Focus on Food Engineering Research and Developments

FOCUS ON FOOD ENGINEERING RESEARCH AND DEVELOPMENTS

FOCUS ON FOOD ENGINEERING RESEARCH AND DEVELOPMENTS

VIVIAN N. PLETNEY EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2007 by Nova Science Publishers, Inc.

All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Focus on food engineering research and developments / Vivian N. Pletney (editor). p. cm. Includes index. ISBN-13: 978-1-60692-567-6 1. Food industry and trade--Research. I. Pletney, Vivian N. TP370.8.F63 2006 664--dc22 2007028557

Published by Nova Science Publishers, Inc.

New York

CONTENTS Preface Expert Commentary

vii Computer-Vision Based Analysis of Color as a Tool for Food Process Control Vural Gökmen and İdris Süğüt

Chapter 1

Transport Phenomena During Drying of Food Materials Kamil Kahveci and Ahmet Cihan

Chapter 2

The Influence of Interactions Occurring Between Micro-Organisms on Predicting the Safety of Lactic Acid Cheese Izabela Steinka

Chapter 3

Chapter 4

Chapter 5

Chapter 6

1

13

165

The Development of Engineering Technology to Improve the Quality of Production of Tropical Fruit in Developing Countries B. Jarimopas, P. Sirisomboon, R. Sothornwit and A. Terdwongworakul

239

Development of Gel Products Containing Fruit Pieces Using Osmotic Treatments without byProduct Generation N. Martínez-Navarrete, M.M. Camacho, E. García-Martínez and M.E. Martín-Esparza

307

Quality Aspects of Dehydrated and Rehydrated Fruit in Relation to Drying Method C. Contreras, M.E. Martín-Esparza, A. Chiralt and N. Martínez-Navarrete Pest Control Using High Pressure Carbon Dioxide as an Advanced Technology Mustafa Bayram

339

361

vi Chapter 7

Chapter 8

Chapter 9

Contents Effects of Permeation on Mass Transfer Coefficient for Laminar Non-Newtonian Fluid Flow in Membrane Modules During Clarification/ Concentration of Fruit Juice Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

397

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings in the Wheat Flour Milling Process Aleksandar Fistes and Gavrilo Tanovic

453

An Effect of Relative Air Humidity on the Content of Volatile Compounds in Roasting Cocoa Beans Wieslawa Krysiak, Teresa Majda and Ewa Nebesny

467

Chapter 10

Bulguration: Combined Cooking and Drying Operation Mustafa Bayram

Chapter 11

Water Sorption on Foodstuffs - Alternative Models 497 Sylwester Furmaniak, Artur P. Terzyk, Leszek Czepirski, Ewa Komorowska-Czepirska, Joanna Szymońska and Piotr A. Gauden

Chapter 12

A Forecast Analysis on Food Nutrition Supply and Demand Worldwide Wenjun Zhang, Wengang Zhou, Xiyan Zhang, Yongkai Xia and Wei He

Index

483

517

531

PREFACE Food engineering refers to the engineering aspects of food production and processing. Food engineering includes, but is not limited to, the application of agricultural engineering and chemical engineering principles to food materials. Genetic engineering of plants and animals is not normally the work of a food engineer. Food engineering is a very wide field of activities. Among its domain of knowledge and action are: • • • • •

Design of machinery and processes to produce foods. Design and implementation of food safety and preservation measures in the production of foods. Biotechnological processes of food production. Choice and design of food packaging materials. Quality control of food production.

Chapter 1 - Drying has been one of the most important techniques used in food preservation for long years. The drying process has to be performed considering energy economy and the quality standards for the product. Therefore, it is of great importance to understand the physical phenomena taking place in the drying processes. Various mass transfer mechanisms such as molecular diffusion, capillary flow and hydrodynamic flow may take place during the drying process of food materials. Drying is generally composed of a series, parallel and/or series-parallel combination of these mechanisms. In addition to the complexity because of these various transport mechanisms in the drying processes, the structures of materials are also too complex. These constitute the main reasons that make the understanding and modeling the drying process difficult. There are three basic approaches used in modeling as empirical, semi-empirical, and theoretical. Empirical and semi-empirical approaches consider only external resistance to mass transfer between product and air while the theoretical approaches consider only internal resistance to mass transfer. At the theoretical modeling two kinds of approaches are used. These are discrete approach and continuum approach. In discrete approach, transport is examined in a network structure representing the material structure and generally the purpose of use of this approach is to determine transport parameters as an alternative to the experimental measurements. On the other hand, continuum approach is commonly used for describing the transport taking place at macroscopic level. In continuum approach, the food material is considered as a fictitious continuum and the effects

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of the physical phenomena taken into consideration are lumped into effective transport coefficients. There are many models suggested based on continuum approach. The main difficulty in using a model based on a continuum approach arises from determination of these effective transport parameters. Most of the transport parameters are strongly dependent on concentration, temperature and material structure. Various models have been suggested to clarify the effect of temperature and concentration on transport parameters. However, relatively little is known on the effect of structure on transport parameters. In conclusion, it may be stated that drying of food materials is a complex unit operation and main problem to overcome on the way to better understand and describe drying processes is to reveal the effect of structure on transport. Chapter 2 - This paper discusses numerous problems occurring in relation to microbiological quality of lactic acid cheese. Lactic acid cheese constitutes the source of various nutritive substances, what results in a possibility of allochthonous micro-flora to grow despite the presence of starter micro-flora. One of the issues discussed herein comprised the results of microbiological research depending on tvarog packing system. The influence of packing system on surface micro-flora population was assessed. Moreover, the problem of growth of enterococci and LAB (Lactic Acid Bacteria) populations depending on stage of tvarog production as well as packing system was also raised. The issue of interactions occurring among micro-organisms that re-infect tvarogs and the influence of these interactions on the growth of individual micro-organisms was also discussed. The author also presented the possibility to apply JMTPH computer program for assessment of the dynamics of changes of tvarog micro-organisms during product storage. Another chapter includes assessment of the influence of lactic acid bacteria on the behaviour of individual groups of micro-organisms occupying tvarog surface, depending on packaging hermetic properties. It was also very important to assess the safety of tvarogs in the context of a possibility of enterotoxin synthesis in conditions of various packing systems. Finally, the models of optimising lactic acid cheese quality were presented, what included application of plant additives of biostatic character, modification of used packaging as well as employing the probabilistic mathematical model helpful in evaluation of enterotoxin synthesis, depending on the level of staphylococci and yeast populations. Chapter 3 - Many developing countries are rich in agricultural and food resources but are unable to maximize the export income they earn from them because they lack value-adding technology. In other words, developing countries typically must sell their products in cheap unfinished form to nations which possess the technology that adds profitability to these goods. Accordingly, if developing countries wish to earn more revenue for the improvement of their people’s employment and education, they must develop food engineering technology alongside other food science technologies. These efforts at technological self-improvement should be supported by the developed countries as the reduction of the knowledge and income gaps between the industrialized and developing worlds will do much to further global peace and happiness. The desired trend for food engineering research is to focus on developing engineering technology that will help to improve tropical fresh produce quality. This chapter discusses three facets of this trend. The first aspect concerns the physical properties of tropical fruit and vegetables, which consist of post-harvest loss, physical characteristics, mechanical properties, firmness, friction, and non-destructive quality grading techniques relating to mangoes, mangosteen, durian, sweet tamarind, guava, tangerines, snake egg plants, white long radish

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and lime. The second aspect concerns innovations in machinery and devices used with mangosteen, durian, young coconut, dry over-mature coconut and baby corn. State of the art design, operating principles and key performance tests of tropical fruit machinery and inventions will be reviewed. The third aspect concerns packaging technology, particularly that which is directed towards the extension of the shelf life of the aforementioned tropical fresh produce. There are three current realities which inform this book. They are as follows: that there is a high incidence of post-harvest loss and a corresponding magnitude of shortage in research and development work on tropical fresh produce; that the global flow of information is increasing while agricultural labor is becoming scarcer and more expensive; and that tropical produce engineering technology must be thoroughly understood. Accordingly, we make two recommendations: for producer countries to instigate a dramatic increase in the research and development that they conduct into tropical fresh produce, and in the support that they provide for this research; and that the research trend should cover all economic tropical fruit and vegetable goods grown in the producer countries and all aspects of engineering technology that they use, with a particular emphasis on developing computerized nondestructive techniques for quality assurance. Chapter 4 - Fruits are products of a very important nutritional interest. Nevertheless, and mainly due to their relatively short shelf-life and modern-day eating habits, the level of consumption is below that recommended by the World Health Organization. In this sense, the development of foods with a high fresh or processed fruit content, that maintain the nutritional and sensorial properties of the fresh fruit, may contribute to stimulating the interest of the consumer, thus increasing the product consumption. Osmotic dehydration (OD) techniques have been widely applied in fruit processing, since they require little energy and allow us to obtain high quality products. However, its industrial use may be limited by the management of the osmotic solution (OS). To solve this problem, the re-use of the OS in more than one OD cycle, with or without a previous re-concentration stage, may be considered. When there is no re-concentration, the re-use will be limited by the possible microbiological contamination and by the progressive dilution that takes place after each OD cycle, which may affect the kinetics of the osmotic process. On the other hand, as some native hydrosoluble compounds, such as volatiles, acids, minerals, vitamins and phytochemicals, will be released together with water into the OS during OD, its management as an ingredient in some product formulation seems to be an interesting alternative. To this end, this work analyses the viability of formulating a fruit-gel product with the osmodehydrated fruit (strawberry, kiwi or grapefruit) and the re-used OS obtained from the dehydration step, in order to diminish the loss of flavour, aroma and functional components of the fruit and avoid the generation of by-products in the process. In this study, the number of OS re-use cycles has been optimized, on the basis of its microbial recounts, the dilution level, the solution enrichment in fruit bioactive compounds, the fruit-solution ratio used during the dehydration step and the fruit-gel ratio in the final product. The kind and concentration of gelling agents, which best favour the properties of aspect (transparency) and texture of the gels, taking the peculiar composition of the re-used OS used as gelling medium into account, have been identified. The conditions in which the fruit pieces are mixed with the gelling solution have also been studied and defined. Finally, the fruit-gel product formulation conditions have been optimized, on the basis of its sensory acceptance and its compositional stability during storage, ensuring the thermodynamic equilibrium between the fruit and the gel when mixed.

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The microbiological stability of the product was of at least 15 days in refrigerated storage. During this time, the evolution of some properties such as phytochemicals, vitamins, acids, volatile compounds, colour and texture was studied. Chapter 5 - The development of new attractive dehydrated fruit-based products, to be consumed as dried or rehydrated, with high quality and reasonable shelf-life, will increase and diversify its availability in the market. In this sense, it is necessary to optimize the dehydration operation conditions to achieve not only the maximum process efficiency and control, but also various characteristics in the final product in relation to colour, texture, water activity, nutritive value, etc. Air drying has been the most frequently selected process for industrial food dehydration, due to its efficiency, versatility and easy management. However, it is known that it provokes considerable changes in sensory and nutritional quality. Some research works refer to the advantages of applying microwaves to convective drying associated with the fast volumetric heating of the product due to its high penetration power. On the other hand, the application of certain pre-treatments before drying operation, such as vacuum impregnation or vacuum pulsed osmotic dehydration, could help to enhance the stability and quality attributes, as high temperatures are not employed and specific solutes can be incorporated into the porous structure. In this chapter the advantages of microwave application to convective drying of apple and strawberry are pointed out. These are related to the great reduction in process time and to the fact that they allow obtaining a dehydrated product with a greater resistance to deformation and fracture and a greater stability during commercialization. Nevertheless, its use is not recommendable when the product has to be used or eaten after its rehydration, as the structural damage caused by microwaves decreases the mechanical resistance and the retention capacity of the incorporated liquid phase. The colour of dehydrated or rehydrated product is more affected by microwave treatments when the fruit pigment content is relevant, as occurs with strawberry anthocyanins. Application of a previous vacuum impregnation/osmotic dehydration step with sugared solutions is always recommended. Chapter 6 - Food products are always under the risk of infestation by pests. In view of the competitive markets, there has been increasing demand for quality in foods in terms of freedom from pest and pesticide contaminants. Also, it is very important for trade purpose suffer economic and quality losses. Zero tolerance of insect pest in foods has been adopted in some of countries and there is a tendency to achieve this goal in overall the world. The governments, the food industries and exporters are dependent on fumigation as a quick and effective tool for insect pest control in food commodities. Fumigants are widely used for pest elimination in these commodities. Toxic substances have therefore been used to destroy for example pests, as well as their eggs, larvae, cocoons and adults. Currently used substances, such as methyl bromide, hydrogen phosphide, ethylene dioxide, malathion etc., are characterized by more or less serious problems. In recent years, that fumigation technology based on the chemical control of products has been facing threats/constraints because of regulatory concerns, the development of resistance, handling hazards, residues, food safety, cost, carcinogenicity, involvement in ozone depletion, resurgence, environmental pollution and other factors. Reliance upon fumigation as an overall solution to infestation problems in food products has become questionable. The chemical action of fumigants upon commodities and the environment has necessitated the withdrawal of many fumigants from the market. Also, some of them are being phase out their uses at the international level.

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Due to becoming the target of increasing criticism of toxic substances, such concerns have led to the development of non-chemical methods for the control of insect pests that infest food commodities. One such method is the high pressure carbon dioxide application, which mainly involves the use of CO2 at high pressure (10-40 bar) for food fumigation. It is a new effective, non-chemical, non-residual, safe, fast and environmentally friendly method for the food industry. It has been generated and developed within last 20 years. Carbon dioxide is a fumigant and being used to control pests in the food industry. After extensive testing, high pressure carbon dioxide fumigation can be accepted as the advanced pest control technology for the future. Nowadays, it is particularly indispensable for the gentle, safe, natural and organic food products. If operation time for the fumigation is constraint and nonchemical treatments are required, this technique is suitable for conventional products. Chapter 7 - Membrane based clarification and concentration of fruit juice has become a popular unit operation in modern fruit juice processing industries. The well known membrane modules used for this purpose are tubular and spiral wound modules. Therefore, design of these modules is of utmost industrial importance. The key parameter for design of membrane modules is mass transfer coefficient. Most of the fruit juices have non-Newtonian rheology, e.g., power law, ellis fluid, etc. Till today, the mass transfer coefficient for such systems used is approximated from the corresponding relations developed for Newtonian fluids. Hence, a detailed fluid flow modeling with non-Newtonian rheology is urgently warranted. In the present work, this aspect is attempted. The expressions of the mass transfer coefficients are derived from the first principles for laminar, non-Newtonian fluid flow in a porous conduit. The effects of the permeation are incorporated quantitatively in the mass transfer coefficient from a theoretical basis. The analysis is carried out for various non-Newtonian rheologies. Effects of the operating conditions, i.e., Reynolds number, permeate flux, etc. on mass transfer coefficient are also investigated. Two flow geometries are considered. Flow through a tube and that through a rectangular thin channel, which are useful for the design of the tubular and spiral wound cross flow membrane modules. The developed relations of mass transfer coefficients would be of tremendous help to the design engineers. Chapter 8 - A laboratory roll stand Variostuhl, equipped with smooth rolls (250 mm diameter, 100 mm length), was used to examine, under simulated commercial conditions, the effect of roll speed and roll differential on the reduction of sizings and coarse middlings from the primary break passages of the wheat flour milling process. The samples were obtained from the industrial mill, intercepting the sizings and coarse middlings from the 1st, 2nd and 3rd break stage that normally would have gone to the purification system, as well as intercepting the purified sizings (cleaned middlings) that normally would have gone to the reduction system of the wheat flour milling process. As roll velocity increases flour release was increased, milling energy consumption rose while flour quality (as determined by ash content) was not affected. By increasing roll velocity it is possible to increase feed rate to the rolls and, therefore, the disposable roll surface is used more efficiently. Flour release rose when differential was increased from 1.1 up to 1.25 but decreased when differential increased from 1.25 up to 5.0. Increasing roll differential led to an increase in milling energy consumption. These effects can be explained by the relative contribution of compressive and shearing forces acting on the particles passing through the grinding zone of the smooth rolls. Considering the results obtained in this study (flour release, flour quality and milling energy

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consumption) a differential of 1.25, relative to a fast roll speed of 5 m/s could be designated as optimal. Chapter 9 - The Ivory Coast cocoa beans were convectively roasted at 135°C, at the air flow rate of 1.0 m/s and relative air humidity (RH) of 0.4%, 2.0% and 5.0%. Volatile components of raw and roasted beans were analyzed by SPME/GC/GCMS and identified by comparing their retention indices with that of standards included in a database and their mass spectra with standard spectra included in NIST computer library. Almost 100 different volatile compounds were identified in examined samples of roasted cocoa. They ranked among aldehydes, ketones, alcohols, esters, monoterpenes, pyrazines, acids, lactones, furan derivatives, and sulfur-containing compounds. It was found that a rise in the relative air humidity from 0.4% to 2.0 and 5.0% increased the contents of pyrazines, volatile acids, esters, furan derivatives, and sulfur-containing compounds in a headspace of roasted cocoa. In contrast, the contents of alcohols and aldehydes in the headspace were considerably lower when the cocoa beans were roasted at the relative air humidity of 5.0% as compared to that when less humid air was used for convective heating. Chapter 10 - Cooking and drying are two main unit operations used widely in food processing. Consecutive cooking and drying operations supplies perfect properties to gain to food products and called as bulguration. Individually, the former method is used nearly for all food products before consumption. Cooking is a well-known way to destruct microorganisms, insect, insect eggs and larvaes for food safety. Also, it increases the digestive property of food with starch gelatinization, protein gelation and textural softening. However, it is very difficult to store this product without drying due to its high moisture content after cooking. Therefore, food products should be dried. Drying is required to prolong storage time of food products. Bulguration is the gaining of the some functional characteristics on the finished product such as the resistance to mold contamination, insect attacks and radiation, inactivation of enzymes and microorganisms, encapsulation of numerous nutritional components in food products, easy preparation after bulguration due to semi and ready-to-eat form, obtaining long shelf-life having economical products with safety, decreasing undesired components e.g. phytic acid in contrast to increasing desired one e.g. folate/folic acid. As raw materials, cereals, pulses, seeds, vegetables, fruits etc. can be used. Recently, the use of bulguration in the food industry dramatically increases as an optimal method due to above situations. Bulguration is an ancient technique; however, the modern technology re-discovered it. In this chapter, the techniques of bulguration are explained with examples. Also, the results of the recent researches are given. Chapter 11 - It is well known that sorption isotherms of foodstuffs are very important for design, modeling and optimization of important processes for example drying, aeration, predicting of stability and quality during packaging and storage of food. Many literature reviews conclude that the BET (and its modifications) and the GAB sorption isotherm equations are the most popular and applicable for description of isotherms of foodstuffs. The authors showed recently the applicability of the GDW model for description of water sorption on different foodstuffs. Moreover, it was also shown that the GAB model (also widely applied in food science) is the special case of the GDW equation. In this review the authors present the current state of art and also an attempt of application of different models of water sorption, namely CMMS, DD and modified CDS for description of water sorption data on different starch samples and other foodstuffs.

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Chapter 12 - This paper aimed to make a longer-term forecast analysis on global food nutrition supply and demand. The forecasts of supplies of food calories and proteins for the world and various regions over the period 2010-2030 were given, and food nutrition supply and demand balance in the forecast period was discussed. If the past pattern continues, the global total food calorie supply would grow at the annual rate of 13.43±0.71 kcal/cap/day and reach 3210.4±67.3 kcal/cap/day in 2030. Total food calorie supplies for all of the regions would grow during the forecast period and, in most regions they are forecast to be greater than 3000 kcal/cap/day from 2015-2020. Total food protein supply for all regions but not Oceania, is forecast to grow during the forecast period. The proportion of animal sourced protein in total food protein supply is in 2030 forecast to increase and reach 35.5%, 61.6%, 56.8%, and 21.7% for Asia, Europe, South America, and Africa. Food calorie supply in the world is expected to exceed the adequate energy intake after around 2015. Strong focus should be worldwide put on the over-intake of food calorie in the near future. Global food protein supply is not expected to be greater than the adequate range during the period 2010-2030. Food protein supply in Africa and Caribbean would be just a little greater than the basic demand in the forecast period. Food protein intake in these regions should be improved in the coming years.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 1-12 © 2007 Nova Science Publishers, Inc.

Expert Commentary

COMPUTER-VISION BASED ANALYSIS OF COLOR AS A TOOL FOR FOOD PROCESS CONTROL Vural Gökmen and İdris Süğüt Department of Food Engineering, Hacettepe University, 06800 Beytepe, Ankara, Turkey

ABSTRACT The color is the first sensation that the consumer perceives and uses as a tool to accept or reject, because the color observation allows the detection of certain anomalies or defects of a product. Commercial color-measuring devices are designed to contact the materials to perform the color measurements. The main drawback of using these devices is the limitations in size and geometry of the area that subjected to color measurement, because they measure a small area with a fixed geometry making the measurement quite unrepresentative for heterogeneous materials like many processed foods. With a digital imaging system, it is possible to register the color of foods using three-color sensors. In this case, surface color of the food is detected online in a non-contact manner and monitored throughout the process. By using appropriately developed computer algorithms, highly accurate and reliable information can be obtained about the color changes in a food during processing. This kind of algorithm can be used as a process control tool for automatic visual inspection in an industrial production process and can improve the overall quality of the product. The advantage of computerized visual inspection over inspection by humans is that machines can evaluate color continuously and objectively. This chapter describes novel approaches for a non-contact computer vision based color measurement system and its potential applications in food processing.

1. INTRODUCTION The overall appearance of any object is a combination of its chromatic and geometric attributes. Both of these attributes should be accounted for when making visual or instrumental assessments of appearance. The color is the first sensation that the consumer

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perceives and uses as a tool to accept or reject, because the color observation allows detecting certain anomalies or defects of a product. The human eye has receptors for short (S), middle (M), and long (L) wavelengths, also known as blue, green, and red receptors. This means, in principle, three parameters are required to describe a color sensation. A specific method for associating three parameters (or tristimulus values) is called a color space which specifies how color information is represented. A color component is also referred to as a color channel. The XYZ is the first color space that mathematically defined by the Commission Internationale d'Eclairage (CIE) in 1931. The L*a*b* color space is perceptually uniform and the most complete model defined by the CIE in 1976 to serve as a device-independent, absolute model to be used as a reference. It is based on the XYZ color space as an attempt to linearize the perceptibility of color differences, using the color difference metric described by the Macadam ellipse. The non-linear relations for L*, a* and b* are intended to mimic the logarithmic response of the human eye. Here, L* is the luminance or lightness component, which ranges from 0 to 100, and parameters a* (from green to red) and b* (from blue to yellow) are the two chromatic components, which range from –120 to 120 [1-3].

2. INSTRUMENTAL MEASUREMENT OF COLOR A lot of color-measuring instruments are available in the market for several applications. Most of them are designed to contact the materials to perform the color measurements. These instruments are successfully used for color measurement of homogenous materials. However, the main drawback of using a color-measuring instrument is the limitations in size and geometry of the area that subjected to measurement. A commercial instrument usually measures a small area with a fixed geometry. This makes the measurement quite unrepresentative for heterogeneous materials like many food items [4]. Repetitive measurements are therefore required to increase the accuracy. In most cases, increasing the repetitions is not a viable approach for irregularly shaped objects. Instead, an approach taking the overall surface into account is required to obtain meaningful information about the color. This is especially important for industrial applications in which the color homogeneity is an important feature of the material. In such a case, commercial color measuring instruments are not fit for purpose as a process control and/or product quality control tool.

3. COMPUTER VISION BASED MEASUREMENT OF COLOR A typical image captured by a digital camera consists of an array of vectors called pixels. Each pixel has red, green and blue color values:

⎡ x r ( n, m ) ⎤ ⎢ ⎥ x[n, m] = ⎢ x g (n, m)⎥ ⎢ x (n, m ) ⎥ ⎣ b ⎦

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where, Xr, Xg and Xb are values of the red, green and blue components of the (m,n)th pixel, respectively. In digital images, Xr, Xg and Xb color components are represented in 8 bits, i.e., they are allowed to take integer values between 0 and 255 (=28-1) [5]. RGB values of an image captured by a digital camera can be converted into device-independent L*a*b* units. Computational approaches that convert RGB values into L*a*b* units have been previously reported using the standard equations [2,6,7]. However, usefulness of a computer vision system as a tool for color measurement depends on the accuracy of color transformation. Digital cameras have built-in white-balancing systems modifying actual color values, therefore pixel values in an image captured by a camera of a machine vision system or a consumer camera may not correspond to true colors of imaged objects. Therefore, it is better to build calibrated models by using the charts which reflect the variations in color space. The necessity of a calibration process to obtain device-independent L*a*b* color units have been previously underlined [8]. However, the information is lacking on how the accuracy of color measurement could be improved by calibration process in these reports.

3.1. Description of the Technique Following section describes a computer-vision based technique calibrated with ANN modeling to measure color in foods. The model allows a user to determine a polygonal region of interest. This feature increases the accuracy in color measurement when compared to commercial color-measuring devices. To perform the color measurement, digital images of food samples are taken using a color digital camera under well controlled conditions (illumination, lamp angle and distance). The calibration of computer vision based color measurement system was performed by using an Agfa 5x7 inch reflective color chart (figure 1) which is an internationally accepted IT8 standard (IT8.7/2-1993) with device-independent color definitions. The chart consists of 288 colored squares, and has been designed to represent the color space from full saturation to near neutrals at highlight, mid-tone and shadows. Figure 2 shows the algorithm used to convert camera RGB values to spectrophotometric CIE L*a*b* values. Based on this algorithm, the first step is the conversion of color values from RGB to L*a*b* using the standard conversion equations, and the second step is the correction of L*a*b* values through an ANN model.

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Figure 1. Agfa 5x7 inch color chart (IT8.7/2-1993) used to build a calibrated ANN model for the correction of monitor L*a*b* values. = 2.4

RGB values by digital camera

RGB to XYZ //Observer:2o ; Illuminant: D50 var_R = ( R / 255 ) var_G = ( G / 255 ) var_B = ( B / 255 )

Conversion of color values RGB CIE L*a*b* by using equations

//Where R = 0 ÷ 255 //Where G = 0 ÷ 255 //Where B = 0 ÷ 255

if ( var_R > 0.04045 ) var_R = ( ( var_R + 0.055 ) / 1.055 ) ^ 2.4 else var_R = var_R / 12.92 if ( var_G > 0.04045 ) var_G = ( ( var_G + 0.055 ) / 1.055 ) ^ 2.4 else var_G = var_G / 12.92 if ( var_B > 0.04045 ) var_B = ( ( var_B + 0.055 ) / 1.055 ) ^ 2.4 else var_B = var_B / 12.92 var_R = var_R * 100 var_G = var_G * 100 var_B = var_B * 100

Correction of color values CIE L*a*b* CIE L*a*b* by using an ANN model

CIE L*a*b* values by ANN

X = var_R * 0.4360 + var_G * 0.3851 + var_B * 0.1431 Y = var_R * 0.2225 + var_G * 0.7169 + var_B * 0.0606 Z = var_R * 0.0139 + var_G * 0.0971 + var_B * 0.7142 XYZ to L*a*b* var_X = X / ref_X // ref_X = 96.422 var_Y = Y / ref_Y // ref_Y = 100.000 var_Z = Z / ref_Z // ref_Z = 82.521 if ( var_X > 0.008856 ) var_X = var_X ^ ( 1/3 ) else var_X = ( 7.787 * var_X ) + ( 16 / 116 ) if ( var_Y > 0.008856 ) var_Y = var_Y ^ ( 1/3 ) else var_Y = ( 7.787 * var_Y ) + ( 16 / 116 ) if ( var_Z > 0.008856 ) var_Z = var_Z ^ ( 1/3 ) else var_Z = ( 7.787 * var_Z ) + ( 16 / 116 ) CIE-L* = ( 116 * var_Y ) - 16 CIE-a* = 500 * ( var_X - var_Y ) CIE-b* = 200 * ( var_Y - var_Z )

Figure 2. Algorithm used to convert camera RGB values to spectrophotometric CIE L*a*b* values.

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An ANN is a nonlinear mathematical model that learns from the examples through iterations. ANNs are made of a large number of nodes or artificial neurons, which are disposed in a parallel structure. Each ANN has one input layer containing one node for each independent variable, one or more hidden layers, where the data are processed, and one output layer, containing one node for each dependent variable. The data from the input layer are propagated through the hidden layer and then to all network, which are associated with a scalar weight. Neurons in the hidden and output layers calculate their inputs by performing a weighted summation of all the outputs they receive from the layer before. Their outputs, on the other hand, are calculated by transforming their inputs using a non-linear transfer function. Then, the network output is compared with the actual output provided by the user. The difference is used by the optimization technique to train the network. Thus, the training process requires a forward pass to calculate an output and a backward pass to update the weights in feed-forward back-propagation networks [9,10]. A great advantage of ANN models is that, they do not require prior knowledge of the relationship between the input and output variables, and instead of that, they figure out these relationships through training. Therefore, complex processes can be optimized to produce the desired outputs using successfully trained ANN models. A feedforward backpropagation network was used for the conversion of monitor color values to spectrophotometric color values in CIE L*a*b* units. Sigmoid function (Eq.1) was used in the hidden layers as the transfer function which gave outputs in the range [-1,1].

f ( x) =

1 1 + exp(− x)

Eq. (1)

In the output layer, the training function was purelin (Eq. 2) which gave outputs in the range [-∞,+∞].

f ( x) = x

Eq.(2)

Representative examples, obtained by experimental data sets, were presented to the network so that it could integrate this knowledge within its structure. The learning function was the gradient descent with momentum and Bayesian regularization was used as the training algorithm. Several ANN topologies were trained using the experimental data sets. The data set consisting of 288 data points were divided into two parts for training (252 data points) and testing (36 data points). Input and output layers consisted of three neurons which corresponded to monitor color values and spectrophotometric color values in CIE L*a*b* units, respectively. After each training session, train and test data sets were simulated by ANN. The error measures used for comparing the performance of various ANN configurations were estimated using Eq. (3); n

SSE = ∑ (VD − VP ) 2 i =1

Eq. (3)

Vural Gökmen and İdris Süğüt

6

where, n is the number of data points, VD and VP are the measured and the predicted color values.

3.2. Accuracy of Color Measurement Among the different network architectures, the 3-6-6-3 network topology appeared as the best performing one which exhibited a great capability for calibrating the monitor color values to the spectrophotometric color values in CIE L*a*b* units (figure 3).

Monitor Color

Spectrophotometric Color values

L*

L*

a*

a*

b*

b*

Figure 3. Topology of the ANN model used to correct monitor L*a*b* values.

The accuracy in color measurement is usually determined as the Euclidian distance (Eq. 4) between two colors (ΔE). ΔE of less than 1.0 is usually desired for accuracy of a color measurement system.

ΔE = ( L1 − L2 ) 2 + (a1 − a2 ) 2 + (b1 − b2 ) 2

Eq. (4)

ΔE of less than 1.0 is usually considered as an indication of high accuracy for a color measuring instrument. The ΔE values for the estimated values and the real spectrophotometric values of 288 colored squares in the IT8 chart used for training and testing is shown in figure 4. The maximum ΔE value was determined to be 0.45 in the computer vision based color measurement system described above.

Computer-Vision Based Analysis of Color as a Tool for Food Process Control

7

a.

b. Figure 4. Change of ΔE value for the patches on IT8 color chart (a) the color patches used for training, (b) the color patches used for testing.

Vural Gökmen and İdris Süğüt

8

The commercial colorimeters measure color in a small area with fixed geometry (usually circle in a few square centimeters). This is the major drawback of the commercial instruments which makes the color measurement problematic especially for heterogeneous materials. It is the usual way to make repetitive measurements over the surface in order to obtain meaningful information about the color. From the industrial point of view, it is unattractive to make several measurements and averaging the results for a single product to get useful information about the color. In practice, a system which has the capability of measuring color in the overall area of a material is advantageous and of commercial interest. In this respect, averaging the values of all pixels in a digital image of a material using a non-contact computer vision technique is absolutely better than averaging the values of repetitive measurements using a contact colorimeter in terms of accuracy and analysis time. For example, after the frying process, different kinds of image pixels appear in a typical fried potato image (figure 5a). The color of this potato crisp can be measured by marking a polygonal area over the image which represents the whole surface as shown in figure 5b. Color measurements were also performed by a color spectrophotometer (Minolta model CM3600d spectrophotometer) or a portable colorimeter (ColorSavy model CM2C colorimeter) for the same potato crisp sample making a dozen of measurements from different regions.

a.

b.

Figure 5. (a) Digital image of a potato crisp sample, (b) polygonal area marked on the image of potato crisp which subjected to color measurement by computer vision based analysis.

Figure 6 shows the results of pixel by pixel analysis of color performed by the computer vision based color measurement system and the results of 12 repetitive measurements performed by the spectrophotometer and the portable colorimeter. In order to emphasize the capability of computer vision based color measurement system, data points are represented in colors as the monitor RGB counterparts of each data points in CIE L*a*b* units determined by the appropriate technique. It is clear from the results that the color values determined by the spectrophotometer and by the portable colorimeter are perceptually not accurate. This

Computer-Vision Based Analysis of Color as a Tool for Food Process Control

9

case exemplifies that commercial instruments have certain limitations when dealing with the color of rough and heterogeneous materials like potato crisps. In addition to the advantage of averaging color values of all pixels, the measurement of color in a region of interest over a material’s surface may be of greater importance under certain circumstances. In that case, measuring the color in a user defined polygonal area may be useful to get appropriate information about the color of different regions over the surface of a single material. A commercial device is not capable of extracting color information for a specific region of a heterogeneous material. Figure 7 shows a cookie sample composed of two regions with and without cocoa. Two regions with different shapes in this image can be processed pixel by pixel to determine average L*, a* and b* values separately by defining polygonal area of interest.

Figure 6. The results of color measurements by means of computer vision technique (pixel by pixel measurements), color spectrophotometer and portable color mouse (12 repetitive measurements).

Vural Gökmen and İdris Süğüt

10

a.

b.

Figure 7. (a) Digital image of a cookie sample composed of two regions, (b) polygonal areas marked on the image of cookie sample which subjected to color measurement by computer vision based analysis.

4. CONCLUSION The computer vision based image analysis system described here offers some advantages over the commercial color-measuring instruments namely the possibility of performing noncontact color measurement without sample preparation, and extracting meaningful information in a specific region of interest over a material surface. This kind of system can be used as a tool for automatic visual inspection of colors in an industrial production process and can improve the overall quality of the product. The advantage of computerized visual inspection over inspection by humans is that machines can evaluate color continuously and objectively.

REFERENCES [1] [2]

[3] [4]

Papadakis, S.E.; Abdul-Malek, S.; Kamdem, R.E.; Yam, K.L. (2000). A versatile and inexpensive technique for measuring color of foods. Food Technology 54(12), 48-51. Segnini, S.; Dejmek, P.; Öste, R. (1999). A low cost video technique for colour measurement of potato chips. Lebensmittel-Wissenschaft and Technologie. 32(4), 216-222. Yam, K.L.; Papadakis, S. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering. 61, 137-142. Antonelli, A.; Cocchi,M.; Fava, P.; Foca, G.; Franchini, G.C.; Manzini, D.; Ulrici, A. (2004). Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Analytica Chimica Acta. 515, 313.

Computer-Vision Based Analysis of Color as a Tool for Food Process Control [5] [6] [7] [8] [9]

[10]

11

Gonzales, R. C.; Woods, R. E. (2002). Digital Image Processing, Prentice Hall, New Jersey. Paschos, G. (2001). Perceptually uniform color spaces for color texture analysis: An empirical evaluation. IEEE Transactions on Image Processing. 10(6), 932-937. Mendoza, F.; Aguilera, J.M. (2004). Application of image analysis for classification of ripening bananas. Journal of Food Science. 69, 471-477. León K.; Mery, D.; Pedreschi, F.; León, J. (2006). Color Measurement in L*a*b* units from RGB digital images. Food Research International. 39, 1084-1091. Gonçalves, E.C.; Minim, L.A.; Coimbra, J.S.R.; Minim, V.P.R. (2005). Modeling sterilization process of canned foods using artificial neural networks. Chemical Engineering Process. 44, 1269-1276. Bishop, M.C., (1994). Neural network and their applications, Review in Scientific Instruments. 65(6), 1803–1832.

APPENDIX 1. MATLAB CODE FOR EXTRACTING THE COLOR INFORMATION OF A POLYGONAL MARKED REGION IN AN IMAGE RGB=imread(‘image.extension’); RGB=im2double(RGB); Z=roipoly(RGB); [d1,d2]=size(Z); c=0; L=[ ]; for a=1:d1 for b=1:d2 if Z(a,b)==1 n=1; c=c+1; L(n,c)=a; n=2; L(n,c)=b; end end end P=[ ]; for n=1:c P(n,:)=impixel(RGB2,L(2*n),L(2*n-1)); end roired=[ ];roigreen=[ ];roiblue=[ ]; sum_red=0;sum_green=0;sum_blue=0; for n=1:c sum_red=sum_red + P(n,1);

12

Vural Gökmen and İdris Süğüt sum_green=sum_green + P(n,2); sum_blue=sum_blue + P(n,3); end roired=sum_red/c; roigreen=sum_green/c; roiblue=sum_blue/c; RGB__value=[roired roigreen roiblue]

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 13-163 © 2007 Nova Science Publishers, Inc.

Chapter 1

TRANSPORT PHENOMENA DURING DRYING OF FOOD MATERIALS Kamil Kahveci and Ahmet Cihan Mechanical Engineering Department, Trakya University, 22180 Edirne, TURKEY

ABSTRACT Drying has been one of the most important techniques used in food preservation for long years. The drying process has to be performed considering energy economy and the quality standards for the product. Therefore, it is of great importance to understand the physical phenomena taking place in the drying processes. Various mass transfer mechanisms such as molecular diffusion, capillary flow and hydrodynamic flow may take place during the drying process of food materials. Drying is generally composed of a series, parallel and/or series-parallel combination of these mechanisms. In addition to the complexity because of these various transport mechanisms in the drying processes, the structures of materials are also too complex. These constitute the main reasons that make the understanding and modeling the drying process difficult. There are three basic approaches used in modeling as empirical, semi-empirical, and theoretical. Empirical and semi-empirical approaches consider only external resistance to mass transfer between product and air while the theoretical approaches consider only internal resistance to mass transfer. At the theoretical modeling two kinds of approaches are used. These are discrete approach and continuum approach. In discrete approach, transport is examined in a network structure representing the material structure and generally the purpose of use of this approach is to determine transport parameters as an alternative to the experimental measurements. On the other hand, continuum approach is commonly used for describing the transport taking place at macroscopic level. In continuum approach, the food material is considered as a fictitious continuum and the effects of the physical phenomena taken into consideration are lumped into effective transport coefficients. There are many models suggested based on continuum approach. The main difficulty in using a model based on a continuum approach arises from determination of these effective transport parameters. Most of the transport parameters are strongly dependent on concentration, temperature and material structure. Various models have been suggested to clarify the

14

Kamil Kahveci and Ahmet Cihan effect of temperature and concentration on transport parameters. However, relatively little is known on the effect of structure on transport parameters. In conclusion, it may be stated that drying of food materials is a complex unit operation and main problem to overcome on the way to better understand and describe drying processes is to reveal the effect of structure on transport.

1. INTRODUCTION Drying of food materials is used as a preservation technique. Microorganisms that cause spoilage and decay can not grow and multiply in the absence of water [1]. In addition, enzymes that cause chemical changes can not function in an environment lacking water. It is therefore necessary to expose food materials to a proper drying process to reduce their water content. It is possible to classify drying processes in several ways. For example, drying processes can be classified as batch and continuous [1]. In batch drying process, food material is inserted into the drying equipment and drying proceeds for a given period of time. In the continuous drying process, however, food materials are continuously added to the dryer and dried material continuously removed. Drying processes can also be categorized depending on the physical conditions used to add heat and remove water vapor. The most common type of process under this categorization is the process in which heat is added by direct contact with heated air at atmospheric pressure and the resulting water vapor is removed by the air. In addition, there are other processes, in which heat is added indirectly through a metal wall or by radiation. Types of driers used in the food industry display a considerable diversity particularly depending on the type of process used in drying. Schematic views of some common driers are shown in figure 1. Brief information on various types of driers is also given below.

Sun Drying and Solar Dryers Drying of food materials by spreading them on an appropriate ground is called sun drying. In this case, part or all of the heat required for drying is supplied by direct radiation from the sun. Solar drying means the processes where solar collectors are used for heating the air.

Tray Dryers In tray dryers, food materials are usually laid on trays as a very thin layer. The required heat is supplied by the air sweeping over the trays or by conduction or radiation from heated trays.

Transport Phenomena During Drying of Food Materials

15

Tunnel Dryers In tunnel dryers, trays or trolleys containing food materials move through a tunnel, in which heat is supplied and water vapor is removed. Food material generally moves along the tunnel parallel or opposite to the direction of air flow.

Roller or Drum Dryers In this type of driers, food material is spread on the surface of a heated drum and the drum is rotated. The food remains on the drum surface for the greater part of the rotation during the time that drying takes place.

Fluidized Bed Dryers In fluidized bed dryers, food material is held suspended during the drying process with the help of the upward flow of the drying air. The air flow may be horizontal to help the food material to convey in the dryer. In this type of dryers a major part of the heat is transferred by convection.

Spray Dryers In this type of driers, liquid or fine solid material is sprayed into a heated air flow as fine droplet dispersion form. Drying speed is extremely high in this type of drying process. For this reason, this process is generally used in drying of food materials which damage due to prolonged exposure to hot air stream.

Pneumatic Dryers In pneumatic dryers, solid food particles are conveyed rapidly with an air stream. The heat necessary for drying is supplied by the air. Generally, a classifier is used in this type of driers. Dried particles are separated in this classifier. The remaining humid part is recirculated for additional drying.

Rotary Dryers In rotary dryers, food material is taken into a horizontal inclined cylinder. The necessary heat for drying is provided by an air stream moving along the cylinder or by the transfer of the heat from the walls of the cylinder by conduction. The cylinder rotates in certain types and in other types the cylinder is stationary and the material is conveyed by a paddle or screw rotating in the cylinder.

16

Kamil Kahveci and Ahmet Cihan

Trough Dryers In this type of dryers, food material is loaded on a trough-shaped conveyor belt made of mesh. Drying air is blown through the bed of material. The movement of the conveyor continually turns over the material and hence exposes wet surfaces to hot air.

Bin Dryers In bin dryers, food material is contained in a perforated bottom bin. Drying process is performed by vertically upward conveyance of hot air along the bin.

Belt Dryers In this type of dryers, food material is laid on a horizontal mesh or a solid belt and hot air is passed over the material. Mostly, the belt is mobile. In some cases, the belt is stationary and material is conveyed by scrappers.

Vacuum Dryers In vacuum drying, material is inserted in an evacuated cabinet and the drying process is performed in this cabinet. The required heat is mainly transferred by conduction or radiation. This process which allows drying at lower temperatures is generally used for food materials that are damaged by exposure to high temperatures.

Freeze Dryers In freeze dryers, food material is loaded on shelves or belts in a chamber under vacuum. Material is generally frozen before loading into the dryer. The heat is transferred to food by conduction or radiation. The resulting vapor is removed by a vacuum pump and is condensed. In certain cases, sheets of expanded metals and heated plates are inserted between food materials to enhance heat transfer and moisture removal.

Microwave Dryers Drying process in microwave dryers is performed using polarization occurring at molecular and atomic level. The heat developed in a material by an alternating electromagnetic field results from the polarization process within the product when molecules within the material rotate and move laterally millions of times per second in an attempt to align with the changing field. Microwave heating provides a uniform heat flux throughout the material.

Transport Phenomena During Drying of Food Materials

17

Radio Frequency Dryers In a radio frequency drying system, a RF generator creates an alternating electric field between electrodes. The material is conveyed between the electrodes. In this case, alternating energy causes polar molecules in the water to continuously reorient themselves to face opposite poles. This frictional movement causes the water content of food material to heat up rapidly and leave the medium by vaporization.

Infrared Dryers In the infrared dryers, the electromagnetic energy of infrared rays is used for drying. The depth of penetration of infrared is a function of its wavelength. Generally, the shorter the wavelength, the greater is its penetration power. The most important advantage of this type of drying is economy. For biological materials, however, infrared heater temperatures greater than 830°C should be avoided as this can char the product and cause surface damage [3].

Heat Pump Dryers Heat pump dryer consists of a drying chamber equipped with a circulation system and the components of an air-conditioning refrigeration system. The drying air is dehumidified by the evaporator and reheated by the condenser of the heat pump. The maximum drying temperature is determined by the condensing temperature of the refrigerant used. Heat pump drying is essentially a low-temperature process which can be controlled from -20°C to 70°C by selecting an appropriate refrigerant and regulating the compressor capacity and air flows within the system [4].

Superheated Steam Dryers In the superheated dryer, wet solids are fed into the flow of pressurized superheated transport steam by means of a pressure tight rotary valve, plug screw or similar. The transport steam is superheated indirectly via a tubular heat exchanger, by a heating media such as medium pressure steam, flue gases or thermal oil [5]. Also, electrical heating can be applied. In the subsequent drying ducts, moisture is vaporized from the product, forming excess transport steam and lowering its degree of superheat. Normally the residence time in the system is 5-60 seconds only. For some materials, a second superheater is necessary to achieve the required dryness. The dry material is separated in a high efficiency cyclone and the material is discharged from the dryer by means of another pressure tight rotary valve. From the cyclone, the transport steam is recycled by a centrifugal fan to the inlet of the first heat exchanger. The excess steam generated is continuously bled off.

18

Kamil Kahveci and Ahmet Cihan

Hybrid Dryers Particularly, dryers manufactured using latest technology consist of a combination of different types of drying processes. They are preferred mostly because the combination allows utilization of the most advantageous aspect of each process for food material and drying process. Common hybrid dryers used in drying are listed below. • • •

Microwave-Convective Dryers, Microwave-Infrared Dryers, Microwave-Vacuum Dryers, Microwave-Superheated Steam Dryers, Infrared-Convective Dryers, Infrared-Vacuum Dryer, Infrared-Heat Pump Dryers, Radio Frequency-Heat-Pump Dryers, Radio Frequency-Vacuum Dryers. centrifugal fan

heater

feed

product

steam

product

ROLLER DRYER

TRAY DRYER

vapour vapour

feed feed

heater product hot air FLUIDISED DRYER

Figure 1. continued on the next page.

air product PNEUMATIC DRYER

19

Transport Phenomena During Drying of Food Materials feed

feed

steam

atomiser

product hot air

condensate

ROTARY DRYER

SPRAY DRYER product

steam heated plates vapour steam jet vacuum pump steam

food

steam jet vacuum pump

compression mechanism FREEZE DRYER

feed

infrared heater

condenser

water

INFRARED DRYER

heating media

aspirator

excess steam

infrared heater

feed product

product SUPERHEATED STEAM DRYER

vacuum pressure controller

cooler

INFRARED-VACUUM DRYER

Figure 1. Schematic view of various types of dryers used in food industry [2].

product

20

Kamil Kahveci and Ahmet Cihan

2. STRUCTURE OF FOOD MATERIALS Food materials are porous and hygroscopic materials and their structure has a strong effect on transport and transport parameters. Structure has a strong effect particularly on mass diffusivity, permeability and thermal conductivity. On the other hand, its effect on thermal diffusivity is relatively weak [6].

Porous Materials A porous medium is a multi-phase system consisting of a solid phase and one or more fluid phases that occupy the pore space. The pore space of the porous medium consists of pores (nodes) and throats (bond) as connections between pores. The pores and throats are distributed randomly inside the porous medium. They have irregular shapes and therefore the structure of porous medium is very complex (see figure 2). Porous materials are divided into two groups as porous and capillary porous materials. This distinction is based on pore size. Materials with pore diameter equal to or higher than 10-7m are called porous materials, and smaller than 10-7m are called capillary porous materials [8]. The majority of food materials are capillary-porous materials [9].

Figure 2. Examples of porous media (10 x magnified): a) beach sand, b) sandstone; c) limestone; d) rye bread; e) wood; f) human lung (adapted from[7]). with permission.

Transport Phenomena During Drying of Food Materials

21

Pores in porous materials may be classified in three groups: (a) interconnected pores, (b) isolated or closed pores and (c) dead-end or blind pores (see figure 3). The interconnected pores are usually accessible from many directions. Blind or dead end pores are accessible from one direction only. Isolated pores are inaccessible and behave as part of the solid. Isolated pores decrease the diffusivity characteristics of the porous medium. Blind Pore

Closed Pore Interconnected Pore

Figure 3. Different types of pores in a porous medium.

Hygroscopic and Non-Hygroscopic Materials In non-hygroscopic materials, pore spaces are filled with liquid if the material is fully saturated, and with air if it is completely dry (see figure 4). In non-hygroscopic materials bound water content is quite low and vapor pressure is the function of temperature only [8]. Non-hygroscopic materials do not shrink during drying process. However, hygroscopic materials contain large amounts of physically bound water and therefore these materials usually shrink during the drying process. Food materials are in the hygroscopic material class where the modeling of drying process is more complex due to shrinkage. Solid Phase

Bound Gas Hygroscopic

Figure 4. continued on next page.

22

Kamil Kahveci and Ahmet Cihan Liquid Water

Gas Phase

Solid Phase

Non-Hygroscopic Figure 4. Hygroscopic and non-hygroscopic materials (adapted from [10]).

3. TRANSPORT PARAMETERS Dimensionless Numbers Various dimensionless numbers are encountered when dealing with the transport phenomena during the drying process. Most of these numbers are given in table 1 together with their physical meanings. Table 1. Dimensionless numbers and heir physical meanings D.less Number

Equation

Physical Meaning

Biot

h L Bi m = m D

mass transfer across the boundary/mass transfer within the solid

Biot

Bi q =

Fourier

Fo =

Dt L2

dimensionless time in the unsteady state regime

Grashof

Gr =

gL3β v ρ 2 ΔT ν

buoyancy forces/viscous forces

Lewis

Le =

α D

thermal diffusivity/mass diffusivity

Nusselt

Nu =

hqL

Peclet

Pe =

vL ν

convection/diffusion

Prandtl

Pr =

ν α

momentum diffusivity/mass diffusivity

Reynolds

Re =

vL ν

inertial force/viscous force

Schmidt

Sc =

ν D

momentum diffusivity/mass diffusivity

Sherwood

Sh =

h mL D

mass transfer/mass diffusivity

hqL λ

λ

heat transfer across the boundary/heat transfer within the solid

convective heat transfer/conductive heat transfer

Transport Phenomena During Drying of Food Materials

23

Table 1. Continued D.less Number Stanton

Equation h St m = m v St q =

Stanton

Physical Meaning wall mass transfer/mass transfer by convection

h q / ρC p v

heat transferred to fluid/heat transported by fluid

where L is the characteristic dimension of a solid body (m).

Relative Humidity Relative humidity is the ratio of the mole fraction of water vapor in a given moist air sample to the mole fraction in a saturated air sample at the same temperature and pressure. By using the perfect gas law, it can be expressed as the ratio of the actual vapor pressure Pv (Pa) to the vapor pressure of the saturated air at the same temperature Pv,sat (Pa). ϕ = Pv / Pv,sat (T)

(1)

Humidity Ratio The humidity ratio of moist air is defined as the ratio of the mass of water vapor Mv (kg) to the mass of dry air contained in the moist air Ma (kg).

ω = Mv / Ma

(2)

Saturation Humidity Ratio The saturation humidity ratio ωsat is the humidity ratio of moist air saturated with respect to water at the same temperature and pressure.

Specific Humidity The specific humidity ωs is the ratio of the mass of water vapor to the total mass of air in a particular volume of air. ωsp =

Mv Mv + Ma

The specific humidity is related to the humidity ratio by the following way:

(3)

24

Kamil Kahveci and Ahmet Cihan ωsp =

ω 1+ ω

(4)

Dry-Bulb Temperature The dry bulb temperature Tdb (°C) is the temperature measured by a (dry) thermometer immersed in a vapor-gas mixture.

Wet-Bulb Temperature The psychrometric wet -bulb temperature Twb (°C) is measured by placing a thermometer having a water-moistened wick covered bulb into a fast moving stream of ambient air. If the air surrounding the wet-bulb thermometer is not saturated, evaporation of water from the wick will occur. This will cause the bulb to cool. The amount of cooling is proportional to the evaporation rate. Having waited long enough, a steady-state is reached. This final equilibrium temperature is called the wet-bulb temperature of the moist air. The psychrometric wet-bulb temperature is not the same as the adiabatic temperature which is the temperature reached by moist air and water if the air is adiabatically saturated by the evaporating water. However, the adiabatic and psychrometric wet-bulb temperatures are nearly equal for moist air.

Dew-Point Temperature The dew-point temperature Tdp (°C) is the temperature at which a given unsaturated airvapor mixture becomes saturated.

Vapor Pressure The vapor pressure Pv (Pa) is the partial pressure exerted by the water vapor molecules in moist air. When air is fully saturated with water vapor, its vapor pressure is called the saturated vapor pressure Pv,sat (Pa).

Gas Pressure The total gas pressure Pg (Pa) is the sum of partial pressures of air and vapor. Pg = Pv + Pa

(5)

If it is assumed that gas phase obeys ideal gas law: ˆ = ρ RˆT Pi M i i

(6)

25

Transport Phenomena During Drying of Food Materials

ˆ is the molecular mass (kg/mol) Rˆ is the universal gas constant (8.314 J/(mol K) where M i and T is the temperature (K). An average molar mass can also be written as: ˆ = (M ˆ +M ˆ −M ˆ )P / P M g a v a v g

(7)

ˆ and M ˆ are the molar mass of gas, air and vapor (kg/mol), respectively. ˆ , M where M a v g

Capillary Pressure Capillarity can be explained by considering the effects of two opposing forces: adhesion, the attractive force between the molecules of two dissimilar substances, and cohesion, the attractive force between the molecules of a single substance. The magnitude of attraction between gas molecules is smaller when compared to liquids due to greater distance. This results in higher attraction of liquid molecules in a liquid gas interface towards the interior liquid compared to the surrounding gas and a surface tension takes place at the interface. Surface tension values between water and air at different temperatures under atmospheric pressure are given in table 2 together with other physical properties of the water. Table 2. Surface tension between water and air and other properties of water (adapted from [11]) T(°C) 0 10 20 30 40 50 60 70 80 90 100

σx103(N/m) 75.64 74.23 72.75 71.20 69.60 67.94 66.24 64.47 62.67 60.82 58.91

ρ(g/cm3) 0.99984 0.99970 0.99821 0.99565 0.99222 0.98803 0.98320 0.97778 0.97182 0.96535 0.95840

Cp(J/gK) 4.2176 4.1921 4.1818 4.1784 4.1785 4.1806 4.1843 4.1895 4.1963 4.2050 4.2159

Pv(kPa) 0.6113 1.2281 2.3388 4.2455 7.3814 12.344 19.932 31.176 47.373 70.117 101.325

η(Pa s) 1793 1307 1002 797.7 653.2 547.0 466.5 404.0 354.4 2314.5 281.8

λx103(W/mK) 561.0 580.0 598.4 615.4 630.5 643.5 654.3 663.1 670.0 675.3 679.1

Surface tension will also occur in a similar way at the solid material/liquid interface, and at the solid material/gas interface. The angle between the edge of the meniscus and the solid material is called contact angle (see figure 5 and figure 6). The contact angle acquires a value in compliance with the balance of surface tensions at the interface between gas, liquid and solid. Thus, the contact angle is a function of the characteristics of the liquid, the gas and the solid material. The forces are balanced if σ sg = σ sl + σ lg cos θ

(8)

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Kamil Kahveci and Ahmet Cihan

where σ is the surface tension (N/m) and the indices sg, sl and lg denote the solid-gas, solidliquid and liquid-gas interfaces, respectively. The contact angle θ (rad) can then be given as: cos θ =

σ sg − σ sl

(9)

σ lg

If the contact angle is between 0<θ<90° the surface is called hydrophile (water-loving) and σsl is smaller than σsg. If the contact angle equals to zero, the liquid wets the solid surface fully and σsg = σlg which in turn means that no work (energy) is needed to create the solidliquid interface. If the contact angle is bigger than 90°, the surface is called hydrophobic (water-hating). In the special case when θ= 180°, no work is needed to create the solid-gas interface. In this case, σsg will be equal to zero. This means that a drop of liquid on the surface of the solid remains separated by a thin film of vapor [12, 13]. θ

a)

θ b)

Figure 5. A liquid drop on a a) hydrophobic and b) hydrophile surface (adapted from [13]). σsg

θ σlg

σsl

Figure 6. The balance of forces that results in a contact angle θ (adapted from [13]).

The combination of surface tension and meniscus due to the capillaries causes the two phases to experience different pressures. If the surface of the tube is hydrophile (as in pore surfaces in a solid food material), there will be an under-pressure in the liquid and the liquid

Transport Phenomena During Drying of Food Materials

27

will penetrate the tube. The magnitude of the pressure difference over a meniscus (Pg - Pl) is calculated from the force acting on the water. This force for a circular tube can be written as follows: F = 2πro σ cos θ

(10)

where σ is the surface tension between liquid and gas (N/m); θ is the contact angle (rad), ro is the tube radius (m). By dividing the driving force by the sectional area of circular tube, the underpressure, or capillary pressure Pc (Pa) is determined as: Pc =

2σ cos θ ro

(11)

For a pore cross section that is not circular, a more general expression for the capillary pressure can be obtained from the equilibrium of forces as: ⎛1 1⎞ Pc = σ cos θ⎜⎜ + ⎟⎟ ⎝ r1 r2 ⎠

(12)

where r1 and r2 are the principal radii of the meniscus in two orthogonal directions. Equation (12) is known as the Laplace formula for capillary pressure. In describing the capillary flow in a porous material, the capillary pressure head h (m) is usually used instead of capillary pressure Pc (Pa). Capillary pressure head h (m) for the case that the air pressure is constant and equals to the atmospheric pressure is defined as: h = Pc /(ρg ) Capillary pressure (head )

(13)

Drying

Wettin

mirr (Sirr)

Liquid content Saturation

Figure 7. Capillary pressure head as a function of liquid content and saturation.

msat (Ssat)

28

Kamil Kahveci and Ahmet Cihan

where ρ is the density (kg/m3) and g is the gravitational acceleration (m/s2). The capillary pressure (head) is a function of the liquid content or the saturation degree and it usually has the shape as shown in figure 7.

Viscosity Viscosity is one of the most important transport properties of liquids and gases. The dynamic viscosity (η) is defined by the following equation for a Newtonian fluid: τ = ηγ

(14)

where τ is the shear stress (Pa), γ is the shear rate (1/s) and η is the dynamic viscosity (Pa s). Viscosity is usually determined by measuring the shear stress via capillary or rotational viscometer at various shear rates.

Chemical Potential

ˆ (J/mol), is a term introduced by Willard Gibbs. He defined it as Chemical potential, μ follows: If to any homogeneous mass in a state of hydrostatic stress we suppose an infinitesimal quantity of any substance to be added, the mass remaining homogeneous and its entropy and volume remaining unchanged, the increase of the energy of the mass divided by the quantity of the substance added is the potential for that substance in the mass considered. The chemical potential of a system is the amount by which the energy of the system would change on condition that an additional particle were added. If a system has more than one species of particle, there is a separate chemical potential associated with each species. If a thermodynamic system containing n constituent species is considered, its total internal energy U (J) can be written to be a function of the entropy S (J/K), the volume V (m3), and the number of moles of each species N1,..., Nn: U = U (S, V, N1 ;..., N n )

(15)

It is emphasized by referring U as the internal energy that the energy contributions resulting from the interactions between the system and external objects are excluded. The chemical potential of the ith species is defined as the partial derivative of the internal energy [14]. ⎛ ∂U μˆ i = ⎜⎜ ⎝ ∂N i

⎞ ⎟⎟ ⎠ S, V , N j ≠ i

(16)

In real systems, it is usually difficult to hold the entropy fixed since this requires complete thermal insulation. It is therefore more convenient to define the Helmholtz free energy A (J), which is a function of the temperature, volume, and number of moles:

Transport Phenomena During Drying of Food Materials A = A(S, V, N1 ;..., N n )

29 (17)

In terms of the Helmholtz free energy, the chemical potential is

⎛ ∂A μˆ i = ⎜⎜ ⎝ ∂N i

⎞ ⎟⎟ ⎠S,V , N j≠i

(18)

If the pressure and temperature are assumed to be constant, the chemical potential can be expressed as the partial derivative of the Gibbs free energy G (J) with respect to mole number of the species. ⎛ ∂G μˆ i = ⎜⎜ ⎝ ∂N i

⎞ ⎟⎟ ⎠S,V , N j≠i

(19)

The chemical potential of water vapor μˆ (J/mol)is related to water activity aw by:

μˆ = μˆ o + RˆT ln a w

(20)

where μˆ o is the chemical potential for pure water (J/mol), Rˆ is the ideal gas law constant (8.314 J/(mol K)) and T is the temperature (K).

Concentration Concentration of a mixture and its components may be expressed in terms of mass and mol. In terms of mass, concentration of a mixture and component i are given by: C = M/V

Ci = M i / V

or

ρ = M/V

ρi = M i / V

(21)

where M (kg) and Mi (kg) are the mass of the mixture and component i and V is the total volume (m3). A related quantity, the mass fraction of component i is defined as: ) m i = M i / M = Ci / C

or

) m i = M i / M = ρi / ρ

(22)

Molar concentration of a mixture and component i and the mole fraction of component i are defined as: ˆ = N/V C ) ˆ /C ˆ ni = Ni / N = C i

ˆ = N /V C i i

(23) (24)

30

Kamil Kahveci and Ahmet Cihan

where N and Ni are the mol of the mixture and component i, respectively. It can easily be shown that: M = ∑ Mi

C = ∑ Ci

i

N = ∑ Ni

i

ˆ = ∑C ˆ C i

i

i

ρ = ∑ ρi i

) ) n = ∑ ni

) ) m = ∑ mi

(25)

i

(26)

i

The mass concentration and molar concentration are related through the molar mass of component i as: ˆ ˆ C Ci = M i i

(27)

Using the ideal gas law for a mixture of gases, the following equation can be written for gas I Pi V = N i RˆT

(28)

where Pi (Pa) is the partial pressure of gas i for which there is Ni moles, and Rˆ is the universal gas constant (8.314 J/(mol K)). Therefore, concentration of any gas in a mixture can be written in terms of its partial pressure as: ˆ = N i = Pi C i V RˆT

(29)

The total concentration is related to the total pressure. Using the equation PV = NRˆT , total concentration can be written as: ˆ= P C RˆT

(30)

r r ˆ ) is assumed to be the driving force for The gradient of concentration ( ∇C or ∇C molecular diffusion, although it does not have the dimensions of force. Fick’s law relating diffusion to a concentration gradient is derived by analogy with heat conduction.

Moisture Content Moisture content of a wet material is defined as the ratio between the total mass of water Mw (kg) and the mass of dry solid Md (kg). m=

Mw Md

(31)

Transport Phenomena During Drying of Food Materials

31

Sometimes a wet basis moisture content mwb (kg water/kg wet substance), which is the moisture ratio based on the total mass of wet material, is used. mw =

Mw Mt

(32)

The two moisture contents are related by the following expression: m=

mw 1 − mw

(33)

Moisture Ratio Moisture ratio is dimensionless moisture content and is defined as: mr =

m − me mo − me

(34)

where m, mo, me are the instantaneous, initial and equilibrium moisture contents respectively. In certain drying processes, materials are not continuously exposed to uniform relative humidity and temperature conditions. In such cases, the following equation not incorporating the equilibrium moisture content is used instead of the equation defined in Eq. (34). mrr =

m mo

(35)

Saturation Saturation degree (or pore saturation) is defined as the volume fraction of void space filled by moisture in a substance. S=

Vw Vvoid

(36)

Saturation takes values from 0 (when the medium is completely dry) to 1 (when the medium is completely saturated). There is a relationship between saturation and moisture content as: m=

ψρ w S (1 − ψ)ρ ds

(37)

32

Kamil Kahveci and Ahmet Cihan

where ρw and ρds are the densities of water and dry solid (kg/m3) and ψ is the porosity of material.

Mass Capacity Mass capacity is a potential for mass transfer and is defined by analogy with that of temperature. The heat capacity of a body is given as: ⎛ ∂H ⎞ CP = ⎜ ⎟ ⎝ ∂T ⎠ P

(38)

where H is the enthalpy (J), T is the temperature (K) and P is the pressure (Pa). Similarly, mass capacity of a body is given as [15]: ⎛ ∂m ⎞ Cm = ⎜ ⎟ ⎝ ∂W ⎠ T

(39)

where W is called moistness (°M). If a mean mass capacity is assumed over a range of moistness, then the quantity of moisture passing from one place at W2 to another at W1 (W2>W1) is given by C m ( W2 − W1 ) [15]. A scale of moistness has been constructed by Luikov [16]. The scale is chosen so the moistness at the maximum hygroscopic moisture content (ϕ=1) is 100°M, and the mass capacity C om of the standard body is taken as one hundredth of the maximum sorptional moisture content m omax . The moistness of the standard body W (°M) at any moisture content mo then becomes: W=

mo mo = o 100 o C m m max

(40)

If the standard body is in contact with another material at hygrothermal equilibrium, the degree of moistness is everywhere the same and the mass capacity of the second material can be obtained from its moisture content.

Cm =

m W

The value of Cm (kg/(kg°M)) is usually determined experimentally based on Eq.( 41).

(41)

Transport Phenomena During Drying of Food Materials

33

Water Activity Water activity is one of the most important parameters of food processing. Many physical and chemical factors are affected by water activity significantly. Water activity is defined as the ratio of the vapor pressure of water in a material (Pv) to the vapor pressure of pure water (Pvo) at the same temperature. aw =

Pv Pvo

(42)

When the vapor pressure and temperature equilibrium are obtained, the water activity of a material is equal to the relative humidity of surrounding air. Some Microorganisms and Their Growth Minima

Water Activity

1.0

Fresh vegetables,Fruits, Meats Poultry, Fish, Mik Cured Meats, such as Ham

0.9

Salami, Some Dry Cheeses

Cl. Botulinum

Salmonella Most Bacteria Most Yeasts Staphylococcus

Some Foods and Their Water Activity Ranges

Flour, Cakes, Rice, Beans, Cereals Most Molds

0.8 Intermediate Moisture Foods Salt, Preserved Foods, Jams

Holophilic Bacteria

0.7

Extremely Osmophilic Microorganisms (Some Molds and Yeasts)

Rolled Oats

Dried Fruits, Caramels

0.6 Dehydrated Foods

0.5 Figure 8. Water activity levels of some foods and growth minima’s of micro-organisms (adapted from [17]).

Water activity levels of some foods and growth minima’s of micro-organisms are given in figure 8. Chemical effects increasing with water activity are enzymatic and nonenzymatic browning reactions (Maillard reactions) and microbial damage. Since enzymes in many food materials are not inactivated during heating process, enzymatic reactions occur also in low moisture contents. Nonenzymatic browning reactions, too, are affected by water activity. In the presence of water, carboxyl and amino compounds are involved as reactants, products, or catalysts in the browning process [17]. In low water activities, enzymatic and browning reaction rates are low due to substrate solubility and mobility. And for high water activity

34

Kamil Kahveci and Ahmet Cihan

values, water dilutes the reactants and the rate of browning falls. Bacterial growth occurs at extremely high water activity levels and is dormant for a water activity below 0.90 [17]. Most yeast and moulds, however, can proliferate at water activity levels as low as 0.80. Physical changes such as texture and aroma can depend greatly on water activity. Textural changes are most often seen in freeze drying and subsequent storing of foods, particularly for meats and fish. The water activity in dried foods can also affect the retention of aroma [17].

Equilibrium Moisture Content One of the most important parameters in drying is the humidity of the air in contact with the solid material. Let us assume that a wet material is exposed to an air stream with constant humidity and temperature and there is no change in conditions of air. If it is waited long enough, the solid material will have a definite moisture content. This moisture content is known as the equilibrium moisture content of the material under the specified humidity and temperature of the air. The moisture content is usually expressed on the dry basis as kg water/kg dry solid. If the material contains more moisture than its equilibrium value in contact with air of a given humidity and temperature, it will dry until it reaches equilibrium value. If the material contains less moisture than its equilibrium value, it will adsorb water until it reaches equilibrium value. For air having 0% humidity, the equilibrium value of all materials is zero. In general, at low relative humidities, the equilibrium moisture content is greater for food materials high in protein, starch and lower for food materials high in soluble solids [1]. Equilibrium moisture content is generally determined experimentally. There are two different approaches called gravimetric and hygrometric [18]. In gravimetric method, air temperature and water activity is kept constant until the moisture content of the sample achieves a constant equilibrium value. The air may be circulated or stagnant. In hygrometric method, the moisture content of the sample is maintained at a constant value until the surrounding air reaches to equilibrium value. Numerous mathematical models also exist to predict the equilibrium moisture content (see table 3). Some of these models are theoretical (such as Modified BET and GAB), some are semi-empirical (such as Henderson and Halsey) and some are empirical (such as Smith and Oswin) (for more details see Ref. [19]). Of these models, Halsey model yields better results for many food materials compared to Henderson model [20]. The BET and Harkins and Jura equations provide acceptable predictions only for ϕ<0.3 and Modified Chung and Pfost equation is suitable for cereal grains [21]. Oswin model provides satisfactory results for protenaceous foods, starchy foods, meats and vegetables [22].

Transport Phenomena During Drying of Food Materials

35

Table 3. Theoretical and empirical sorption models Name

Model Equation

Freundlich

m e = a (a w )1 / b

BET

m e = m ml c a w /[(1 − a w )(1 − a w + ca w )]

Harkins and Jura

ln a w = b − (a / m e2 )

Oswin

m e = a (a w /(1 − a w )) b

Smith

m e = a − b ln(1 − a w )

Halsey

m e = [a / ln a w ]

Henderson

m e = [ln(1 − a w ) /(−a )]

Chung-Pfost

m e = −[ln(− ln a w / a )] / b

Kuhn

m e = a / ln a w + b

Iglesias and Chirife

ln m e + (m e2 + m 0.5 ) 0.5 = ba w + a

GAB

m e = (m ml cka w ) / [(1 − ka w )(1 − ka w + cka w )]

Anderson

m e = (abca w ) / 1 + (b − 2)ca w + (1 − b)c 2 a w

Peleg

m e = aa + ba

1/ b 1/ b

[

]

[

c w

]

d w

Caurie

m e = exp[a w ln a − 1 /(0.045b)]

Modified BET

m e = m m ca w /[(1 − a w )(1 − c ln(1 − a w ))]

Modified Oswin

m e = (a + bT)(a w /(1 − a w )) c

Modified Halsey

m e = [exp(a + bT ) / ln a w ]

Modified Henderson

m e = {− ln(1 − a w ) / [a (T + b)]}

Modified Chung-Pfost

m e = −{ln[(−(T + b) ln a w ) / a ] / c)}

1/ c 1/ c

where a, b, c, k

constants

aw T me mml m0.5

water activity temperature (°C), equilibrium moisture content ( kg water/kg dry solid) monolayer moisture content ( kg water/kg dry solid) moisture content at aw=0.5 ( kg water/kg dry solid)

Sorption Isotherms A sorption isotherm is the graphic representation of the sorption behavior of a substance. It represents the relationship between the equilibrium moisture content of a product and the water activity of the material at a particular temperature. As shown in figure 9, there are three basic shapes for sorption isotherms. There is a closed loop hysterisis between desorption and adsorption isotherms as shown in figure 10. The desorption isotherm always has the larger equilibrium moisture content.

36

Kamil Kahveci and Ahmet Cihan

Moisture Content, m

1

2 3

Water Activity, aw

Moisture Content, m

Figure 9. Basic shapes of sorption isotherms. 1. Highly hygroscopic, 2. Medium hygroscopic, 3. Low hygroscopic, sensitive to high air humidity (adapted from [23]). Strongly bound monolayer

Less strongly bound water layers and capillary adsorbed water

Solvent and free water

Desorption Adsorption

A

0.0

C

B

0.2

0.4

0.6

0.8

1.0

Water Activity, aw Figure 10. Sorption isotherms.

Bound and Unbound Water If the equilibrium moisture content of a material is continued to its intersection with the 100% humidity line, the moisture is called bound water (see figure 11). This water in the material exerts a vapor pressure less than that of liquid water at the same temperature. If such a material contains more water than indicated by intersection with the 100% humidity line, it exerts vapor pressure equal to that of pure liquid at the same temperature. This excess moisture content is called unbound water. The unbound water is held primarily in the voids of the solid.

Transport Phenomena During Drying of Food Materials

37

Free Moisture Free moisture is the moisture above the equilibrium moisture content (see figure 11). Free moisture is the moisture that can be removed by drying under a given relative humidity. bound moisture

unbound moisture

Relative Humidity, φ

1.0

free moisture 0.5

0.0 me

mc Moisture Content, m

Figure 11. Types of moisture involved in the drying.

Thermophysical Properties Thermophysical properties of a material are those which control the thermal energy transport and/or storage within it, as well as the transformations undergone by the material under the action of heat [24]. The density, specific heat, thermal conductivity and thermal diffusivity are regarded as thermophysical properties. These properties are dependent on the temperature, the material chemical composition and the physical structure.

Density There are various density definitions for a porous material. The most important ones are apparent density and substance density. Their values are generally determined by experimental measurements. Volumetric displacement method, dimension method, stereopycnometer method and buoyant force method are the commonly used methods to determine apparent density [25]. Apparent volume of the sample is measured by these methods. Subsequently, density is calculated by dividing the measured sample weight by this determined volume. In volumetric displacement method, the apparent volume is determined by placing the sample in a container of known liquid volume and measuring the volume displacement. In dimension method, the apparent volume is determined by averaging a number of dimension measurements with micrometers. In stereopycnometer method, the sample is covered with silicone grease to make it impervious to gases and its apparent volume

38

Kamil Kahveci and Ahmet Cihan

is measured by a stereopycnometer. In buoyant force method, the apparent volume is determined by measuring the buoyant force applied on the sample when coated sample is immersed in a known liquid. Substance density is mostly determined using a gas (helium) stereopycnometer. Density of a wide variety of foods at different moisture content can be found in Refs. [19], [25] and [26]. The density is dependent on moisture content of the material. It is difficult to measure the density as a function of moisture content and hence empirical correlations are often used for this purpose. The correlations are generally in the following forms: ρ = a + bm w

ρ = a + bmw + cm2w

ρ = a + bm w + cm 2w + dm 3w

(43a)

ρ = a + b exp(cm w )

ρ = a + b exp(cm 2w )

ρ = a + b ln m w

(43b)

ρ = a + b ln(m w + c)

ρ = a + b / mw + c / m2w

ρ = a + b / mw + clnmw

(43c)

where ρ is the density (kg/m3) and mw is the moisture content in wet basis (kg water/kg wet substance) and a, b, c and d are empirical constants. Alternatively, densities of food materials can be determined using the densities of components constituting the material [27]: ρ=

1 ) m ∑ i / ρi

(44)

i

) where m i is the mass fraction of component i and ρi is the corresponding density. The mass fractions of components of food materials can be obtained from USDA Handbook 8 [28]. The densities for basic components can be estimated using the equations given in table 4. Table 4. Densities of food components as a function of temperature. (adapted from [27]) Component

ρ (kg/m3)

Carbohydrate

ρ = 1.5991x10 3 − 0.31046T

Protein

ρ = 1.3299 x10 3 − 0.51840T

Fat

ρ = 9.2559x10 2 − 0.41757T

Ash

ρ = 2.4238x10 3 − 0.28063T

Fiber

ρ = 1.5991x10 3 − 0.31046T

Water

ρ = 997.18 + 3.1439 x10 −3 − 3.7574 x10 −3 T 2

Ice

ρ = 916.89 − 0.13071T

Transport Phenomena During Drying of Food Materials

39

Apparent or Bulk Density Apparent or bulk density is defined as the ratio between the current weight of the material and its overall volume. ρ app =

Ms + M w Vs + Vw + Va

(45)

where Ms (kg) and Mw (kg) are the masses of dry solid and water, respectively; and Vs (m3), Vw (m3) and Va (m3) are the volumes of dry solid, water and air respectively.

Substance Density Substance density is defined as the ratio between the total mass of the material and its overall volume excluding all pores. ρs =

Ms + M w Vs + Vw

(46)

Particle Density Particle density is defined as the ratio between the total mass of the material and its overall volume excluding only externally connected pores. ρp =

Ms + M w Vs + Vw + Vcp

(47)

Dry Solid Density Dry solids density is defined as the ratio between the mass of solid in the material and the volume occupied by the solid. ρ ds =

Ms Vs

(48)

The dry solid density is related with the apparent density as follows: ρ app = ρ ds (1 − ψ) + ψSρ w where ψ is the porosity and S is the saturation degree.

(49)

40

Kamil Kahveci and Ahmet Cihan

Equilibrium Density Equilibrium density is defined as the ratio between the mass of the material at the equilibrium with environmental air and its overall volume. ρe =

Me Ve

(50)

True Density The density of a pure component substance i of a material is calculated using its mass and volume as follows. ρi =

Mi Vi

(51)

Specific Heat Specific heat C P (J/(kg K)) is defined as the amount of energy Q (J) needed to increase the temperature of one kilogram of matter by one degree Celcius.

CP =

Q M (T2 − T1 )

(52)

Specific heat of food materials are generally determined experimentally by the following methods: mixture method, comparison method, adiabatic method and differential scanning calorimeter method (see Ref. [19] for details). Specific heat for a wide variety of foods at different moisture content can be found in Refs. [19] and [26]. Specific heat is dependent on composition and temperature. These dependences are generally expressed using the correlations in the following forms: C P = a + bm w

CP = a +

C P = a + bm w + cm 2w

b b c + c ln m w + 2 CP = a + mw mw mw

C P = a + bm w + cT

C P = a + bm w + cm 2w + dm 3w

(53a)

C P = a + bm w /(1 + m w )

(53b)

C P = a + bm w + cm 2w + dT C P =

am w b + 1 + mw 1 + mw

(53c)

41

Transport Phenomena During Drying of Food Materials

where mw is the moisture content in wet basis (kg water/kg wet substance), T is the temperature (°C) and a, b, c, and d are empirical constants. Specific heat of food materials may also be obtained using specific heats of components constituting the material. In the context of such approach, Leninger and Baverloo [29] have suggested the following correlation: ) ) ) C p = (0.5m f + 0.3m s + m w )4.18

(54)

) ) ) where m f , m s and m w are the mass fraction of fat, solid and water respectively. This equation references the specific heat of water (4.18 kJ/kg) at 20°C. Charm [30] has proposed another correlation in the form of Equation (54) as follows: ) ) ) C p = 2.094m f + 1.256m s + 4.187m w

(55)

This equation references the values of specific heat at 75°C. Heldman and Singh [31] have proposed the following correlation for 20°C or lower temperature: ) ) ) ) ) C p = 1.424m c + 1.549m p + 1.675m f + 0.837m a + 4.187m w

(56)

where the coefficients represent the specific heats of carbohydrate, protein, fat, ash and water. A more general equation in the form of Eqs. (54)-(56) can be expressed as follows:

) C P = ∑ m i C pi

(57)

i

)

where m i is the mass fraction of component i and Cpi is the corresponding specific heat. The mass fractions of components of food materials can be obtained from USDA Handbook 8 [28]. Table 5. Specific heat capacities of food components as a function of temperature. (adapted from [27]) Component

CP [kJ/(kg°C)]

Carbohydrate

C P = 1.5488 + 1.9625x10 −3 T − 5.9399 x10 −6 T 2

Protein

C P = 2.0082 + 1.2089 x10 −3 T − 1.3129 x10 −6 T 2

Fat

C P = 1.9842 + 1.4733x10 −3 T − 4.8008x10 −6 T 2

Ash

C P = 1.0926 + 1.8896x10 −3 T − 3.6817 x10 −6 T 2

Fiber

C P = 1.8459 + 1.8306 x10 −3 T − 4.6509 x10 −6 T 2

Water Ice

C P = 4.0817 − 5.3062 x10 −3 T + 9.9516 x10 −4 T 2 (-40
C P = 2.0623 + 6.0769 x10 −3 T

The specific heats for basic components can be estimated using the equations given in table 5.

42

Kamil Kahveci and Ahmet Cihan

Thermal Conductivity Thermal conductivity expresses the efficiency of a material as a heat conductor and it is defined by the Fourier equation: r r J q = − λ ∇T

(58)

r where J q is the heat flux vector (W/m2), λ is the thermal conductivity (W/(m K)) and T is the temperature (°C). Thermal conductivity of food materials depends on its composition and temperature and it is generally determined experimentally. For measurement of thermal conductivity, there are steady state techniques such as guarded hot plate and radial heat flow method, quasi-steady techniques such as Cenco-Fitch and Rahman-Fitch method and transient techniques such as line source method. The details on thermal conductivity measurement methods can be obtained from Ref. [19]. In addition, thermal conductivity values of a wide variety of foods at different moisture content can be found in Refs. [19] and [26]. Measurement of thermal conductivity as a function of composition and temperature is difficult. Hence, generally empirical correlations are used. Thermal conductivity of food materials decreases with a decrease in moisture content. Therefore, it is usually found a linear relation between thermal conductivity and moisture content [19]. However, the linear correlation of thermal conductivity with moisture content is limited to small changes in moisture. Hence, nonlinear correlations are also needed to cover the whole range of moisture content. λ = a + b exp(cm w )

(59a)

λ = a + b / m w + c / m 2w λ = a + b / m w + c ln m w λ = a + bm w + cT

(59b)

λ = a + bm w + c / T

(59c)

λ = a + bm w

λ = a + bm w + cm 2w

λ = (a + bT + cT 2 )(d + em w )

where mw is the moisture content in wet basis (kg water/kg wet substance) and a, b, c, and d are empirical constants. Thermal conductivity correlations given above are limited to specific materials and varieties. However, there are also correlations that can be used for various types of foods. Sweat [32] has proposed the following linear model for predicting the thermal conductivity of fresh fruits and vegetables giving predictions within 15% of most experimental values. λ = 0.148 + 0.439m w

(60)

The above model is limited to water content above 60% (wet basis). Another correlation in the form of Eq. (60) has been suggested by Sweat [33] for thermal conductivity prediction of the meat.

Transport Phenomena During Drying of Food Materials λ = 0.08 + 0.52m w

43 (61)

This correlation is limited to water content from 5% to 88% (wet basis) and temperature from 0°C to 60°C. The correlation developed by Sweat [32] (Eq. (60)) shows that thermal conductivity is strongly dependent on the water content for all fruits and vegetables except apple. This is probably because of the fact that apple is a highly porous fruit. Therefore, it will be suitable to use more general correlations containing the porosity term [19]. One of this type of correlations has been suggested by Rahman [34] for thermal conductivity prediction of apple, beef, pear, potato and squid as follows: m λ 1 = 1.82 − 1.66 exp(−0.85 w ) λ o 1 − εa m wo

(62)

where ε a is the volume fraction of air. This correlation is limited to water content between 5% to 88% (wet basis), porosity from 0 to 0.5 and temperature from 20 to 25°C. Another frequently used approach for thermal conductivity prediction involves the use of thermal conductivities of components constituting the material. Sweat [26] has proposed the following correlation for solid and liquid foods. ) ) ) ) ) λ = 0.25m c + 0.155m p + 0.16m f + 0.135m a + 0.58m w

(63)

The following equation has been suggested by Choi and Okos [27] for liquid foods. ) ) ) ) ) λ = 0.2051m c + 0.2m p + 0.175m f + 0.135m a + 0.61m w

(64)

Riedel [35] has proposed the following equation for fruit juices, sugar solutions and milk over a temperature range from 0°C to180°C. λ = (326.58 + 1.0412T + 0.00337T 2 )(0.46 + 0.54m w )(1.73x10 −3 )

(65)

A more general model for thermal conductivity prediction can be written as follows [27]: λ = ∑ λi εi

(66)

i

where εi is the volume fraction of component i and λi is the corresponding thermal conductivity. The volume fraction is used in Eq. (66) since the thermal conductivity is dependent on the spatial structure of the material. The volume fraction can be defined as follows. εi =

) m i / ρi ) ∑ m i / ρi i

(67)

44

Kamil Kahveci and Ahmet Cihan

) where m i is the mass fraction of component i. The mass fractions of components of food materials can be obtained from USDA Handbook 8 [28]. The thermal conductivities for basic components can be estimated with the equations given in table 6. Table 6.Thermal conductivities of food components as a function of temperature. (adapted from [27]) Component

λ [W/(m°C)]

Carbohydrate

λ = 0.20141 + 1.3874x10 −3 T − 4.3312x10 −6 T 2

Protein

λ = 0.17881 + 1.1958x10 −3 T − 2.7178x10 −6 T 2

Fat

λ = 0.18071 + 2.7604x10 −4 T − 1.7749x10 −7 T 2

Ash

λ = 0.32962 + 1.4011x10 −3 T − 2.9069x10 −6 T 2

Fiber

λ = 0.18331 + 1.2497 x10 −3 T − 3.1683x10 −6 T 2

Water

λ = 0.5710 + 1.7625x10 −3 T − 6.7036x10 −6 T 2

Ice

λ = 2.2196 − 6.2489x10 −3 T + 1.0154x10 −4 T 2

There are also more different models proposed for thermal conductivity prediction. The following equation has been developed by Marinos-Kouris and Maroulis [18] using thermal conductivity data for more than 100 food materials in 11 different categories: λ=

⎡ E ⎛1 ⎡ E ⎛1 1 1 ⎞⎤ m 1 ⎞⎤ ⎟⎟⎥ + ⎟⎟⎥ λ o exp ⎢− ao ⎜⎜ − λ i exp ⎢− ai ⎜⎜ − ˆ ˆ 1+ m ⎣⎢ R ⎝ T TR ⎠⎦⎥ 1 + m ⎣⎢ R ⎝ T TR ⎠⎦⎥

(68)

where E ao (J/mol) is the activation energy for heat conduction in dry material at m=0, Eai (J/mol) is the activation energy at m=∞, Rˆ is the ideal gas constant (8.3143 (J/(mol K)), T is the material temperature (°C) and TR is a reference temperature (°C). The reference temperature has been chosen as 60 °C by Marinos-Kouris and Maroulis [18]. In determining thermal conductivity for heterogeneous materials, structural models must be used and the effect of geometry must be taken into consideration. Some structural models available in the literature are given in table 7. In series model, layers of components are placed normal to the heat flow. On the other hand, in parallel model, layers of components are placed in the direction of the heat flow. In mixed model, heat conduction occurs by parallel and vertical heat flow. The random model assumes that both phases are dispersed randomly. Maxwell model is for the case where one phase in continuous and the other phase is dispersed as uniform spheres [18].

Transport Phenomena During Drying of Food Materials

45

Table 7. Structural models for thermal conductivity in heterogeneous materials (adapted from [18]) Model

Equation

Perpendicular (series)

1 / λ = (1 − ε) / λ1 + ε / λ 2

Parallel

λ = (1 − ε)λ1 + ε λ 2

Mixed

1/ λ =

Random

λ = λ(11−ε ) λε2

Effective medium theory

Maxwell

⎛1− ε 1− F ε ⎞ ⎟ + F ⎜⎜ + ⎟ (1 − ε) λ1 + ε λ 2 λ λ 2 ⎠ ⎝ 1

[

λ = λ1 b + (b 2 + 2(λ1 / λ 2 ) /( Z − 2)1 / 2

]

b = [Z(1 − ε) / 2 − 1 + (λ 2 / λ1 )(εZ / 2 − 1)]/( Z − 2)

λ=

λ 2 [λ 1 + 2λ 2 − 2(1 − ε)(λ 2 − λ 1 )] λ 1 + 2λ 2 + (1 − ε)(λ 2 − λ 1 )

where λ is the effective thermal conductivity, λi is the thermal conductivity of phase i, ε is the volume fraction of phase 2, and F and Z are the parameters.

Thermal Diffusivity Thermal diffusivity α (m2/s) indicates how heat will be diffused in a material when it is heated and is defined by the Fourier equation. ∂T = α∇ 2 T ∂t

(69)

Thermal diffusivity is related to the thermal conductivity as: α = λ / ρC P

(70)

Thermal diffusivities of food materials are affected considerably by the composition of the material. In addition, temperature also affects the thermal diffusivity. Pressure, too, may have a considerable effect. The effect of moisture content is generally linear for moisture higher than 10% and is non-linear for lower levels of moisture. Thermal diffusivity can be measured experimentally or may be obtained indirectly from the specific heat and density data. Typical thermal diffusivity value for most food is 1.0-2.0x10-7 m2/s [6]. For the prediction of thermal diffusivity as a function of moisture content and temperature, empirical correlations are generally used. Empirical correlations are mostly in the following forms. α = a + bm w

α = a + bm w + cm 2w

α = a + b exp(cm w )

α = a + b / m w + c / m 2w α = a + b / m w + c ln m w α = a + bm w + cT

(71a) (71b)

46

Kamil Kahveci and Ahmet Cihan

where α is the thermal diffusivity (m2/s), mw is the moisture content in wet basis (kg water/kg wet substance), T is the temperature (°C) and a, b and c are the empirical constants. Thermal diffusivities of food materials can also be calculated using the thermal diffusivities of the components of food material as follows [27]: ) α = ∑ αi mi

(72)

i

) where m i is the mass fraction of component i and αi is the corresponding thermal diffusivity. The mass fractions of components of food materials can be obtained from USDA Handbook 8 [28]. The thermal diffusivities for basic components can be predicted by the equations given in table 7. Table 8.Thermal diffusivities of food components as a function of temperature. (adapted from [27]) Component

α (m2/s)x106

Carbohydrate

α = 8.0842x10 −2 + 5.3052x10 −4 T − 2.3218x10 −6 T 2

Protein

α = 6.8714 x10 −2 + 4.7578x10 −4 T − 1.4646x10 −6 T 2

Fat

α = 9.8777 x10 −2 + 1.2569 x10 −4 T − 3.8286x10 −8 T 2

Ash

α = 1.2461x10 −1 + 3.7321x10 −4 T − 1.2244x10 −6 T 2

Fiber

α = 7.3976x10 −2 + 5.1902 x10 −4 T − 2.2202x10 −6 T 2

Water

α = 0.1317 + 6.2477 x10 −4 T − 2.4022x10 −6 T 2

Ice

α = 1.1756 − 6.0833x10 −3 T + 9.5037 x10 −5 T 2

Inverse Approaches for the Prediction of Thermophysical Properties Inverse modeling is a general mathematical method to determine unknown causes on the basis of observation of their effects, as opposed to modeling of direct problems whose solution involves finding effects on the basis of a description of their causes [36]. Inverse approaches for the prediction of the thermophysical properties become widely used in the last few decades. Inverse approaches can also be used to obtain other physical properties such as heat and mass transfer coefficients. To use this approach, first the governing equations and boundary conditions are defined. If physical properties are known, the problem is a direct one. The objective of the direct problem is to determine the temperature and moisture content fields in the drying material. On the other hand, for the inverse problems, physical properties are unknown parameters. Assumed values for these properties are used to solve the model equations. Desired parameters are determined by systematically minimizing the differences between observed and simulated state variables.

Transport Phenomena During Drying of Food Materials

47

Enthalpy The enthalpy is defined as the heat content per unit mass. Enthalpy is relative, that is, the actual heat content is dependent on the datum or zero point chosen. The enthalpy of moist air per unit mass is defined as the sum of the enthalpies of dry air and superheated water vapor per unit mass.

h ma = h a + ωh v

(73)

where ω is the humidity ratio of air (kg water/ kg dry air). hma (J/kg), ha (J/kg) and hv (J/kg) are the enthalpies of the moist air, dry air and superheated water vapor per unit mass, respectively. The enthalpy of dry air per unit mass is defined as: h a = C Pa (T − TR )

(74)

where CPa (J/(kg K)) is the specific heat of dry air at constant pressure and TR (°C) is the reference temperature. The enthalpy of the superheated water vapor per unit mass can be expressed as follows: h v = C Pv (T − Tdp ) + Δh vap + C Pw (Tdp − TR )

(75)

where Tdp is the dew point temperature (°C) and Δh vap is the enthalpy of vaporization (J/kg).

Sensible Heat The sensible heat Q (J) is the heat energy absorbed or released when a body changes temperature. Q = mC P (T1 − T2 )

(76)

where m is the mass (kg), CP is the specific heat at constant pressure (J/kg K)) and T is the temperature (°C).

Enthalpy of Vaporization Enthalpy of vaporization Δhˆ vap (J/mol) is the quantity of heat required to transform a substance from a liquid phase to vapor phase at a constant temperature. Slightly higher energy is required to evaporate water from solid material than that required to evaporate free water because water is partially bound in materials. The enthalpy of vaporization is a function of the temperature at which the vaporization occurs and the moisture content of material. It

48

Kamil Kahveci and Ahmet Cihan

decreases as the temperature increases and increases as the moisture content decreases. Enthalpy of evaporation of free water at various temperatures is given in table 9. Table 9. Enthalpy of vaporization of water as a function of temperature. (adapted from [11]) T (°C)

0

25

40

60

80

100

Δhˆ vap (kJ/mol)

45.054

43.990

43.350

42.482

41.585

40.657

Heat Transfer Coefficients The surface heat transfer coefficient hq (W/(m2K)) and overall heat transfer coefficient Uq (W/(m2K)) are defined as: J q = h q ΔT

J q = U q (ΔT) LM

(77)

where Jq is the heat flux (W/m2), T is the temperature (°C) and LM is the log-mean temperature difference between the two media. Surface heat transfer coefficient is not a property of the material, but rather a property of convective heat transfer system between solid surface and fluid. The surface heat transfer coefficient is dependent on the thermophysical properties of fluid and solid (density, specific heat and thermal conductivity), characteristics of the solid (shape, dimensions, surface temperature, surface roughness, outgoing fluxes), and the characteristics of fluid flow (velocity, turbulence intensity) and the systems (heat transfer equipment) [19]. The surface heat transfer coefficient can be determined experimentally or calculated from empirical correlations. Empirical correlations generally involve the following dimensionless numbers: Nusselt, Reynolds and Prandtl. Luikov [37] states that empirical correlations should also include the Gukhman number defined as: Gu = (Ta − Tm ) / Ta

(78)

where Ta is the temperature of drying air (K) and Tm is the temperature of moist surface (K). The Gukhman number is a generalized variable determining the peculiarities of simultaneous heat and mass transfer with evaporation.

Mass Transfer Coefficients The mass flux density Jm (kg/(m2s)) is defined by the following equation in which the driving force can be the partial pressure difference, the concentration difference or the mol fraction difference [6]: J m = h m ΔC

J m = h P ΔP

J m = h n Δn f

(79)

Transport Phenomena During Drying of Food Materials

49

where hm (m/s), hP (kg/(m2s Pa)), hn (-) are the mass transfer coefficients. The mass transfer coefficients can be determined experimentally or calculated from empirical correlations. Empirical correlations generally involve the following dimensionless numbers: Sherwood, Schmidt and Reynolds. Luikov [37] states that these correlations should also include the Gukhman number, which defines the capacity of a mobile gas to evaporate the liquid for capillary porous bodies. Under certain simplifications, mass transfer coefficient hm may also be calculated through the following Lewis equation: h q / h m = ρC P

(80)

Mass Diffusivity Mass diffusivity D (m2/s) is defined by the following diffusion equation: ∂C = D∇ 2 C ∂t

(81)

Mass diffusivity is generally determined using experimental techniques. There are also empirical correlations for mass diffusivity prediction. Marinos-Kouris and Maroulis [18] have developed the following correlation using the diffusivity data for more than 100 food materials in 11 different categories: D=

⎡ E ⎛1 1 1 D o exp ⎢− ao ⎜⎜ − ˆ 1+ m ⎢⎣ R ⎝ T TR

⎡ E ⎛1 ⎞⎤ m 1 ⎟⎟⎥ + D i exp ⎢− ai ⎜⎜ − ˆ ⎢⎣ R ⎝ T TR ⎠⎥⎦ 1 + m

⎞⎤ ⎟⎟⎥ ⎠⎥⎦

(82)

where E ao (J/mol) is the activation energy for diffusion in dry material at m=0, Eai (J/mol) is

ˆ is the ideal gas constant the activation energy for diffusion in wet material at m=∞, R (8.3143 (J/(mol K)), T is the material temperature (°C) and TR is a reference temperature (°C). The reference temperature has been selected as 60 °C by Marinos-Kouris and Maroulis [18].

Diffusibility The diffusion coefficient in a porous material (Deff) is lower than the diffusion coefficient in the absence of a porous material (D). The ratio of these diffusion coefficients is called the diffusibility of porous material. Q dif = D eff / D

(83)

50

Kamil Kahveci and Ahmet Cihan

Phase Change Criterion The phase change criterion εpc is defined as the ratio of the vapor diffusion coefficient Dv to the coefficient of total moisture diffusion Deff and it has a value between 0 and 1. ε pc = D v / D eff

(84)

If the diffusion is mainly in the liquid phase inside the material, phase change criterion has a value around εpc ≈ 0. If the controlling mechanism of diffusion is mainly by water vapor diffusion, phase change criterion has a value around εpc ≈ 1. Phase change criterion varies with moisture content, but it is usually taken constant.

Porosity The porosity of a porous material is defined as the ratio of the total void or pore volume to the total volume of the material ψ=

Vvoid Vtotal

(85)

Porosity

Porosity

Porosity

b

a Moisture content

Porosity

Porosity is the most important geometrical properties of a porous material and has a direct effect on the physical properties. Porosity of food materials is within a broad range (0≤ψ≤0.99). Porosity data for numerous food materials can be obtained from Ref. [25]. Usually, porosity is not measured directly. Instead, it is calculated by Eq. (86) through measured apparent and substance density. Methods for direct measurement of porosity are also available such as direct method, gas expansion method and optical method [25]. For hygroscopic materials, porosity shows a change during the drying process. In many cases, variation of porosity with moisture content is expressed by empirical correlations. Linear, exponential and power law equations are most often used equations for this purpose. The formation of pores in drying has been grouped into two generic types by Rahman [38]. These types are: pore formation with an inversion point and that without an inversion point (see figure 12). These models have been suggested based on experimental data available in the literature. Porosities of most food materials decrease with the increase of moisture content as in the case shown in figure 12 c.

Moisture content

c Moisture content

Figure 12. Change of porosity with moisture content (adapted from [38]).

d Moisture content

51

Transport Phenomena During Drying of Food Materials

Apparent Porosity Apparent porosity is the ratio of total enclosed void volume to the total volume of the material and is given by: ψ app = 1 −

ρ app ρs

(86)

where ρ app is the apparent density (kg/m3) and ρs is the substance density (kg/m3).

Open Pore Porosity Open pore porosity is the ratio of the volume of pores connected to the outside to the total volume:

ψ op = 1 −

ρ app ρp

(87)

where ρ p is the particle density (kg/m3).

Closed Pore Porosity Closed pore porosity is the difference between apparent porosity and open pore porosity: ψ cp = ψ app − ψ op

(88)

Permeability Permeability refers to the resistance of a solid food matrix against the pressure driven flow (see figure 13) and it is one of the most important physical properties of a porous medium. If a porous medium is considered as a bundle of tubes of varying diameter embedded in the solid matrix (see figure 14), permeability can be defined as follows [8]: K=

1 ∑ Δε pi ri2 8τ i

(89)

where τ is the tortuosity, Δε pi is the volume fraction of pores in the ith class with radius ri. Permeability is related to the hydraulic conductivity KH (m/s) as follows:

52

Kamil Kahveci and Ahmet Cihan KH =

ρl g K ηl

(90)

where ρl is the density of the liquid water (kg/m3), g is the gravitational acceleration (m/s2) and ηl is the viscosity of the liquid water (Pa s). Permeability is a transport parameter depending on material structure and is independent of fluids flowing along the material. However, in case of a fluid consisting of a liquid and a gas phase, the permeability will depend on the saturation degree of the porous medium, since the voids available for each of the fluids change. The liquid, for example, will block pores for gas transport and, consequently, changes the gas permeability. In order to express models for the permeabilities for two-phase flow in a porous medium, the overall permeability is divided into two parts as intrinsic and relative permeability.

Porous, impermeable

Porous, permeable

bili

bili

High porosity, low permeability

Low porosity, high permeability

Figure 13. Permeability and porosity (adapted from [39]).

Figure 14. Idealized porous permeable media as bundle of tubes of varying diameters (adapted from [40]).

~ ~ ~ K = Ki ⋅ kr

(91)

Transport Phenomena During Drying of Food Materials

53

where Ki is the intrinsic permeability (m2), which only depends on the geometrical porous structure; kr is the relative permeability, which depends on the saturation degree of the liquid used. Permeabilities are generally obtained from experiments in which the flow of a given fluid is measured as a function of the pressure difference over the relevant sample. In this way, it is possible to determine intrinsic permeability using dry samples. It is more difficult to determine relative permeability requiring partially saturated samples [41]. For example, if a partially saturated sample is exposed to liquid moisture, the sample may be fully saturated. For determining relative permeabilities of gases drying of the sample must be minimized by applying highly saturated gas phase. In addition, the strength of the material is equally important. Since cracks may be formed in a material of low strength, the obtained permeability values will be useless. The saturation S takes values in the range 0≤S≤1. In this case, limit values for relative permeability may be given as follows [41]: ~

~

~

lim k r = 0

~

lim k r = I

S→0

(92)

S→1

Below a certain critical saturation degree, the liquid phase in the pores breaks up into isolated islands and continuous liquid pathways across the sample no longer exist. This means that below this critical saturation degree, which is called irreducible saturation degree Sir, the relative permeability for liquid moisture drops to zero. Thus: ~ ~ k rl = 0

for

S ≤ Sir

(93)

Different forms of relations have been suggested for relative permeabilities depending on the degree of saturation. Among these relations, the following are widely used which assumes that porous structure is isotropic [41, 42]. ⎛ S − Sir k rl = ⎜⎜ ⎝ 1 − Sir

⎞ ⎟⎟ ⎠

a

⎛ 1− S k rg = ⎜⎜ ⎝ 1 − Sir

⎞ ⎟⎟ ⎠

b

(94)

where a and b are constants. In some cases Sir is taken equal to zero. Widely used relations in the form of Eq. (94) for relative permeabilities are as follows [8]: ⎧⎛ S − S ir ⎪⎜ k rl = ⎨⎜⎝ 1 − S ir ⎪ ⎩0

⎧1 − 1.1S k rg = ⎨ ⎩0

⎞ ⎟⎟ ⎠w

⎫ S > S ir ⎪ ⎬ ⎪ S < S ir ⎭

S < 1 / 1.1⎫ ⎬ S > 1 / 1.1⎭

(95)

(96)

These relations are not based on a firm physical basis. Thus, they do not always yield consistent results with experimental observations.

54

Kamil Kahveci and Ahmet Cihan

Tortuosity Tortuosity factor is defined as the ratio between the actual path traveled by a fluid element between two points divided by the straight line path between the same two points. τ = L eff / L

(97)

where Leff is the traveled distance through the porous medium and L is the simple orthogonal distance across the medium. Some prefer to use (L eff / L) 2 and refer to this quantity as tortuosity. Tortuosity of porous materials is generally determined experimentally by using different techniques, such as conductivity and diffusion techniques, the ions transit-time technique and the pore-distribution technique by using the capillary-pressure curve. It can also be obtained theoretically by mathematical models. Using Fick’s first law to describe fluid diffusion through cylindrical paths the following empirical expression can be obtained [43] for tortuosity factor: τ = 2.23 − 1.13Vvoid ρ(0.92γ p )1+ n

(98)

where γ is a pore shape factor, ρ is the bulk density of the solid and n is the pore shape factor exponent. Pore shape factor is defined as γp =

4Vvoid A p = A Ad

(99)

where A is the total surface area by BET adsorption (m2), d is the average pore diameter (m) and Ap is the total pore area (m2). In porous materials, effective diffusivity differs from the theoretical diffusivity by a factor related to the structure of the solid as follows: D eff = D the ψ / τ

(100)

where ψ is the porosity and τ is the tortuosity factor. The tortuosity factor lumps all deviations from straight diffusion paths into a single dimensionless parameter.

Flux and Driving Force A system not in equilibrium thermodynamically heads towards maximum entropy irreversibly and tries to achieve equilibrium. Dissipation of driving forces occurs with the flux toward equilibrium. In mass transport, this is the flux of matter. The flux Ji of a component i is defined as the mass or the number of moles of i crossing a unit surface area in sec. The flux

Transport Phenomena During Drying of Food Materials

55

is obviously related to the velocity of transport. If a mass of concentration Ci is moving with a velocity vi in a direction perpendicular to a surface, the flux Ji and velocity vi are related by [44]: J i = v i Ci

(101)

In irreversible processes, a force driving the system to equilibrium is the negative of the gradient of a potential energy. Driving forces dissipate energy irreversibly and cause fluxes. Potentials for various types of transport are seen in table 10. When the potentials become uniform throughout the system, no force exists, and all net fluxes go to zero. The general equation for the movement of a substance through a medium where there is no absolute barrier is given by [44]:

J = LΦ

(102)

where J is amount/unit time = flux, (kg or moles)/( sec) per m2 surface area, Φ driving force and L conductance =1/ resistance = conductance per unit area. Table 10. Potentials for various types of transport Process Heat conduction Diffusion Convection

Potential Temperature Concentration or Partial Pressure Total Pressure

Current Heat Molecules Fluid Flow

Dissipation Function The dissipation function φ is the rate at which internal heat energy is produced. It is equal to the sum of the products of the fluxes Ji and the driving forces Φi [44]: φ = ∑ ΦiJi

(103)

i

Velocity and Mass Average Velocity The velocity of a bulk mixture and of its components due to the diffusion in z direction can be illustrated as in figure 15.

56

Kamil Kahveci and Ahmet Cihan

vB

vB-v

v=mAvA+ mBvB vA

vA-v

z

Figure 15. Velocities due to diffusion (adapted from [45]).

In this figure, v is the mass average velocity, vA-v and vB-v are the diffusional velocities of components A and B. In the case of a mixture, the mass and molar bulk velocity can be written as follows: n ) n C v = ∑ mi vi = ∑ i vi i =1 i =1 C

,

ˆ n ) n C vˆ = ∑ n i v i = ∑ i v i ˆ i =1 i =1 C

(104)

) ) where m i and n i are the mass and molar fractions of component i.

Shrinkage Shrinkage DR (Sb) expresses a relative or reduced dimensional change of volume, area or thickness and it is often represented by the following way [46]: Sb = V / Vo

(105)

where V is the volume of the material (m3) and Vo is the initial volume (m3).

Isotropic Shrinkage Isotropic shrinkage is the uniform shrinkage in all dimensions of the material.

Anisotropic Shrinkage Anisotropic shrinkage is the nonuniform shrinkage in different dimensions of the material.

Transport Phenomena During Drying of Food Materials

57

4. THERMODYNAMIC EQUILIBRIUM RELATIONSHIPS For a mixture of n components, the equilibrium between a liquid (l) and its vapor (v) can be expressed using the chemical potential as: μˆ li = μˆ vi

i=1,2,..n

(106)

The chemical potential for a pure substance is equal to molar Gibbs function of a phase gˆ (J/mol). The equilibrium can then be expressed as follows [47]: gˆ l = gˆ v

(107)

The differential of a chemical potential for a single phase of a pure substance can be written as [47]:

) dgˆ = −sˆdT + υˆ dP + μˆ dn − dw σ

(108)

) where sˆ is the molar entropy (J/(mol K)), υˆ is the molar volume (m3/mol), n is the molar fraction and dw σ is the amount of work made by the surface tension to extend the phase

separation surface (J/mol). If the phase interface is assumed to have a constant area and the molar fraction to be constant, Eq. (108) becomes as follows: dgˆ = −sˆdT + υˆ dP

(109)

Since dμˆ l = dμˆ v and thermodynamic equilibrium requires thermal equilibrium: − sˆ l dT + υˆ l dPl = −sˆ v dT + υˆ v dPv

(110)

Clapeyron-Clasius Equation The Clapeyron–Clausius equation can be obtained from Eqs. (106-110) if at equilibrium on a flat interface the pressures are equal ( Pv = Pl = P ) and the system has only one degree of freedom (Gibbs rule applied to a phase change) [47]. dP sˆ v − sˆ l = dT υˆ v − υˆ l The enthalpy of evaporation Δhˆ vap (J/mol) is equal to T(sˆ v − sˆ l ) . Therefore:

(111)

58

Kamil Kahveci and Ahmet Cihan Δhˆ vap dP = dT T(υˆ v − υˆ l )

(112)

This equation is known as Clapeyron-Clasius equation that can be applied to a phase change of a pure vapor at a constant temperature and pressure. If the specific volume of the liquid water is negligible with respect to that of the vapor and the specific volume of vapor can be expressed by the ideal gas law, Eq. (112) becomes as follows [47]: dP Δhˆ vap PΔhˆ vap ≈ = dT Tυˆ v RˆT 2

or

d ln P Δhˆ vap = dT RˆT 2

(113)

Kelvin Equation From the definition of capillary pressure, the following equation can be written: dPc = dPv − dPl

(114)

Liquid and the vapor phase are both governed by a Gibbs-Duhem equation [47]: sˆ l dT − υˆ l dPl + dμˆ l = 0

sˆ v dT − υˆ v dPv + dμˆ v = 0

(115)

If the temperature is assumed to be constant and we use Eq. (106), Eq. (115) becomes: υˆ l dPl = υˆ v dPv

(116)

If Eq. (116) is substituted into Eq. (114): dPc =

υˆ l − υˆ v dPv υˆ l

(117)

Assuming that the vapor obeys to the ideal gas law: dPc = dPv −

RˆT dPv υˆ l Pv

(118)

If Eq. (118) is integrated from Pc=0 and Pv=Pvo to Pc, Pv and we assume that the molar volume for the liquid water is constant: Pc = (Pv − Pvo ) −

RˆT ⎛ Pv ln⎜ υˆ l ⎜⎝ Pvo

⎞ ⎟⎟ ⎠

(119)

Transport Phenomena During Drying of Food Materials

59

If the molar volume of the liquid is negligible with respect to that of the vapor (i.e., the vapor pressure is not too high), the first term on the right hand side of Eq. (119) can be omitted [47]. Therefore: Pc = −

RˆT ⎛ Pv ⎞ ⎟ ln⎜ υˆ l ⎜⎝ Pvo ⎟⎠

(120)

Equation (120) is known as Kelvin’s equation.

Equilibrium between Water and Moist Air Both the Clapeyron-Clasius and Kelvin equations are valid only for pure water. For the case of a liquid in equilibrium with a mixture of ideal gases with a total pressure Pg, the following relation can be written instead of Eq. (119): Pc = (Pg − Pgo ) −

RˆT ⎛ Pv ⎞ ⎟ ln⎜ υˆ l ⎜⎝ Pvo ⎟⎠

(121)

Because of the equilibrium between water and moist air, the total gas pressure can be considered to be constant. Therefore: Pc = −

RˆT ⎛ Pv ⎞ ⎟ ln⎜ υˆ l ⎜⎝ Pvo ⎟⎠

(122)

Furthermore, since the molar volume of the liquid phase is much smaller than that of the vapor, the influence of the gas pressure on the vapor tension of the liquid Pvo can be neglected. In this case, Pv has the meaning of the partial pressure in the gas mixture and Pvo of the vapor tension of the liquid for a flat surface when Pc=0 [47].

5. DRYING KINETICS OF FOOD MATERIALS The drying process of food materials has a typical character (see figure 16 ). Drying rate displays differences at certain periods during drying. In the beginning, the temperature adjusts itself until reaching a steady state. This period is usually quite short and it is generally ignored in the drying models. After this period, it is entered a period in which the drying rate remains constant. This period is called constant rate period. In this period, the temperature of wet solid surface is the same as the wet bulb temperature of the drying air. The moisture level at the end of constant rate period is called the critical moisture content. If the material has lower moisture content than the critical moisture content, a decrease is observed in the moisture transfer from the material. This period is called falling rate period. In the falling rate period,

60

Kamil Kahveci and Ahmet Cihan

the surface temperature rises. In many food materials the falling rate period has several stages. These stages are called first falling rate period, second falling rate period and so on. m A mc

dm dt

B C

B A

dm dt

C

T

C B A

D

D

E D

B C

D

E

m

E

E t

t

mc

m

A t

Figure 16. Characteristic drying, drying rate and temperature curves for hygroscopic materials.

In figure 16; A-B is the heating period, B-C is the constant rate period of drying, C is the critical moisture content, C-D is the first falling rate period, D-E is the second falling rate period.

Drying in the Constant-Rate Period In the constant rate drying period, the surface of the solid is very wet and a continuous film of water exists on the drying surface. This water is entirely unbound water and acts as if the solid are not present. The rate of evaporation under the given air condition is independent of the solid and is essentially the same as the rate from a free liquid surface. Increased roughness of the solid surface, however, may lead to higher rates than from a flat surface. The drying rate in the constant rate period is determined by conditions external to the material being dried including temperature, air humidity, flow, area of material surface, and pressure. The water evaporated in the constant-rate period is supplied from the interior of the material. This period continues as long as the water is supplied to the surface as fast as it is evaporated. Evaporation during this period is similar to that in determining the wet bulb temperature, and in the absence of heat transfer by radiation or conduction, the surface temperature is approximately that of the wet bulb temperature [1]. If it is assumed that the heat transfer to the material surface is only by convection and the mass transfer from the surface to the drying medium, the mass and heat transfer for the constant-rate period can be expressed as: J m = h m (ωS − ω)

(123)

J q = h q (T − Twb )

(124)

where Jm is the total mass flux (kg/(m2s)), Jq is the total heat flux (J/(m2s)), hm is the mass transfer coefficient (kg/(m2s)) and hq is the heat transfer coefficient (W/(m2K)). ωS is the moisture content of the air in equilibrium with the surface of the product (kg moisture/kg dry

Transport Phenomena During Drying of Food Materials

61

air), ω is the moisture content of the bulk air (kg moisture/kg dry air), T is the drying temperature (°C) and Twb is the wet bulb temperature (°C).

Drying in the Falling-Rate Period Constant rate period is followed by the falling rate period. Critical moisture content is between these two drying rates. The critical moisture content is the minimum moisture content that will sustain a rate of flow of free water to the surface of the solid equal to the maximum rate of removal of water vapor from the solid under the drying conditions [1]. Point C in figure 16 is at the critical free moisture content. At this point, there is insufficient water on the surface to maintain a continuous film of water. The entire surface is no longer wetted, and the wetted area continually decreases in this first falling period rate until the surface is completely dry at point D in figure 16. In the falling rate period, the rate of internal mass transfer to the material surface controls the drying process.

Controlling Resistance The drying process may be divided in three different categories, namely, externally controlled drying, internally and externally controlled drying and internally controlled drying. The category in which a considered drying process is included is estimated by the Biot number for the mass transfer, which is the ratio of external mass transfer to internal mass transfer. The drying process of food materials can be classified as externally controlled when Bi<0.1, the process is both internally and externally controlled when 1100. In externally controlled drying processes, the mass transfer inside the material is neglected, and it is assumed that the water content inside the material is uniform. In the case that the drying process is partly or fully internally controlled the profiles of water content is nonuniform inside the material. The moisture content gradients are higher when Bi is larger or the evaporation rate from the surface is greater. In food materials, the constant rate period is very rarely observed due to the high critical moisture content of most of the foods. Similar to the Biot number for mass transfer, the Biot number for heat transfer can be used for predicting which resistance, convection or conduction, dominates the heat transfer. A large Biot number (Bi>100) indicates that the conductive resistance controls the heat transfer. In this case, it is easier for heat to leave the surface by convection than to reach it by conduction. Under this condition, large temperature gradients within the solid exist. A small Biot number (Bi<0.1) represents the case where conduction resistance is negligible and the temperature gradient within the solid is quite small. For values between 0.1 and 100, both internal and external resistance to heat transfer should be taken into account.

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Kamil Kahveci and Ahmet Cihan

6. TRANSPORT PHENOMENA In porous materials, mass and heat transfer may occur in quite a number of varying mechanisms (see table 11 and figure 17). Drying is generally composed of a series, parallel and/or series-parallel combination of these mechanisms. Dominant mass transfer mechanism is capillary flow for materials with large water content. On the other hand for materials with low moisture content diffusion in the vapor phase is dominant mechanism. In some cases, a considerable amount of evaporation and a consequential pressure rise may occur. In this case, the dominant mass transport mechanism may be hydrodynamic flow.

Poiseuille Flow

Knudsen Diffusion

T2

T1

T1
Molecular Diffusion

h

Stefan Diffusion

Surface Diffusion Capillary Flow

Figure 17. Various mass transport mechanisms in a porous material (adapted from [9]).

Table 11.The heat and mass transport mechanisms in a porous material

Gas Transport

Transport Mechanism

Cause and Potential of Transport

Knudsen Diffusion

concentration or pressure

Slip Flow

total pressure

Poiseuille Flow

total pressure, gravity

Molecular Diffusion

concentration or partial pressure

Transport Phenomena During Drying of Food Materials

63

Table 11. (continued)

Liquid Transport

Heat Transport

Transport Mechanism

Cause and Potential of Transport

Stefan Diffusion

partial pressure

Condensation-Evaporation

temperature, …

Molecular Diffusion

concentration

Capillary Flow

capillary force

Surface Diffusion

concentration

Hydrodynamic Flow

total pressure, gravity

Heat Conduction

temperature

Heat Radiation

temperature in 4th power

Air Flow

total pressure, density differentials

Enthalpy Flow

moisture movement

GAS TRANSPORT MECHANISMS Molecular mass transfer processes in which molecules move under a concentration gradient or a partial pressure gradient are called diffusion processes. If the molecules move under a total pressure gradient, transfer is called bulk mass transfer. Mass transfer mechanism in gas phase varies depending on the nature of momentum transfer occurring due to molecular collisions, which generally are of two types. Collisions between molecules are referred to as intermolecular collisions and collisions between molecules and pore walls are referred to as molecule wall collisions. The type of collision depends on the length of the mean free path relative to the diameter of the tube. The mean free path length λ is the average distance traveled by a molecule between two successive collisions. The mean free path length λ depends on the gas type, the temperature and the mean pressure and is determined with the following equation [48, 49]: λ=

ηg Pg

πRˆT 2

(125)

where ηg is the dynamic viscosity of gas (Pa s), Pg is the mean gas pressure (Pa), Rˆ is the

ideal gas constant (J/(mol K)), T is the temperature (K). For vapor and air at atmospheric pressure and room temperature, the mean free path length is of the order magnitude of 5x10-8 m [50]. The ratio between the mean free path and the pore diameter is called the Knudsen number. Kn =

λm d

(126)

Mass transfer mechanisms according to the value of Knudsen number are given in table 12. When the value of Knudsen number is much less than unity, the probability of intermolecular

64

Kamil Kahveci and Ahmet Cihan

collisions is much greater that that of molecule-wall collisions. In this case, if a total pressure gradient exists, there will be a viscous flux. If a partial pressure gradient exists, then the collisions between the molecules in the mixture will result in molecular diffusion. When Knudsen number is much larger than unity, molecule-wall collisions reach to much more significant levels. In this case, mass transfer occurs with Knudsen diffusion. Since Knudsen diffusion is not affected by the existence of any other gas, both total pressure gradient in a single phase system and partial pressure gradient in a multicomponent system result in Knudsen diffusion. When the Knudsen number has the value around 1, intermolecular collisions and molecule-wall collisions are at comparable levels. In this case, two different flow regimes are available, which are not new mechanisms but transitions between the mechanism described above. The transition between Knudsen diffusion and viscous flux is called slip flux. And the transition between Knudsen diffusion and molecular diffusion is called transition diffusion. Table 12. Various gas transport mechanisms based on the Knudsen number (adapted from [50]) Knudsen Number Kn<<1 Kn ≅ 1 Kn>>1

Total Pressure Gradient Viscous Flux Slip Flux Knudsen Diffusion

Partial Pressure Gradient Molecular Diffusion Transition Diffusion Knudsen Diffusion

Knudsen or Free Molecule Diffusion Since porous materials have irregularly shaped pores, it is difficult to describe mass transfer from them. A single capillary tube is generally considered to obtain a basic model. The Knudsen diffusion between two points in a capillary tube is defined as: ˆ −C ˆ ) Jˆ mz = w p v z (C g2 g1

(127)

where Jˆ m is the molecular flux (mol/(m2s)), wp is a dimensionless probability factor, v z is the mean molecular speed (m/s) and Cˆ g is the molar concentration (mol/m3). The mean molecular speed can be calculated from the kinetic theory of gases as: vz =

8RˆT ˆ πM g

(128)

ˆ is the where Rˆ is the gas constant (8.3143 J/(mol K)), T is the temperature (K) and M g

molecular mass of gas (kg/mol). Calculation of probability factor wp is much more complex. The value of the probability factor is known for some simple geometries. For a long straight circular tube of radius r and length L, wp is 2r/(3L). In this case, Eq. (127) may be expressed as follows:

Transport Phenomena During Drying of Food Materials 2rp Jˆ mz = 3L

8RˆT ˆ ˆ ) (C g 2 − C g1 ˆ πM g

65

(129)

Eq. (129) can be written in differential form as: 2rp Jˆ mz = − 3

ˆ 8RˆT dC g ˆ dz πM g

or

ˆ dC g Jˆ mz = D k ,sp dz

(130)

where D k ,sp is the Knudsen diffusion coefficient for a single pore and is proportional to the pore radius and the mean molecular velocity. The Equation (130) is valid for a circular tube. For different geometries, an equation in the same form can be obtained but with different geometrical parameters. For this reason a general equation is defined using a Knudsen coefficient Ko (m). Knudsen diffusion coefficient Dk,sp (m2/s) is related to Knudsen coefficient Ko as. D k ,sp =

4 K o vz 3

(131)

Considering transfer in all directions, Knudsen diffusion in a porous material may be expressed as follows: r r ˆ Jˆ m = D k ∇C g

or

r r J m = D k ∇C g

(132)

Knudsen diffusion coefficient for porous materials is generally determined experimentally. It can also be determined by the following equation by means of the diffusion coefficient of the single pore: Dk =

ψ D k ,sp τ

(133)

where ψ and τ are the porosity and tortuosity factor.

Slip Flux Slip flux is a transition regime between viscous flux and Knudsen diffusion. In this regime, there exists a partial interaction among molecules. Momentum loss resulting from molecule-wall collision is not fully transferred to the rest of the fluid. For this reason, the velocity of gas stream does not approach to zero when it approaches to the wall. In other words, the Hagen-Poiseuille equation, which assumes a zero wall velocity, is no longer valid. Gas flows like it is slipping at the wall, so there appears a positive velocity along the wall.

66

Kamil Kahveci and Ahmet Cihan

To simplify the derivation of transport equations for slip flux, the following assumptions can be made. Flow is isothermal and is at steady regime, bulk viscosity effects are negligible, there is not external force except the gravitational force and the change of convected momentum is negligible. In addition, it can be assumed that the axial velocity is more strongly dependent to radial coordinate compared to axial coordinate. Therefore, the NavierStokes equation takes the following form for a long cylindrical capillary [51]: −

dPg dz

+

dΨg dz

+ ηg

1 d ⎛ dv z ⎞ ⎜r ⎟=0 r dr ⎝ dr ⎠

(134)

where Pg is the gas pressure (Pa), ηg is the dynamic viscosity of gas (Pa s), Ψg is the gravity potential of gas (m2/s2), vz is the axial velocity (m/s) and r and z are the radial and axial coordinates, respectively. Integrating Eq. (137) yields: vz = −

1 ηg

⎛ dPg dΨg ⎞ r 2 ⎟ ⎜ ⎜ dz − dz ⎟ 4 + c 2 ⎠ ⎝

(135)

When the Maxwell slip boundary condition given below is applied. r = rp

v z = −G

dv z dr

(136)

Equation (135) becomes: vz = −

1 4η g

⎛ dPg dΨg ⎜⎜ − dz ⎝ dz

⎞ 2 ⎟⎟(rp − r 2 + 2Grp ) ⎠

(137)

where rp is the radius of capillary (m). The total volumetric flow rate Q vol (m3/s) can then be written as: rp

Q vol = 2π ∫ v z rdr = − 0

πrp2 ⎛ dPg dΨg ⎞ 2 ⎟ rp + 4Grp ⎜ − 8ηg ⎜⎝ dz dz ⎟⎠

(

)

(138)

The average velocity over the cross section can be expressed as: rp2 Q vol vz = 2 = − 8ηg πrp

⎛ dPg dΨg ⎞⎛ 4G ⎞⎟ ⎜⎜ ⎟⎟⎜1 + − dz ⎠⎜⎝ rp ⎟⎠ ⎝ dz

where G is the slip modulus defined as:

(139)

Transport Phenomena During Drying of Food Materials 1/ 2

ηg ⎛ 8RˆT ⎞⎟ G ≈ 1.19 ⎜ ˆ ⎟ Pg ⎜⎝ πM g ⎠

=

2ηg Pg rp

Ko

67

(140)

where Ko is the Knudsen coefficient (m) and can be defined as: Ko=0.89Dk

(141)

where Dk is the Knudsen diffusion coefficient (m2/s). The factor 0.89 comes fitting to the experimental data [51]. If Eq. (140) is substituted into Eq. (139), one can find: ⎛ rp2 K ⎞⎛ dPg dΨg − v z = −⎜ + o ⎟⎜⎜ ⎜ 8ηg Pg ⎟⎝ dz dz ⎠ ⎝

⎞ ⎟⎟ ⎠

(142)

A general form of Eq. (142) can be written as: ⎛B K ⎞⎛ dPg dΨg ⎞ ⎟ − v z = −⎜ k + o ⎟⎜⎜ ⎜ ηg Pg ⎟⎠⎝ dz dz ⎟⎠ ⎝

(143)

where Bk (m2) is a viscous flow parameter evaluated for the mean pore radius of the porous medium. Bk is more commonly known as permeability (Kg) and is generally obtained from experiments. Equation (143) can be written in vectorial form as: r ⎛ Kg Ko ⎞ r r ⎟( ∇Pg − ∇Ψg ) v z = −⎜ + ⎜ ηg Pg ⎟⎠ ⎝

(144)

Molar flux Jˆ m (mol/(m2s)) can then be expressed as follows: r r ⎞ r Pg r ˆ 1 ⎛⎜ K g Jˆ m = v g C Pg + K o ⎟( ∇Pg − ∇Ψg ) =− g = vg ⎟ RˆT RˆT ⎜⎝ ηg ⎠

(145)

Multiplication by the molecular weight of gas gives the result in (kg/(m2s)). ˆ ⎛K r r ⎞ r M g ⎜ g Pg + K o ⎟( ∇Pg − ∇Ψg ) Jm = − ⎟ ⎜ ˆ RT ⎝ η g ⎠

(146)

Flux equation may alternatively be defined as follows: ˆ r r r Pg M g Jm = − K g 1 + b K / Pg ( ∇Pg − ∇Ψg ) RˆTηg

(

)

(147)

68

Kamil Kahveci and Ahmet Cihan ˆ r r r Pg M g Jm = − K app ( ∇Pg − ∇Ψg ) RˆTηg

(148)

where Kapp is the apparent gas permeability K app = K g (1 + b K / Pg ) , and bK is the Klinkenberg parameter (Pa), which depends on the geometry of the pore spaces. By plotting Kapp as a function of 1 / Pg , bK and the Kg can be determined from the slope and the intercept, respectively.

Viscous Flux Collision of molecules with the wall causes gas molecules to lose momentum. The loss of momentum in gas molecules adjacent to the wall is transferred to other molecules through intermolecular collisions. This leads to a decrease in molecule flow rates from the wall towards the tube center. If the gas flow is laminar, the momentum exchange results in a smooth velocity profile. This flow regime is called viscous flow regime. The viscous flow is described by the Navier-Stokes equation. The derivation of the transport equation is quite similar to that of slip flux. The only difference is due to boundary condition. Similar to those for the slip flux, it can be assumed that the flow is isothermal and is at steady regime, bulk viscosity effects are negligible, there is no external force except the gravitational force, change of convected momentum is negligible and axial velocity is more strongly dependent on radial coordinate compared to the axial coordinate. In this case, the Navier-Stokes equation takes the following form for a long cylindrical capillary: −

dPg dz

+

dΨg dz

+ ηg

1 d ⎛ dv z ⎞ ⎜r ⎟=0 r dr ⎝ dr ⎠

(149)

where Pg is the gas pressure (Pa), ηg is the dynamic viscosity of gas (Pa s), Ψg is the gravity potential of gas (m2/s2), vz is the axial velocity (m/s) and r and z are the radial and axial coordinates, respectively. Integration of Eq. (149) yields: vz = −

1 ηg

⎛ dPg dΨg ⎞ r 2 ⎟ + c2 ⎜⎜ − dz ⎟⎠ 4 ⎝ dz

(150)

No-slip condition is valid on the surface for viscous flow: r = rp

vz = 0

where rp is the radius of capillary (m). Therefore Eq. (150) becomes as follows:

(151)

Transport Phenomena During Drying of Food Materials vz = −

1 4η g

⎛ dPg dΨg ⎞ 2 ⎜⎜ ⎟⎟(rp − r 2 ) − dz dz ⎝ ⎠

69

(152)

The total volumetric flow rate Q vol (m3/s) can then be written as: Q vol =

π rp4 ⎛ dPg dΨg ⎞ ⎜ ⎟ − 8ηg ⎜⎝ dz dz ⎟⎠

(153)

where Qvol is the volumetric flow rate (m3/s), rp is the capillary radius (m), ηg is the dynamic viscosity (Pa s) Pg is the pressure (Pa). This equation is known as Hagen-Poiseuille equation. The average velocity over the cross section can be defined as: vz =

rp2 Q vol = − 8ηg πrp2

⎛ dPg dΨg ⎜⎜ − dz ⎝ dz

⎞ ⎟⎟ ⎠

(154)

A general form of Eq. (154) can be expressed as: vz = −

Bk ηg

⎛ dPg dΨg ⎜⎜ − dz ⎝ dz

⎞ ⎟⎟ ⎠

(155)

where Bk (m2) is a viscous flow parameter evaluated for the mean pore radius of the porous medium. Bk is generally determined experimentally and is more commonly known as the permeability Kg. vz = −

K g ⎛ dPg dΨg ⎞ ⎟ ⎜ − ηg ⎜⎝ dz dz ⎟⎠

(156)

Equation (156) can be written in vectorial form as: r Kg r r vz = − ( ∇Pg − ∇Ψg ) ηg

or

r K i k rg r r vz = − ( ∇Pg − ∇Ψg ) ηg

(157)

This equation is known as Darcy equation. Molar and mass flux equations may then be expressed as follows: r r r ˆ = v Pg = − K i k rg Pg ( ∇P − ∇Ψ ) Jˆ m = v g C g g g g ηg RˆT RˆT

(158)

70

Kamil Kahveci and Ahmet Cihan ˆ P r r r K i k rg M g g J m == − ( ∇Pg − ∇Ψg ) ηg RˆT

(159)

Molecular Diffusion Molecular diffusion means the relative motion of different species of a mixture according to each other under concentration gradient. For a binary mixture, the diffusive fluxes of species can be written according to the Fick’s first law as: r r J mA = −D gAB∇C gA

r r J mB = −D gAB∇C gB

(160)

r where J m is the mass flux vector (kg/(m2s)) and Cg is the concentration of diffusing

substance (kg/m3). Dg is the gas diffusion coefficient (m2/s) and it describes the transfer rate of diffused gas with random molecular motion. In a two component system, satisfying the condition of zero volume change on mixing, the diffusion coefficient of each component is equal, i.e. DgAB=DgAB. Therefore the behavior of the system can be described in terms of a single diffusion coefficient Dg. The free gas diffusion coefficient Dg,free in a binary gas mixture at low to moderate pressures can be accurately predicted using the kinetic theory. The following semi-empirical equation has been proposed by Gilliland [52]: D g ,free = 4.3x10 −3

T3/ 2 1 1 1/ 2 ( + ) ˆ ˆ Pg (υˆ + υˆ 1gB/ 3 ) 2 M M gB gA 1/ 3 gA

(161)

where D g ,free is the diffusion coefficient (cm2/s), T is the temperature (K), Pg is the pressure ˆ is the molecular mass of gas i (g/mol), and υˆ is the molar of the gas mixture (bar), M gi gi

volume of gas i (cm3/(g mol)). As it can be seen from Eq. (161) that the gas diffusion coefficient in a binary mixture is inversely proportional to the pressure of the gas mixture. Another equation predicting the diffusion coefficient more accurately has been proposed by Fuller et. al. [53] as follows: D g ,free = 1.01x10 −3

[

Pg (∑ ϑ

T1.75

)

1/ 3 a gA

+ (∑ ϑ

)

]

1/ 3 2 a gB

(

1 1 1/ 2 + ) ˆ ˆ M gA M gB

(162)

where D g ,free is the diffusion coefficient (cm2/s), T is the temperature (K), Pg is the pressure ˆ is the molecular mass (g/mol). The diffusion volume ∑ ϑ is the sum of the (bar) and M g a atomic volumes for all the atoms in each molecule. These atomic parameters are determined by a regression analysis of many experimental data. Equation (162) usually gives results within four percent of experimental data when water is the solvent [54].

Transport Phenomena During Drying of Food Materials

71

Another way to predict the diffusion coefficient for gases is to take into account the forces acting between molecules. Hirschfelder et al. [55] derived the following equation for diffusivity for non-polar gases using the Lennard-Jones potential, which describes the attractive and repulsive forces between atoms. D g ,free = 1.88x10 −3

T3/ 2 1 1 1/ 2 ( + ) ˆ ˆ Pg L2 Ω D M M gA gB

(163)

ˆ is the molecular where D g ,free is the diffusion coefficient (cm2/s), Pg is the pressure (bar), M g o

mass (g/mol) and L is the characteristic length ( A ) and ΩD is the diffusion collision integral, which is dimensionless. The characteristic length in Eq. (163) may be estimated as: L=

L1 + L 2 2

(164)

where L1 and L2 are the characteristic lengths of gas species in the mixture. The diffusion collision integral can be determined approximately by the following equation [56, 57]:

Ω D = (1.06036T ′) −0.1561 + 0.193 exp(−0.47635T ′) + 1.03587 exp(−1.52996T ′) + 1.76474 exp(−3.89411T ′)

(165)

where T′ =

σbT E

1/ 2

⎛E E ⎞ E = ⎜⎜ c1 + c 2 ⎟⎟ ⎝ σB σB ⎠

(166)

where σ b is the Boltzmann constant (1.3805x10-23 J/K), T is the temperature (K), Ec1 and Ec2 are the characteristic energies of gas species (J). The gas diffusion coefficient through the porous medium is usually related to the free gas diffusion coefficient in an open space as follows: Dg =

ψ D g ,free τ

(167)

where ψ and τ are the porosity and the tortuosity factor.

Stefan Diffusion Diffusion of water vapor through a layer of stagnant air is called Stefan diffusion. This type of diffusion is encountered during evaporation of water from a wet surface to bulk air. Water and water vapor are present at the same time in a system. The quantity of vapor

72

Kamil Kahveci and Ahmet Cihan

increases in time due to water evaporating in the vicinity of water and gas interface. This results in an increase in the absolute pressure in the vicinity of gas and liquid phase interface. The absolute pressure gradient causes water vapor to move away from the interface. The real flux of water vapor is then higher than it would correspond to the gradient of its partial pressure [58]. Since the process is relatively slow, the physical situation may be simplified assuming that absolute pressure remains constant ( P = Pv + Pa =constant). In this case, total air flux in the vicinity of liquid surface must be equal to zero. Otherwise, there would be an increase in total pressure due to the increase in the quantity of air in the vicinity of the interface. While water vapor moves away from the interface, the air flows towards the interface according to its concentration gradient. Since the pressure is constant, this should lead to the flow of water vapor and air mixture from the interface with convection. Flux for vapor and air can be expressed as [58]: r r r J mv = − D va ∇ρ v + ρ v v

(168)

r r r J ma = −D av ∇ρ a + ρ a v

(169)

where D is the diffusion coefficient (m2/s), ρ is the concentration (kg/m3), and v is the velocity (m/s). In Equations (168) and (169), the first terms represent the flux arising from diffusion and the second terms represent the flux due to convection. Under assumptions P=constant and T=constant, it follows from the ideal gas law that ρg =

ˆ P M g g =constant ˆ RT

(170)

ˆ is the molar mass of the vapor air mixture (kg/mol), Rˆ is the gas constant (8.314 where M g

J/(kg K)) and T is the temperature (K). Therefore: ρ v + ρ a = ρ g =constant

(171)

r r ∇ ρ v = −∇ ρ a

(172)

and

For the diffusion coefficients, we can write Dva=Dav. The velocity of the mixture can then be written from Eq. (169) as: vg =

D av r D va r ∇ρ a = ∇ρ v ρa ρg − ρ v

If Eq. (173) is substituted into Eq. (168), we have

(173)

Transport Phenomena During Drying of Food Materials r r J mv = − D va ∇ρ v − D va

r r J mv = −D va ∇ρ v (1 +

ρv r ∇ρ v ρg − ρ v

ρg r ρv ) = −D va ∇ρ v ρg − ρv ρg − ρ v

73

(174)

(175)

Using ideal gas law, the following relations can be written for water vapor and water vaporair mixture: ρv =

ˆ P M v v ˆ RT

for water vapor

(176)

ρg =

ˆ P M g g ˆ RT

for water vapor-air mixture

(177)

ˆ ≈M ˆ yields: Assuming M v g ˆ r Pg r M J mv = − D va v ∇ρ v RˆT Pg − Pv

(178)

Equation (178) is valid for diffusion of water vapor in the air. To express diffusion in porous materials, Krischer [59] proposed introduction of a factor called resistance factor. ˆ r Pg r D M J mv = − va v ∇Pv ζ RˆT Pg − Pv

(179)

where ζ (>1) is the diffusion resistance factor and describes the decrease of the vapor flow in the solid in comparison with that in stagnant gas.

Condensation-Evaporation Diffusive transport of water vapor can be obstructed by the presence of liquid islands in the pore throats. However, under a thermal gradient, a vapor pressure gradient develops in the gas phase and causes water to evaporate from one side of the liquid island, and diffuse in the gas phase to a liquid island of lower temperature where it condenses. The evaporationcondensation process repeats itself on the other side of the liquid island. Heat and mass balance equations for this kind of transport was first expressed by Henry [60] assumed that moisture migrates entirely in the gaseous phase and that the continuous network of spaces included in the solid, the amount of vapor in the solid varies linearly with

74

Kamil Kahveci and Ahmet Cihan

the concentration of vapor and temperature, and the diffusion coefficient is constant. In this case, the balance equations can be written as follows:

Mass Balance The net amount of water entering an element by diffusion equals to the increase in moisture in the air and the increase in moisture in the solid. ∂C v ∂m + (1 − ε a )ρ s (180) ∂t ∂t where εa is the volume fraction of air in pores, b is a factor taking into account the tortuosity of the diffusion path, Dv is the vapor diffusion coefficient (m2/s), Cv is the vapor concentration (kg/m3), ρs is the solid concentration (kg/m3) and m is the moisture content in dry basis ( kg water/kg dry solid). ε a bD v ∇ 2 C v = ε a

Energy Balance The increase in the heat content of the solid equals to the amount of heat entering by conduction and the heat involved in the desorption (or absorption) of water by solid: ε a ρs C P

∂T ∂m = λ∇ 2 T − q vol ∂t ∂t

(181)

where CP is the specific heat capacity (J/(kg K)), λ is the heat conductivity (W/(m K), qvol is heat involved in the desorption (or absorption) of water by solid (J/m3). The mass and energy balance equations can be written in more compact form as follows [21]: γ

∂C ∂m = D1v ∇ 2 C v − v ∂t ∂t

δ

∂m ∂T 2 = D11 v∇ T− ∂t ∂t

(182)

where γ=

λ 1 − εa q vol , D11 ρ s , D1v = bD v , δ = v = ε a ρs C P ε a ρs C P εa

LIQUID TRANSPORT MECHANISMS Molecular Diffusion Molecular diffusion of fluids is defined by Fick’s first law as in gases.

(183)

Transport Phenomena During Drying of Food Materials r r J ml = − D l ∇C l

75 (184)

r where J ml is mass flux vector (kg/(m2s)), Cl is concentration of diffusing substance (kg/m3)

and Dl is the liquid diffusion coefficient (m2/s). Liquids have strong intermolecular forces. However, they are not ordered as atoms and molecules in a solid. Therefore, describing the liquid state quantitatively is more difficult. Three different approaches are available for the prediction of diffusion coefficient in fluids. These are: hydrodynamical theory, quasicrystalline theory, and fluctuation theory. The most commonly used one is the hydrodynamical theory, which relates diffusion to the viscosity of liquid movement. Stokes described the force acting on an atom and Einstein proposed the following equation relating the diffusion coefficient to the mobility of the atom [54]: D lAB =

σbT 6π r ηlB

(185)

where σb is the Boltzmann’s constant (1.3805x10-23 J/K), r is the radius of the spherical solute (m) and ηl is the viscosity of the solvent (Pa s). This model has many drawbacks because of its simplifications of molecular interaction, but it does predict diffusion coefficients within an order of magnitude [61, 62]. Another correlation, which is widely used for diffusion coefficient is the Wilke-Chang equation, which is, in essence, an empirical modification of the Stokes-Einstein relation given in Eq. (185): D lAB =

ˆ )1/ 2 T 7.4x10 −8 (ξ B M lB ηlB υˆ 0lA.6

(186)

where DlAB (cm2/s) is the diffusion coefficient of solute A at very low concentrations in ˆ is the molecular mass of solvent B (g/mol), T is the temperature (K), ηlB is solvent B, M lB the viscosity of solvent B (cP) and υˆ lA is the molar volume of solute A at its normal boiling temperature, (cm3/g mol) and ξB is the association factor of solvent B, dimensionless. The association factor takes values in the range from 1.0 to 2.6 and accounts for interactions of the solvent. The factor is 1.0 for non-polar solvents, 1.5 for ethanol, and 2.6 for water [62]. This correlation usually gives results within ten percent of experimental data when water is the solvent. The error increases slightly when using organic solvents and is not suited for predicting diffusivity when water is the solute [62, 63]. The Wilke-Chang equation does not give accurate results for concentrated solutions. Vignes [64] states that this is because of the fact that most concentrated solutions are nonideal. Therefore, Leffler and Cullinan [65] included the changes in viscosity at different concentrations resulting in Eq. (187): ) ⎛ ∂ ln γ lA ⎞ ⎟ D lABηlm = (D olABηlm )(D olABηlA ) n A ⎜⎜1 + ∂ ln x A ⎟⎠ ⎝

(187)

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Kamil Kahveci and Ahmet Cihan

where D oAB (cm2/s) is the diffusion coefficient, independent of concentration, ηlm is the ) viscosity of the mixture (cP), n A is the mole fraction, γ is the activity coefficient. If viscosity data are known for the mixtures and the individual components, the Lefler and Cullinan equation gives accurate predictions for the diffusion coefficients in concentrated solutions.

Capillary Flow Capillarity refers to the flow of a liquid through the voids and over the surface of a solid due to molecular attraction between the liquid and solid. This form of transport was first analyzed by Buckinghan [66], who suggested capillary potential ΨH (m) as driving force for unsaturated capillary flow. Depending on capillary potential, mass flux is expressed as: r r J m,vol = −K H ∇ΨH

(188)

r where J m,vol is the volumetric flux (or specific charge) (m3/(m2s)) and KH is the hydraulic

conductivity (m/s). Under isothermal conditions, capillary potential is usually assumed to be proportional to the gradient of moisture concentration. In this case, mass flux may be defined as follows: r r J m = − K l ρ s ∇m

(189)

where Kl is the liquid conductivity (m2/s) and it is defined as: Kl =

σ cos θ r1 2 ∫ r f (r )dr 4ηl r 2 f (r ) ro

(190)

where σ is the surface tension (N/m), f(r) is the differential curve for distribution of pore sizes by radius r, ro and r1 are the minimum and maximum values of radii of capillaries (m), ηl is the dynamic viscosity of liquid water (Pa s).

Surface Diffusion Hill [67] regarded the surface diffusion phenomenon as a random walk process of the molecules on a solid surface. When a molecule adsorbs onto a surface, it tends to sit at the bottom of the potential well of the underlying solid. These minima correspond to potential adsorption sites. The possible adsorption sites vary from one adsorption system to another. The case EA> ES>ET can be chosen as the basis for the surface diffusion phenomenon, where EA (J/mol), ES (J/mol) and ET (J/mol) are the potential barrier among adsorption sites, the differential heat of adsorption, and the thermal motion of molecules, respectively. When EA< ES surface flow does not take place. When an adsorbed molecule gains energy E between EA and ES, this molecule hops from site to site in the adsorbed states. After several hopping

77

Transport Phenomena During Drying of Food Materials

have occurred, the molecules may finally detach (desorb) from the surface. An adsorbed molecule, however, remains on the adsorption site when E<ES and is desorbed when E>EA. Various types of hopping possible are shown in figure 18. Molecules randomly hop to a neighboring site after various holding times. It is generally assumed that when a molecule hit a site occupied by another molecule it would immediately bounce off and continue until finding an unoccupied site. hopping molecule

adsorbed molecule

Figure 18. Modes of jumping (adapted from [68]).

The surface diffusion flux is defined as: r r J m = − D lS∇C lS

(191)

where DlS is the surface diffusion coefficient (m2/s). Surface diffusion is not taken directly into account by any of the existing drying models. Philip and De Vries [69] state that it seems unlikely that diffusion in the adsorbed phase will affect the total heat and mass transfer process in significant amount. Their reasoning is that part of the water that evaporates from or condenses on the surfaces may recirculate in a single air filled pore through the surface migration process [21].

Hydrodynamic Flow Hydrodynamic flow of liquid water is defined by Darcy law similar to that for gases. r r K r v w = − l (∇Pl − ∇Ψw ) ηl

(192)

r where v w is the mass average velocities (m/s), K i is the permeability of liquid (m2), and Ψw

is the gravity potential of liquid water (m2/s2). The range of validity of the Darcy equation is expressed in terms of the Reynolds number. The upper limit of the validity of the Darcy equation is at a value of Re between 1 and 10 [70]. The importance of Darcy flow compared to diffusion is decided taking the Peclet number into consideration. If Peclet number is small enough Darcy flow may be omitted.

78

Kamil Kahveci and Ahmet Cihan

ENERGY TRANSPORT MECHANISMS Heat transfer in drying process of food materials may take place by different mechanisms such as conduction, convection and radiation. In general, the main mechanism for heat transfer is heat conduction. Heat transfer by conduction is defined by the Fourier’s law: r r J q = −λ∇T

(193)

r

where λ is the thermal conductivity (W/(m K)), ∇T is the temperature gradient. In addition to the conduction, heat transfer will also be accomplished by convection due to the movement of the three phases: solid, liquid and gas. The evaporation may also have significant thermal effects.

DRYING MODELS Mathematical modeling of drying behavior of food materials is important because it enables scientific process design, minimization of energy cost and minimization of cost due to the quality constraints. However, it is very hard to describe the drying process with a single and simple model due to the complexity of the drying process. The difficulties arise from the following reasons: There is a mixture of various transport mechanisms and the contribution of different mechanisms to the total transport varies from place to place and as drying proceeds, The most of transport properties are strongly affected by concentration, temperature and physical structure, The modeling of structural parameters like porosity, tortuosity and permeability is complicated, Structure of the material may be heterogeneous showing regions of different permeability to the transport of moisture, Material may have complex shape, which are difficult to describe mathematically, Material may shrink significantly in the process, Drying temperature, humidity etc. may change during the drying process.

Drying models for porous materials may be categorized into three distinct groups, theoretical, empirical, and semi-empirical. The theoretical models consider only internal resistance to moisture transfer while the empirical and semi-empirical models consider only external resistance.

Theoretical Models Theoretical models can be classified into two categories: the continuum approach and discrete approach. In discrete approach, transport from porous medium is considered at pore level. Porous materials have a quite complex structure. The difficulty in modeling due to complex structure is overcome by describing a pore network which takes account only the basic features of geometry and topology of the porous structure (see figure 19). Transport process is considered in this network structure representing the porous medium. The most

79

Transport Phenomena During Drying of Food Materials

important advantage of this type of modeling is to facilitate the understanding of the macroscopically observed mass transfer phenomena. Modeling based on discrete approach is used for two different objectives. One is the computation of the effective parameters at the scale of a representative volume of the microstructure. The other is to describe transport process at the scale of the product. However, approach that is generally used in defining transport at macroscopic level is continuum approach. In continuum approach, there is no need to define the structure of the medium at microscopic level. Therefore, the purpose of use is only to analyze the drying process at product level. Continuum modeling is a classical approach used for media having complex microstructure. Continuum modeling is also a phenomenological approach because it requires determination of transport coefficients experimentally. In this type of approach, transition from microscopic level to macroscopic level may be considered as an operation of smoothing out the microscopic heterogeneity of the relevant properties. In other words, porous media is assumed to be a fictitious continuum. The effects of the physical phenomena taken into consideration are lumped into effective transport coefficients. The greatest difficulty in using a model based on a continuum approach arises from determination of these transport parameters which are dependent moisture content, temperature and material structure. Many models have been suggested based on continuum approach. The most important ones are molecular diffusion model, receding front model, Philip and de Vries model, Luikov model, Krischer model and Berger and Pei model. The continuum approach has gained a firmer basis through the Whitaker model [71, 72] by a volume averaging technique. The most important feature of the volume averaging technique is the possibility of determining the effective parameters through the solution of closure problems defined over regions representative of the pore microstructure. In this way, a fundamental aspect of the problem which is the disordered nature of the porous microstructure could be analyzed [73]. In this chapter, the models only based on continuum approach will be considered.

Solid Phase

Pore (Site)

Solid Phase

Figure 19. Conversion from the porous medium to the pore network (adapted from [73]).

Molecular Diffusion Model Fick's first law does not consider the fact that the gradient and local concentration of a diffusing substance in a material decreases with an increase in time. In such cases, Fick’s second law is used for expressing diffusion. The Fick's second law states that the change in concentration of a diffusing substance in a material over time is equal to the change in local diffusion flux.

80

Kamil Kahveci and Ahmet Cihan

Let us consider the control volume seen in figure 20 to derive the Fick’s second law. Let us assume that there are mass fluxes towards the inside and outside of this control volume. The mass fluxes into the control volume can be written as follows: jm , x = − D

∂C ∂x

jm , y = − D

∂C ∂y

jm , z = − D

∂C ∂z

(194)

where D is the diffusion coefficient (m2/s) and C is the concentration (kg/m3). Mass conservation can be expressed as: Mass in-Mass out+Mass generated=mass stored

(195)

Quantities of mass entering and exiting control volume at Δt time can be defined as follows: jm ,x ΔyΔzΔt jm,x + Δx ΔyΔzΔt

jm, y ΔxΔzΔt

jm ,z ΔxΔyΔt

jm,y+ Δy ΔxΔzΔt

jm,z+ Δz ΔxΔyΔt

(196) (197)

Generated or depleted mass and stored mass in control volume at Δt time may be expressed as: IΔxΔyΔzΔt (generated amount)

CΔxΔyΔz (stored amount)

(198)

where I is the rate of mass generation or depletion per unit volume (kg/(m3s)). Substituting all the terms into Eq. (195) gives the following differential equation called Fick’s second law: ∂c ∂ ⎛ ∂c ⎞ ∂ ⎛ ∂c ⎞ ∂ ⎛ ∂c ⎞ = ⎜D ⎟ + ⎜D ⎟ + ⎜D ⎟ + I ∂t ∂x ⎝ ∂x ⎠ ∂y ⎝ ∂z ⎠ ∂z ⎝ ∂z ⎠

(199)

In cylindrical and spherical coordinate, the diffusion equation is as follows [74]: ∂c 1 ⎧ ∂ ⎛ ∂c ⎞ ∂ ⎛ D ∂c ⎞ ∂ ⎛ ∂c ⎞⎫ = ⎨ ⎜ rD ⎟ + ⎜ ⎟ + ⎜ rD ⎟⎬ + I ∂t r ⎩ ∂r ⎝ ∂r ⎠ ∂θ ⎝ r ∂θ ⎠ ∂z ⎝ ∂z ⎠⎭

(200)

∂c ⎞ 1 ∂ ⎛ ∂c ⎞⎫ 1 ∂ ⎛ ∂c 1 ⎧ ∂ ⎛ 2 ∂c ⎞ ⎜ D ⎟⎬ + I = 2 ⎨ ⎜r D ⎟ + ⎜ D sin θ ⎟ + ∂θ ⎠ sin 2 θ ∂φ ⎜⎝ ∂φ ⎟⎠⎭ ∂r ⎠ sin θ ∂θ ⎝ ∂t r ⎩ ∂r ⎝

(201)

All these diffusion equations can be expressed in vectorial form as: ∂C r r = ∇ ⋅ Jm + I ∂t

r r J m = − D∇C

(202)

Transport Phenomena During Drying of Food Materials

∂C −D ∂x

Δx

−D

Δz x

∂C ∂x

81

x + Δx

Δy

Figure 20. Control volume showing inflows and outflows of a substance by diffusion.

This model is based on the assumption that mass transfer occurs only with molecular diffusion. However, there are also other mass transfer mechanisms. The effects of other transfer mechanisms can be considered to be lumped into diffusion coefficient. So, the diffusion coefficient becomes an effective parameter. r ∂C = ∇ ⋅ (D eff ∇C) + I ∂t

(203)

Although molecular diffusion model is the most commonly used model to express drying behavior of food materials, it is also the most criticized model. The reason, of course, is that it assumes that mass transfer occurs only by molecular diffusion. Fortes and Okos [21] attributes widely usage of this model to the logarithmic behavior resembling experimental drying curves. Sherwood [75] states that the success of diffusion equation lies in the fact that the calculations are made by integration techniques, which compensate for the error caused by wrong distribution assumption.

Solutions of Diffusion Equation for Some Simple Geometries It is possible to obtain analytical solution of diffusion equation for some simple geometries under certain assumptions. Suppose that: • • • • • • •

the diffusion coefficient is independent of moisture content for a given temperature, the material is isothermal during drying, the moisture is initially uniformly distributed throughout the material, surface moisture content of the samples instantaneously reaches equilibrium with the conditions of the surrounding air, the material size and geometry remain constant during drying, there is no mass generation or depletion inside the material.

Let us use the following dimensionless variables:

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Kamil Kahveci and Ahmet Cihan

C − Ce D 1 t* = 2 t (204) ∇*2 = 2 ∇ 2 , , Co − Ce L L where L is the characteristic dimension (half-lengths for a slab, radius for a cylinder, a sphere and a spheroid) (m) and t is the time (s). The diffusion equation and initial and boundary conditions can then be written as follows: C* =

∂C* = ∇ 2 C* * ∂t C*

t =0

=1

(205)

,

dC * dt

=0

,

C* = 0

(206)

S

s .a .

In order to predict a drying curve, it is necessary to determine an equation defining the average moisture content. Therefore, calculated average moisture content can be compared with the corresponding experimental results. This equation can be expressed as follows: mr =

m − me 1 = ∫ C*dV mo − me V V

(207)

where m, mo, me are the instantaneous, initial and equilibrium moisture contents respectively, and V is the volume of the material. For the geometries given in figure 21, analytical solutions of diffusion equation subjected to the initial and boundary conditions defined by Eq. (206) are given below.

Slab

Cardioid

Cylinder

Hexagon

Sphere

Corrugated (loop 1/8)

Figure 21. Some conventional and nonconventional shapes.

Ellipsoid

Epitrochoidal

Transport Phenomena During Drying of Food Materials

83

Conventional Shapes Infinite Slab mr =

2 ⎛ 8 ∞ 1 2 π D eff t ⎞ ⎜ ⎟ exp ( 2 n 1 ) − − ∑ ⎜ 4L2 ⎟⎠ π 2 n =1 (2n − 1) 2 ⎝

[76, 77]

(208)

Semi-Infinite Slab 2 2 ⎛ ⎞⎤ 1 ⎛ 8 ⎞ ⎡∞ ⎜ − (2n − 1) 2 π D eff t ⎟⎥ × mr = ⎜ 2 ⎟ ⎢ ∑ exp 2 2 ⎜ ⎝ π ⎠ ⎢⎣n =1 (2n − 1) 4L1 ⎟⎠⎥⎦ ⎝ 2 ⎡∞ ⎛ ⎞⎤ 1 ⎜ − (2n − 1) 2 π D eff t ⎟⎥ exp ⎢∑ 2 2 ⎟ ⎜ 4L 2 ⎠⎥⎦ ⎢⎣n =1 (2n − 1) ⎝

[78 ,79]

(209)

Finite Slab 3 2 ⎛ ⎞⎤ 1 ⎛ 8 ⎞ ⎡∞ ⎜ − (2n − 1) 2 π D eff t ⎟⎥ × exp mr = ⎜ 2 ⎟ ⎢ ∑ 2 2 ⎜ ⎝ π ⎠ ⎢⎣n =1 (2n − 1) 4L1 ⎟⎠⎥⎦ ⎝ 2 * ⎞⎤ ⎡∞ ⎛ 1 ⎜ − (2n − 1) 2 π D eff t ⎟⎥ × [78 ,79] exp ⎢∑ 2 2 ⎜ 4L 2 ⎟⎠⎦⎥ ⎝ ⎣⎢n =1 (2n − 1)

(210)

2 ⎡∞ ⎛ ⎞⎤ 1 ⎜ − (2n − 1) 2 π D eff t ⎟⎥ exp ⎢∑ 2 2 ⎜ 4L 3 ⎟⎠⎥⎦ ⎢⎣n =1 (2n − 1) ⎝

Sphere mr =

− n 2 π 2 D eff t ⎤ 6 ∞⎡1 exp( )⎥ ∑ ⎢ L2 π 2 n =1 ⎣ n 2 ⎦

[76, 80]

(211)

Hemisphere ∞ ∞

mr = ∑ ∑ b nm exp(−D eff α 2nm t ) n =0m =1

[81, 82]

(212)

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Kamil Kahveci and Ahmet Cihan

Here αnm are the roots of Cnth order Bessel function J Cn ( x ) = 0 and coefficients bnm are defined as:

b nm

1 ⎡1 ⎤ (24n + 18) ⎢ ∫ P2 n +1 ( x * )dx * ∫ (r * ) 3 / 2 J Cn (α nm r * )dr * ⎥ 0 ⎣0 ⎦ = J C2 n +1 (α nm )

2

(213)

Here P2n+1 is the (2n-1)th order Legendre function. The eigenvalues Λn are obtained using: Λ2n = 2(n + 1)(2n + 1)

(214)

The eigenvalues determine the order (Cn) of the Bessel function (JCn) according to: C n = (Λ2n + 1 / 4)1 / 2

(215)

Infinite Cylinder ∞ ⎡ 4 D t ⎤ mr = ∑ ⎢ 2 exp(−α 2n eff2 )⎥ L ⎦ n =1 ⎣ α n

[76, 83]

(216)

Here α n (n=1,2,.....) are the roots of zeroth order Bessel function J o ( x ) .

Finite Cylinder mr =

D t ⎤ 8 ∞ ∞⎡ 1 exp((−α 2n + β 2m ) eff2 )⎥ *2 ∑ ∑ ⎢ 2 2 L ⎦ l n =1m=1⎣ α n β m

[84, 85]

(217)

where l* is the dimensionless half-length of the cylinder. β m is defined as: βm =

(2m − 1)π 2l

m = 1, 2,.....

(218)

Prolate and Oblate Spheroid mr =

(

)

D eff t 1 N * * ∫ ∑ c n Ω n r , z exp( −Λ n 2 ) V V n =1 L

[86, 87]

(219)

85

Transport Phenomena During Drying of Food Materials

Here cn (n=1,2,...) are constants and Λ n and Ω n (n=1,2,...) are the eigenvalues and eigenfunctions respectively. Eigenvalues Λ n in the Eq. (219) can be obtained by the following equation:

(

Ω n (r, z ) = ∑ d njf j r * , z* N

n =1

)

(220)

where fj is an element of a group of base functions and d nj are constants to be determined. Function f j is called the Galerkin function and it is obtained by the multiplication of function Ω by an element of a complete set of functions. Function Ω is selected so as to satisfy the homogeneous boundary condition. Function f j with j varying from 1 to N constitutes a set of base functions. Using the Galerkin procedure eigenvalues γ n and constants d nj can be obtained. Thus the constants cn in Eq. (219) can be obtained by the following equation: * * * * ∫ f i C (r , z )dV = ∫ ∑ f i c n Ω n (r , z )dV N

(221)

V n =1

V

Nonconventional Shapes Analytical solution of diffusion equation for some nonconventional shapes seen in figure 21 has been obtained by Rotstein et al. [88] using conformal mapping. Cross sectional areas of these nonconventional shapes are given in table 13. Table 13. Cross sectional area for some nonconventional shapes (L is the characteristic length) cardioid

corrugated

3 2 πL c 2

129 2 πL r 128

epitrochoid 41π 2 Lc 36

Hexagon 3 3 2 Lh 2

Cardioid m 4 32 ∞ ∞ 1 1 2 = (1 − 2 ) exp(−Fmc Dt ) ∑∑ m o 3 π 2 n =1m=1(2n + 1) 2 α 2m αm

(222)

where αm (m=1, 2,.....) are the roots of zeroth order Bessel function J o ( x ) and Fm is the i

shape factor defined as:

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Kamil Kahveci and Ahmet Cihan

⎛ (2m + 1)2 ⎞ ⎟ + m (223) F = ⎜⎜ 2 ⎟ L2i ⎝ 4l ⎠ where i is the shape index (i=c, cardioid, i=h, hexagon, i=g, corrugated, i=e, epitrochoid), l is the vertical semi-length (m). 2

i m

Hexagon m 32 ∞ ∞ 1 1 h = 0.9060 2 ∑ ∑ Sm exp(−Fmh Dt ) 2 2 mo π n =0m=0 (2n + 1) α m

(224)

where Shm = a 2 + b 2 f 6 + c 2 f12 + d 2 f18 + e 2 f 24 + f 2 f 30

(225)

where f k is given by the following functional relation: 2

⎛ k! ⎞ −2 j ⎟⎟ α m f k = ∑ (−1) 2 ⎜⎜ j=0 ⎝ (k − j)! ⎠ k

j

2j

(226)

Corrugated (Loop 1/8) m 32 ∞ ∞ 1 1 ⎡ 13 ⎤ = 0.9524 2 ∑ ∑ 1 + ( ) 2 f12 ⎥ exp(−Fmg Dt ) 2 2 ⎢ mo 16 π n =0m=0 (2n + 1) α m ⎣ ⎦

(227)

Epitrochoid m 32 ∞ ∞ 1 1 ⎡ 25 ⎤ = 0.9119 2 ∑ ∑ 1 + f 4 exp(−Fme Dt ) mo π n =0m=0 (2n + 1) 2 α 2m ⎢⎣ 36 ⎥⎦

(228)

DIFFUSION MODEL FOR SHRINKING MEDIA Mass balance in a shrinking porous material may be expressed as follows: ∂ρ s r r + ∇(ρ s v s ) = 0 for solid phase ∂t

(229)

Transport Phenomena During Drying of Food Materials ∂ρ l r r + ∇(ρ l v l ) = 0 for liquid phase ∂t

87

(230)

r where ρs and ρl are the concentrations of the solid and liquid (kg/m3), v s is the solid

r

displacement velocity (m/s) and v l is the liquid velocity (m/s). Also: r r r ρv = ρ s v s + ρ l v l

(231)

ρ = ρs + ρl

(232)

where ρ is the total concentration (kg/m3). The mass flux can be divided into two separate terms in terms of diffusion and convection: r r r ρ k v k = J mk + ρ k v

k=l,s

(233)

The moisture content m is the ratio of the two phase mass concentrations: m = ρl / ρs

(234)

If the liquid flow is assumed to be diffusive, it can be expressed with respect to the solid phase frame reference. Using Eqs. (231), (233), and (234) yields: r r r ρ ρl (v l − vs ) = − D eff ∇m 1+ m

(235)

If this relation is combined with the two mass balance equations (Eqs. (229) and (230)), the liquid transport takes the following form: ρs

r ρ r r r ∂m + ρs v s ⋅ ∇m = ∇( D eff ∇m) ∂t 1+ m

(236)

Relation Between Solid Displacement and Water Loss The solid and liquid phase concentrations depend on the dry solid concentration and can be given as follows [89]: ρs =

ρso 1 + βm

ρl =

mρ so 1 + βm

(237)

where ρso is the initial solid concentration (kg solid/m3 total volume) and β is the volumetric shrinkage coefficient. Using Eq. (237), we obtain:

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Kamil Kahveci and Ahmet Cihan

ρ (1 + βm) − ρsoβm ρ m ρso = ρso − β so = so 1 + βm 1 + βm 1 + βm Therefore: ρs =

(238)

ρs = ρso − δρl

(239)

Equations (229) and (239) lead to: −β

r ∂ρ l r − ∇ ⋅ ( ρ sv s ) = 0 ∂t

(240)

−β

r r r r ∂ρ l + ρ s∇ ⋅ v s + v s ⋅ ∇ ρ s = 0 ∂t

(241)

−β

r r r r ∂ρ l + ρ s∇ ⋅ v s − δv s ⋅ ∇ ρ s = 0 ∂t

(242)

r r ∂ρ l r r + v s ⋅ ∇ ρ s ) + ρs∇ ⋅ v s = 0 ∂t

(243)

− β(

From the conservation of the liquid phase, we can write: r r r ∂ρ l r r + ∇(ρ l v l − ρ l v s ) + ∇(ρ l v s ) = 0 ∂t

(244)

If we use the diffusion flux: r r ∂ρ l r r + ∇ ⋅ J m + ∇ ⋅ (ρ l v s ) = 0 ∂t

(245)

r r r r ∂ρ l r r + ∇ ⋅ J m + ρl∇ ⋅ v s + v s∇ ⋅ ρl = 0 ∂t

(246)

r r r r ∂ρ l r r + v s ⋅ ∇ρ l = − ∇ ⋅ J m − ρ l ∇ ⋅ v s ∂t

(247)

If we replace Eq. (247) into Eq. (243): r r r r r r − β(−∇ ⋅J m −ρ l ∇ ⋅ v s ) + ρs ∇ ⋅ v s = 0

(248)

r r r r β ∇ ⋅J m +(βρ l + ρ s ) ∇ ⋅ v s = 0

(249)

Transport Phenomena During Drying of Food Materials

89

r r r r β ∇ ⋅ J m + ρ so ∇ ⋅ v s = 0

(250)

r r r r β ∇ ⋅ J m = −ρ so ∇ ⋅ v s

(251)

r The diffusion flux J m is defined as. r ρ D r J m = − so ∇m 1 + βm

(252)

Therefore Eq. (251) becomes: r ⎛ ρ D r ⎞ r r β ∇ ⋅ ⎜⎜ − so ∇m ⎟⎟ = −ρso ∇ ⋅ v s ⎠ ⎝ 1 + βm

(253)

which can be simplified in: r ⎛ D r ⎞ r r β ∇ ⋅ ⎜⎜ ∇m ⎟⎟ = ρso ∇ ⋅ v s ⎠ ⎝ 1 + βm

(254)

Rheological Behavior The deformation of drying material can be partially characterized by the solid-liquid momentum equation. These deformations define structural displacements and therefore the solid velocity. The momentum conservation can be defined as follows: ρ

r r r ~ r rr ∂v + ρv ⋅ ∇v = ρF + ∇ ⋅ σ ∂t

(255)

r ~ is the stress tensor (Pa) and F where σ is the external force (N) applied to the sample. If it is assumed that there are no external forces and the inertial term is negligible, Eq. (255) becomes as:

r ~ r ∇⋅σ =0

(256)

with the boundary condition below which implies that the loading to the material surface is absence. ~ ⋅ nr = 0 σ S

(257)

The solid liquid momentum conservation equation is not sufficient to describe the response of a specific material to an applied loading. An equation is required which defines rheological behavior. Mihoubi et al. [90] considered both elastic and viscoelastic cases. They separated

90

Kamil Kahveci and Ahmet Cihan

strain tensor in two parts for each case. One is directly bound to the material behavior ( ~εM ) and the other is bound to the moisture and temperature variation ( ~ε ) r

~ε = ~ε + ~ε M r

(258)

The strain tensor ~εr depends on the moisture migration ~εH and the temperature variation ~εT . ~ε = ~ε + ~ε r T H

(259)

The strain tensors depending on the moisture migration and temperature variation can be derived from the thermodynamics of linear irreversible process as [90]: ⎧~εT = α T ΔT ⎨~ ⎩ εH = β T ΔT 1 ε ij = ( v i , j + v j,i ) 2 v i, j =

∂v i ∂x j

(260)

vi =

∂ (ν p s ) i ∂t

(261)

where αT is the thermal expansion coefficient (1/K), βT is the volumic expansion coefficient (1/K), and νp is the Poisson coefficient.

Elastic Behavior Stress-strain relation in the classic elasticity is related by the Hooke law generalized in three dimensions: ~ = λ′(~ε ) T ⋅ ~ σ 1 + 2μ′~εm m

(262)

~

where 1 is a unit tensor, λ′ and μ′ are the Lame coefficients (Pa). Lame coefficients are related the Young modulus E (Pa) and the Poisson ratio ν by the following equations [90]: λ′ =

E yν (1 + ν)(1 − 2ν)

μ′ =

Ey 2(1 + ν)

where Ey is the Young modulus (Pa).

(263)

Transport Phenomena During Drying of Food Materials

91

Viscoelastic Behavior Strain rate in a viscoelastic material changes in time. The most appropriate approach for describing the deformation of many real products is the viscoelastic approach. The application of the principle of correspondence in the space of Laplace space for a viscoelastic material responding to the deformation solicitations leads to the following generalized law of Hooke [90]: 2 ⎞ ⎛ σij (s) = ⎜ P (s) − Q (s) ⎟ ε m ,kk (s)δ ij + 2Q (s) ε m ,ij 3 ⎠ ⎝

(264)

Returning to actual space, several shapes are derived according to the regime of solicitation. Among all these shapes, the one bound to a stepwise solicitation represents well because we have the effect of the deformation and the rate of the deformation [90]. t t ∂ε m ,ij (τ) 2 ⎛ ⎞ ∂ε m ,kk (τ) σ ij ( t ) = ∫ ⎜ K V ( t − τ) − G r ( t − τ) ⎟ δ ijdτ + 2 ∫ G r ( t − τ) dτ δτ 3 ∂τ ⎠ 0⎝ 0

(265)

In the matrix form ~ ~ ~ t t ~ ( t ) = ⎛⎜ K ( t − τ) − 2 G ( t − τ) ⎞⎟ ∂ εm (τ) : 1 dτ + 2 G ( t − τ) ∂ εm (τ)dτ σ ∫ V ∫ r r δτ 3 ∂τ ⎠ 0⎝ 0

(266)

where t is the time (s), KV is the volume model (Pa) and Gr is the relaxation model (Pa).

Mathematical Transformation The flux and continuity equations for liquid phase can be written as follows: r r J m = −ρs D∇m

(267)

r 1 r ∂m v r + v s ⋅ ∇m = ∇ ⋅ (Dρs ∇m) ∂t ρs

(268)

The actual position in space of a small mass element of the moving solid can be denoted by a r time dependent vector r ( t ) . However, it might be more advantageous to use the Lagrangian description, in which the initial position of each small mass element is indicated by a vector r r rL = r (0) . The partial time derivatives for both coordinate systems are related by [41]: r r ∂m ∂m + v s ⋅ ∇m = ∂t rrL ∂t rr

(269)

92

Kamil Kahveci and Ahmet Cihan

The transformations for the gradient and divergence are defined as. r −T r r ⎛ ∂r ⎞ ∇ rr (*) = ⎜ r ⎟ ⋅ ∇ rrL (*) ⎝ ∂z ⎠

r r ⎛ 1 ⎛ ∂rr ∇ rr ⋅ (*) = f ∇ rrL ⋅ ⎜ ⎜⎜ r ⎜ f ⎝ ∂ rL ⎝

(270)

−1 ⎞ ⎞ ⎟⎟ ⋅ (*) ⎟ ⎟ ⎠ ⎠

(271)

where r ⎛ ∂r 1 = Det⎜⎜ r f ⎝ ∂ rL

⎞ ρso ⎟⎟ = ⎠ ρs

(272)

where f is the shrinkage function, ρs is the solid concentration (kg/m3) and ρso is the dry solid concentration (kg/m3). Applying these transformation rules to the flux and continuity equations yields: r −T r r ⎛ ∂r ⎞ J m = −ρso f D ⎜⎜ r ⎟⎟ ⋅ ∇ rL m ⎝ ∂ rL ⎠ ∂m ∂t

r rL

r −1 r r ⎧⎪ ⎛ ∂r ⎞ ⎛ ∂r r = ∇ rL ⋅ ⎨D eff ⎜⎜ r ⎟⎟ ⋅ ⎜⎜ r ⎝ ∂ rL ⎠ ⎝ ∂ rL ⎪⎩

(273)

⎞ ⎟⎟ ⎠

−T

⎫⎪ r ⋅ ∇ rrL m ⎬ ⎪⎭

(274)

r r The deformation of a material is described by means of ∂ r / ∂rL . The amount of solid remains

constant during drying. Therefore, the following restriction has to be satisfied [41]: r ⎛ ∂r Det⎜⎜ r ⎝ ∂ rL

⎞ 1 ρso ⎟⎟ = = ⎠ f ρs

(275)

The deformation in the case of isotropic shrinkage is mathematically given by [91]: −1 / 3 r ⎛⎜ f ∂r ⎜ r = 0 ∂ rL ⎜ ⎜0 ⎝

0 f 0

⎞ ⎟ 0 ⎟ ⎟ f −1 / 3 ⎟⎠ 0

−1 / 3

Assuming no gradients in the ρso , the continuity equation becomes:

(276)

Transport Phenomena During Drying of Food Materials r r ∂m = ∇ rrL ⋅ (D eff f 2 / 3∇ rrL m) ∂t rrL

93

(277)

One-Dimensional Shrinkage The deformation in one dimensional shrinkage is defined as [41]: −1 r ⎛f ∂r ⎜ r = ⎜0 ∂ rL ⎜ ⎜0 ⎝

0 1 0

0⎞ ⎟ 0⎟ ⎟ 1 ⎟⎠

(278)

The flux and continuity equations can then be written as: J m,ξ = −ρso f 2 D

∂m ∂ξ

∂ ∂m ∂m ) = (D eff f 2 ∂ξ ∂t ξ ∂ξ

(279)

(280)

ANOTHER DIFFUSION MODEL FOR SHRINKING MEDIA Mass balance equations for solid and liquid phases can be written as follows: r ∂ρ s r + ∇ ⋅ (ρs v s ) = 0 for solid ∂t

(281)

r ∂ρ l r + ∇ ⋅ (ρ l v l ) = 0 for liquid ∂t

(282)

If we use the relation ρs = ρ so /(1 + βm) , the mass balance equation for solid phase becomes as follows: r r ∇ ⋅ vs =

β ∂m β r r v s ⋅ ∇m + 1 + βm 1 + βm ∂t

(283)

The balance equation for liquid phase can be rearranged as: r r r ∂ρ l r r + ∇(ρ l v l − ρ l v s ) + ∇(ρ l v s ) = 0 ∂t

(284)

94

Kamil Kahveci and Ahmet Cihan

r Using the diffusion flux J m , we obtain:

r r ∂ρ l r r + ∇ ⋅ J m + ∇ ⋅ (ρ l v s ) = 0 ∂t

(285)

r r r r ∂ρ l r r + ∇ ⋅ J m + ρ l ∇ ⋅ v s + v s ⋅ ∇ρ l = 0 ∂t

(286)

and from Eq. (252): r r r r ∂ρ l r ⎛ ρ so D r ⎞ + ∇⎜⎜ − ∇m ⎟⎟ + ρ l ∇ ⋅ v s + v s ⋅ ∇ρ l = 0 ∂t ⎠ ⎝ 1 + βm

(287)

If we use the relation ρ l = mρs and write Eq. (283) into Eq. (287), we find:

(

)

r ⎡ β r r ∂ρs ∂m ∂ β ∂m ⎤ + ρs + − ρso D∇m + ρ l ⎢ v s ⋅ ∇m + + ∂t ∂t ∂x 1 + βm ∂t ⎥⎦ ⎣1 + β m r r r v s ⋅ m∇ρ s + ρ s ∇m = 0

m

(

)

(288)

Recalling that ρs = ρ so /(1 + βm) and ρs = ρso − δρl : 1 ∂m r ⎛ D r ⎞ 1 r r = ∇ ⋅ ⎜⎜ ∇m ⎟⎟ − v s ⋅ ∇m 1 + βm ∂t ⎝ 1 + βm ⎠ 1 + βm

(289)

Equations (283) and (289) define the mass transfer from a shrinking body and can be solved for vs, and m.

RECEDING FRONT MODEL Experimental observations show that during drying of some products evaporation takes place inside the material at a certain depth which divides the system into two regions as dry and wet, as shown in figure 22. For a hygroscopic material, the dry zone is called the sorption zone due to the adsorptive nature of moisture retention. In the dry zone, moisture is in vapor phase only and, in the wet zone, it is in liquid or mixed form. As drying proceeds the evaporating front recedes increasing the ratio of dry to wet zones. The simplest case for this model assumes that the saturation S is 1 in the wet region and 0 in the dry region. Balance equations related to this case can be defined as follows [92, 93]:

95

Transport Phenomena During Drying of Food Materials Evaporation Front

Drying Surface

Symmetry Plane

Drying Air

Heat Flux

Wet Zone

Dry Zone

Moisture Flux

z=L

z=zF(t)

z=0

Figure 22. Receding front model (adapted from [93]).

Wet Zone (1) ∂m fw ∂ ⎛ ∂m fw ⎞ = ⎜ Dl ⎟ ∂z ⎝ ∂z ⎠ ∂t ρC pl

∂T1 ∂ ∂T = (λ eff 1 ) ∂t ∂z ∂z

(290)

(291)

where Dl is the liquid transfer coefficient (m2/s), CPl is the specific heat of water (J/(kg K)), mfw is the moisture content of free water (kg water/kg dry solid) and λ eff is the effective thermal conductivity (W/(m K)). The thermal conductivity is calculated from the following equation: λ eff = λ l +

ˆ ∂Pv,sat (T) D′v M v Δh vap ∂T RˆT

(292)

ˆ is the molecular mass of the where λl is the thermal conductivity of liquid (W/(m K)), M v vapor, Δh vap is the evaporation enthalpy (J/kg), Rˆ is the ideal gas constant (8.314 J/(mol K)), Pv,sat (T ) is the saturation vapor pressure (Pa). D′v is the vapor transfer coefficient (m2/s), which includes the contribution of both convective and diffusive flows and it is defined as:

96

Kamil Kahveci and Ahmet Cihan ⎞ ⎛ K i k rg Pv / η v ⎟ D′v = D v ⎜1 + ⎜ D v + K i k rg (Pg − Pv ) /(ε av ηv ) ⎟ ⎠ ⎝

(293)

where Dv is the vapor diffusion coefficient (m2/s), Ki is the intrinsic permeability (m2), k rg is the relative permeability of gas phase, ηv is the dynamic viscosity (Pa s) and εav is the ratio of air and vapor diffusion coefficients.

Dry or Sorption Zone (2) ρ

ˆ ∂P ∂m sorb ∂m sorb ∂ ∂ D′ M = ρ (D sorb )+ ( v v v) ∂t ∂z ∂z ∂z RˆT ∂z

ρC Pv

∂T2 ∂ ∂T = (λ v 2 ) ∂t ∂z ∂z

(294)

(295)

where CPv is the specific heat capacity of vapor (J/(kg K)), λv is the thermal conductivity of vapor (W/(m K)), msorb is the adsorbed water content (kg water/kg dry solid) and Dsorb is the transfer coefficient of adsorbed water (m2/s). For a non-hygroscopic material, msorb is zero and Dsorb is negligible.

Evaporation Front The following conditions must be satisfied at the moving boundary: ˆ ∂P ∂m fw ∂m sorb D′v M v v = ρD sorb + ∂z ∂z RˆT ∂z

(296)

ˆ ∂P D′ M ∂ (λ eff Tl ) ∂ (λ v T2 ) + Δh vap v v v = ∂z ∂z RˆT ∂z

(297)

T1 = T2 ,

(298)

ρD l

m fw = 0

The most important difficulty in using this model arises from determination of the boundary for the moving evaporation front [93].

PHILIP AND DE VRIES MODEL In this model, the moisture content and temperature gradient are assumed to be the driving force in moisture transport. In addition, mass transport in liquid phase is assumed to

Transport Phenomena During Drying of Food Materials

97

occur with the effect of capillarity. The Flux and balance equations for this model can be defined as follows:

Liquid Water Darcy law is used in this model to express mass transfer in liquid phase [69]. r r Kk r J ml = −ρ l i rl (∇Pl − ∇Ψl ) ηl

(299)

where ρl is the density of the liquid water (kg/m3), Ki is the intrinsic permeability (m2), krl is the relative permeability of the liquid water and ηl is the dynamic viscosity of the liquid water (Pa s). Pl and Ψl are the pressure (Pa) and gravity potential (m2/s2) of the liquid water, r respectively. If the term ∇Pl is expressed as a function of moisture content and temperature, liquid water flux can be written as: r r r Kk r J ml = −D lm ∇m − D lT ∇T + ρ l i rl ∇Ψl ηl

(300)

where T is the temperature (°C) and m is the moisture content (kg water/kg dry solid). Dlm and DlT are the isothermal and thermal diffusivities of water (m2/s) and are given by D lm = ρ l

K i k il ∂Pl ηl ∂m

D lT = ρ w

K i k rl ∂Pl ηl ∂T

(301)

Water Vapor The water vapor transport is described by Fick’s first law and by using the assumption of a steady diffusion in a closed system between an evaporation source and a condensation sink. The commonly used expression for the vapor flux in terms of moisture and temperature gradients is as follows [93]: r r r J mv = − D vm ∇m − D vT ∇T

(302)

where Dvm and DvT are the isothermal and thermal diffusivities of vapor and are defined as [73]: D vm = f (ψ)D va

ˆ g ρ ∂P M v v l Pg − Pv RˆT ρ l ∂m Pg

(303)

98

Kamil Kahveci and Ahmet Cihan D vT = f (ψ )D va

r ρ v (∇T) av 1 dPv,sat r Pg − Pv ρ l (∇T) Pv*,sat dT Pg

(304)

In these equations f (ψ ) is a function of porosity and moisture content, Dva is the diffusion coefficient of vapor in air (m2/s), g is the gravitational acceleration (m/s2), P v, sat (T) is the r saturation vapor pressure (Pa), (∇T) av is the average air temperature gradient, ρv and ρw are the concentrations of vapor and liquid (kg/m3).

Balance Equations If the flux equations are taken into consideration, the mass and energy conservation equations can be written as follows: r r r r ⎛K k r ⎞ ∂m r = ∇ ⋅ (D T ∇T) + ∇ ⋅ (D m ∇m) + ∇ ⋅ ⎜⎜ i rl ∇Ψl ⎟⎟ ∂t ⎠ ⎝ ηl

∂ (ρC p T) ∂t

r r r r = ∇ ⋅ (λ∇T) + Δh vap∇ ⋅ (D vm∇m)

(305)

(306)

where DT=DwT+DvT is the overall thermal diffusivity (m2/s), Dm=Dwm+Dvm is the overall isothermal mass diffusivity (m2/s), λ is the thermal conductivity (W/(m K)) and ρCP is the volumetric heat capacity of the porous material (J/(m2K)).

LUIKOV MODEL This model is one of the most frequently used drying models. In this model, heat and mass transfer equations are derived by employing the principles of nonequilibrium thermodynamics theory. As it is well known, if two points of a system are at different potentials, a potential difference in the system is established which causes the flow of entity from the point of higher potential towards that of lower potential. The entity may be either of matter or heat or electricity. When two or more irreversible processes take place simultaneously in a thermodynamic system, they may interfere with each other and produce a cross effect. This model is based on taking into consideration such cross effects involved in heat and mass transport in a porous material.

Potentials for Heat and Mass Transfer The total entropy increases in an irreversible process, whereas in a reversible process there is no change in the total entropy. The entropy production in an irreversible process gives

Transport Phenomena During Drying of Food Materials

99

a dissipation of energy, i.e. energy is lost. Entropy production can be calculated from the dissipation function φ (J/(m3s)) as: φ=T

n dS = ∑ JiΦi dtdV i =1

(307)

where J i and Φ i are the conjugated fluxes ([…]/(m2s)) and forces (J/(kg[…])), respectively, T is the temperature (K), S is the entropy (J/K)), t is the time (s) and V is the volume (m3). Although transport processes are irreversible, in nonequilibrium thermodynamic theory, these processes are assumed to be in local thermodynamic equilibrium. Local equilibrium means that the Gibbs equation (Eq.(308)) holds for a small region of the space and for changes in the variables that are actually not infinitely small. TdS = dU + pdV − ∑ μ i dN i

(308)

i

where U (J) is the internal energy, P (Pa) is the pressure, μ (J/kg) is the chemical potential and N is the number of moles. The moisture in a porous material may exist in the frozen, liquid or vapor state; in addition, there will be some air. It is generally assumed that the change in chemical potential of air is negligibly small. In addition, the frozen moisture is immobile, and therefore no chemical changes take place. In this case, assuming also that the pressure is constant, the thermodynamic forces of heat and mass transfer in a porous material can be derived from Eq. (308) to be: 1 Φ q = − ∇T T

(309)

Φ l = −T∇(μ l / T)

(310)

Φ v = −T∇(μ v / T)

(311)

The dissipation function can then be expressed as follows: φ = J q Φ q + J l Φ l + J v Φ v = −J q

1 ∇T − J l T∇(μ l / T) − J v T∇(μ v / T) T

(312)

Transfer Fluxes The all thermodynamic forces or gradients give rise to fluxes of heat, mass and other forms of energy. If these gradients are not too large, it is a fundamental postulate of irreversible thermodynamics that the fluxes are linear, homogeneous function of the gradients. Thus:

100

Kamil Kahveci and Ahmet Cihan n

J i = ∑ L ik Φ k

(313)

k =1

The coefficients Lik ([…]2/s) are called phenomenological because they are determined by the rate at which the phenomena proceed. According to the Onsager’s fundamental theorem, cross coefficients are symmetrical if a proper choice for the conjugated fluxes and forces is made. L ik = L ki

(314)

These identities are called the Onsager reciprocal relations. They express a connection between two reciprocal phenomena. These reciprocal relations can be derived from the principle of microscopic reversibility, using the statistical thermodynamics. Since the entropy in an irreversible process must increase, Eq. (307) must be positive, which gives the two additional restrictions to the phenomenological coefficients. L ii ≥ 0 L ii L kk ≥ L2ik

(i=1,2…n) (i ≠ k ;

(315) i,k=1,2,…n)

(316)

In other words, the main coefficients must be positive or zero and the coupling coefficients are limited in magnitude to the square root of the product between the corresponding main coefficients. If there is no metastable equilibrium and all the forces and flows are independent, the inequality sign holds in Eqs. (315) and (316). The following set of phenomenological equations can be written for the mass and heat transport in a porous material: J q = L qq Φ q + L ql Φl + L qv Φ v

(for heat transfer)

(317)

J ml = L lq Φ q + L ll Φ w + L lv Φ v

(for liquid water transfer)

(318)

J mv = L vq Φ q + L vw Φ w + L vv Φ v

(for water vapor transfer)

(319)

In the material, vapor and liquid phases are in equilibrium. Therefore, the chemical potentials and their gradients are equal: μl = μ v = μ w

and

r r r ∇μ l = ∇μ v = ∇μ w

(320)

Consequently: J q = L qq Φ q + (L ql + L qv ) Φw

(321)

Transport Phenomena During Drying of Food Materials

101

J ml = L lq Φ q + L l Φ w

(322)

J mv = L vq Φ q + L v Φw

(323)

1 Φ q = − ∇T T

(324)

Φ w = −T∇(μ w / T)

(325)

and

The equations (321-323) expresses that heat transfer depends not only on thermal conduction but also on the redistribution of mass (Dufour effect) and mass transfer is determined not only by differences in chemical potential but also by thermal diffusion (Soret effect). From Onsager reciprocal relations, the cross coefficients are equal. Lql = Llq

and

Lqv = Lvq

(326)

Since the chemical potential is a function of the intensive and specific state parameters, the following equation can be written when pressure is constant: r ∂μ r ∇Tμ w = w ∇T m ∂m

(327)

where m is the moisture content (kg moisture/kg dry solid). Taking all of these into account, the mass and heat flux equations can be written as follows: r ∂μ r 1 J ml = −L lw T w ∇ T m − L lq ∇T ∂m T

(328)

r ∂μ r 1 J mv = −L vw T w ∇ T m − L vq ∇T ∂m T

(329)

r ∂μ r 1 J q = −L qq ∇T − (L ql + L qv )T w ∇ T m T ∂m

(330)

In practice, it is quite usual to present Eqs. (328-330) as follows: r r r J ml = −ρs D l (∇ T m + δ Tl ∇T)

(331)

r r r J mv = −ρs D v (∇ T m + δ Tv ∇T)

(332)

102

Kamil Kahveci and Ahmet Cihan r r ∂μ r J q = −λ∇T − ρ s (D l δ Tl + D v δ Tv )(T w ∇ T m) ∂m

(333)

where Dl (m2/s) and Dv (m2/s) are the diffusivities of the liquid and of the vapor, δTl (1/°C) and δTv (1/°C) are the thermal gradient coefficients and λ (W/(m K) is the thermal conductivity. Any potential gradient can result in moisture transfer. Under certain circumstances a pressure gradient can arise in a moist body. Then there will be an associated moisture flow. r r J mP = δ P ∇Pg

(334)

where δP is the moisture filtration coefficient (kgm/(sN)). In drying applications Dufour effect is generally omitted since it is not at significant levels [16, 94]. In addition, flux for liquid and vapor phases are expressed in one single equation using effective parameters, instead of defining separately. In this case, Eqs. (331)-(333) become as follows: r r r J m = −ρ s D(∇ T m + δ T ∇T)

(335)

r r J q = −λ∇T

(336)

Balance Equations An analytical description of drying in a porous material can be obtained by using the foregoing equations for the fluxes in mass and energy balances over the material.

Temperature Equation The energy balance can be written as: ρs C P

r r 4 ∂T = −∇ ⋅ J q − ∑ h i I i ∂t i =1

(337)

4

where CP is the specific heat capacity (J/(kg K)) and ∑ h i I i is the heat source or sink. The i=1

source or sink term is due to the phase change of the water within the porous body and can be defined as follows [95]:

∑ h i I i = −Δh vap ε pc ∇ ⋅ [ρ s D(∇m + δ T ∇T)] − Δh vap ε pc ∇ ⋅ (δ P ∇Pg ) 4

i =1

r

r

r

r

r

(338)

Transport Phenomena During Drying of Food Materials

103

where Δh vap is the enthalpy difference for a change between the condensed and vapor states (J/kg), ε pc is the ratio of vapor diffusion coefficient to the coefficient of total moisture diffusion. Substituting Eqs. (336) and (338) into Eq. (337), we obtain: ρs C P

∂T = Δh vapε pcρs D∇ 2 m + (λ + Δh vapε pc δ T )∇ 2 T + Δh vapε pc δ P ∇ 2 Pg ∂t

(339)

Luikov [16] expresses moisture content in terms of moisture potential (m=CmW). Therefore: ρs C P

∂T = Δh vap ε pc ρ s C m D∇ 2 W + (λ + Δh vap ε pc δ T )∇ 2 T + Δh vap ε pc δ P ∇ 2 Pg ∂t

(340)

Moisture Content Equation The mass balance for each species in the material can be written as:

ρs

r r ∂m i = −∇ ⋅ J mi + I i ∂t

(341)

where I is the volumetric capacity of the source of the material (kg/(m3s)). The overall mass can be obtained from Eq. (341) as: ρs

r r 4 ∂m = −∇ ⋅ J m + ∑ I i ∂t i =1

(342)

4

Since the moisture is neither lost nor created ( ∑ I i = 0 ). Substituting Eqs. (334) and (335) i =1

into (342), we have:

ρs

∂m = ρ s D∇ 2 m + ρ s Dδ T ∇ 2 T + δ P ∇ 2 Pg ∂t

(343)

Expressing moisture content in terms of moisture potential, Eq. (343) becomes:

ρs C m

∂W = ρs C m D∇ 2 W + ρs Dδ T ∇ 2 T + δ P ∇ 2 Pg ∂t

(344)

Pressure Equation Assuming that the moisture filtration of liquid is small as compared with that of vapor and air, the mass flux equation can be written as follows:

104

Kamil Kahveci and Ahmet Cihan r r r r J m = ∑ J mi = J mv + J ma

r r J m = −δ P ∇Pg

(345)

i

If the mass transfer equations for liquid and gas are summed, the following equation is obtained: ρs

r r r ∂(u v + u a ) = −∇ ⋅ ( J mv + J ma ) + I v + I a ∂t

(346)

where u is the mass average velocity (m/s). The specific mass content of the vapor and the gas mixture can be determined by gas equation as follows: ρs ( u v + u a ) =

ˆ Pg M g ψS ˆ RT

(347)

ˆ is the molar mass (kg/mol), Rˆ is the ideal gas law constant where P is the pressure (Pa), M (8.314 J/(mol K)), ψ is the bulk porosity of the body, and S is the saturation of the pores and capillaries in the body. On differentiating and assuming T2>>cP and T>>dS and letting cP =

ˆ ψ SM g ρ TRˆ

(348)

s

we obtain: ρs

∂Pg ∂(u v + u a ) = ρs c p ∂t ∂t

(349)

where cp is the air capacity (kg m2/(kg N)). Substituting Eqs. (345) and (349) into Eq. (346) yields:

ρs c P

∂Pg ∂t

= δ P ∇ 2 Pg − ρs ε pc C m

∂W ∂t

(350)

With the substitution Eq.(342) into Eq. (348), we obtain:

ρs c P

∂Pg ∂t

= −ε pc ρs C m D∇ 2 W − ε pc ρs Dδ T ∇ 2 T + (1 − ε pc )δ P ∇ 2 Pg

(351)

Let us write the balance equations again: ρs C P

∂T = Δh vap ε pc ρ s C m D∇ 2 W + (λ + Δh vap ε pc δ T )∇ 2 T + Δh vap ε pc δ P ∇ 2 Pg ∂t

(352)

Transport Phenomena During Drying of Food Materials ρs C m

ρs c P

∂W = ρs C m D∇ 2 W + ρ s Dδ T ∇ 2 T + δ P ∇ 2 Pg ) ∂t

∂Pg ∂t

105

(353)

= −ε pc ρs C m D∇ 2 W − ε pc ρs Dδ T ∇ 2 T + (1 − ε pc )δ P ∇ 2 Pg

(354)

A general set of boundary conditions for this system can be defined as follows [16]: W = WS δm

δ δ ∂T ∂W + h m ( W − Wa ) = 0 + Jm + m T C m ∂n ∂n

T = TS λ

∂T + J q + h q (T − Ta ) + h m Δh vap (1 − ε pc )( W − Wa ) = 0 ∂n

Pg = PS

on

Γ1

(355)

on

Γ2

(356)

on

Γ3

(357)

on

Γ4

(358)

on

Γ5

(359)

where hm is the convective mass transfer coefficient (kg/(m2s)) and hq is the convective heat transfer coefficient (W/(m2K)). Subscripts a and S stand for ambient and surface respectively. The first term in Eq. (358) is the amount of heat passing into the body, the second term and the third term are the heat supplied at the surface, and the last term is the amount of heat expended in the phase change of the fluid. The first term in Eq. (356) is the moisture flux in the direction normal to the surface, while the last two terms describe the amount of moisture removed from the surface. Equations (356) and (358) can be written in a general form as [95]: k11

∂T + J *q* = 0 ∂n

k 22

∂W + J *m* = 0 ∂n

(360)

where J *q* = a q (T − Ta ) + a ε ( W − Wa ) + J *q J *m* = a δ (T − Ta ) + a m ( W − Wa ) + J *m

aε =

Δh vap h m λ

(1 − ε pc )K 11

aq =

K11h q λ

DJ q ⎤ ⎡J J *m = K 22 ⎢ m − ⎥ ⎣ h m Cmλ ⎦

δ T Δh vap K 22 δ T h q ⎡ 1 ⎤ a m = K 22 h m ⎢ − (1 − ε pc )⎥ a δ = − Cmλ Cm ⎣hm ⎦

J *q = (

K11 ) λ

(361)

(362)

(363)

106

Kamil Kahveci and Ahmet Cihan

The partial differential equations for temperature, pressure and moisture potential are not symmetric; however, by multiplying Eq. (352) by δ T / C m , Eq. (353) by Δh vap ε pc and Eq. (354) by −Δh vap δ p / δ m .a symmetric set of equations can be obtained. c′q

∂T = k11∇ 2 T + k12 ∇ 2 W + k13∇ 2 Pg ∂t

(364)

c′m

∂m = k 21∇ 2 T + k 22 ∇ 2 W + k 23∇ 2 Pg ∂t

(365)

c′p

∂P = k 31∇ 2 T + k 32 ∇ 2 W + k 33∇ 2 Pg ∂t

(366)

where c′q = ρ s C P δ′T

k11 = (λ + Δh vapε pc δ T )δ′T k12 = k 21 = Δh vap ε pc δ m δ′T

(367)

k13 = k 31 = Δh vap ε pc δ P δ′T

(368)

c′m = ρ s Δh vap ε pc C m

c ′P =

−ρ s c P Δh vap δ P δm

k 22 = Δh vap ε pc δ m

k 33 =

− Δh vap (1 − ε pc )δ 2P δM

k 23 = k 32 = Δh vap ε pc δ P

(369)

where δ m = ρs C m D

δ′m = δ m / C m

(370)

Analytical solution of Luikov heat and mass transfer differential equation system are only obtained for simple geometrical shapes and boundary conditions. For a slab, a cylinder, and a sphere, Luikov and Mikhailov [96] used the Laplace transform technique to obtain their solutions. These same problems were also taken into account by Mikhailov and Özisik [97] using the finite integral transform technique. They obtained the same solutions as those of Luikov and Mikhailov [96]. However, it was seen subsequently that these solutions ignored the possible existence of complex eigenvalues. If complex eigenvalues exist, these solutions can be grossly in error [98]. More satisfactory solutions were also obtained for Luikov’s differential equation system using different approaches (see Ref. [98, 99]).

KRISCHER MODEL This model assumes that moisture transfer occurs with the combined effect of capillary flow of liquid and diffusion of vapor. The main difference between Luikov and this model is that, in the Luikov model, the total moisture content is assumed as the driving force for both

Transport Phenomena During Drying of Food Materials

107

water and water vapor transport, while this model considers liquid water and water vapor transport separately. In this model, the driving force for water vapor transport is assumed to be the gradient of its partial pressure in the air and the driving force for liquid water transport is taken as the gradient of the liquid moisture content. The flux and balance equations for this model are as follows.

Liquid Phase r r J ml = −ρ l K l ∇u

(371)

r where J ml is the liquid flux (kg/m2s), ρl is the density of liquid water (kg/m3), Kl is the liquid

diffusivity (m2/s) and u is the liquid content by volume (m3 liquid/m3 solid).

Vapor Phase ˆ r r D M J mv = − v,free v ∇Pv ζ RˆT

(372)

r where J mv is the flux of water vapor (kg/m2s), Pv is the partial pressure of water vapor in the

air (Pa), D v,free is the gas diffusion coefficient in open space (m2/s), ζ (>1) is the diffusion resistance factor, which describes the decrease of the vapor flow in the porous medium in comparison with that in stagnant gas. Rˆ is the gas constant (8.314 J/(mol K)) and T is the temperature (K).

Balance Equations Let us look at, before expressing balance equations, what the approach of Krischer was while constructing flux equations. ρ l u term in liquid mass flux equation can be written as follows [58]. ρl u =

m l Vl m l = = Cl Vl V V

(373)

If this equation is substituted into the flux equation, we obtain: r r J ml = −K l∇C l

(374)

As can be seen, the equation used by Krischer for liquid transport is analogous to Fick’s law.

108

Kamil Kahveci and Ahmet Cihan If the state equation is used, vapor transport equation can be expressed as follows: r D 1 r RˆT J mv = − ∇( Cv ) ˆ ζ R vT M v

(375)

Let us assume that the temperature is constant too. In this case, it is possible to write: r r D J mv = − v,free ∇C v ζ

(376)

In addition, if D v = D v,free / ζ is considered as the diffusion coefficient for porous material, again a relation analogous to Fick’s law is obtained. Assuming that transport parameters are constant, balance equations may be expressed as follows. ∂u = K l∇ 2 u ∂t

(for the liquid water)

(377)

∂ρ v D v,free 2 = ∇ Cv ∂t ζ

(for the water vapor)

(378)

In order to get the same physical dimensions in both transport equations, the transport equation for liquid water can be multiplied by ρw and the equation for water vapor transport ˆ /(RˆT) . Therefore: by M v

ρl

∂u = ρl K l∇ 2 u ∂t

ˆ D ˆ ∂P M M v v = v v ,free ∇ 2 Pv RˆT ζ RˆT ∂t

(379)

(380)

Krischer introduced two additional corrections in the mass balance equation for water vapor. The first is the reduction of the accumulation term on the left-hand side by the multiplication factor (ψ-u), which expresses the fact that water vapor can appear only in that part of the porous space where water in the liquid state is not present, and its accumulation is therefore limited to only that space [58]. There is no need for reduction in the term at the right side of the equation. Because, the reduction for this term has already been made through the diffusion resistance factor ζ. The second correction contains the inclusion of Stefan diffusion [58]. Mass flux for Stefan diffusion is defined as follows (see Eq. (178)):

Transport Phenomena During Drying of Food Materials r Pg r D 1 J v = − v,free ∇Pv ζ R v T Pg − Pv

109

(381)

where Stefan diffusion is defined by the multiplication factor Pg/(Pg-Pv). Therefore, mass balance equation for vapor phase becomes: (ψ − u )

ˆ D ˆ ∂P Pg M M v v = v v,free ∇ 2 Pv ˆ ˆ RT ζ Pg − Pv RT ∂t

(382)

The total mass balance of moisture can then be written as follows: ρl

ˆ ∂P ˆ D Pg M M ∂u ∇ 2 Pv + (ψ − u ) v v = ρ l K l ∇ 2 Pv + v v ,free ˆ ˆ ∂t RT ∂t RT ζ Pg − Pv

(383)

BERGER AND PEI MODEL In this model, the total internal moisture transfer is assumed to consist of two different mechanisms. These are capillary flow of liquid due to gradient in liquid content, and the diffusion of vapor through empty pores due to a gradient in partial vapor pressure. The internal heat transfer is assumed to be governed by heat conduction and enthalpy of vaporization. In addition, it is assumed that external heat and mass transfer is proportional to the temperature and the partial vapor pressure difference between the surface of the drying solid and the external drying media. The main difference of this model from the Krischer model is that this model takes sorption isotherms into consideration, as opposed to Krischer model, by using two coupling equations between the three dependent variables u, ρv and T. These two equations are: the Clasius-Clapeyron’s equation and the equation of the sorptional isotherm of the system. Flux and balance equations for this model where Fick’s law is used to express both internal mass transfer mechanisms are as follows:

Liquid Phase r r J ml = −K l ρ l ∇u

(384)

where Kl is the liquid conductivity in the solid (m2/s), ρl is the density of the liquid (m3/kg) and u is the liquid content by volume (m3 liquid/m3 solid).

110

Kamil Kahveci and Ahmet Cihan

Vapor Phase ˆ r r M J mv = −D v (ε void − u ) v ∇Pv RˆT

(385)

where Dv is the vapor diffusivity (m2/s) and εvoid is the void fraction of solid (m3 air/m3 solid), Rˆ is the gas constant (8.314 J/(mol K)), T is the temperature (K), Pv is the vapor pressure (Pa). It is assumed that the temperature changes are small. Therefore: ˆ ˆ r r⎛ P M M v ∇Pv = ∇⎜ v v ⎜ ˆ RˆT ⎝ RT

⎞ r ⎟ = ∇C v ⎟ ⎠

(386)

r r J mv = −D v (ε void − u )∇C v

(387)

Balance Equations If the Kl and Dv are assumed to be constant, the mass balance equations for the liquid and vapor transfer can be written as follows: ρl

∂u = K l ρ l ∇ 2 u − I ev ∂t

(388)

[

]

r r ∂ [(ε void − u )C v ] = D v∇ ⋅ (ε void − u )∇C v + I vap ∂t

(389)

where Ivap is the rate of evaporation (kg/(m3s). Using the two mass balance equations above, the total moisture transfer equation can be expressed as: ∂C v = K lρl∇ 2 u + ∂t r r − u )∇ 2 C v − (∇u ) ⋅ (∇C v )

(ρ l − C v ) ∂u + (ε void − u ) ∂t

[

D v (ε void

]

(390)

The rate of enthalpy of vaporization can be defined as: q vap = Δh vap I vap , where Δh vap is the evaporation enthalpy (J/kg). In this case, the energy balance equation can be written as follows:

r r Δh vap ⎧⎪ ⎡ ⎤ ∂ 2ρ v ∂T = α∇ 2 T + − ∇ ⋅ ∇ ( u ) ( C v )⎥ ⎨D v ⎢(ε void − u ) 2 ∂t ρ s C p ⎪⎩ ⎢⎣ ∂x ⎥⎦ ∂C ∂u ⎫ − (ε void − u ) v + C v ⎬ ∂t ⎭ ∂t

(391)

Transport Phenomena During Drying of Food Materials

111

where α is the thermal diffusivity (m2/s). In the original model, proposed by Berger and Pei [100] it is considered a one dimensional system seen in figure 23 and the following boundary conditions at x=0: K lρl

∂C ∂u + D v (ε void − u ) v = h m (C v − C va ) ∂x ∂x

h q (Ta − T) = Δh vap K l ρ l

∂u ∂T −λ ∂x ∂x

(392)

(393)

where hm is the mass transfer coefficient (m/s), hq is the heat transfer coefficient (W/(m2K)) ∂u denotes the amount of and λ is the thermal conductivity (W/(m K)) the term Δh vap K l ρ l ∂x x =0 heat required to evaporate the liquid flux at the surface. It is assumed that no mass and heat is transferred across the surface of the drying material at x=L. Therefore: K lρl

∂C ∂u = D v (ε void − u ) v = 0 ∂x ∂x

∂T =0 ∂x

(394)

(395)

In the original model, moisture content with respect to volume has been used when expressing the liquid flux. Today, it is preferred to use moisture content with respect to mass. Furthermore, in the original model the temperature changes were assumed to be small and therefore vapor concentration gradient has been used as driving force instead of partial vapor pressure gradient. The flux and balance equations may be expressed as follows in terms of the moisture content with respect to mass and vapor pressure: r r J ml = −K l ρ s ∇m

(396)

ˆ ⎛ εg − εl ⎞ r r r r M ⎟∇Pv J mv = −D v (ψ − u )∇ρ v J mv = −D v v ⎜⎜ RˆT ⎝ ε s ⎟⎠

(397)

K lρs

Kv

∂ 2m ∂m − I ev = ρ s 2 ∂t ∂x

ˆ ∂ ⎡⎛ ε − ε l ⎞ ⎤ ˆ r ⎡⎛ ε g − ε l ⎞ r ⎤ M M v ⎟⎟∇Pv ⎥ + I ev = v ⎢⎜⎜ g ⎟Pv ⎥ ∇ ⋅ ⎢⎜⎜ RˆT RˆT ∂t ⎢⎣⎝ ε s ⎟⎠ ⎦⎥ ⎣⎢⎝ ε s ⎠ ⎦⎥

(398)

(399)

112

Kamil Kahveci and Ahmet Cihan K l ρs ∇ 2 m + D v

ˆ ⎡⎛ ε g − ε l ⎞ 2 r ⎤ ρ r M v ⎜⎜ ⎟⎟∇ Pv − s ∇m ⋅ ∇Pv ⎥ = ⎢ ρl RˆT ⎣⎢⎝ ε s ⎠ ⎦⎥ ˆ ⎛ ε g − ε l ⎞ ∂P ˆ P ⎞ ∂m M ⎛ 1 M v v ⎟ ⎟⎟ v ρ s ⎜1 − + v ⎜⎜ ⎜ ρ RˆT ⎟ ∂t ˆ ε R T l s ⎝ ⎠ ∂t ⎝ ⎠

( )( )

ˆ ⎧⎪ ⎡⎛ ε g − ε l λ ∂ 2 T Δh vap M v ∇ 2T 2 + ⎨D v ⎢⎜ ρs C p ρ s C p RˆT ⎪⎩ ⎣⎢⎜⎝ ε s ∂x ⎛ εg − εl ⎜⎜ ⎝ εs

(400)

r ⎤ ⎞ 2 ρ r ⎟⎟∇ Pv − s ∇m ⋅ ∇Pv ⎥ − ρl ⎥⎦ ⎠

( )( )

(401)

⎞ ∂Pv ρ s ∂m ⎫⎪ ∂T ⎟⎟ + Pv ⎬= ∂t ⎪⎭ ∂t ⎠ ∂t ρ l

where m is the moisture content by mass (kg moisture/kg dry solid) and εs, εl and εg are the volume fraction of solid, water and gas, respectively. External Drying Media Jm

Jq,ext

x=0 Jmv

Jml -Jq

q& vap

x Ivap

Drying Material

x=L

Figure 23. Schematic view for Berger and Pei model (adapted from [100]).

WHITAKER MODEL This model was proposed by Whitaker [71, 72] to describe the simultaneous heat, mass and momentum transfer in a porous media. The biggest advantage of this model is that the physics of the model is better understood, the assumptions are very clear and the parameters are well defined. In addition, all mechanisms for mass and heat transfer were taken into consideration: liquid flow due to capillary forces, vapor and gas flow due to convection and diffusion, internal evaporation of moisture and heat transfer by convection, diffusion and conduction.

Transport Phenomena During Drying of Food Materials

113

Microscopic Equations In this model a representative volume is considered as shown in figure 24 to express mass and heat transfer. The figure at the left represents the microscopic scale and the figure at the right the macroscopic scale. Porous structure is assumed to consist of three phases, namely, solid, liquid and gas. The solid phase will be represented here by s, the liquid phase by l and gas by g. The gas phase is assumed to consist of air (denoted by a) and vapor (denoted by v).

Figure 24. Pore and macroscale in a porous body (adapted from [93]).

At microscopic level, mass and heat transport equations can be derived using the conservation laws as follow:

Mass Conservation If it is assumed that no chemical reaction exists during the transport process, mass conservation equation for each phase can be written as follows: r ∂ρ r + ∇ ⋅ (ρv) = 0 ∂t

(402)

where ρ is the concentration of phase considered (kg/m3). The mass conservation equation for each species in the phase may be expressed as follows. r ∂ρ i r + ∇ ⋅ (ρ i v i ) = 0 for i=1,2,..n ∂t

(403)

where ρi is the concentration of species i (kg/m3) and vi is the velocity of species i (m/s). The following relations can be written for the velocities and concentrations of species:

114

Kamil Kahveci and Ahmet Cihan r ρ r v = ∑ i vi , i ρ

ρ = ∑ ρi

(404)

i

where v is the mass average velocity (m/s) and ρ is the total concentration (kg/m3).

Linear Momentum Principle For each phase, momentum conservation can be written as: r Dv r ~ ρ =∇⋅σ Dt

(405)

~ is the stress tensor (Pa). In the above equation, body forces such as gravitational where σ force are neglected. The angular momentum principle requires this tensor to be symmetric. ~=σ ~T σ

(406)

Energy Conservation Energy conservation for each phase can be written as follows: r r r ∂ (ρh ) r DP ~ r r + ∇ ⋅ (ρhv) = −∇ ⋅ J q + + τ : ∇v Dt ∂t

(407)

r

where h is the enthalpy per unit mass (J/kg), J q is conductive heat flux vector (W/m2), ~τ is rr the viscous stress tensor (Pa). The term ~τ : ∇v is the viscous dissipation, P is the pressure (Pa) and DP / Dt is the compression work. In the above equation, the source or sink of electromagnetic radiation was neglected. The viscous dissipation and the compression work for the liquid and gas phase can also be neglected. Therefore, the energy equation becomes as follows: r r r ∂ (ρh ) r + ∇ ⋅ (ρhv) = −∇ ⋅ J q ∂t

(408)

We can also assume that the enthalpy is independent of pressure. Therefore: h = C P (T − TR )

(409)

where CP is the specific heat capacity (J/(kgK)) and TR is the reference temperature (°C). The conservation laws can then be written for each phase as follows:

115

Transport Phenomena During Drying of Food Materials

Solid Phase r The solid phase is considered to be rigid and fixed in space ( v s = 0 ). In this case, we

must deal only with the conservation of energy. ρs

r r ∂h s = −∇ ⋅ J qs ∂t

(410)

If the assumption defined in Eq. (409) is taken account and the conductive heat flux is

r

r

expressed as J q = −λ s ∇Ts according to the Fourier law, the energy equation can be written as: ρs C Ps

∂Ts = λ s ∇ 2 Ts ∂t

(411)

where λs is the thermal conductivity of the solid phase (W/(mK)).

Liquid Phase The mass and energy conservation equation for the liquid phase can be expressed asllows: r ∂ρ l r + ∇ ⋅ (ρ l v l ) = 0 ∂t

ρ l C Pl

(412)

r r ∂Tw + ρ l C Pl v ⋅ ∇Tl = λ l ∇ 2 Tl ∂t

(413)

Gas Phase The gas phase is more complicated than the other phases since it contains two components: air and vapor. The total mass conservation can be expressed as follows: ∂ρ g ∂t

r r + ∇ ⋅ (ρ g v g ) = 0

v

(414)

v

The species velocity v gi can be written in terms of the mass average velocity v g and the

r

diffusion velocity u i : r r r v gi = v g + u i

(i=a,v)

Therefore, the mass conservation of air and vapor can be given as follows:

(415)

116

Kamil Kahveci and Ahmet Cihan ∂ρ gi ∂t

r r r r + ∇ ⋅ (ρg i v g ) = −∇ ⋅ (ρ gi u i ) i =a,v

(416)

Moreover, by expressing the diffusive flux as: r r ρ gi u i = −ρ g D va ∇(ρ gi / ρ g )

(417)

where D va is the binary molecular diffusion coefficient for vapor and air (m2/s), we obtain:

∂ρ gi ∂t

[

r r r r + ∇ ⋅ (ρg i v g ) = ∇ ⋅ − ρ gi D va ∇(ρgi / ρ g

]

i=a,v

(418)

For a multicomponent phase, the energy conservation equation can be written as:

[

]

r r r r ∂ ∑ (ρ gi h gi ) + ∇ ⋅ ∑ ρ gi h gi v gi = −∇ ⋅ J q ∂t i i

(419)

The mass average enthalpy can be defined in a similar manner to the mass average velocity as: h g = ∑ (ρ gi h gi / ρ g )

(420)

i

Therefore, Eq. (419) becomes as follows: ρ g C Pg

∂Tg ∂t

r r r r r + ρ g C Pg v g ⋅ ∇Tg = λ g ∇ 2 Tg − ∇ ⋅ (ρ a h a u a + ρ v h v u v )

(421)

where C Pg = (ρ a C Pa + ρ v C Pv ) / ρ g

(422)

For the species of gas phase, ideal gas law can be assumed.

ˆ Pi = ρi RˆT / M i

i=a,v

(423)

ˆ is the molar mass of species i where Rˆ is the ideal gas constant (8.314 J/(mol K)) and M i (kg/mol). The total gas pressure can be written as: Pg = Pa + Pv

(424)

Transport Phenomena During Drying of Food Materials

117

Boundary Conditions A general set of the boundary conditions must be defined to complete the set of equations. Let us assume that Γlg represents the interface between liquid and gas phases, Γsl the interface between solid and liquid and Γsg the interface between solid and gas. The following relations are valid for these interfaces: Γlg=Γgl

Γsl=Γls

Γsg=Γgs

(425)

The boundary conditions for the solid-liquid interface Γsw can be written as:

r vl = 0

(426a)

r r r r J qs ⋅ n ls = J ql ⋅ n ls

(426b)

Ts = Tl

(426c)

r r r where n ls ( n ls = −n sl ) is the unit normal vector directed from the liquid phase toward to the

solid phase. The boundary conditions for the solid-gas interface Γsg can be given as follows: r vg = 0

(427a)

r r r r J qs ⋅ n sg = J qg ⋅ n sg

(427b)

Ts = Tg

(427c)

The boundary conditions for the liquid-gas interface Γwg can be written as follows: r r r r r r ρ v ( v v − w ) ⋅ n gw = ρ w ( v l − w ) ⋅ n gw

(428a)

r r r ρ a ( v a − w ) ⋅ n gw = 0

(428b)

r r r r r r ρ g ( v g − w ) ⋅ n gw = ρ w ( v l − w ) ⋅ n gw

(428c)

r r r r r r ρ l (h v − h l )(v l − w ) ⋅ n gl = ( J ql − J qg ) ⋅ n gl

(428d)

Tl = Tg

(428e)

r where w is the velocity of water-gas interface (m/s).

118

Kamil Kahveci and Ahmet Cihan

Volume Averaging Method The equations defined above cannot be solved on a microscopic level since the geometry of the porous medium and the distribution of the phases are not observable and are too complex to describe. Whitaker [71, 72, 101] introduced the concept of the averaging elementary volume to relate the microscopic geometrical and physical properties of the real porous medium to the macroscopic properties of the continuum model. With this method, governing equations are spatially smoothed leading to continuum equations defining the transport process. Although the equations become solvable in this case, the method has its own drawbacks, especially difficulties in determination of effective parameters that appear in the macroscopic equations. In volume averaging method, an averaging volume is associated to each point in porous material. This volume can be in any shape. In figure 24 averaging volume is represented by a circle. This representative volume provides upscaling of transport equations from pore scale to macroscopic scale. There are three types of average used in the study of drying. These are: spatial average, phase average and intrinsic average and they are defined as: 〈 y〉 =

1 ∫ ydV VV

〈 yi 〉 =

1 ∫ y i dV V Vi

〈 yi 〉 i =

1 ∫ y i dV Vi Vi

spatial average

(429)

phase average

(430)

intrinsic average

(431)

When it is interested in the average of some quantity related with a single phase, the phase average is employed. However, it should be noted that if yi is constant, the phase average is not equal to this value. In this situation, the use of intrinsic average is more appropriate. The phase average and intrinsic average are related as. 〈 yi 〉 = εi 〈 yi 〉 i

(432)

where ε i is the volume fraction of the phase i ( ε i = Vi / V ). Common expressions used in averaging are given below: r r 1 r 1 r 〈 ∇y w 〉 = ∇ 〈 y w 〉 + n ws y w dΓ + ∫ ∫ n wg y w dΓ V Γws V Γwg

(433)

r r r r 1 r r 1 r r 〈 ∇ ⋅ v w 〉 = ∇〈 v w 〉 + vw ⋅ n ws dΓ + ∫ ∫ v w ⋅ n wg dΓ V Γws V Γwg

(434)

Transport Phenomena During Drying of Food Materials 〈

r r r r ∂〈 y w 〉 1 ∂y w 1 − y w w ⋅ n wg dΓ − y w w ⋅ n ws dΓ 〉= ∫ ∫ ∂t V Γwg V Γws ∂t

119

(435)

Macroscopic Equations The following macroscopic transport equations can be obtained by averaging the pore scale transport equations over the averaging volume [71, 93]:

Mass Conservation liquid phase ρl

r r ∂ε l + ρl∇ ⋅ 〈 v l 〉 + 〈 J m 〉 = 0 ∂t

(436)

vapor in the gas phase

(

)

(

[

)

r r r r ~ ∂ ε g 〈ρ v 〉 g + ∇ ⋅ 〈ρ v 〉 g 〈 v g 〉 − 〈 J m 〉 = ∇ ⋅ 〈ρ g 〉 g D eff ⋅ ∇(〈ρ v 〉 g / 〈ρ g 〉 g ∂t

]

(437)

air in the gas phase

(

)

(

)

[

r r r r ∂ ~ ε g 〈ρ a 〉 g + ∇ ⋅ 〈ρ a 〉 g 〈 v g 〉 = ∇ ⋅ 〈ρ g 〉 g D eff ⋅ ∇(〈ρ a 〉 g / 〈ρ g 〉 g ∂t

]

(438)

Energy Conservation 〈ρ〉 C P

[

]

[

r r r ~ r ∂〈T〉 + ρ l C P l 〈 v l 〉 + 〈ρ g 〉 g 〈 C P 〉 g 〈 v g 〉 ⋅ ∇〈T〉 + Δh vap 〈 J m 〉 = ∇ ⋅ λ eff ∇(T〉 ∂t

]

(439)

where 〈ρ〉 C P = ε s ρ s C Ps + εl ρl C Pl + ε g 〈ρ v 〉 g C Pv + ε g 〈ρ a 〉 g C Pa

(440)

〈ρ〉 = ε s ρs + εl ρl + ε g 〈ρ v 〉 g + ε g 〈ρ a 〉 g

(441)

with

and Δhvap is the evaporation enthalpy (J/kg). In Eq. (439), local thermal equilibrium is assumed:

120

Kamil Kahveci and Ahmet Cihan 〈T〉 = 〈Ts 〉 s = 〈Tl 〉 l = 〈Tg 〉 g

(442)

~ ~ The effective diffusivity D eff and effective thermal conductivity λ eff in Eqs. (437)-(438) are obtained from the process of upscaling from pore scale transport equations to macroscopic equations [71]. The capillary pressure is defined as the difference between gas pressure and liquid pressure: 〈 Pc 〉 = 〈 Pg 〉 g − 〈 Pl 〉 l . Vapor pressure is determined from sorption isotherm. With the analysis of the conservation of linear momentum, the following equations are obtained for the liquid and gas phase: ~ ~ K i ⋅ k rg r r 〈vg 〉 = − .∇〈 Pg 〉 g ηg

~ ~ r K ⋅k r 〈 v l 〉 = − i rl .∇〈 Pl 〉 l , ηl

(443)

~ ~ ~ where K i is the intrinsic permeability tensor, k rl and k rg are the relative permeability

tensors. To apply control volume method, Eqs. (436)-(439) must be reformulated as follows: ∂y r r −∇⋅J ∂t

(444)

r where y is a scalar quantity, and J is mass or energy flux vector. To reformulate the system, a set of main variables must be selected to define the whole drying process. One possible choice can be average temperature 〈T〉 , volume fraction of the liquid water ε l and intrinsic

phase average of air density in the gas phase 〈ρ a 〉 g . In this case, the system can be written as follows if additionally the average notation 〈 〉 is dropped for simplicity [93]:

[

r r r r r r ∂ ρ l ε l + ε g ρ v + ∇ ⋅ ρl v l + ρ v v g = ∇ ⋅ ρ g D eff ⋅ ∇(ρ v / ρ g ∂t

(

)

(

)

[

r r r r r ∂ ε g ρ a + ∇ ⋅ ρa v g = ∇ ⋅ ρ g D eff ⋅ ∇(ρ a / ρ g ∂t

(

(ε s ρ s

)

(

)

]

]

∂h s ∂h ∂h ∂h + ε lρl l + ε g ρ v v + ε g ρa a ) + ∂t ∂t ∂t ∂t r r v r r r ρ l C pl v l + (ρ v C Pv + ρ a C Pa ) v g ⋅ ∇T + Δh vap J m = ∇ ⋅ (λ eff ∇T)

[

]

(445)

(446)

(447)

In addition, the following relations can be written: ∂ ∂ ∂ (ε i ρ i h i ) = ε i ρ i ( h i ) + h i (ε i ρ i ) ∂t ∂t ∂t

(448)

Transport Phenomena During Drying of Food Materials ∂ (ε s ρ s ) = 0 ∂t

hs

121

(449)

∂h s ∂h v ∂h a ∂h l + εlρl + εgρv + εgρa )= ∂t ∂t ∂t ∂t

(ε s ρ s

∂ (ε s ρ s h s + ε l ρ l h l + ε g ρ v h v + ε g ρ a h a ) − ∂t ∂ ∂ ⎤ ⎡ ∂ ⎢h l ∂t (ε l ρ l ) + h v ∂t (ε g ρ v ) + h a ∂t (ε g ρ a )⎥ ⎦ ⎣

(450)

r r r r r r ∇ ⋅ (ρhv) = ρ v ⋅ ∇h + h∇ ⋅ (ρ v)

(451)

In this case, the second term on the left hand side of Eq. (447) becomes as follows:

[ρ C l

]

[

]

r r r r r r v l + (ρ v C Pv + ρ a C Pa ) v g ⋅ ∇T = ∇ ⋅ ρ l h l v l + (ρ v h v + ρ a h a ) v g − r r r r r r h l∇ ⋅ (ρ l v l ) + h v ∇ ⋅ (ρ v v g ) + h a ∇ ⋅ (ρ a v g )

[

Pl

]

(452)

The enthalpy of vaporization can be expressed as: Δh vap = h v − h l at temperature T. Therefore: Δh vap J m ,vap = h v J m,vap − h l J m,vap

(453)

The evaporation rate Jm,vap can be computed in two different ways: one from the conservation equation for liquid water, the other from the conservation equation for water vapor. J m,vap = −ρ l

J m,vap =

r r ∂ (ε l ) − ρ l ∇ ⋅ ( v l ) ∂t

(454)

[

r r r r r ∂ (ε g ρ v ) + ∇ ⋅ (ρ v v g ) − ∇ ⋅ ρ g D eff ⋅ ∇(ρ v / ρ g ) ∂t

]

(455)

With the substitution of the equations given above, the energy equation becomes as follows: r r r ∂ ∑ ε i ρi h i + ∇ ⋅ ρl h l v l + ( ρv h v + ρa h a ) v g = ∂t i=s,w ,v,a r r r r r r r r r h a ∇ ⋅ ρ g D eff ⋅ ∇(ρ a / ρ g + h v ∇ ⋅ ρ g D eff ⋅ ∇(ρ v / ρ g + ∇ ⋅ λ eff ∇T

(

[

)

]

[

]

(

)

(456)

The energy equation can be further simplified by assuming that, within the averaging volume, the variation of enthalpy is small compared with its value.

122

Kamil Kahveci and Ahmet Cihan r r r ∂ ∑ ε i ρi h i + ∇ ⋅ ρl h l v l + ( ρv h v + ρa h a ) v g = ∂t i=s,w ,v,a r r r r r r r r r ∇ ⋅ ρ g h a D eff ⋅ ∇(ρ a / ρ g + ∇ ⋅ ρ g h v D eff ⋅ ∇(ρ v / ρ g + ∇ ⋅ λ eff ∇T

(

[

)

]

[

]

(

)

(457)

CAPILLARY FLOW MODEL If the capillarity is the primary mode for transport, two different formulations may be expressed under isothermal conditions. One of these is capillary pressure formulation and the other capillary diffusivity formulation [8].

Capillary Pressure Formulation ⎞ ∂C l r ⎛ Kk r + ∇ ⋅ ⎜⎜ − ρ l i rl ∇(P − Pc ) ⎟⎟ = −I vap ∂t ηl ⎝ ⎠

(458)

where C is the concentration (kg/m3), ρ is the density (kg/m3), Ki is the intrinsic permeability (m2), kr is the relative permeability, η is the viscosity (Pa s), Pc is the capillary pressure (Pa) and Ivap is the rate of evaporation (kg/(m3s)). If Pc >>P (capillary pressure is large) and Ivap=0 (no significant evaporation): ∂C l r ⎛ K i k rl r ⎞ + ∇ ⋅ ⎜⎜ ρ l ∇Pc ⎟⎟ = 0 ∂t ηl ⎝ ⎠

(459)

In this form of transport equation both water concentration and capillary pressure are involved. This equation is also known as Richard’s equation. Generally, capillary head h (m) is used instead of capillary pressure [8]. In this case, Eq. (459) can be written as: ∂C l r ⎛ K i k rl ρ l g r ⎞ + ∇ ⋅ ⎜⎜ ρ l ∇h ⎟⎟ = 0 ∂t ηl ⎝ ⎠

(460)

⎛ ∂C l ⎞ ∂h r ⎛ K i k rl ρ l g r ⎞ + ∇ ⋅ ⎜⎜ ρ l ∇h ⎟⎟ = 0 ⎜ ⎟ ηl ⎝ ∂h ⎠ ∂t ⎝ ⎠

(461)

or

where g is the gravitational acceleration (m/s2), ∂C l / ∂h is the specific moisture capacity.

Transport Phenomena During Drying of Food Materials

123

Capillary Diffusive Formulation Another formulation equivalent to capillary pressure formulation for capillary flow may be expressed as follows [8]: ∂C l = D l∇ 2 Cl ∂t

(462)

This equation is similar to the commonly used diffusion equation. In Eq. (462), Dl is the capillary diffusivity of liquid water and is defined as: D l = −ρ l2 g

K i k rl ∂h ηl ∂C l

(463)

Datta [8] reported that it will be better to use capillary pressure formulation in multi-domain foods, since the capillary pressure variation with moisture content will be different in different domains of material. When a formulation is used in which capillary pressure (head) is the driving force, the most important problem arises from the determination of the relationship between capillary head and moisture content. Such a relation can be obtained only using other material properties data. One of the relations to be used for this purpose is Kelvin’s equation [102].

EMPRICAL AND SEMI-EMPRICAL MODELS Due to the complexity of transport mechanisms, empirical and semi-empirical models are often used to describe the thin-layer drying behavior of food materials. Of these models, those used frequently are given in table 14. The empirical models constitute a direct relationship between the average moisture content and the drying time. They neglect the fundamentals of the drying process and therefore their parameters have no physical meaning. The semitheoretical models are generally derived by simplifying general series solutions of Fick's second law or they are modified forms of simplified models. The empirical and semiempirical models require small time compared to theoretical models and do not need assumptions of geometry of a typical food, its mass diffusivity and conductivity, et cetera. [103]. Therefore they are useful for automatic control processes. Empirical and semiempirical models are valid within the temperature, relative humidity, air flow velocity and moisture content range for which they were developed [104]. Among these drying models, the Page models, the Henderson and Pabis model, the two-term exponential model and the Midilli et al. model are widely used models to simulate the thin layer drying behavior of food materials.

124

Kamil Kahveci and Ahmet Cihan

Lewis Model In this model, mass transfer is expressed by analogy to heat flow from a body immersed in a cool fluid, in other words, to Newton’s law of cooling [105]. The drying rate is assumed to be proportional to the difference in moisture content between the material being dried and equilibrium moisture content at the drying air condition. The most important drawback of this model is that it generally overpredicts the early stages and underpredicts late stages of the drying process.

Page and Modified Page Models Page model has been proposed by Page [106] to eliminate the drawback of the Lewis model. For this purpose, Page [106] introduced an exponent to time term in Levis model. However, the introduction of the exponent causes the model to become a purely empirical model. This parameter has an effect of moderating the time and the model in this case gives better results for the prediction of moisture loss.

Henderson and Pabis Model Various approximations and variations of the diffusion model have been used to simulate the drying behavior of food materials. The model proposed by Henderson and Pabis [107] is of this type and corresponds to the first term of a general series solution of Fick’s second law. The slope of this model, coefficient k, is related to effective diffusivity when drying process takes place only in the falling rate period and liquid diffusion controls the process [108].

Two Term Exponential, Diffusion Approach, Verma et al., Logarithmic, Midilli et al., Jena and Das Models These models are modified forms of the Henderson and Pabis model, based on simplification of a general series solution of Fick's second law. Among these models, two term exponential and Midili et al. model have been used frequently because they produce better fit to experimental data as compared to other models.

Two Term, Modified Henderson and Pabis Models It is not possible to express drying behavior of food materials having several compartments with the first term of a general series solution of Fick’s second law with the sufficient accuracy due to different drying resistance in each compartment. In such cases, exponential terms equal to the number of compartments must be used to define drying in each compartment. Two term and Modified Henderson and Pabis models have been proposed to simulate the drying process of such type of materials. Sharma et al. [109] used these two- and

Transport Phenomena During Drying of Food Materials

125

three-term models to simulate the thin-layer drying behavior of rough rice with three compartments, the hull, bran and endosperm.

Geometric, Thompson, Wang and Singh, Logistic, Weibull Distribution Models All of these models are empirical models and have been proposed for simulating the drying behavior of various types of food materials. For example, Thompson model has been suggested to simulate the drying behavior of shelled corn in the temperature range 60–150°C [110]. Wang and Singh model has been proposed to simulate the drying of medium-grain rough rice [111]. The coefficient of correlation (r) can be used to determine the suitability of the empirical and semitheoretical models in describing the experimental drying data. Correlation coefficient values close to one mean the fit is good. In addition to correlation coefficient; standard deviation (es) and mean squared deviation (χ2) can be used to determine suitability of the fit. These parameters are defined as follows [112]: n obs

n obs

n obs

n obs ∑ mrpre ,i mrexp,i − ∑ mrpre ,i ∑ mrexp,i

r=

i =1

i =1

⎛ ⎞ n obs ∑ (mrpre ,i ) 2 − ⎜ ∑ mrpre ,i ⎟ i =1 ⎠ ⎝ i=1 n obs

n obs

2

i =1

⎛ n obs ⎞ n obs ∑ ( mrexp,i ) 2 − ⎜ ∑ mrexp,i ⎟ i =1 ⎝ i=1 ⎠ n obs

2

(464)

n obs

∑ (mrpre,i − mrexp,i ) 2

es =

i =1

n obs

(465)

n

χ2 =

∑ (mrpre,i − mrexp,i ) 2 i =1

n obs − n const

(466)

where mrpre,i is the ith predicted moisture ratio, mrexp,i is the ith experimental moisture ratio, nobs is the number of observations and nconst is the number of constants in the drying model.

126

Kamil Kahveci and Ahmet Cihan Table 14. Thin-layer drying models

Name Lewis

Model equation mr = exp(− kt )

N.C.

Page

mr = exp(−kt )

2

Modified Page

mr = exp(−(kt ) )

2

Modified Page II

mr = exp(−c( t / L2 ) n )

2

Henderson&Pabis

mr = a exp(− kt )

2

1

n

n

Geometric

mr = at

−n

2

Wang&Singh

mr = 1 + at + bt 2

2

Two term exponential

mr = a exp(− kt ) + (1 − a ) exp(− kat )

2

Thompson

t = a ln mr + b(ln mr ) 2

2

Logarithmic

mr = a 0 + a exp(−kt )

3

Logistic

mr = a 0 /(1 + a exp(kt ))

3

Diffusion approach

mr = a exp(− kt ) + (1 − a ) exp(− kbt )

3

Verma et al.

mr = a exp(−kt ) + (1 − a ) exp(−gt )

3

Two term

mr = a 1 exp(− k 1 t ) + a 2 exp(− k 2 t )

4

Midilli et al.

mr = a exp(− kt ) + bt

4

Jena and Das

mr = a exp(−kt + b t ) + c

4

Weibull distribution

mr = a − b exp(−(kt ))

4

Modified Henderson and Pabis

mr = a exp(− kt ) + b exp(−gt ) + c exp(−ht )

6

n

n

where mr is the moisture ratio, t is the time (s), L is the characteristic length (m) and a, b, c, n, k, g and h are the empirical constants

8. SHRINKAGE The drying process causes shrinkage in volumes and heat and mass exchange areas of food materials. This shrinkage in the drying process particularly affects the diffusion coefficient of the material significantly. Heat and mass transport and shrinkage are generally coupled. The following procedure is followed in coupling. Shrinkage is modeled with respect to moisture content, including sometimes other physical properties, and this predetermined relationship of dimensional changes is used to modify the geometry with time in the simulation [8]. Shrinkage models used in the literature are given in the comprehensive review prepared by Mayor and Sereno [46]. Of these models, those given in tables 15 and 16 are empirical models. Mayor and Sereno [46] states that linear empirical models are adequate to describe materials and process conditions leading to negligible porosity development during the drying process, or to a uniform development of porosity, corresponding to a linear decrease of volume in the whole range of humidity. If development of porosity increases sharply during the final stage of drying, linearity is lost and the behavior is best described by nonlinear models seen in table 16 such as exponential models or a quadratic model [46, 113]. These models usually produce a good fit with the experimental data, but their use is limited because of their dependence on the drying conditions and on the material and they require

Transport Phenomena During Drying of Food Materials

127

extensive experimental testing and should not be extrapolated. For modeling shrinkage, some fundamental models are also used. They are based on mass balances, density and porosity definitions and assume in most cases additivity of the volumes of the different phases in the system [46]. These fundamental methods have been classified by Mayor and Sereno [46] in three groups: models which show a linear shrinkage behavior throughout the whole drying process (table 17); models which include deviations of this linear behavior (table 18) and models which include explicitly variations of the porosity through the drying process (table 19). The fundamental models allow the prediction of moisture content and/or change in volume to be obtained without complicated mathematical calculations. Furthermore, it is not usually necessary to obtain experimental shrinkage values at every process conditions, as in the case of empirical models. Table 15. Linear empirical models for shrinkage (adapted from [46]) Model

Geometry and reduced dimension slab-thickness; cylinder-volume sphere-radius ellipsoid-(x,y,z coordinates); sphere-radius cylinder-volume

Material Apple Soybean Apricot Carrot, Amylose starch gel, Broccoli stem

slab-(thickness,width,length); sphere-volume; (cube,cylinder)-volume sphere-volume cylinder-(volume, radial, axial) slab-(thickness, width, length) slab-(thickness, width, length) sphere-volume

Grape Green bean Fish muscle (shark) Fish muscle (ocean perch) Cherry

D R = a 1ε w + a 2

slab-(thickness, width, length)

Fish muscle (ocean perch)

D R = 1 + βm

cube-volume slab-thickness cylinder-volume

Apple, carrot, potato gelatin gel, carrot Apple, carrot, banana, potato

E ⎤ ⎡ D R = 1 + ⎢a 1 exp(− a )⎥ m RˆT ⎦ ⎣ T (Kelvin)

sphere-volume

Grape

D R = a 1 + (a 2 + a 3 φ + a 4 T)Δm

sphere-bed volume

wheat, canola

D R = (a 1T + a 2 ) + (a 3 T + a 4 ) m

cylinder-volume

Potato

D R = a 1m + a 2

Potato

where ai is the empirical constants, m moisture content (kg water/kg dry solid), εw the volume fraction of water (volume of water/total volume), β shrinkage coefficient, Ea activation energy (J/mol), Rˆ universal gas constant (8.314 J/(mol K)), T temperature (°C), φ relative humidity.

128

Kamil Kahveci and Ahmet Cihan Table 16. Non-linear empirical models for shrinkage (adapted from [46])

Model D R = 0.16 + 0.816(m / m o ) +

Geom.& Red. dim.

Material

cylinder-volume

Carrot, pear, potato, sweet potato

slab-volume

Garlic

A v / A vo = a 1 + a 2 m + a 3 m 2 + a 4 m 3

cylinder-surface area to volume ratio

Apple, carrot, potato

D R = a1 + a 2 m + a 3m 2 + a 4 m3

cylinder-bed volume

Apple, carrot, potato

D R = a 1 + a 2 exp(−a 3 t )

slab-surface area slab-thickness

Potato, squash Apple

hemispherediameter; cylinder-length

Cauliflower

D R = a 1 + a 2 m + a 3 m 3 / 2 + a 4 exp(a 5 m)

slab-thickness

Garlic

D R = a 1 + a 2 ( m / m o ) + a 3 (m / m o ) 2

(cylinder, slab)-volume

D R = a 1 exp(a 2 m / m o )

(cylinder, slab)-volume

0.022 exp(

0.018 m ) + b1 (1 − ) m + 0.025 mo

b1 = 0.209 − b 2

D R = a1 + a 2

b2 =

0.966 m o + 0.796

m m + exp(a 3 ) 1+ m 1+ m

Apple, carrot, potato, squid Apple, carrot, potato, squid

where ai is the empirical constants, m moisture content (kg water/kg dry solid), Av the surface area to the volume ratio (1/m).

Table 17. Linear fundamental models for shrinkage (adapted from [46]) Model

Geom.& Red. dim.

V ⎛ m + 0.8 ⎞ A ⎛ V ⎞ ⎟ ⎟; =⎜ =⎜ Vo ⎜⎝ m o + 0.8 ⎟⎠ A o ⎜⎝ Vo ⎟⎠ V m = b1 + b 2 Vo mo b1 =

Material

2/3

m o (ρ s / ρ w ) 1 , b2 = m o (ρ s / ρ w ) + 1 m o (ρ s / ρ w + 1

Vegetables

volume

Sugar beet root

cube-area

Carrot, potato, sweet potato, radish

cube-area

Carrot, potato, sweet potato, radish

Uniform drying model A ⎛ V =⎜ A o ⎜⎝ Vo

⎞ ⎟ ⎟ ⎠

2/3

a)

m + b1 1 1 V = ; b1 = m e ( − 1) + ρe ρe Vo m o + b1

b)

ρo V = b1 m + b 2 , b 1 = , b 2 = 1 + b1 − ρ o Vo mo + 1

Core drying model 1 − b2 V = b1 m + 1 , b1 = , Vo mo − me (m e + 1)ρ o A V b2 = , = ( )2/3 (m o + 1)ρ e A o Vo

where m is the moisture content (kg water/kg dry solid), A area (m2), V volume (m3), ρ density (kg/m3).

Transport Phenomena During Drying of Food Materials

129

Table 18. Non-linear fundamental models for shrinkage (adapted from [46]) Model Semi-core drying model V = b1 m + b 2 Vo b1 =

Material

cube-area

Carrot, potato, sweet potato, radish

cylinder-volume

Cassava root

A V = ( )2/3 Ao Vo

1 − b3 m o − m e + b 4 (b 3 m o − m e + b 3 − 1)

b m − m e − b 4 (b 3 m o − m e + b 3 − 1) b2 = 3 o m o − m e − b 4 (b 3 m o − m e + b 3 − 1) b3 =

Geom.& Red. dim.

(m e + 1)ρ o ρ − (1 − m)ρ e , b4 = e ρo (m o + 1)ρ e

D r = b1 + b 2 (m / m o ) + 0.26b 3 (1 − m / m o ) 3 b1 =

m o (ρ s / ρ w ) 1 , b2 = m o (ρ s / ρ w ) + 1 m o (ρ s / ρ w + 1

b3 =

0.966 m o + 0.796

where m is the moisture content (kg water/kg dry solid), A area (m2), V volume (m3), ρ density (kg/m3).

Table 19 Fundamental models for shrinkage including porosity (adapted from [46]) Model

Geom.&Red.dim.

Material

cylinder-volume

Carrot, pear, potato, sweet potato

cylinder-volume

Carrot, pear, potato, sweet potato

Model A (inclusion of initial porosity) ⎛ ⎞ m D R = ⎜⎜ b1 + b 2 (m) ⎟⎟ b 3 ⎝ mo ⎠ −1

(

)

χ sg. + ρ sn (m)b 4 b1 ⎛ χ sg. ρ sn ,o ⎞ + b1 = ⎜⎜1 + b 4 ⎟⎟ , b 2 = mo mo mo ⎝ ⎠

b3 =

χ χ 1 − ψ (m o )ρ sn (m o ) , b 4 = cw + st 1 − ψ (m)ρ sn (m) ρ cw ρ st

Model B (without inclusion of initial porosity) 1 (b1 + χ sg / ρ sg + χ / ρ sn )ρ o DR = (1 − ψ ) mo + 1

χ χ b1 = cw + st ρ cw ρ st DR =

1 ⎡ ρ o (m − m o ) ⎤ ⎢1 + ⎥ (1 − ψ ) ⎣ ρ w (1 + m o ) ⎦

slab-volume

Beef meat

DR =

ρo 1 + m (1 − ψ ex − ψ ) , ρ= m ) ρ 1 + mo ∑ m i /(ρ T ) i

slab-volume

Squid

⎤ 1 ⎡ ρ o (m − m o ) − ψo ⎥ ⎢1 + ρ w (1 + m o ) (1 − ψ ) ⎣ ⎦

cylinder-volume

Apple, potato, carrot, squid

i =1

DR =

) where m is the moisture content (kg water/kg dry solid), m mass fraction (kg/kg total mass), χ constituent concentration 3 (kg/kg dry solid), ψ porosity, ρ density (kg/m ).

130

Kamil Kahveci and Ahmet Cihan

9. DIFFUSIVITY In a porous material, various diffusion coefficient definitions exist depending on the transport mechanism. Diffusion may in general be divided into two parts as molecular and capillary diffusion. Molecular diffusion can occur in liquid and gas phase. Molecular diffusion in the gas phase becomes more important when the water saturation decreases. Generally, liquid and gas diffusion are expressed in one single equation instead of defining individually. In this case, diffusivity becomes an effective parameter. This effective diffusivity value is different from diffusivity value which is only for liquid or for gas. Capillary diffusivity has two components one due to moisture gradient and the other due to temperature gradient. Capillary diffusivity due to the temperature gradient is mostly omitted. Capillary diffusivity data is generally unavailable. However, effective diffusivity data available in the literature is close to capillary diffusivity when the material is very wet because the molecular diffusion in that condition is insignificant [102]. For materials with low moisture content, effective diffusivity is close to molecular diffusivity. Diffusion coefficient for a porous material is generally determined experimentally. Commonly used techniques are: sorption kinetics method, permeation method, concentrationdistance curve method and drying method. Among these techniques, drying method is the one widely used. Although there are alternative approaches to determine diffusivity by drying method, all approaches are based on diffusion equation. Different approaches based on the drying method are given below.

Simplified Methods In these methods, solution of diffusion equation is used to determine diffusion coefficient. Series solutions of the diffusion equation for some simple geometries are as follows. mr =

8 π2

⎡ π 2 D eff t 1 π 2 D eff t π 2 D eff t 1 − + exp( − 9 ) + exp( − 25 )+ exp( ) ⎢ 9 25 4L2 4L2 4L2 ⎣ ⎤ π 2 D eff t 1 exp(−49 ) + ..⎥ 2 49 4L ⎦

mr =

(467)

π 2 D eff t π 2 D eff t π 2 D eff t 1 6 ⎡ 1 exp( 9 )+ − + exp( − 4 ) + − exp( ) ⎢ 9 4 L2 π2 ⎣ L2 L2 ⎤ π 2 D eff t 1 exp(−16 ) + ..⎥ 2 16 L ⎦

(468)

Deff t D t ) + 0.131 exp(−30.5 eff2 ) + L L2 D t D t 0.0534 exp(−74.9 eff2 ) + 0.029 exp(−139.1 eff2 ) + .. L L

(469)

m r = 0.692 exp(−5.78

Transport Phenomena During Drying of Food Materials

131

where L is the characteristic length (m). Since the values of the exponential terms of Eq. (467) (for infinite slab), Eq. (468) (for sphere) and Eq. (469) (for infinite cylinder) except the first terms contribute little to moisture ratio when the value of D eff t / L2 is greater than 0.2, the first term can be taken into account for finding the effective moisture diffusivity. If, also, natural logarithms are taken of these equations, they become as: ln(m r ) = ln

8 π 2 D eff − t π2 4L2

(470a)

ln(m r ) = ln

6 π 2 D eff − t π2 L2

(470b)

π 2 D eff (470c) t L2 From Eqs. (470), plots of ln(mr) versus drying time t give straight lines with the slopes of ln(m r ) = ln 0.692 − 5.78

Slope =

π 2 D eff 4L2

Slope =

π 2 D eff L2

Slope = 5.78

π 2 D eff L2

(471)

These slopes are used as a measure for the diffusivity (see figure 25). x

ln(mr)

x x x

Slope=f(Deff;L)

x x

x x x x

t Figure 25. Experimental drying curve.

This method can not be used when the diffusion coefficient is strongly dependent on concentration. In this case, the following procedure is followed in determining the diffusivity. The theoretical moisture ratio is evaluated numerically for a range of the Fourier numbers. Then, the same ratio is evaluated using experimental data. Subsequently, both theoretical and experimental moisture ratio curves are plotted versus time and the Fourier number on a semilogarithmic diagram as shown in figure 26. Moisture diffusivity is determined with the help of the following equation by comparing the slopes of both curves [114]:

132

Kamil Kahveci and Ahmet Cihan ⎛ ∂m r ⎞ ⎜ ⎟ ⎝ ∂t ⎠ exp 2 L D= ⎛ ∂m r ⎞ ⎜ ⎟ ⎝ ∂t ⎠ the 1.0

(472)

x x x

mr

x x

experimental x

0.1

x

theoretical

x x x

x

0.0 0

2

4

6

0.0

0.2

0.4

0.6

8 t (h) 10 0.8 Fo

1.0

Figure 26. Theoretical and experimental drying curves (adapted from [114]).

Regular Regime Method The regular regime method with which concentration dependent diffusivity can be calculated is based on the experimental measurement of the regular regime curve. Regular regime curve is the drying curve when it becomes independent of the initial concentration profile. The method assumes that material is homogeneous and nonporous, however it can also be applied to cellular tissue foods such as apples and potatoes [115]. In this method, the diffusion equation is solved numerically for the regular regime period of an isothermal diffusion process to obtain diffusivity. Regular regime method is rather complicated and needs successive interpolations and differentiations of the experimental drying data. However, a short-cut method has also been proposed to avoid this rigorous procedure [116].

Regression Analysis Method In this method, first partial differential equations governing drying process are created. Geometry and particular boundary conditions are taken into consideration. Diffusivity is mostly expressed as a parametric model of local moisture content, temperature, or any other property. Following assignment of initial guess for parameters contained in the diffusion coefficient, the partial differential equation system is solved numerically. The values obtained are compared with experimental values with non-linear regression analysis. If the criterion of the least sum of squares is not satisfied, a new guess of the model parameters is fed back to the numerical calculation. Procedure is continued until a final convergence is obtained.

Transport Phenomena During Drying of Food Materials

133

Diffusion coefficient of food materials are mostly obtained by using one of the drying techniques given above. For various food materials, diffusion coefficients or correlations obtained using the diffusivity data are given in table 20. Diffusion coefficient is affected by many physical properties. Brief explanations on these factors are given below.

Temperature The dependence of the diffusivity on temperature is generally described by the Arrhenius equation as follows: D eff = D o exp(−

Ea ) RˆT

(473)

Potential Energy

Here Do is the diffusion coefficient factor, Rˆ is the universal gas constant (8.314 J/(mol K)), T is the air temperature (K) and Ea is the activation energy (J/mol). Activation energy values for various food materials are given in table 21. Activation energy is used to describe temperature dependence of diffusion coefficient. According to the energy levels involved in a reaction and to the collision theory of reactive molecules, enough energy must be generated to provide the necessary activation energy to be able to develop the reaction (figure 27) [114, 117]. Activation energy will not itself provide any idea of the reactivity of a given system, only information on temperature dependence of the reaction. Activation energy is also related to moisture content. The activation energy for diffusion increases at lower moisture contents since generally the interaction forces between moisture and solid are higher at lower moisture contents.

energy level in an activated state

activation energy for reverse reaction

average energy of reactants

activation energy

average energy of reactants heat of reaction

Figure 27. Potential energy levels during a given endothermic reaction [114, 117].

134

Kamil Kahveci and Ahmet Cihan

Pressure Under certain conditions, there may be a significant increase in the gas pressure inside the material during drying. As it can be seen from Eqs. (161)-(163), gas pressure is inversely proportional to diffusion coefficient and any increase in the gas pressure causes gas diffusion coefficient to decrease.

Composition Moisture diffusivity of most food materials shows a strong dependence on composition. Diffusivity is generally small for small values of moisture content and shows an increase with increasing moisture content and approaches to a constant value for values of moisture content higher than a certain value. Concentration dependence of diffusion coefficient is generally expressed by relations in linear, polynomial and exponential forms. Also, some studies show that an increase in the salt content of food materials leads to a decrease in moisture diffusivity particularly for high temperature and low moisture content. A decrease is also observed in the diffusion coefficient of some food materials with an increase in fat content. Some other studies show that with an increase in protein content, moisture diffusion coefficient can take higher values.

Shrinkage Diffusion coefficient is highly affected by shrinkage. This effect is due to the decreasing diffusion path in shrinking media [118]. Some researchers incorporated the volume change into the diffusion coefficient in order to take account the shrinkage effect on transport properties. They suggested multiplying the diffusion coefficient by a power of the volume changing factor, which is the ratio between the actual volume and a reference volume that is either the basic initial volume or the volume of the totally dried samples [119]: ⎛V⎞ D eff = ⎜⎜ ⎟⎟ D eff ,R ⎝ Vo ⎠

n

(474)

The power exponent used by Crank [74] was 2 and by Fish [120], 2/3. Another approach used in the literature is based on calculating first a reference diffusion coefficient by using the initial thickness of the product. The diffusion coefficient obtained can thereafter be corrected for the shrinkage by applying: ⎛ L ⎞ D eff = ⎜⎜ ⎟⎟ D eff ,R ⎝ L o ⎠

n

(475)

The shrinkage during drying is sometimes neither ideally three-dimensional, nor onedimensional. For these cases, the following equation is suggested for the diffusion

Transport Phenomena During Drying of Food Materials

135

coefficient [121, 122]: ⎛V⎞ D eff = ⎜⎜ ⎟⎟ D eff ,R ⎝ Vo ⎠

2/ n

(476)

where the exponent n equals to 1 for one-dimensional shrinkage and equals to 3 for isotropic three-dimensional shrinkage. This parameter n may be viewed as a measure of the degree of isotropicity of the deformation and is related to volume shrinkage by [119]: Sb = Sdn

(477)

where Sb and Sd are defined as Sb =

V Vo

Sd =

L d or d o Lo

(478)

Other Factors In food materials enzymatic and microbiological changes may also have an effect on diffusion coefficient. Enzymatic reactions may change the internal properties of food material. Furthermore, the micro-organisms may also influence the surface of the food material, changing the equilibrium moisture content of food at the surface. Another factor acting on diffusion coefficient is pretreatment. Food materials may be exposed various types of pretreatment before drying. Pretreatment generally causes a change in the porosity of food materials and this leads to a change in diffusion coefficient.

CONCLUSION A large number of models have been proposed to simulate drying process of food materials. The basic reason of such an abundance of model propositions is the variety of transport mechanisms involved in drying process and complexity of material structures. Models based on continuum approach are preferred generally to simulate drying at macroscopic level. Among proposed models based on continuum approach, Whitaker model is one step ahead of other models in that physical basis of this model is stronger. However, the empirical character of transport coefficients as in other models based on the continuum approach constitutes the most important drawback of this model. The majority of transport coefficients show a stronger dependence on concentration, temperature and material structure. The effect of concentration and temperature on transport parameters is relatively well known and various models have been proposed to express these effects. However, there is relatively limited knowledge on the effect of structure on transport. It may be said that determination the effects of structure on transport will hereafter be one of the basic topics in studies related drying behavior of food materials.

Table 20. Moisture diffusion coefficients of various food materials Material

Meth&Geom

Apple (Red Delicious)

Slope Cylinder

Apple (organic)

Slope Slab

Apple pomace

Slope Slab

Apricot

Slope Slab

Banana

Regression Cylinder

Banana

Slope Slab

Barley

-

Basil leave

Slope Slab

Bean

Regression Sl.-Sph.

Drying Conditions Hot air m=0.06-8.5, T= 40-70°C v=3 m/s ,d=0.7cm , L=10d PT: water bath Hot air mw=0.11-0.82, T=40-60°C v=0.8m/s, L=5-9 mm PT: 5% lemon solution Microwave mw=0.25-0.40 N=150-600W Hot air mw=0.16-078, T=50-80°C v=0.2-1.5m/s Hot air m=0.2-3.8, T=60-80°C v=1.3 m/s Hot air (Tunnel) m=0.24-4, T=40-60°C v=0.3-0.7m/s, L=4mm

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5 D eff = 1.65x10 −6 exp(−

19.34 ) RˆT

Ref. 123

ab

Deff= 2.27x10-10 –Deff=4.97x10-10

124

Deff=1.0465x10-8 for N=150W , Deff=2.2130x10-8 for N=30W Deff=2.7157x10-8 for N=450W , Deff=3.6854x10-8 for N=600W

125

D eff = 1.14x10 −7 + 2.25x10 −9 T + 1.47 x10 −7 v

126

D eff = 1.36 x10 −7 exp(−

13.4 ) RˆTab

Deff =6.61x10-11 for T=40°C, v=0.3 m/s Deff =1.95x10-10 for T=60°C, v=0.3 m/s Deff =1.61x10-10 for T=50°C, v=0.5 m/s Deff =9.80x10-11 for T=40°C, v=0.8 m/s Deff =2.41x10-10 for T=60°C, v=0.8 m/s

127 , , , ,

Deff =1.57x10-10 for T=50°C, v=0.3 m/s Deff =7.33x10-11 for T=40°C, v=0.5 m/s Deff =2.23x10-10 for T=60°C, v=0.5 m/s Deff =1.74x10-10 for T=50°C, v=0.8 m/s

128

m=0.10-0.27 T=30-70°C Open sun mw=0.04-0.87 Te=30-36.5°C ϕe=0.24-0.28 qs=195-796 W/m2

Deff=1.31x10-11-6.52x10-10

129

Deff =6.44x10-12

130

T=25-40°C

Deff=4.35x10-11-3.79x10-9

131

Table 20. (Continued). Material

Meth&Geom

Beef Meat

Slope Slab

Biscuit

Black grape

Black tea particle

Broccoli floret

Carrot

-

Slope Sphere

Slope Sphere

Drying Conditions Hot air m=0.1-2.8, T=6.6-40.4°C d=38mm h=10mm m=0.10-0.60 T=20-100°C Hot air mw=0.25-0.79, T=60°C v=1.1 m/s PT: Potassium carbonate solution: 5% K2CO3 + 0.5% olive oil PT: Ethyl oleate plus potassium carbonate solution PT: Ethyl oleate plus potassium hydroxide solution PT: Ethyl oleate plus sodium carbonate solution Hot air T=80-120°C v=0.25-0.65m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5 D eff = 5.09x10 −6 exp(

24.643 ) RˆT

Ref. 132

ab

Deff=8.6x10-10-9.4x10-10

133

Deff =3.82x10-10

Deff =1.05x10-9 134

Deff =1.28x10-9

Deff =8.64x10-10 Deff =7.97x10-10 D eff = 1.68x10 −7 exp(−

406.02 ) RˆT

135

ab

Regression

Hot air T=50-75°C v=1.2-2.25m/s l=10mm

Deff=3.5780x10-6 for T=50°C, v=1.2m/s , Deff=5.9747x10-6 for T=60°C, v=1.2m/s Deff=10.6760x10-6 for T=75°C, v=1.2m/s , Deff=6.0112x10-6 for T=50°C, v=1.75m/s Deff=8.2654x10-6 for T=60°C, v=1.75m/s , Deff=12.8230x10-6 for T=75°C, v=1.75m/s Deff=4.8886x10-6 for T=50°C, v=2.25m/s , Deff=7.8897x10-6 for T=60°C, v=2.25m/s Deff=16.6770x10-6 for T=75°C, v=2.25m/s

136

Slope Sphere

Hot air mw=0.06-088, T=50-70°C v=0.5-1.0m/s Dims: 1x1x1cm 2x2x2cm

Deff=0.776x10-9 - 9.335x10-9

137

Table 20. (Continued). Material

Carrot

Meth&Geom

Slope Slab

Drying Conditions Hot air m=0.2-14, T=55-75°C v=1.6 m/s, Dims:1x1x1cm PT: NaCl

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5 D eff = 3.0x10 −6 exp(−

22.1426 ) RˆT ab

D eff = 4.0 x10 −8 exp(−

10.0017 ) RˆT ab

PT: Sucrose

Ref.

D eff = 2.0 x10

−7

14.8729 exp(− ) RˆT

D eff = 3.0x10

−7

16.2130 exp(− ) RˆT

138

ab

PT: Sucrose+NaCl

ab

Carrot

Slope Slab

Carrot core

Slope Cylinder

Carrot cortex

Slope Cylinder

Celery

Inverse M.

Chelwa

Slope Cylinder

Cherry (sweet)

Regression Sphere

Infrared mw=0.0-0.9, T=50-80°C L=1-2mm Hot air m=1.17-6.39, T=40-70°C v=1.5,3m/s, d=0.7cm PT: water bath Hot air m=1.05-5.31, T=40-70°C v=1.5-3m/s, d=0.7cm PT: water bath m=0.09-9.82 T=49.1°C ρc=461-1428kg/m3 L=3-10mm Open sun m=0.1-2.5,Te=32.5-42.5°C ϕe=0.15-0.32 qs=460-820W/m2 Hot air m=0.1-2.6, T= 50-80°C φ=0.05-0.5, v=1-5 m/s

Deff =7.295x10-11 for T=50°C , Deff =9.309x10-11 for T=60°C Deff =1.140x10-10 for T=70°C , Deff =1.501x10-10 for T=80°C

139

Deff =6.42x10-10 -14.7x10-10

140

Deff =6.68x10-10 -13.6x10-10

140

Deff = 7.98x10−4 exp(0.130m −

3217.3 1323.7 + ) ρc Tab

141

D eff = 17.57 x10 −11 exp(−1.591m m )

142

Deff =6.814x10-11 for T=50°C , Deff =12.516x10-11 for T=60°C Deff =25.026x10-11 for T=70°C , Deff =34.739x10-11 for T=80°C

143

Table 20. (Continued). Material

Meth&Geom

Cherry (sour)

Slope Sphere

Drying Conditions Hot air mw=0.2-0.82, T=55-65°C v=1 m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

Ref.

Deff =4.75x10-10 for T=55°C, Deff =5.57x10-10 for T=65°C

144

D eff = 1.2152 x10 −5 exp(− Chestnut

Slope Sphere

Hot air m=0.04-0.50, T=70-90°C

22.578 ) RˆT

for Longal variety

21.926 ) RˆT

for Martainha variety

20.458 ) RˆT

for Viana variety

ab

D eff = 9.8684 x10 −6 exp(−

145

ab

Chestnut Chickpea

Coconut presscake

Coffe cherry

Corn

Slope Sphere

Hot air m=0.04-0.40, T=70-90°C

D eff = 6.6979 x10 −6 exp(−

Regression Sphere

T=15-40°C

9.71x10-11-5.98x10-10

Slope Slab Regression Sphere Slope Sphere

Vacuum m=0.02-1.038 P=65 mmHg T=65-75°C, L=2-4 mm Hot air mw=0.11-0.66T=45,60°C Hot air mw=0.14-0.3, T=55-75°C PT: alkali solution

145

ab

D eff = 0.3753x10 −8 exp(549.8L −

129 171.9 ) T

146

Deff =0.1x10-10-1x10-10 for T=45°C , Deff =0.3x10-10-3x10-10 for T=60°C

147

Deff =9.488x10-11 for T=55°C,Deff =1.153x10-10 for T=65°C,Deff =1.768x10-10 for T=75°C Deff =1.424x10-10 for T=55°C,Deff =1.733x10-10 for T=65°C,Deff =2.716x10-10 or T=75°C

148

Cracker

-

m=0.03-0.14 T=40-90°C

Deff=1.41x10-11-1.81x10-9

129

Date (red soft )

Slope Sphere

Hot air m=0.4-1.6, T=50-80°C v=1.5 m/s

Deff =5.89x10-10 for T=50°C , Deff =7.48x10-10 for T=60°C Deff =1.45x10-9 for T=70°C , Deff =1.78x10-9 for T=80°C

149

Date (tempo 2)

Slope Sphere

Hot air m=0.4-1.5, T=50-80°C v=1.5 m/s

Deff =3.22x10-9 for T=50°C , Deff =4.52x10-9 for T=60°C Deff =7.32x10-9 for T=70°C , Deff =8.16x10-9 for T=80°C

149

Table 20. (Continued). Material

Meth&Geom

Drying Conditions

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

Ref.

Date (tempo 3)

Slope Sphere

Hot air m=0.5-1.5, T=50-80°C v=1.5 m/s

Deff =7.53x10-9 for T=50°C , Deff =1.11x10-8 for T=60°C Deff =2.30x10-8 for T=70°C , Deff =2.98x10-8 for T=80°C

149

Dough

-

M=0.20-0.50 T=15-203°C

Deff=1.30x10-10-1.00x10-9

129

Dill leave

Slope Slab

Hot air mw=0.05-0.82, T=50-70°C v=1.1 m/s

Deff =6.69x10-10 for T=50°C , Deff =9.21x10-10 for T=60°C , Deff =1.43x10-9 for T=70°C

150

Eggplant

Slope Slab

Fig

Slope Sphere

Vacuum m=0.0-0.16, T=30-50°C P=2.5,5,10kPa Dims: 45x25x20mm Open sun mw=0.25-0.74,Te=3547°C

D eff = 2.012x10 −4 exp(−

Fig

Slope Sphere

151

Deff =2.47x10-10 D eff = 4.83x10 −4 exp(−

Hot air (conditioned) m=0.14-1.9, T=55-85°C ϕ=0.10 , v=0.5-3 m/s

29.52 ) Tab

152 37.27 30.81 ) , D eff = 8.77 x10 −5 exp(− ) for v=0.5 and 1 m/s RˆT RˆT ab

D eff = 2.01x10

−2

45.81 exp(− ) , RˆT ab

ab

D = 5.99 x10

−2

48.47 exp(− ) eff RˆT

for v=2.0 and 3

153

ab

m/s Garlic

Garlic

Slope Slab Regression Slab

Hot air m=0.08-1.56, T=40-60°C v=0.8m/s, L=3-5mm Hot air m=0.27-1.35, T=50-80°C v=2-4m/s

D eff = 1.61x10 −6 exp(−

23.48 ) RˆT

154

ab

D eff = 7.490x10 −6 exp(−

27.84 ) RˆT ab

155

Table 20. (Continued). Material

Meth&Geom

Grape

Regression Sphere

Grape (Sultana)

Slope Slab

Green bean

Slope Slab

Green pepper

Slope Slab

Kale

Slope Slab

Kiwi

Regression Slab

Lentil

Regression Slab

Mango

Slope Slab

Minced meat

-

Milk (skimmed) Minced meat

Drying Conditions Hot air m=0.2-2.4, T=40-70°C v=1-2.3m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

PT: alkali solution (1% of sodium hydroxide)

D eff = 1.6x10 −3 exp(−

Hot air m=0.18-3 , T=65°C v=0.45m/s Hot air mw=0.14-0.9, T=50-70°C v=1 m/s , L=4cm (length) Hot air mwo=0.94, T= 25-45°C v=4.1 m/s , l=10mm Hot air mw=0.16-0.86, T= 3060°C v= 1 m/s L=10-50mm (layer thick) Hot air m=0.15-4.65, T=30-90°C L=6mm

D eff = 0.522 exp(−

54 ) exp[− (0.0075Tab + 1.829)m ] RˆTab

Ref.

156

49 ) exp[− (0.0012Tab + 0.309)m ] RˆT ab

Deff=6.44 x10-10

157

D eff = 5.53x10 −4 exp(−

35.43 ) RˆT

158

51.4 ) RˆTab

159

ab

D eff = 1.320x10 −2 exp(−

Deff =14.8894x10-10 – 55.9451x10-10

D eff = 1.476 x10 −5 exp(−

27 ) RˆTab

160

161

T=15-40°C

Deff=3.53x10-10-1.33x10-9

131

Hot air m=0.08–8, T=55-65°C L=2.8 mm Hot air m=0.3-1.8, T= 30-120°C

Deff =2.62x10-10 for T=55°C , Deff =2.95x10-10 for T=60°C , Deff =3.19x10-10 for T=65°C

162

Deff =5.0x10-11-53.0x10-11

114

-

m=0.3-0.8, T=30-50°C

Deff= 0.24x10-10-2.1x10-10

163

-

Hot air m=0.3-1.8, T= 30-120°C

-11

Deff =5.0x10 -53.0x10

-11

114

Table 20. (Continued). Material

Meth&Geom

Mint leave

Slope Slab

Mint leave

Slope Slab

Mulberry

Regression Slab

Mullet roe

Slope Slab

Murici

Regression Slab

Olive cake

Slope Slab

Olive extraction

oil

Olive extraction

oil

Slope Slab Regression Slab

Okra

Slope Sphere

Onion

-

Drying Conditions Open sun mw=0.1-0.86, Te=30-36.5°C φe=0.24-0.28 qs=195-796W/m2 Hot air mw=0.1-0.85, T=35-60°C v=4.1m/s Hot air m=0.1-4.6, T=60-80°C v=1.2 m/s Hot air m=0.3-1, T= 20-40°C v=1.0 m/s Dims.20x10x3cm Hot air mwo=0.88, T= 50-70°C v=1.5 m/s Hot air mw=0.05-0.45 T=50-110°C, v=1.2m/s Hot air m=0.1-2 , T=20-80°C v=1.0m/s Hot air mw=0.05-0.45 T=80-110°C, v=1.2m/s Hot air mw=0.15-0.9 , T=50-70°C v=1m/s m=0.10-10 T=40-80°C

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

Ref.

Deff =7.04x10-12

130

Deff =3.067x10-9 for T=35°C , Deff =5.837x10-9 for T=45°C, Deff =1.237x10-8 for T=55°C , Deff =1.941x10-8 for T=60°C

164

Deff =2.32x10-10, Deff =2.84x10-10, Deff =3.58x10-10 for T=60,70,80°C (CRP) Deff =5.03x10-10, Deff =6.68x10-10, Deff =8.43x10-10 for T=60,70,80°C (FFRP) Deff =2.63x10-9 , Deff =2.30x10-9 , Deff =2.76x10-9 for T=60,70,80°C (SFRP)

165

D eff = 19.8x10 −4 exp(−

37.2 ) RˆTab

166 D

Deff =1.275x10-9 for T=50°C , Deff =1.975x10-9 for T=60°C, Deff =2.906x10-9 for T=70°C

167

17.97 ) RˆT

168

15.77 ) RˆT

169

D eff = 3.128x10 −6 exp(−

ab

D eff = 1.615x10 −7 exp(−

ab

Deff=4.89x10-8 - 9.98x10-8

170

Deff =4.27x10-10 for T=50°C , Deff =7.76x10-10 for T=50°C , Deff =1.30x10-9 for T=50°C

171

Deff=1.38x10-11-6.60x10-9

129

Table 20. (Continued). Material

Meth&Geom

Orange skin

Regression Slab Regression Slab

Drying Conditions Hot air m=0.25-3.66, T=30-90°C v=2.5 m/s, L=0.45cm Hot air mw=0.3-088, T=40,60°C v=1.25-3.25m/s

Papaya PT: osmotical

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5 D = 3.957 x10 −4 exp(−

36.36 −0.0496 m ) RˆT

Ref. 172

ab

Deff =1.72x10-9 v=1.25m/s Deff =2.21x10-9 v=3.25m/s Deff =1.03x10-9 v=1.25m/s Deff =1.11x10-9 v=3.25m/s

for T=40°C and v=1.25m/s , Deff =2.71x10-9

for T=60°C and

for T=40°C and v=3.25m/s , Deff =4.78x10-9

for T=60°C and

for T=40°C and v=1.25m/s , Deff =1.48x10-9

for T=60°C and

for T=40°C and v=3.25m/s , Deff =1.78x10-9

for T=60°C and

173

Parsley leave

Slope Slab

Open sun mw=0.06-0.84 Te=30-36.5°C ϕe=0.24-0.28 qs=195-796 W/m2

Parsley leave

Slope Slab

Hot air mw=0.05-0.82, T=50-70°C v=1.1 m/s

Deff =9.00x10-10 for T=50°C , Deff =1.36x10-9 for T=60°C , Deff =2.34x10-9 for T=70°C

150

Pasta

-

mw=0.05-0.27, T=40-74°C

Deff=0.8x10-11-9.3x10-11

163

Pasta (Semolina)

Regression Slab

Hot air m=0.14-0.50, T=40-100°C Dims:100.0x20.0x1.3mm

Deff = 1.2x10−11 exp(−3036.95(

Pea

-

T=30-65°C

Deff=3.1x10-10-6.6x10-10

Slope

Hot air m=0.16-7.52, T= 55-65°C v=0.8 m/s, L=3.5mm

Deff =3.04x10-6 for T=55°C , Deff =3.62x10-6 for T=65°C

PT: 1% KMS

Deff =3.44x10-6 for T=55°C , Deff =4.41x10-6 for T=65°C

PT: 1% ascorbic acid

Deff =3.51x10-6 for T=55°C , Deff =4.04x10-6 for T=65°C

Peach

Deff =4.53x10-12

130

1 1 − ) exp(6.46m) T 293

174 163

175

Table 20. (Continued). Material

Meth&Geom

Pear

Regression Sphere

Pineapple

-

Pistachio nuts

Slope Sphere

Drying Conditions Hot air (conditioned) mw=0.2-0.8, T=30-50°C v=0.5-1.5m/s, ϕ=0.4-0.6 L=4mm

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

Ref.

1 + 1.2987 x10 −2 m 24.3 exp(− ) 1 + 4.5111x10 −1 m s RˆTab

176

D eff = 1.1276 x10 −5

m=3.80-5, T=30-50°C

Deff=5.38×10-12-2.64×10-9

Hot air mw=0.37-0.05 T=25-70°C ϕ=0.05- 0.2 v=0.5-1.5m/s

Deff=1.37x10-10 Deff =2.26x10-10 Deff =5.24x10-10 Deff =7.01x10-10 Deff =5.42x10-11 Deff =2.29x10-10 Deff =4.91x10-10 Deff =8.82x10-10 Deff =1.70x10-10 Deff =3.26x10-10 Deff =5.25x10-10 Deff =8.07x10-10 Deff =1.36x10-11 Deff =2.98x10-10 Deff =5.82x10-10 Deff =8.40x10-10 Deff =1.80x10-10 Deff =3.48x10-10 Deff =5.75x10-10 Deff =9.29x10-10 Deff =1.81x10-11 Deff =3.25x10-10 Deff =5.90x10-10 Deff =8.90x10-10

for for for for for for for for for for for for for for for for for for for for for for for for

T=25°C, φ=0.05, v=0.5 m/s T=40°C, φ=0.05, v=0.5 m/s T=55°C, φ=0.05, v=0.5 m/s T=70°C, φ=0.05, v=0.5 m/s T=25°C, φ=0.20, v=0.5 m/s T=40°C, φ=0.20, v=0.5 m/s T=55°C, φ=0.20, v=0.5 m/s T=70°C, φ=0.20, v=0.5 m/s T=25°C, φ=0.05, v=1.0 m/s T=40°C, φ=0.05, v=1.0 m/s T=55°C, φ=0.05, v=1.0 m/s T=70°C, φ=0.05, v=1.0 m/s T=25°C, φ=0.20, v=1.0 m/s T=40°C, φ=0.20, v=1.0 m/s T=55°C, φ=0.20, v=1.0 m/s T=70°C, φ=0.20, v=1.0 m/s T=25°C, φ=0.05, v=1.5 m/s T=40°C, φ=0.05, v=1.5 m/s T=55°C, φ=0.05, v=1.5 m/s T=70°C, φ=0.05, v=1.5 m/s T=25°C, φ=0.20, v=1.5 m/s T=40°C, φ=0.20, v=1.5 m/s T=55°C, φ=0.20, v=1.5 m/s T=70°C, φ=0.20, v=1.5 m/s

129

177

Table 20. (Continued). Material

Meth&Geom

Drying Conditions

Pistachio nuts

Slope Sphere

Hot air (conditioned) mw=0.37-0.05 T=25-70°C ϕ=0.05- 0.2 v=0.5-1.5m/s

Plantain (Musa AAB)

-

Plum

Slope Slab

Hot air (conditioned) m=0.12-1.2, T=37-62°C φ=0.01, v=3.6m/s L=7.2-20mm Hot air m=0.09-9.86, T=55-65°C L=3.5 mm

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5 Deff=1.37x10-10 for T=25°C, φ=0.05, v=0.5 m/s Deff =2.26x10-10 for T=40°C, φ=0.05, v=0.5 m/s Deff =5.24x10-10 for T=55°C, φ=0.05, v=0.5 m/s Deff =7.01x10-10 for T=70°C, φ=0.05, v=0.5 m/s Deff =5.42x10-11 for T=25°C, φ=0.20, v=0.5 m/s Deff =2.29x10-10 for T=40°C, φ=0.20, v=0.5 m/s Deff =4.91x10-10 for T=55°C, φ=0.20, v=0.5 m/s Deff =8.82x10-10 for T=70°C, φ=0.20, v=0.5 m/s Deff =1.70x10-10 for T=25°C, φ=0.05, v=1.0 m/s Deff =3.26x10-10 for T=40°C, φ=0.05, v=1.0 m/s Deff =5.25x10-10 for T=55°C, φ=0.05, v=1.0 m/s Deff =8.07x10-10 for T=70°C, φ=0.05, v=1.0 m/s Deff =1.36x10-11 for T=25°C, φ=0.20, v=1.0 m/s Deff =2.98x10-10 for T=40°C, φ=0.20, v=1.0 m/s Deff =5.82x10-10 for T=55°C, φ=0.20, v=1.0 m/s Deff =8.40x10-10 for T=70°C, φ=0.20, v=1.0 m/s Deff =1.80x10-10 for T=25°C, φ=0.05, v=1.5 m/s Deff =3.48x10-10 for T=40°C, φ=0.05, v=1.5 m/s Deff =5.75x10-10 for T=55°C, φ=0.05, v=1.5 m/s Deff =9.29x10-10 for T=70°C, φ=0.05, v=1.5 m/s Deff =1.81x10-11 for T=25°C, φ=0.20, v=1.5 m/s Deff =3.25x10-10 for T=40°C, φ=0.20, v=1.5 m/s Deff =5.90x10-10 for T=55°C, φ=0.20, v=1.5 m/s Deff =8.90x10-10 for T=70°C, φ=0.20, v=1.5 m/s

Ref.

Deff =2.32x10-10 -18.01x10-10

178

Deff =3.04x10-10 for T=55°C , Deff =3.44x10-10 for T=60°C , Deff =3.69x10-10 for T=65°C

179

177

Table 20. (Continued). Material Plum (Stanley)

Pollen

Meth&Geom Slope Sphere Sphere

Pork lean

-

Potato

Regression Slab

Potato

Slope Cylinder

Prawn

Slope Cylinder

Prune

Regression Sphere

Pumpkin

Slope Slab

Drying Conditions Hot air T=60-80°C, v=1-3 m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5 -9

Deff =1.179x10 -6.671x10

180

PT: 2% NaOH solution

Deff =1.197x10-7-4.551x10-7

Fluidized bed m=0.03-0.1, T=40-45°C

D eff = 1.0285x10 −6 exp(−

Hot air m=0.4-1.0, T= 10-30°C Hot air m=0.25- 2, T=40-85°C v=0.5, 1 m/s, Dims:45x 20x10 mm Hot air m=0.24-3.55, T=40-70°C v=1.5,3m/s, d=0.7-1.4cm PT: water bath Open sun m=0.1-3, Te=32.5-42.5°C ϕe=0.15-0.32 qs=460-820W/m2 Hot air (Tunnel) m=0.02-0.2, T=70-80°C v=5m/s Hot air mw=0.1-0.92, T=50-60°C v=1 m/s , L=0.7 cm

Ref.

-9

29.69 ) RˆT

181

ab

Deff =1.1x10-11-2.0x10-11 D eff = 7.18x10 −5 exp(−

114 31580 ) exp[(−0.0025Tab + 1.22)m ] RˆT

182

ab

Deff =4.55x10-10 -10.2x10-10

140

D eff = 18.09 x10 −11 exp(−1.279m m )

142

Deff =4.32x10-10 for T=70°C , Deff =5.48x10-10 for T=70°C , Deff =7.64x10-10 for T=70°C

183

Deff=3.88x10-10 for T=50°C, Deff=6.58x10-10 for T=55°C, Deff=9.38x10-10 for T=60°C

184

Table 20. (Continued). Drying Conditions

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

Hot air m=0.04 -0.67, T=40-60°C v=0.8 m/s

D eff = 1.95x10 −5 exp(−

Open Sun m=0.05 -0.67, Te=21.6-39.7°C ϕe=12.1-51.5 qs=205.1-796.2 W/m2

Deff=1.66 x10-11

Solar Tunnel m=0.05 -0.67

Deff=1.94 x10-11

Quince

Regression Slab

Hot air (conditioned) m=0.3-4.7, T=35-55°C v=0.2-0.6m/s, ϕ=0.4-0.7 L=4mm

Deff =0.65x10-10-6.92x10-10

Quinoa seed

Regression Sphere

Hot air m=0.09-0.26, T=30-90°C

D eff = − 2.11x10 −7 m o + 7.95x10 −7 exp(−

Raisin

-

M=0.15-2.40, T=60°C

Deff=5.0x10-11-2.5x10-10

133

Red bell pepper (Lamuyo)

Slope Slab

Hot air m=0.02-10.11, T=50-80°C v=2.5 m/s, Dims:1x1x1cm

Deff=3.2x10-9 for T=50°C , Deff=6.9x10-9 for T=60°C Deff=10.2x10-9 for T=70°C , Deff=11.2x10-9 for T=80°C

188

Red pepper

Slope Slab

42.8 ) RˆTab

159

Red pepper

Slope Cylinder

37.76 ) RˆT

189

24.76 ) RˆT

190

Material

Pumpkin seed (hull-less)

Red pepper

Meth&Geom

Slope Slab

Slope Cylinder

Hot air mwo=0.91, T= 25-45°C v=4.1 m/s , l=10mm Hot air m=3.25-0.11, T=50-65°C v=0.4 m/s Rotary dryer m=1.05-3.3, T=50-65°C v=0.8 m/s

Ref.

33.15 ) RˆT ab

185

186

(

)

37.98 ) RˆT

187

ab

D eff = 0.043x10 −2 exp(− D eff = 4.92x10 −3 exp(−

ab

D eff = 1.95x10 −5 exp(−

ab

Table 20. (Continued). Drying Conditions Hot air m=0.06-1.22 T= 110–180°C v=0.5-3 m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

-

M=0.50-0.53 T=40-100°C

Deff=2.38x10-11-4.84x10-10

129

Rose hip

Regression Sphere

Hot air m=0.07-0.49, T=50-80°C v=15 m/s

Deff =7.501x10-11 for T=50°C , Deff =15.622x10-11 for T=60°C Deff =22.639x10-11 for T=70°C , Deff =33.674x10-11 for T=80°C

192

Rough rice (long)

-

Hot air mo=0.13, T=12-50°C

D eff = 8.24 x10 −4 exp(−

Hot air mo=0.22, T=40-70°C

D eff = 5.08x10 −4 exp(−

Hot air mo=0.32,T=35-54°C

D eff = 9.33x10 −3 exp(−

Regression Cylinder

Hot air m=0.24-1.22 T= 40–60°C v=1.5-3 m/s

D eff = 1.64v −0.85 exp(−

Rough rice

Regression Ellipsoid

Hot air m=0.24-1.22 T= 40–60°C v=1.5 m/s

Deff= 4.176x10-8 Deff= 4.644x10-8 Deff= 5.508x10-8 Deff= 5.976x10-8 Deff= 7.524x10-8

Salami

-

Soya bean (Nidera A6381)

Regression Sphere

Material

Meth&Geom

Rice (cooked)

Slope Cylinder

Rice (parboiled)

Rough rice (medium)

-

Rough rice (short)

-

Rough rice

Hot air m=0.4-0.45, T= 10-20°C Hot air m=0.14-0.27, T=19-75°C v=0.23m/s

D eff = 2.5x10 −5 exp(−

36.44 ) RˆT

Ref. 191

ab

49.19 ) RˆT

193

43.56 ) RˆT

194

53.37 ) RˆT

195

4706 ) v 0.076 Tab

84

ab

ab

ab

for T=40°C for T=45°C for T=50°C for T=55°C for T=60°C

87

Deff =0.03x10-11-0.37x10-11

114

Deff =1.78x10-11-7.28x10-11

196

Table 20. (Continued). Material

Meth&Geom

Soya bean (Nidera A5409)

Regression Sphere

Soya bean

SlopeSlab

Soya bean (Brazilian Doko)

Regression Sphere

Squid mantle

Regression Hollow cylinder

Sugar beet

-

Tomato

Slope Slab

Drying Conditions Hot air m=0.10- 0.30, T=25-70°C v=0.23m/s

Hot air m=0.10-0. 3, T=30,50°C v=0.5-1.5m/s

Hot air mw=0.11-0.82 T= 31.5–58.5 C v= 0.33–3.17 m/s Hot air m=0.148-2.74, T= 34.3°C v=1.05 m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

(

)

D eff = 4.51x10 −7 + 1.9x10 −8 m o exp(−

27 ) RˆTab

Deff =2.38x10-10 for T=30°C and v=0.5m/s v=0.5m/s Deff =2.62x10-10 for T=50°C and v=0.5m/s v=1.0m/s Deff =2.59x10-10 for T=40°C and v=1.0m/s v=1.0m/s Deff =2.65x10-10 for T=30°C and v=1.5m/s v=1.5m/s Deff =2.92x10-10 for T=50°C and v=1.5m/s

Ref. 196

, Deff =2.50x10-10

for T=40°C and

, Deff =2.43x10-10

for T=30°C and

, Deff =2.84x10-10

for T=50°C and

, Deff =2.83x10-10

for T=40°C and

197

Deff 4848.5 = 8.64 exp(− ) exp(3.8m) Tab R2

198

D eff = 8.23x10 −11 exp(0.0646mr)

199

T=40-84°C

Deff=0.4x10-10-1.3x10-10

163

Hot Air mw=0.11-0.94, T=55-70°C v=1.5 m/s

D eff = 6.987 x10 −5 exp(−

32.94 ) RˆT ab

200

17.40 exp(− ) RˆT

PT: alkaline ethyl oleate solution

D eff = 3.284x10

Deff =1.31x10-9

201

Deff=7.61×10-12-3.62×10-9

129

Tomato (organic)

Slope Slab

Solar tunnel mw=0.12 -0.93 T=22.4-35.6°C ϕ=14.5-50.9 qs=202.3-767.4W/m2

Turnip

-

m=0.31-7, T=20-100°C

−7

ab

Table 20. (Continued). Material

Meth&Geom

Wheat (broom)

Regression Ellipsoid

Wheat (parboiled)

Slope Sphere

White mulberry

Slope Sphere

Yam (Dioscorea alata)

Yam (Dioscorea rotundata)

Yoghurt

Regression Slab

Regression Slab

Slope Slab

Drying Conditions Hot air m=0.14-0.29, T=64-75°C v=0.64m/s Hot air mw=0.1-0.45, T=40-50°C v=3.7 m/min Hot air mw=0.17-0.82, T=50°C v=1 m/s Hot air mwo=0.69, T= 50-80°C v=1.5 m/s, Dims.=50x20x10mm PT: water bath PT: sodium metabisulphite solution Hot air mwo=0.71, T= 50-80°C v=1.5 m/s , Dims.=50x20x10mm PT: water bath PT: sodium metabisulphite solution Hot air m=0.09-3.81 ,T=40-50°C v=2 m/s

Effective Diffusion Coefficient (m2/s) Dmin=4.13x10-12 Dmax=1.041x10-5

[

]

D eff = 3.3x10 −6 + 36 x10 −6 ( m o − 0.222) exp(− D eff = 2 x10 −4 exp( −

32 ) RˆTab

37 ) RˆTab

Ref. 202

203

Deff =2.231x10-10-6.909x10-10

204

Deff =1.11x10-7 for T=50°C , Deff =1.92x10-7 for T=60°C Deff =1.93x10-7 for T=70°C , Deff =4.00x10-7 for T=80°C Deff =9.21x10-8 for T=50°C Deff =1.41x10-7 for T=70°C Deff =1.53x10-8 for T=50°C Deff =2.54x10-7 for T=70°C

, , , ,

Deff =1.23x10-7 for T=60°C Deff =2.84x10-7 for T=80°C Deff =2.37x10-7 for T=60°C Deff =4.01x10-7 for T=80°C

205

Deff =1.120x10-6 for T=50°C , Deff =2.867x10-6 for T=60°C Deff =4.902x10-6 for T=70°C , Deff =8.029x10-6 for T=80°C Deff =1.386x10-6 for T=50°C Deff =5.121x10-6 for T=70°C Deff =3.392x10-6 for T=50°C Deff =9.008x10-6 for T=70°C D eff = 2.11x10 −5 exp(

26.07 ) RˆT

, , , ,

Deff =2.548x10-6 for T=60°C Deff =1.041x10-5 for T=80°C Deff =5.449x10-6 for T=60°C Deff =1.298x10-5 for T=80°C

205

206

ab

where: Deff is the effective diffusion coefficient (m2/s), m moisture content in dry basis (kg moisture/kg dry solid), mw moisture content in wet basis (kg water/kg wet substance), mm hourly mean moisture content (kg water/kg dry matter), ms sugar concentration (kg sugar/kg dry solids), T temperature (°C), Tab

ˆ absolute temperature (K), v velocity (m/s), L thickness (m), l length (m) h height (m), d diameter(m), ϕ relative humidity, P pressure ( mmHg or kPa), R 2 universal gas constant (8.314 J/(molK)), N power (W), qs solar radiation (W/m ), PT pretreatment, CRP constant rate period, FFRP first falling rate period, SFRP second falling rate period, (Subscripts: e ambient air, o initial)

151

Transport Phenomena During Drying of Food Materials Table 21. Activation energies of various food materials for mass transfer Ea (kJ/mol)

Ref.

Materials

Apple (red delicious)

19.96-22.62

123

Mullet roe

37.2

166

Avocado Banana Banana Beef meat Black tea particle Broccoli Carrot Carrot Carrot core Carrot cortex Cauliflower leave Cherry (sweet) Chestnut (Longal) Chestnut (Martainha) Chestnut (Viena) Corn Corn Date (Red Soft) Date (Tempo 2) Date (Tempo 3) Dill leave Eggplant Fig Garlic Garlic Grape (Sultanin) Grape (Chasselas) Green bean Green bean Green pea Green pepper Kale Kiwi Lettuce leave Mango Mint leave Mint leave Mulberry

39.8 13.4 15.5-25.3 -24.64 406.02 26.2 12.7-28.7 22.43 24.78 16.53 19.82 53 22.578 21.926 20.458 29.56-30.56 27.61 35.17 29.50 44.02 35.05 29.52 30.81-48.47 23.48 27.84 54 49 35.43 39.47 24.7-28.40 51.4 36.12 27.00 19.82 22.95-27.68 82.93 62.96 21.2

207 127 208 132 135 209 207 139 140 140 210 143 145 145 145 148 211 149 149 149 150 151 153 154 155 156 156 158 212 213 159 160 161 210 214 215 164 165

Mushroom (Ag. Bisp.) Mushroom (Pl. florida) Olive cake Olive oil extraction Olive oil extraction Okra Orange skin Parsley leave Pear Pistachio nuts Plantain Pollen Potato Potato Prune Pumpkin Pumpkin seed (hulles) Quince Quinoa seed Red bell pepper Red pepper Red pepper Red pepper Rice (cooked) Rose hip Rough rice (long) Rough rice (medium) Rough rice (short) Soya bean (Nid. A6381) Soya bean (Nid. A5409) Sugar beet Tomato Wheat (broom) Wheat (parboiled) Yam (Dios. alata) Yam (Dios. rotundata) Yoghurt

19.79 23.59 17.97 15.77 26.71 51.26 36.36 43.92 24.3 30.79 38.81 29.69 16.3-108 31.5 57.00 78.93 33.15 33.83-41.52 32.50-40.99 39.70 42.80 37.76 24.76 36.44 46.00 49.19 43.56 53.37 28.80 27.00 28.8 32.94 32.00 37.13 41.71-69.45 25.26-46.46 26.07

210 210 168 169 170 171 172 150 176 177 178 181 207 216 183 184 185 186 187 188 159 189 190 191 192 193 194 195 197 197 217 201 203 204 206 206 207

Materials

Ea (kJ/mol)

Ref.

152

Kamil Kahveci and Ahmet Cihan

NOMENCLATURE a aw A A b bK Bk Bim Biq c cp C ˆ C

constant water activity Helmholtz free energy [J] area (m2) constant Klinkenberg parameter (Pa) viscous flow parameter [m2] Biot number for mass transfer Biot number for heat transfer constant air capacity [kgm2/(kgN)] concentration [kg/m3] molar concentration [mol/m3]

Cm CP d d D DR d e es Ey Ea Ec f f 〈f 〉

mass capacity [kg/(kg°M)] specific heat [J/(kgK)] diameter [m] constant Diffusion coefficient [m2/s] shrinkage dimension [volume, area, thickness] constant constant standard deviation Young modulus [Pa] activation energy [J/mol] characteristic energy [J] constant function spatial average of function f

〈f s 〉 〈f s 〉 F Fmi

phase average of a function fs, which represents a property of the s phase s

intrinsic phase average of a function fs, which represents a property of the s phase force [N] shape factor [1/m2]

Fo g gˆ

Fourier number gravitational acceleration [m2/s] molar Gibbs function [J/mol]

G G Gr Gr Gu h h hm hn hP hq H I J Jm Jm,vol

Gibbs free energy [J] slip modulus [m] relaxation model [Pa]. Grashof number Gukhman number capillary pressure head [m] specific enthalpy [J/kg] mass transfer coefficient [kg/(m2s) or m/s] mass transfer coefficient mass transfer coefficient [kg/(m2sPa)] heat transfer coefficient [W/(m2K)] enthalpy [J] the volumetric capacity [kg/(m3s)] transfer flux [[…]/(m2s)] moisture flux density [kg/(m2s)] volumetric flux density [m3/(m2s)]

Transport Phenomena During Drying of Food Materials Jˆ m

molecular flux density [mol/(m2s)]

Jn(x) Jq k kr K Ki Ko KH Kl Kn KV l L L Le M ˆ M

Bessel function of the first kind of order n heat flux density [J/(m2s)] constant relative permeability permeability [m2] intrinsic permeability [m2] Knudsen flow parameter [m] hydraulic conductivity [m/s] liquid conductivity [kg/(ms)] Knudsen number volume model [Pa] half length [m]

m mw ) m mr r n ) n N Nu P Pn(x) Pe Pr qvol q&

moisture content in dry basis [kg moisture/kg dry solid] moisture content in wet basis [kg water/kg wet substance] mass fraction

the number of moles Nusselt number pressure [Pa] Legendre polynomial of the first kind of order n Peclet number Prandtl number heat of desorption or absorption [J/m3] volumetric heating [W/m3]

Q Qdif Qvol r r r Rˆ

heat energy [J] diffusibility volumetric flow rate [m3/s] radius [m] radial coordinate [m] correlation coefficient in Eq. (463) universal gas constant [8.314 J/(molK)]

Re s sˆ

Reynolds number Laplace transformation parameter molar entropy [J/(molK)]

S S S Sb Sc Sh Stm Stq t T u u

entropy [J/K] saturation degree Surface area [m2] relative volumetric shrinkage [V/Vo] Schmidt number Sherwood number Stanton number for mass transfer Stanton number for heat transfer time [s] temperature [°C or K] velocity [m/s] Moisture content by volume [m3 liquid/m3 solid]

characteristic length (m) phenomenological transport coefficient [[…]2/s] Lewis number mass [kg] molecular mass [kg/mol]

moisture ratio unit normal vector molar fraction

153

154

Kamil Kahveci and Ahmet Cihan U Uq v V w wp W x y y z ~ 1

internal energy [J] overall heat transfer coefficient [W/(m2K)] velocity [m/s] volume [m3] velocity [m/s] dimensionless probability factor mass-transfer potential or degrees of moistness [°M] coordinate [m] coordinate [m] scalar quantity in Eq. (443) coordinate [m] unit tensor

Greek Letters α α αT β

thermal diffusivity [m2/s] root of Bessel function in Eqs. (212)-(218) thermal expansion coefficient [1/K] shrinkage coefficient root of cosine function in Eqs. (217) and (218)

βn βv βT δ δm χ χ2 δP δT Δhvap Δhˆ

thermal expansion coefficient [1/K] volumic expansion coefficient [1/K] Kronecker delta in Eqs. (264)-(266) moisture conductivity coefficient [kg/mh°M] constituent concentration (kg/kg dry matter) mean square deviation moisture filtration coefficient [kgm/(sN)] thermo-gradient coefficient [1/°C] evaporation enthalpy [J/kg] molar evaporation enthalpy [J/mol]

ε ε εav εpc φ φ γ γ γp Γ η ϕ Φ λ λ λ′

volume fraction strain ratio of air and vapor diffusion coefficient phase change criterion dissipation function [J/(m3s)] zenith angle [rad] in Eq. (201) shear rate [1/s] in Eq. (16) activity coefficient in Eq. (187) pore shape factor in Eqs. (102) and (103) Boundary surface [m2] dynamic viscosity [Pa s] relative humidity thermodynamic force [J/(kg[…])] thermal conductivity [W/(mK)] mean free path length [m] Lame coefficient [Pa]

Λ μ μˆ

eigenvalue chemical potential [J/kg] chemical potential [J/mol]

vap

μ′ ν νp

Lame coefficient [Pa] kinematic viscosity [m2/s] Poisson coefficient

Transport Phenomena During Drying of Food Materials θ θ ρ σ σ σb

contact angle [rad] azimuth angle [rad] in Eq. (200) and (201) density or concentration [kg/m3] surface tension [N/m] stress [Pa] Boltzmann constant [1.3805x10-23J/K]

υˆ

tortuosity shear stress [Pa] in Eq. (16) molar volume [m3/mol]

τ τ ω ΩD Ω ξ ξ ψ Ψg ΨH ζ

humidity ratio [kg moisture/kg dry air] diffusion collision integral eigenfunction in Eqs. (229)-(221) association factor solid based coordinate [m] porosity gravity potential [m2/s2] capillary potential [m] diffusion resistance factor

Subscripts a a ab app av c c cp cw c db dp ds e e eff ex exp f fw F h irr g g k

air ash absolute apparent average capillary cardioid closed pore cell wall material carbohydrate dry bulb dew point dry solid equilibrium epitrochoide effective excess experimental fat free water moving evaporation front haxagon irreducible gas corrugated Knudsen

l lg LM m max ml o

liquid liquid-gas log-mean moist maximum monolayer standard

155

156

Kamil Kahveci and Ahmet Cihan Subscripts (Continued) o op p P p pre R s s sat s.a. sg sg sl sn sp sp sorb st S the v vap w w wb *

initial open pore protein particle pore predicted reference solid substance saturated symmetry axis solid-gas sugar solid-liquid solution specific single pore adsorbed water starch surface theoretical vapor evaporation water wet basis wet bulb dimensionless

Superscripts * o T

dimensionless standard transpose

Overlines ∼ ^

average tensor molar fraction

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[159] Ertekin, F. K. J. Food Sc.. 2002, 67(1), 168-175. [160] Mwithiga, G.; Olwal, J. O. J. Food Eng. 2005, 71, 373–378. [161] Simal, S.; Femenia, A.; Garau, M. C.; Rossell, C. J. Food Eng. 2005, 66, 323–328. [162] Goyal, R. K.; Kingsly, A. R. P.; Manikantan, M. R.; Ilyas, S. M. Biosystems Eng. 2006, 95(1), 43–49. [163] Doulia, D.; Tzia, K.; Gekas V. (1999). A knowledge Base for the Apparent Mass Diffusion Coefficient (Deff ) of Foods. Available: www.nelfood.com [164] Doymaz, I. J. Food Eng. 2006, 74, 370–375. [165] Maskan, M.; Gogus, F.; J. Food Eng. 1998, 37, 437-449. [166] Fan, C. C.; Liaw, S. P.; Fu, W. R.; Pan, B. S. J. Food Sc. 2003, 68(3), 886-891. [167] Giraldo-Zuniga1, A. D.; Arévalo-Pinedo, A.; Rezende, J. R.; Silva, C. S.; Monteiro, J. A.; J. Food Proces. Preserv. 2006, 30(6), 699–705. [168] Akgun, N. A.; Doymaz, I. J. Food Eng. 2005, 68, 455–461. [169] Celma, A. R.; Rojas, S.; Lo´pez, F.; Montero, I.; Miranda, T. J. Food Eng. 2007, 80, 1261–1271. [170] Doymaz, I.; Gorel, O.; Akgun, N. A. Biosystems Eng. 2004, 88(2), 213–219. [171] Doymaz, I. J. Food Eng. 2005, 69, 275–279. [172] Garau, M. C.; Simal, S.; Femenia, A.; Rossello, C. J. Food Eng. 2006, 75, 288–295. [173] El-Aouar, A. A.; Azoubel, P. M.; Murr, F. E. X. J. Food Eng. 2003, 59, 85–91. [174] De Temmerman, J.; Verboven, P.; Nicolai, B.; Ramon, H. J. Food Eng. 2007, 80, 892– 903. [175] Kingsly, R. P.; Goyal, R. K.; Manikantan, M. R.; Ilyas, S. M. Int. J. Food Sc. Tech. 2007, 42, 65–69. [176] Guine´, R. P. F.; Rodrigues, A. E.; Figueiredo, M. M. Ap. Math. Comp. 2007 (in press). [177] Kashaninejad, M.; Mortazavi, A.; Safekordi, A.; Tabil, L. G. J. Food Eng. 2007, 78, 98–108. [178] Johnson, P. N. T.; Brennan, J. G.; Addo-Yobo, F. Y. J. Food Eng. 1998, 37(2), 233242. [179] Goyal, R. K.; Kingsly, A. R. P.; Manikantan, M. R.; Ilyas, S. M. J. Food Eng. 2007, 79, 176–180. [180] Menges, H. O.; Ertekin, C. Energy Conv. and Manag. 2006, 47, 2337–2348. [181] Vizcarra-Mendoza, M. G.; Martínez-Vera, C.; Ruiz-Martínez, R. S. 15th International Drying Symposium (IDS 2006). 2006, pp 504-507, Budapest, Hungary. [182] Hassini, L.; Azzouz, S.; Peczalski, R.; Belghith, A. J. Food Eng. 2007, 79, 47–56. [183] Sabarez, H. T.; Price, W. E. J. Food Eng. 1999, 42, 167–172. [184] Doymaz, I. J. Food Eng. 2007, 79, 243–248. [185] Sacilik, K. J. Food Eng. 2007, 79, 23–30. [186] Kaya, A.; Aydin, O.; Demirtas, C.; Akgün, M. Desalination. 2007, 212, 328–343. [187] Gely, M. C.; Santalla, E. M. J. Food Eng. 2007, 78, 1029–1033. [188] Vega, A.; Fito, P.; Andre´s, A.; Lemus, R. J. Food Eng. 2007, 79, 1460–1466. [189] Kaleemullah, S.; Kailappan, R. J. Food Eng. 2006, 76, 531–537. [190] Kaleemullah, S.; Kailappan, R. Biosystems Eng. 2005, 92(1), 15–23. [191] Ramesh, M. N. Lebensm-Wiss u-Technol. 2003, 36, 245–255. [192] Marquez, C. A.; De Michelis, A.; Giner, S. A. J. Food Eng. 2006, 77, 566–574. [193] Lu, R.; Siebenmorgen, T. J. Tr. ASAE. 1992, 35(6), 1955–1961. [194] Aguerre, R.; Suarez, C.; Viollaz, P. E. J. Food Tech. 1982, 17(6), 679–686.

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In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 165-237 © 2007 Nova Science Publishers, Inc.

Chapter 2

THE INFLUENCE OF INTERACTIONS OCCURRING BETWEEN MICRO-ORGANISMS ON PREDICTING THE SAFETY OF LACTIC ACID CHEESE Izabela Steinka* Gdynia Maritime University Department of Commodity and Cargo Sciences Poland, 81-225 Gdynia, Morska 83

ABSTRACT This paper discusses numerous problems occurring in relation to microbiological quality of lactic acid cheese. Lactic acid cheese constitutes the source of various nutritive substances, what results in a possibility of allochthonous micro-flora to grow despite the presence of starter micro-flora. One of the issues discussed herein comprised the results of microbiological research depending on tvarog packing system. The influence of packing system on surface micro-flora population was assessed. Moreover, the problem of growth of enterococci and LAB (Lactic Acid Bacteria) populations depending on stage of tvarog production as well as packing system was also raised. The issue of interactions occurring among micro-organisms that re-infect tvarogs and the influence of these interactions on the growth of individual micro-organisms was also discussed. Author presented also the possibility to apply JMTPH computer program for assessment of the dynamics of changes of tvarog micro-organisms during product storage. Another chapter includes assessment of the influence of lactic acid bacteria on the behaviour of individual groups of micro-organisms occupying tvarog surface, depending on packaging hermetic properties. It was also very important to assess the safety of tvarogs in the context of a possibility of enterotoxin synthesis in conditions of various packing systems. Finally, the models of optimising lactic acid cheese quality were presented, what included application of plant additives of biostatic character, modification of used packaging as well as employing the probabilistic mathematical model helpful in evaluation of enterotoxin synthesis, depending on the level of staphylococci and yeast populations. *

Izabela Steinka: [email protected]

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INTRODUCTION Tvarog is a product formed as a result of dehydration of lactic acid curds after the processes of reheating and pressing, produced in Europe. Tvarog is manufactured from milk soured with a starter consisting of the following bacteria cultures: Lactococcus lactis spp. lactis v. diacetylactis, Lactococcus lactis spp. cremoris, Lactococcus lactis spp. lactis and 4 % Leuconostoc lactis. Hence, the non-regional name – lactic acid cheese – renders the product characteristics. This product, together with other similar ones, belongs to the group of unripened cheese. The rennin is not applied during its production. The commonly used names vary depending on the region where it is produced: in Russia it occurs as domasznij syr, while in Poland as tvarog. Slightly similar products comprise: Italian mozzarella, German quark, cottage cheese produced in North America or its form coming from Latin America - Queso blanco. Apart from these, there are also other products known in the market, however they are less similar to typical tvarogs. These include: impastata, bakers cheese, cream cheese, petit susie, gervais, fromage frias ala crème, ymer, lactofil. The above-mentioned cheese differs significantly from typical unripened lactic acid cheese with regard to production technology (acid and rennin), the presence of dressing or cream, the fat content and the consistency. Unripened lactic acid cheese such as cottage cheese, quark or tvarog constitute products of significant protein content and they are especially susceptible to the influence of microorganisms. In literature, there are numerous data available concerning in particular the microbiological quality of cottage cheese, however there are hardly any information on lactic acid cheese.

MICROBIOLOGICAL QUALITY OF TVAROGS AND COTTAGE CHEESE From the literature, it results that the improperly produced cottage cheese can constitute the source of mould comprising the following strains: Candida famata, Candida spherica Candida robusta, Pichia membranofaciens, Saccharomyces exiquus (Westal 1998). The research of Rosenthal shows that the undesired changes of taste and odour of hermetically packed cottage cheese result from the growth of mould belonging to species: Penicillium, Geotrichum, Mucor and Alternaria (Rosenthal et al. 1996). Our research conducted on cottage cheese showed the various level of mould in these products (Steinka 2000). In 37 cheese samples, yeast at the level from 0 up to 7.45 log10cfu/g and mould at the level from 0 up to 3.92 log10cfu/g were observed. Carem cheese was characterised by much better quality: in no sample yeast count exceeded the level of 3.72 log10cfu/g, and for mould - the count of 3.20 log10cfu/g. The microbiological quality of cottage cheese products is discussed in publications of Sims 1989, Ashenafi 1990, Maniar 1994. From data obtained by Asfhenafi, it results that the quality of majority of these products is influenced not only by the presence of yeast and enterococci, but also faecal coliforms. The presented results concerning cheese coming from the market arouse hygienic reservations. Bacillus cereus and Staphylococcus aureus are observed at the level of 102-103, while 55% of the samples contain faecal coliforms. It appears that cottage cheese stored in different temperature conditions and devoid of sorbic acid supports the growth of Pseudomonas fluorescens. Different types of Salmonella

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show the ability to survive in these products (Sims et al. 1989). The authors observed 100fold increase of Salmonella typhimurium count in tvarogs with pepper and garlic, especially in those products which were not enriched with sorbic acid. During further laboratory research conducted by the authors as regards addition of proteins or vegetable ingredients, the decrease in count of staphylococci was observed at the temperature of 10oC and 20oC. Whereas in the same conditions, Bacillus cereus and S. typhimurium counts increased in cottage cheese. The count of Yersinia enterocolitica cells increased only at 10oC, while the decrease of these bacteria number was observed at the temperature of 20oC. Addition of sorbic acid to these products resulted in reduction or stabilisation of bacteria count at the constant level. Pathogenic micro-flora present in cottage cheese some in cases can include: Listeria monocytogenes and Clostridium sporogenes (Chen et al. 1993, Stañczak et al. 2000). There exist many controversies concerning the unequivocal assessment of adverse influence of certain environmental conditions on the survival rate of Listeria monocytogenes in cottage cheese. Majority of authors (Benkeroum and Sandine 1988, George and Lund 1988, Ryster and Marth 1985) prove the growth of populations or at least the presence of this pathogen at constant level in the product. Hicks et al. (1991) and Piccinnin (1995) agree that storage in refrigerating conditions results in reduction of Listeria count in cottage cheese. The condition determining survival rate in tested cottage cheese was neither concentration of H+ ions nor hermetic packaging. Listeria is able to grow in environment of acidity lower than those occurring in lactic acid cheese (George et al. 1988). From data presented by Chen et al. 1993, it results that inoculation of cottage cheese with these species gives the possibility to observe their behaviour during storage in conditions of atmosphere modified with carbon dioxide. In this research, during two months of storage at three temperatures, the reduction in count of Clostridium sporogenes was observed. The decrease in number of Listeria monocytogenes cells was observed in cottage cheese at the temperature of 4oC, while as soon as at 7o it was possible to determine the growth of these micro-organisms by one logarithmic cycle. In traditionally packed cottage cheese, the literature data showed the increase of these bacteria count by 1000 times. The significant number of conducted research concerned also the problem of survival rate of pathogenic bacteria in cottage cheese, indicating the possibility of their occurrence in these products (Piccinin and Shelef 1995, Hicks and Lund 1991 and Farrag et al. 1992). These data suggest that such products as unripened lactic acid cheese can support the growth of pathogenic micro-flora, since they do not include special biostatic additives. This is especially notable, because the composition of products includes significant number of lactic acid bacteria, what should be sufficient for the absence of pathogenic microflora resulting from the inter-microorganism antagonism. However, the prolonged refrigerating storage of products can be the reason for the presence of psychrotrophic micro-flora in lactic acid cheese. This concerns mostly food of animal origin. While listing the psychrotrophs isolated from dairy products, Champagne et al. (1994) mentions above all such bacteria as: Listeria monocytogenes, Yersinia enterocolitica or Bacillus cereus. As far as psychrotrophic micro-flora is concerned, the strains of Pseudomonas fluorescens and Enterobacter aglomerans were identified in cottage cheese (Lund et al. 1988). The behaviour of psychrotrophs depending on the level of added carbon dioxide was investigated by Maniar 1994, Fedio 1994, Moir et al. 1993.

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Tvarogs have no dressing or cream, what makes their micro-flora differ both qualitatively and quantitatively from the one being predominant in cottage cheese. Micro-organisms occurring in cottage cheese comprise such species as: Escherichia coli, Enterobacter sp., Bacillus subtilis, Enterococcus faecalis, Enterococcus faecium, Staphylococcus aureus, Micrococcus sp., Candida quilierimonde, Candida famata, Candida lusitaniae, Geotrichum candidum, Aspergillus flavus (Steinka et al. 1999, 2001, 2002). Due to the product instability, tvarogs are stored in refrigerating conditions. There exist many data on changes of physico-chemical and sensory properties of these products. However, there are only few micro-biological tests connected with quality of these products (Cais et al. 1998, Steinka et al. 1998, 1999a,b, 2000, 2001c, 2006b, Steinka 1999c, 2001a,b,d, 2003a, 2004, 2005a,b, 2006b,c, Ziółkowski et al. 2003). Data obtained by Steinka et al. (2002a) showed the presence of Bacillus sp. bacteria in lactic acid cheese stored in refrigerating conditions. During refrigerating storage, the intensive growth of Staphylococcus epidermidis was also observed in hermetically-packed tvarogs. The number of samples, in which the presence of staphylococci was detected increased by 77.7% in relation to amount observed on the day of buying the tvarog by the customers (Steinka et al. 2002a). Table 1 presents the micro-organisms that could be isolated from stored lactic acid cheese prior to implementation of HACCP system to their production. Table 1. Type and count of micro-organisms isolated from tvarogs during refrigerating storage Type of micro-flora

Enterococcus Haemolysing Streptococcus á Micrococcus Bacillus Staphylococcus epidermidis Candida Mould

Storage time (days) 0 7 Micro-organism count (cfu/g) 1.82 ·106 9.93·105 6 2.2 ·10 5.6·104 0 4·104 1.22 ·106 9·104 0 9·104 4 9 ·10 4·104 7.45 ·105 3.71·104

14 5.19·105 0 5·101 3.13·105 5.98·105 0 3.2·104

Steinka et al. 2002a.

From our previous research, it results that the psychrotrophic micro-flora present in lactic acid cheese could also include Micrococcus sp. and Staphylococcus sp. Prior to implementation of HACCP system into the production of lactic acid cheese, the count of psychrotrophic micro-organisms in tvarogs coming from the market was equal at the maximum to 1.4·103 cfu/g. In tvarogs packed into PA/ PE and stored for the period of 7 days after purchase, the count of populations of these micro-organisms could reach the level of 3.0•105 cfu/g. In more than 52% of tvarog samples, and after 14 days of refrigerating storage from the date of purchase, the count of psychrotrophic micro-flora increased tenfold in relation to the level observed after one week (Steinka et al. 1999b). Unripened lactic acid cheese is also illustrated by only a few mathematical descriptions (Steinka 2003, 2005c).

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With the help of mathematical models, it is possible to predict most of all the technological details in tvarogs such as: the influence of salt level and the initial level of a starter on pathogen survival rate (Bozukart 2001). Another subject of prediction can be evaluation of time of cutting cottage cheese, taking the growth of starter cultures into account (Crofcheck et al. 1999). In ripening cheese (white cheese), the prediction concerned e.g. diffusion during long-term brining (Turhan et al. 1992). Many years of research on lactic acid cheese showed that microbiological quality of tvarogs was various, depending not only on applied technology, but also on the production plant as well as layer, from which the sample is taken for tests. Observations presented in Table 2. confirmed the differences in count of microorganisms populations on the surface and inside the cheese mass (Steinka et al. 2006b). Table 2. Level of micro-organisms in tvarogs, depending on place of taking a sample Type of microflora Escherichia coli Mould Yeast

Place of taking a sample from tvarogs Surface Inside Surface Inside Surface Inside

Average cfu/g

Standard deviation

2.48 2.10 0.31 0.13 2.52 2.43

0.2 0.2 0.09 0.07 0.1 0.2

Steinka et al. 2006 b.

Count of mould on the surface was higher by 58.1% than the number observed inside the cheese mass. In the case of yeast and Escherichia coli these values were equal to 15.0 and 3.6% respectively. Statistic analysis showed that there existed a weak correlation between count of yeast inside the a/m product mass and on its surface ( r 0.136) ( Steinka et al. 2006b). Table 3. illustrates the model of changes of micro-organisms count present in several batches of lactic acid cheese packed into PA/PE, taking surface and inside layers of tvarog cubes into account. Table 3. Models illustrating total count of micro-organisms in tvarogs Type of microflora Escherichia coli Mould Yeast

Model of changes Z=0.091-0.291x-0.33y-0.006x2 –-0.2xy-0.225y2 Z=3.844-0.897x-0.175y-0.082x2 +0.549xy-0.205y2 Z=-0.151-3.811x+4.993y+0.345x2 +0.125xy2 0.479y

Steinka et al 2006b; x-tvarog surface, y-inside of tvarog mass, z-number of micro-organisms in tvarog.

During his research, Parisi compared the behaviour of chosen bacteria and mould in dependence on the distance of product surface from the packaging laminates (Steinka and Parisi 2006). Research was conducted in order to provide knowledge on the differences

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between cheese produced using citric acid and using lactic acid. Cheese manufactured using citric acid was packed into cryovac, while lactic acid cheese was packed into PA/PE laminates. Determination of micro-organisms was performed from the four layers: upper sample 0-5 mm from the packaging surface, middle sample 5-15 mm and lower sample 15-30 mm from the packaging surface. The averaged sample contained micro-organisms from layers of 0-30 mm. In cheese packed into cryovac, the Escherichia coli bacteria grew better in lower and middle layers. In the surface cheese layer, the reduction in bacteria count was observed. The growth of fungi (yeast and mould) was observed by Parisi in samples taken from all cheese layers, however the dynamics of growth were the greatest in a lower layer. The behaviour of fungi in sample taken from the layers 0-30 mm from the packaging surface was similar to the one observed in cheese packed into PA/PE laminates (Steinka et al. 2006b). Test results obtained by other authors show that the intensity of micro-organism occupation of a certain layer of food product is dependant on type of bacteria and type of food. Research conducted by Ingram in meat products showed the growth of staphylococci count by 0.7 cfu/g inside the ham, whereas on its surface the population size varied by only 0.1 cfu/g (Ingram 1996). The results of both research show a different expansion of many species of micro-organisms in external and internal lots of food product.

THE DYNAMICS OF CHANGES OF FACULTATIVE ANAEROBIC MICRO-FLORA IN LACTIC ACID CHEESE The tables present average values of certain types of bacteria and fungi occurring in lactic acid cheese prior to implementation of HACCP system. Population sizes were different, depending on the packaging type. The microbiological requirements for tvarogs valid during that time (1991-2003) assumed the absence of coagulase-positive staphylococci in 0.1 g, what was later verified for the presence of these bacteria in number not greater that 10 cfu in one gram of a product. Salmonella and Listeria monocytogenes could not occur in 1 g of a product. The limits for 4

yeast assumed the maximum presence equal to 10 cfu/g, whereas mould was not permissible 2

at the level higher than 5•10 cfu/g. The presence of coliforms was accepted at the level not greater than 0.001 g (the Standard and the Decree of Polish Minister). Results of testes conducted in individual years, and presented in Table 4., show that the level of many micro-organisms present in these products was inconsistent with the a/m requirements. Table 5. below presents the levels of fungi detected after implementation of HACCP system, depending on type of packaging. From these data it results that PA/PE packaging is the best type of packaging, at high level of initial contamination. However, results concerning the behaviour of populations of other micro-organism species in tvarogs packed with different systems do not entirely confirm these observations.

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171

Table 4. Levels of contamination of tvarogs with allochthonous micro-flora Type of microorganisms

Type of packaging

Staphylococci E. coli

PA/PE

Staphylococcus aureus Yeast E.coli

PA/PE/ Cryovac

Initial contamination cfu/g/ log10cfu/g 1.1 •10-1 1.2•101

Count after 14 days of storage cfu/g / log10cfu/g 2.3.103 0

2.1•103

1.1•103

1•104 1.6•103/1.6•102

1.4•106 1.7•101/0

Staphylococcus aureus Yeast

5.4•102 / 9.1•101

4.6•102/2.1•102

9.1•104 /6.6•103

4.4•106/8.2•105

Enterococci

6.1•103/1.5•105

4.8•105/1•104

2.97

1.6

2.85

1.39

E. coli

PA/PE

Staphylococcus aureus Yeast Enterococci

4.17 Parchment paper

Staphylococcus aureus Yeast

Author Steinka 1998 Steinka Stankiewicz Steinka, Morawska 1999

Steinka Zukowski, Hildebrand 2000

Steinka Kukulowicz 2002

5.25 3

3.3•10

3.7•105

6.4•101

4•101

2.9•105

6.3•105

Steinka Mieczkowska 2003

Enterococci

Steinka – collective study conducted in Microbiological Laboratory in years 1998-2003.

Table 5. Level of fungi (yeast and mould) in tvarogs prior to HACCP implementation Type of fungi

Type of packaging

Market quality log10cfu/g

Count of fungi after storage log10cfu/g

Yeast Mould Yeast Mould Yeast

Traditionally- parchment paper

4.46 4.73 6.79 4.0 3.81

7 days 4.95 5.73 6.11 3.86 4.25

14 days 3.79 6.27 6.11 4.11 5.91

1.71

2.2

2.36

Mould

Own Study.

Vacuum system - PA/PE Atmosphere modified with nitrogen addition

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It should be noticed that the kinetics of populations changes is different, depending on way of conducting the production. These changes are determined together by the state of raw material, packing system as well as the presence of background micro-flora and re-infecting micro-flora. Model of changes of populations occurring in lactic acid cheese produced without taking GHP rules into account was dveloped by Steinka 2002a, Steinka 2003a, 2005a. However, in order to compare the dynamics of population changes depending on the production system, the kinetics of growth should be determined.

2 1,8 1,6 y= -1,075x2 + 4,895x- 3,82 R2 = 1

1,4 1,2 1

y= 0,595x- 0,2367 R2 = 0,4789

Log cfu/h 0,8 0,6 0,4 0,2 0 0

7

14

-0,2 Time of population growth (days)

Figure 1. Kinetics of changes of Enterococcus sp. population in tvarogs produced without GHP.

Attempts were made to compare the growth of Enterococcus sp., Staphylococcus aureus bacteria and yeast during refrigerating storage of tvarogs produced in HACCP system as well as manufactured by dairy plants without implemented quality system. Significant differences were noticed in the dynamics of growth of e.g. enterococci count in tvarogs. Changes in enterococci count during 7 days of storage were equal to 1.67 log10cfu/g up to the day 7, and 1.19 log10cfu/g between day 7 and 14 (figure 1). The growth of staphylococci was not as dynamic – the observed change was equal on the average to 0.26 log10cfu/g in the first week of storage, and yeast was equal to 0.48 log10cfu/g during that period. The presented figures (figure 1 and figure 2), reflecting the behaviour of enterococci observed during 14 days of product storage, present the different behaviour of these bacteria in both production systems.

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Evaluation of kinetics of changes of tested streptococci populations show that in the case when GHP rules are not applied, the rate of population growth is ca. 3.7-fold higher (3.68) than in the case of tvarog production conducted in HACCP system. From obtained data it results that variability of population count is significantly diversified, unless critical checkpoints are introduced.

0,0045 0,004 0,0035 0,003 y= 0,0021x- 0,0024

0,0025

R2 = 0,9196

0,002 log cfu/h

y= 0,0011x2 - 0,0022x+ 0,0011 R2 = 1

0,0015 0,001 0,0005 0 0

7

14

-0,0005 -0,001 Time of population growth (days)

Figure 2. . Kinetic changes of Enterococcus population in tvarog produced in HACCP system.

For packing lactic acid cheese, several types of packaging are applied: PA/PE laminates, foamed polystyrene trays wrapped with a thin PE film – so called: frischaltenfolie, aluminium foils with parchment paper, the parchment paper and cryovac. The hermetic level of packaging as well as its barrier properties are responsible for the behaviour of surface microflora behaviour. The rate of micro-flora growth can be inhibited or stimulated by conditions occurring inside the packaging. A specific ecological niche is created between surfaces of packaging and the product characterised by conditions dependant on the packaging hermetic level. A very significant influence of packaging material of tvarog quality is also observed. Research conducted on the influence of packaging on cottage cheese and cream cheese quality showed that both the packaging and the storage of these products in polystyrene containers also did not meet microbiological expectations. Whereas applying the packaging made of waxed cardboard significantly favoured maintaining good quality (Steinka 1999c). Cottage cheese packed into containers with sealed covers provided better protection against

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contamination than packaging with separate moveable covers. Sealable covers made of aluminium film allowed additional sterilisation and limiting the presence of surface microflora. During further research on cottage cheese and cream cheese it was additionally observed that contamination with yeasts was significantly dependant on technology of cheese production, while the packaging hermetic properties had no influence on mould level in products (Steinka et al. 2000). Majority of research available in literature is devoted to the influence of packing system on organoleptic properties of the products. Only a few studies raise the issue of interactions occurring among product micro-flora, depending on the packing system. It has a first-grade significance for the quality and safety of food. The packaging hermetic properties accompanied by application of vacuum packing system constitutes a special hygienic problem.

PACKING AS A FACTOR DETERMINING THE PRODUCT QUALITY Apart form packaging type and packing system, the time and conditions of product storage constitute very significant factors responsible for quality of stored cheese. In figures 3 and 4, the sizes of populations of facultative anaerobic bacteria in tvarogs coming from the market are presented. Tvarogs were packed into different types of packaging: parchment paper, PA/PE laminate and cryovac. 6 5 4 Log cfu/g 3 2 1 0 Paper

Cryovac

PA/PE

Packaging type E.coli

Enterococcus sp.

Staphylococcus aureus

Figure 3. The Influence of packaging type on growth of facultative anaerobic bacteria in tvarogs.

Influence of Interactions Occurring Between Micro-Organisms…

175

7

6

5

4 Log cfu/g

Yeast 3

Mould

2

1

0 Paper

Cryovac

PA/PE

Packaging type

Figure 4. The influence of packaging type on growth of fungi populations in tvarogs.

From conducted research it results that the level of contamination with bacteria and fungi was significantly dependent on the packaging type. The greatest count of micro-organisms was detected in cheese packed into parchment paper. Application of HACCP system and the packaging made of plastics such as Cryovac showed better protective properties in comparison to PA/PE laminate. It is probable that in the case of Cryovac application, blowing nitrogen through the surface of cheese contributed to modification of atmosphere after closing the packaging. From literature analysis it results that food products hermetically packed into plastic packaging can pose a hazard connected with production of bacterial toxins (Post et al. 1988, Adams et al. 2000, Steinka et al. 2001c, 2004, Duffranse 2000). The safety of hermetically packed products is not always improved by applying modified gaseous atmosphere into their packing. From numerous reports, it results that selection of appropriate composition of atmosphere can be adapted to inhibition of certain types of microflora, since every species reacts differently to the presence of CO2. Therefore, for example: the growth of Staphylococcus aureus is intensively inhibited at 50-100% CO2, however the 0

obligatory condition is temperature of 10 C. From research of Kimura, it appears that staphylococci are more sensitive to the influence of certain concentrations of carbon dioxide in comparison to Escherichia coli rods, what stands in contradiction to the previous opinions on reactions of these bacteria in relation to application of modified atmosphere (Kimura 1999).

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From literature it results that addition of carbon dioxide causes changes of generation time in laboratory conditions, depending on its concentration and type of micro-flora (table 6). Table 6. The influence of carbon dioxide on micro-organisms populations Type of micro-organisms

Pseudomonas, Flavobacterium Achromobacter Moraxella-Acinetobacter

Carbon dioxide concentration

Reaction of micro-organisms

10-20 %

Inhibiting

20-70 %

Time prolongation

,

Lactic acid bacteria

Sustaining of growth

Yeast

50%

50-100-fold increase in count

Clostridium botulinum Clostridium perfringens Listeria monocytogenes

< 50%

Minimal growth inhibiting

< 50%

Staphylococcus aureus

50-100%

Depending on the temperature – inhibiting or sustaining of growth

Own study on the basis of Nguyen and Carlin 1994, FDA 2001, Zagory 1995, Philips 1996, Adams 2000.

The presence of bacterial toxins in hermetically packed food products is not always preceded by obvious organoleptic symptoms. From data presented in literature, it results that at room temperature, 60% of CO2 addition, 25% of oxygen presence and 15% of nitrogen concentration in packed fish fillets, the organoleptic changes occur at the same time as the presence of toxins in products. Whereas at the same composition of gaseous atmosphere in packaging and the temperature of 4.40C, the presence of toxins precedes the indicators of spoiled fish fillet by 7 days (Tyszkiewicz 1992). Control of parameters responsible for physico-chemical changes of products is possible in the case of meat and its products packed with application of modified atmosphere or using active packaging. In the case of lactic acid cheese, application of the above-mentioned technologies is much more difficult due to the delicate product structure. Attempts to pack cottage cheese in modified atmosphere were made by Dixon, 1988, Honer, 1988, Brody, 1989, Fedio, 1994, Maniar, 1994. The stability of quality features of mozzarella cheese was described by Alves et al. (1996) and Elliot et al. Tvarogs packed with MAP system were investigated by PanfilKuncewicz et al. (1997). However, application of modified atmosphere in technology of tvarog packing revealed that there existed a relationship between concentration of carbon dioxide and the adverse sensory changes (Fedio 1994). In Poland, there are several systems of hermetical packing of cheese into plastic packaging, among others: vacuum-packing into

Influence of Interactions Occurring Between Micro-Organisms…

177

PA/PE films and shrink-wrapping with Cryovac laminate. The non-hermetic packing methods include: packing into aluminium films with parchment paper and into the parchment paper. Evaluation of physico-chemical changes of tvarogs packed in a traditional way was conducted by Pieczonka 1993, Kujawski et al. 1994, Chojnowski et al. 1999, Molska et al. 1992, Œmietana et al. 1997. There are not many data on assessment of quality of lactic acid cheese hermetically packed with vacuum and non-vacuum systems (Panfil-Kuncewicz 1997a,b, Steinka et al. 1998, 1999c, 2001b). Technological and technical aspects of packing tvarogs and soft cheese into various types of packing films were presented by Œmietana et al. 1997 and Marcotte et al. 2001. Significant information on the influence of modified atmosphere on unripened cheese quality is presented by Gonzales-Fandos et al. 1999. However, the subject of his study is fresh cheese Cameron, which acidity differs from acidity of tvarogs and is equal to pH ~6.35. This products is characterised by the fat content of 54-56% and 7 days of stability period. Research on the effect of 100% CO2 addition during packing Cameron cheese, as well as prolonged time of storage result in decrease in number of psychrotrophic bacteria only in the early period. Whereas, their increase can be detected between day 7 and day 30 of product storage. In the case of mesophilic micro-flora, this growth is observed between day 12 and day 30 of cheese storage. From the research of Fandos, it also results that the growth of Enterobacteriaceae rods is noticeable between day 7 and 15. The research results of Fandos are similar to data obtained by Moir (1993), who observed the growth of psychrotropic bacteria in tested products despite the presence of CO2. In tvarogs vacuum-packed into PA/PE laminates, where atmosphere was not modified, Steinka et al. (1999b) observed the increase of psychrotrophs count during product storage. However, vacuum-packing did not favour the growth of Listeria monocytogenes rods in tvarogs. The number of samples indicating the presence of this rod after 14 days of storage could evidence the change of atmosphere during product breathing or the metabolism of other micro-organisms. Number of tvarog samples, where Listeria monocytogenes was present decreased by 24% after 14 days of storage (Steinka et al. 1999b). The significant factor determining the growth of micro-organisms in tvarogs is pH value characterising the products. From data obtained by Moir, it results that when CO2 additive does not exceed the value of 40%, then the acidity value does not change while packing the cheese using MAP technology. In tvarogs packed into both types of laminates, the acidity varied from 4.26 to 5.03 pH. During 14 days of storage, no significant changes were observed in acidity of vacuum-packed cheese. In tvarogs packed into Cryovac laminates, the decrease in content of organic acids was observed up to the day 7 of storage, what could result from the growth of alkalising micro-flora of the mould (Steinka 2003a). In tvarogs of low count of contaminating micro-organisms, the acidity variations during storage are insignificant (table 7).

178

Izabela Steinka Table 7. Acidity of tvarogs of low level of contamination with allochthonous micro-flora Storage time of tvarogs in PA/PE packaging (days) Average 0 2 4 acidity values pH 4.73±0.06 4.59±0.05 4.63±0.04

7

14

4.56±0.03

4.63±0.08

Own study.

From the research of Steinka (2003a), it results that during the period up to day 7, no reduction is observed in number of all tested groups of micro-organisms isolated from tvarogs. It often happens that the aesthetics and convenience play a decisive role in purchase of a product by a consumer. The results of study of consumer preferences concerning tvarogs packaging revealed that among the selection factors connected with packaging, very significant were such indicators as: tightness (71.8% of respondents), aesthetic appearance (59.4% of respondents) and information on the packaging that is important for 64% of those polled (Steinka 2003a). High water content in lactic acid cheese is the reason for their low stability and the difficulties connected with selection of packaging as well as storage problems. Research conducted in recent years concerning the possibility to apply hermetic packaging for tvarogs still has not solved many problems and has not answered numerous arousing doubts (Panfil–Kuncewicz et al. 1997 a,b, Panfil–Kuncewicz et al. 1997b, Steinka 1999c, Steinka 2001b, Steinka et al. 2001c). The research of Panfil–Kuncewicz et al. proved the improvement of microbiological quality of tvarogs stored in modified atmosphere, however the composition of applied atmosphere that allowed obtaining that effect is considered to worsen the sensory quality of the products. Microbiological tests concerning hermetic packing of tvarogs (Steinka et al. 2001c, Steinka 2004) showed a possibility of staphylococcal enterotoxin to occur in stored products. In subject literature, there are no such models which would take all parameters into account at the same time: packaging type, packing system, biological and biogenic factors dependent on variable conditions of product storage in packaging. Biological factors should be understood as live organisms belonging to Eucaryota and Procaryota, constituting autochthonic micro-flora and secondary food contamination. The notion ‘biogenic factors’ refers to products of basic and secondary metabolism of these organisms formed as a consequence of transformations resulting from their physiology. The biogenic factors include fermentation products (organic acids, alcohols), aldehydes, gases, toxins, bacteriocins, antibiotics as well as products of proteolysis, liopolysis and decomposition of specific organic substances. The table below presents few mathematical models that are valid in predictive microbiology and which concern cheese quality issues. The type of tests conducted in such cheese and its products are presented in table 8. There exists only one predictive model concerning tvarog micro-flora, which presence results from certain contamination and re-infection level (Steinka 2003a). Appropriate selection of packaging type and packing system determines quality of the product and safety

179

Influence of Interactions Occurring Between Micro-Organisms…

of a consumer. This results from the fact that in hermetically-packed tvarogs, there occur interactions between product and micro-flora as well as among product, micro-flora and the packaging. The influence of the above-mentioned biological factors and biogenic factors on the type of interactions is dependent on packing system and decides about the quality of products stored in refrigerating conditions. Multidirectional character of interactions influences also safety of these products. This arouses difficulties in adapting mathematical description to changes of interaction types having the influence on safety of hermeticallypacked lactic acid cheese. Table 8. Predictive models of the micro-organisms behaviour in cheese Authors of a model Brouillaud –Delante 1997 Bolton 1999 Bozukart 2001 Steinka 2003 Tamagnini 2005

Type of described changes

Type model Linear

Evaluation of probability of growth of Listeria monocytogenes Behaviour of Yersinia enterocolitica

Type of dairy product Micro-organisms in dairy products Mexican cheese Feta cheese

Surface micro-flora

Lactic acid cheese

Polynomial

Crottin Sheep’s milk cheese Cheese

Vitalistic, Churchil, Gompertz Neural network

Poirazi 2006

Streptococcus macedonicus

Listeria monocytogenes

Yersinia enterocolitica, typhimurium

Salmonella

of

Logistic Gompertz

Own study.

Nevertheless, in recent years, actions aiming at improvement of product safety has been more and more often observed. These tendencies include taking packaging into account in predictive models, as well as application of a new generation of packaging or using bacteriostatic additives. However, taking these factors into account and excluding them require new research to be conducted as regards evaluation of interactions occurring among micro-flora both in raw material and with possible re-infections.

ASSESSMENT OF INTERACTIONS OCCURRING AMONG TVAROG MICRO-FLORA IN MODEL CONDITIONS The significant element taken into account in predicting the quality and safety of products should be interactions occurring between micro-organisms. Among interactions occurring between micro-organisms based on the presence of numerous nutritive substances, there are such which result from the severe competition for food. The developed defence mechanisms work in the form of such interactions which ensure the possibility of using nutrition substances to all types of micro-organisms.

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Among micro-organisms, the complete dependence of species on reciprocal metabolism is commonly known. The interactions of syntrophic character cause that in order to inhibit the growth of one micro-organism, it is enough to limit the growth of another one. In some fermented dairy products, the syntrophic phenomenon is responsible for proper organoleptic properties. A very special case of syntrophic character is the dependence between cells of Streptococcus thermophilus and Lactobacillus delbrueckii bulgaricus in yoghurt. However, most often the interactions occurring between cells of Procaryota are based on the phenomena of competition for food, and the interactions are most often of antagonistic character. Knowledge on food requirements of certain type of micro-flora is necessary for proper prediction of direction of micro-organism metabolism in the presence of other species. If only two micro-organisms species are present in the environment, then their growth is different than in the situation when there are much more of them. The interactions are also different in laboratory conditions and in food. In tvarogs, the growth of micro-organisms in a monoculture also occurs differently than in the presence of many cultures. The behaviour of micro-organisms in tvarogs is significantly dependent also on other factors, which include: • • • • •

type and count of technical micro-flora (starters) in tvarog, type and count of other micro-organisms, type of packaging, packing system, production stage, during which penetration of micro-organisms occurs: raw material, curd, unpacked finished products, packed finished product.

The example of micro-organism behaviour in tvarogs, depending on the moment of infection, contamination size or the presence of other micro-organisms, can be the behaviour of Staphylococcus aureus and Enterococcus faecalis, if they are added to milk or to the produced lactic acid curd. Tests were conducted in laboratory conditions. Enterococci and staphylococci were added to milk, which was then soured using a typical starter for production of lactic acid cheese. The starter composition contained of bacteria of Lactococcus sp. type in amount of 96% and Leuconostoc lactis in amount of 4%. The table below presents changes in count of micro-flora infecting raw material and the curd, in the case when inoculum of this bacteria is added to the curd or the milk, then a curd is produced and such a semi-product is stored in refrigerating conditions (table 9). In the case of traditional packing of tvarogs into parchment paper, the growth of faecal streptococci could be observed in the final period of refrigerating storage of products (figure. 5). The behaviour of population can evidence that re-infection with enterococci occurred not during milk souring, but at further stages of tvarog production.

Influence of Interactions Occurring Between Micro-Organisms…

181

Table 9. Changes of staphylococci and enterococci in model conditions depending on production stage Type of microorganism

Time of curd storage (days) Production /inoculum log10cfu/g

Staphylococcus aureus Enterococcus faecalis

stage

Milk 2.30 cfu/mL Curd 2.75cfu/g Milk 2.74 cfu/mL Curd 2.0 cfu/g

2

4

7

14

2.30

0

0

0

2.30 3.33

1 2.90

1 2.25

0 3.74

2.47

3.14

2

3.46

Own study.

4,5 4 3,5 y= 0,0833x3 - 0,7236x2 + 1,9331x+ 2,144

3

R2 = 0,9974

2,5 log cfu/g 2 1,5 1 0,5 0 0

2

4

7

14

Storage time (days)

Figure 5. The influence of refrigerating storage on enterococci population in tvarogs packed into parchment paper.

Enterococci and yeast constitute predominant micro-flora in tvarogs (Steinka et al. 2001c). The modelling research was conducted, in order to evaluate interactions between micro-flora most often present in lactic acid cheese. The behaviour of starter micro-flora, yeast and staphylococci was tested in the presence of certain inoculum of enterococci present in milk or in semi-products (lactic acid curd). The influence of enterococci of technical micro-flora (Lactococcus spp. and Leuconostoc lactis) and their interactions with other micro-organisms occurring depending on inoculum is presented in the figures.

182

Izabela Steinka

As a result of souring milk with starter consisting of pure dairy cultures, lactic acid curds were obtained, in which count of lactic acid bacteria after 14 days of souring was equal from 8.22 to 8.51 log10cfu/g. Number of Lactococcus bacteria decreased after 14 days of storage to 7.25-7.9 log10cfu/g on the average. Inoculation of milk with Entrococcus faecalis cultures resulted in changes of count of these bacteria after curd formation by 0.67 log10cfu/g. In the moment of curd formation, the count of faecal enterococci was equal to 3.33 log10cfu/g. During curd storage, the insignificant changes of enterococci population count occurred up to day 4 of its storage (figure. 6). However, after 14 days the level of enterococii was different from the initial value only by 0.11 log10cfu/g. These changes were described by a polynomial equation of the third order. As much as 99.4% of variability of enterococci population could be explained with the help of this model (table 10.).

5 4.5 4 3.5 3 Log cfu/g 2.5 2 1.5 1 0.5 0

Curd C Curd B Milk 0

2

4

7

14

Tim e (days)

Figure 6. Changes of enterococci count depending on curd production stage and applied inoculum.

Addition of faecal streptococci of initial inoculum 2 log log10cfu/g to the formed curd resulted in detecting inhibition of growth of these bacteria after 14 days of storage (figure 6).

Influence of Interactions Occurring Between Micro-Organisms…

183

Table 10. Tendency of changes of enterococci depending on tvarog production stage and the presence of allochthonous micro-flora Experimental variant Soured milk with Enterococcus faecalis ATTC 29241 inoculum 4log cfu/g. A-produced curd

Equation form y = -0.0142x3 + 0.1839x2 - 0.6919x + 3.85

r2 0.994

B and C-curds with Enterococcus faecalis ATTC 29241 inoculum 2 (B) and 4 log cfu/g ( C)

y = 0.0883x3 - 0.8729x2 + 2.4788x + 0.84 (B)

0.844

y = 0.0467x3 - 0.2593x2 + 0.114x + 4.772 (C)

0.954

4

3

2

D-curd with Enterococcus faecalis ATTC 29241 and Staphylococcus aureus ATTC 25923 after milk fermentation

y = 0.1483x - 1.8483x + 8.0267x - 14.057x +10.42

0.999

E-curd with Enterococcus faecalis ATTC 29241 and Candida sp. after milk fermentation

y = -0.035x3 + 0.3864x2 - 1.1386x + 4.8

0.951

F–industrially produced tvarogs

y = -0.005x4 + 0.1383x3 - 1.0775x2 + 2.8392x + 0.84

0.555

y- Enterococcus faecalis count, x-storage time.

The tendency of observed population changes was characterised by polynomial equation of the third order (table 10). The coefficient of determination of this model showed that only 84.4% of observed variability of enterococci populations could be explained by the influence of time of exposure to low temperature. The addition of 4 log cfu/g of enterococci to the curd showed different dynamics of population changes (figure 6). Equation describing changes of Enterococcus faecalis during storage of a curd with additive in amount of 4 log cfu/g reflected to a great extent the real variability of streptococci populations in stored curd (table 10). Only 5.6% of observed streptococci variability did not result from the influence of curd storage time of the behaviour of enterococci. The presence of Staphylococcus aureus population in stored curd caused that count of enterococci in a curd insignificantly decreased after 14 days of storage. However, the reduction of size of streptococci population did not exceed the value of 0.28 log10cfu/g (figure 7).

184

Izabela Steinka

10 8 6 Log cfu/g 4 2 0

Lactococcus sp. Enterococcus sp. 0

2

4

7

14

Time (days) Figure 7. Changes in count of enterococci in the presence of populations: Lactococcus sp. and Staphylococcus aureus (variant D).

The addition of yeast of Candida kefyr species to the curd (inoculum 3 log cfu/g) and the presence of Enterococcus faecalis of inoculum equal to 4 log cfu/g resulted in detecting a small decrease in count of Candida and a small increase in count of faecal streptococci after 14 days of curd storage. However, the magnitude of changes of Enterococcus population did not exceed the value of 0.38 log10cfu/g (figure 8). Changes in count of Enterococcus population in the presence of lactic acid bacteria and Candida could be expressed with the help of a polynomial equation of the third order characterised by a high value of coefficient of determination (table 10).

9 8 7 6 5 Lo g cf u/ g 4 3 2 1 Lactococcus sp. 0

Candida kef yr 0

2 4

T i me ( d ays)

Enterococcus sp. 7 14

Figure 8. Changes of enterococci count in the presence of populations: Lactococcus sp. and Candida kefyr (variant E).

Influence of Interactions Occurring Between Micro-Organisms…

185

3.5 3 2.5 2 log cfu/g

1.5 1 0.5 0 0

2

4

Time (days)

7

14

Figure 9. Changes of enterococci count in vacuum-packed tvarogs.

Observation of enterococci population in vacuum-packed tvarogs showed that during storage of the products, the increase in count of these bacteria occurred by 0.82 log log10cfu/g on the average up to the second day of storage. Between day 2 and day 10 of storage, the reduction in faecal streptococci count by ca. 1 log10cfu/g was detected (figure 9). Enterococci population in tvarogs showed dynamics of changes similar to the enterococci population added to the curds before their storage at low temperature (Variant B). Equation describing changes of bacteria count in hermetically-packed tvarogs is a polynomial equation of the fourth order characterised by a low coefficient of determination value. The value of coefficient r2 showed the presence of other, apart from storage time, factors influencing the population behaviour (table 10). The polynomial equation determined for describing changes did not describe 44.5% of variability of faecal streptococci population as time-dependant function. The behaviour of enterococci population observed in variants B, C, D and E was similar, no matter if additional type of re-infecting micro-organisms was present in the curd or enterococci were present there as a monoculture. Another dynamics of changes were observed in the case when curd subjected to final technological treatment i.e. lactic acid cheese was closed in hermetic packaging (Variant F). In this experiment, no significant influence of interactions occurring between staphylococci and streptococci as well as between yeast and enterococci was observed on the dynamics of changes of bacteria present in curds in model conditions. No apparent antagonism was observed between lactic acid bacteria and enterococci. At ratio 2:1 this phenomenon was expressed as lack of evident reduction in number of faecal streptococci in a curd.

186

Izabela Steinka

In physiologically optimum conditions of the monoculture growth, the antagonistic influence of lactic acid bacteria in relation to significant number of pathogenic bacteria is known (Usajewicz 2000). The author investigated the influence of lactic acid rods on pathogenic strains of staphylococci and Salmonella. Research on interactions of enterococci with other bacteria concerned the microflora responsible for defects in ripening rennet cheese (Usajewicz 1995). The quoted research indicates the significant influence of enterococci on proteolytic activity of some bacteria of Lactococcus type that can have the influence on determining the sensory and physico-chemical properties of obtained curds (Usajewicz 1995). There exist only a few data concerning reciprocal interactions between faecal streptococci and facultative pathogenic micro-organisms (yeast and staphylococci) in conditions of lactic acid curd production (Steinka 2003a). From other research conducted e.g. by Kornacki et al. (2002), it results that the effect of inhibiting micro-organisms of faecal origin by technical micro-flora is obtained in tvarogs only in certain technological conditions. However, the observed effect concerned faecal rods of coli group, and not streptococci of Enterococcus type. Lack of significant changes of streptococci population count and their apparent synergistic interactions with yeast and staphylococci, at certain inoculum, causes that enterococci remain at the constant level for a long time of curd storage. Obtained results indicate also the low sensitivity of enterococci to the presence of Lactococcus sp., staphylococci or yeast. In model conditions, regardless of the level of infection with enterococci within the inoculum range 2-4 log cfu/g, changes of these bacteria count were not significant after 14 days of curd storage. It was also observed that at low initial contamination of tvarogs with enterococci, the reduction of these bacteria count after 14 days of storage was insignificant and did not exceed the value of 1 log10cfu/g. A small change in environmental conditions is able to change the intensity of observed phenomena or the total effect of interactions detected during production or manufacture of tvarogs. For interactions occurring among bacteria cells, the size of population occurring together in a given environment is of a great significance. The example is interaction between Staphylococcus aureus and Lactococcus rods presented in figure 10.

Influence of Interactions Occurring Between Micro-Organisms…

187

10 9 8 7 6 Log cfu/g 5 4 3 2 1 0 0

2

4

7

14

Time (days) inoculum9

inoculum7

inoculum5

inoculum3

Figure 10. The behaviour of Staphylococcus aureus in lactic acid curd during storage.

At constant number of Lactococcus sp. cells in lactic acid curds produced in model conditions, the dying-out time of staphylococci is dependent on the initial inoculum of Staphylococcus aureus. Analyses of predictions of staphylococci changes in model conditions, where only one population occurs (apart from staphylococci), show a completely different behaviour of these bacteria than the one observed in environment containing several populations (figure 11). The reduction time for monoculture in laboratory model conditions is different than for dicultures in the same conditions (table 11). Table 11. Inhibition of growth of staphylococci tvarog curd depending on the inoculum level Inoculum Log jtk/g

10 9 8 6 3

Size of reduction of Staphylococcus areus population count log10cfu number 4 5 6 Reduction time (days) 7 10 11 3 9 12 4 5 6 5 6 7 3 cycles 2 days -

Own research.

The behaviour of staphylococci investigated in lactic acid curds in the presence of three other strains of micro-organisms is presented in the figure below.

188

Izabela Steinka

8 7 6 5 Log cfu/g 4 3 2 1 0 0

2

4

7

14

Time (days) Yeast

Enterococcus sp.

Staphylococcus aureus

Figure 11. The influence of interactions among staphylococci, enterococci, yeast and lactic acid bacteria in a curd on population behaviour during storage.

Changes in count of yeast, enterococci and staphylococci populations show rather synergistic interactions among these cells as well as small inhibiting effect on staphylococci caused by present populations of Lactococcus, Enterococcus and yeast. Changes observed in the so-called ‘co-cultures’ could be determined as logit and described with the help of polynomial equations. 4

3

2

2

Y1 = 0.3771x - 4.7058x + 20.293x - 34.744x + 23.85 r =0.99 3

2

4

3

2

Y2= -0.0783 x + 0.5279 x - 0.4838x + 3.654-yeast r = 0.98 2

2

Y3= -0.1733 x + 1.9933 x - 7.2567 x + 7.6767x + 4.5 r = 0.99 where: Y1- enterococci count, Y2 - yeast count, Y3 - staphylococci count, x – time of refrigerating storage 2

r – equation coefficient of determination

(1) (2) (3)

Influence of Interactions Occurring Between Micro-Organisms…

189

THE INFLUENCE OF HERMETIC PACKING ON STARTER MICROFLORA AT HIGH CONTAMINATION LEVEL OF TVAROGS The influence of packing on micro-organism populations in tvarogs has to be considered in the aspect of the behaviour of both technological and re-infecting micro-flora. Irrespective of the number of populations infecting raw material or a curd constituting semi-product for the manufacture of tvarogs, the technical micro-flora occurs in the conditions of hermetic packing. The conducted research on the influence of vacuum packing on starter bacteria populations showed a varied behaviour of these micro-organisms, depending on whether they occur as the only cultures present in soured raw material or a curd, or whether they are present in ready products (tvarogs). Tests were performed during 14 days of curd and tvarog o storage at the temperature of 6±2 C. Evaluation of dynamics of changes of lactic acid bacteria populations was conducted during storage of lactic acid curds and tvarogs manufactured in model conditions, in dependence on vacuum packing. It was observed that vacuum packing did not influence changes of allochthonous microflora count up to day 7 of storage of curds manufactured in model conditions. The dynamics of changes of lactic acid bacteria were varied during storage of model lactic acid curds and tvarogs manufactured in industrial conditions.

9 8 4

3

2

y = 0,0363x - 0,5158x + 2,4938x - 4,7542x + 11,15 R2 = 1

7 6 4

3

2

y = -0.0742x + 0.7433x - 2.4308x + 3.0217x + 4.54

5 log cfu/g 4

2

R =1

3 2 1 0 0

2 Curd B

4

7

Time of storage (days) Curd F polynom.

Figure 12. Changes of Lactic Acid Bacteria in curd B and tvarog (F) during storage.

14 polynom.

190

Izabela Steinka

As a result of souring the milk with pure lactic acid bacteria cultures, the lactic acid curds were obtained, in which count of lactic acid bacteria after 14 hours of souring was equal from 8.22 to 8.41 log10cfu/g. In tvarogs produced industrially, number of lactic acid bacteria ranged from 5.44 log10cfu/g to 6.32 log10cfu/g (figure 12). Statistically significant differences were observed in sizes of Lactococcus sp. population in tvarogs (C) and curd manufactured in model conditions (B) for á=0.05. The average differences between count of lactic acid bacteria for C and B were significant and were equal to t = - 1.9280 and t = –3.9967. Number of lactic acid bacteria in vacuum–packed tvarog LT could be expressed with the help of mathematical equation presented below: 2 L T = 31.295-3.05•LSP r -0.706 , r 0.498 , p 0.16

á0.05

(4)

LT – count of lactic acid bacteria SP – vacuum-packed curd T – vacuum-packed tvarog Correlation coefficient between variables indicated high correlation between counts of lactic acid bacteria in both tested products, however the value of coefficient of determination 2 R showed that only 33% of variability of Lactococcus sp. count in tvarogs (C) could be explained with changes observed in stored curds produced in model conditions (B). During storage of curds A and B (Curd A was a control sample and remained without packaging, while curd B was vacuum-packed and manufactured in model conditions), no statistically significant differences in the dynamics of changes of Lactococcus sp. count were observed. However, from obtained data it results that vacuum packing constitutes factor influencing the dynamics of growth of technological micro-flora population in lactic acid curd during final period of storage in refrigerating conditions (figure 13). Regardless of the presence of packaging, the significant differences were detected in the dynamics of changes of Lactococcus sp. population between day 7 and 14 of curd storage (figure 13). In control curds remaining without packaging (A) the level of lactic acid bacteria after 14 days was the same as before storage (A), while in vacuum-packed curd (B) the decrease in population count by 1 logharithmic cycle was observed (figure 13).

191

Influence of Interactions Occurring Between Micro-Organisms…

Log cfu/g 8,5 y= -0,0133x4 + 0,1083x3 - 0,2167x2 + 0,0117x+ 8,33 R2 = 1

8,4 8,3 8,2 8,1 8 7,9

y= 0,0363x4 - 0,5158x3 + 2,4938x2 - 4,7542x+ 11,15 R2 = 1

7,8 7,7 7,6 0

2

4

7

14

Time of storage (days) Curd A

Curd B

polynom.

polynom.

Figure 13. Changes of Lactic Acid Bacteria count in curds A and B during storage.

Polynomial equation of the fourth order describing variations of Lactococcus sp. population count in curds A and B showed a different direction of observed changes (figure 13). LSP=0.25951+0.96344LSN

2

r 0.509, r 0.259 , p =0.381

á 0.05

(5)

L- number of lactic acid bacteria, SP - vacuum-packed curd SN - non-vacuum packed curd However, in the case of curds produced in similar technological conditions, the differences in dynamics of changes of lactic acid bacteria for vacuum-packed curds and curds remaining without packaging were not as significant as differences between micro-flora of curds and lactic acid cheese (figures 12 and 13). In vacuum-packed products of animal origin, changes in lactic acid bacteria population count and their metabolism are varied and dependant on type of the product, packing system, time and way of storage as well as secondary micro-flora present in a product (Holly et al. 1992, Jackson et al. 1992, Nicolai et al. 1993). Confirmation of the importance of vacuum-packing in stabilising the growth of lactic acid bacteria can also be the model developed for tvarog produced in industrial conditions.

192

Izabela Steinka

From the presented model, it results that in lactic acid cheese manufactured in industrial conditions, the behaviour of applied autochthonous micro-flora is influenced by other factors than in the case of non-packed semi-products (curds). Probably, it is dependent not only on technologies applied in production plants, but also on the presence of micro-flora re-infecting milk. Determined equation of linear regression for vacuum-packed and unpacked curds show that only 25.8% of changes of lactic acid bacteria count can be explained by application of vacuum-packing. This is consistent with observations of Maniar et al., who detected significant discrepancy between lactic acid bacteria count in cottage cheese, depending on level of atmosphere modification of the above-mentioned packaging wherein the product was stored (Maniar et al. 1994). Whereas, the predictive model of Nicolai describing the dynamics of changes in vacuumpacked meat indicates the significant influence of the environmental pH value on changes of lactic acid bacteria count during storage of packed products. The observed behaviour of lactic acid bacteria during refrigerating storage of the curds without re-infection is not significantly dependent on the presence of packaging up to day 7 of curd storage. Changes in fermentation bacteria population count during storage of vacuumpacked curds produced in model conditions and industrially manufactured curds show significant differences in relation to the semi-product.

INVESTIGATION OF INTERACTIONS IN TVAROGS OF HIGH MICRO-FLORA CONTAMINATION LEVEL From numerous research on lactic acid cheese (Steinka 2003a), it results that time of tvarog storage is not the only one and the most significant factor determining the population size in products. In minor part of available literature, the changes of microbiological quality of cottage cheese and tvarogs packed using vacuum or in modified atmosphere are described Maniar 1994, Fedio 1994, Panfil –Kuncewicz 1997a,b, Severini 1998, Steinka 1999c, Steinka 2001a,b). From data it results that packing system has a significant influence on the behaviour of micro-flora present in a product during its storage (table 12). Table 12. Equations of linear correlation for the influence of tvarog storage time on allochthonous micro-flora count Type of organisms

micro-

Enterococcus sp. Escherichia coli Staphylococcus aureus CP Mould Yeast Psychrotrophs

Lactic acid cheese packed into PA/PE laminate Equation r Y=38473+43157t 0.299 Y=1725.3-141.6t -0.348 Y=861.67-6.600t -0.021

Lactic acid cheese packed into Cryovac laminate Equation r Y=1575e2-126et -0.239 Y=170.42-14.29t -0.344 Y=129.17+10.714t 0.157

Y=7170.4+307.86t Y=6845e2+53759t Y=8445e2-102E2t

y=72.917+15.893t Y=-148e3+66775t Y=-295.6+274.02t

Steinka 2003a, t - storage time.

0.000 0.481 -0.090

0.170 0.631 0.261

193

Influence of Interactions Occurring Between Micro-Organisms…

Application of modified atmosphere in packing of lactic acid cheese does not guarantee inhibition of growth of all types of micro-organisms present in a product before storage (Kuncewicz 1997a,b). The research of Kuncewicz showed that application of 80% of CO2 additive for packing tvarogs limited the growth of mould and coliforms during refrigerating storage, however it did not inhibit the growth of yeast. Vacuum packing of tvarogs in conditions without Good Manufacture Practice also does not guarantee the reduction of micro-flora during storage of products in refrigerating conditions (Steinka 1999c). An attempt to create model predicting changes of quality and safety of hermetically packed lactic acid cheese had to take interactions occurring micro-flora present in tvarogs into account. Changes of micro-flora population count present in tvarogs could result from the change of conditions occurring in the space between the packaging and the products during storage. In lactic acid cheese packed with vacuum system and into Cryovac, the different tendency in behaviour as well as varied dynamics of change of micro-organism population count is observed. Table 13. Tendency of changes of tvarog allochthonous micro-flora Type of micro-flora

Enterococci Escherichia coli Moulds Yeasts Psychrotrophic bacteria Coagulase positive Staphylococci Coagulase negative Staphylococci

Type of packaging PA/PE Equation describing tendency of changes

R2

Cryovac Equation describing tendency of changes

r2

y = -0.82x2 + 4.28x + 0.32 y = 0.07x2 - 1.27x + 4.5 y = 1.21x2 - 4.77x + 7.6 y = -0.27x2 + 1.61x + 3.71 y = -0.15x2 + 0.59x + 5.31

0.999 0.997 0.999 0.999 0.999

y = 0.74x2 - 3.55x + 8.08 y = -1.15x + 3.4167 y = -1.675x2 + 5.515x+0.97 y = 0.41x2 - 0.44x + 3.6 y = 0.49x2 - 1.41x + 3.43

0.999 0.997 0.999 0.999 0.999

y = 0.205x2 - 0.355x + 2.97

0.999

y = 0.21x2 - 1.02x + 3.58

0.999

y = 0.255x2 - 0.525x + 3.71

0.999

y = -0.43x2 + 1.54x + 1.8

0.999

x-storage time.

Analyses of data also indicate the existence of significant variation in the course of function describing changes in count of all other tested groups of facultative anaerobic bacteria such as enterococci, staphylococci, E. coli after 14 days. Linear equations cannot be used for describing changes of tvarog micro-flora (table 12). The courses of these functions and consistency of predicted data with empirical data for both tvarog packing systems are not identical. Coefficients of linear correlation r indicate weak or lack of linear correlation between storage time and the behaviour of surface micro-flora population. Differences in tendencies of changes of micro-organisms present in tvarogs depending on packing system can be observed. It is confirmed by the derived equations describing tendencies of changes presented in table 13. Due to the possibility of occurrence of multi-species population occupying the surface of tvarogs as well as lack of predictive models concerning tvarogs, it became important and

194

Izabela Steinka

necessary to create such a predictive model for evaluation of quality of stored products, which will take interaction occurring among surface micro-flora into account. The research of Steinka et al. proved that the level of micro-flora present in tvarogs showed diversification dependent on packing method (Steinka et al. 1998, 2001c Steinka 2001a). In tvarogs packed into cryovac, after 14 days of storage, the lower number of Enterococcus sp. and Escherichia coli by 1 log cfu/g on the average was detected in comparison with tvarogs packed into PA/PE. The count of coagulase-negative Staphylococcus aureus, mould and psychrotrophs was higher by 2 logarithmic cycles on the average in tvarogs packed into PA/PE. The level of yeast and coagulase-positive staphylococci in tvarogs packed into both types of packaging was similar after storage (Steinka 2003a). Equation of linear correlation determined for evaluation of influence of storage time on the dynamics of population changes showed that only 1.53-19.7 % of variability could be explained with the help of linear models. Models of changes of micro-flora populations present in tvarogs packed into Cryovac laminates were characterised by low coefficients of determination R2 at the level of 0.0260.3716. Microbiological data were subjected to multiple regression analysis. Determined coefficients of semi-partial correlation are presented in tables 14 and 15. Values of partial correlations allowed evaluation of the influence of interactions among micro-organisms on changes in count of individual groups of micro-organisms in lactic acid cheese (table 14). The obtained test results showed that enterococci, mould and psychrotrophs had the greatest influence on determining changes of surface micro-flora of tvarogs packed into PA/PE had. In lactic acid cheese packed into cryovac, the significant reciprocal influence of facultative anaerobic bacteria on changes of other micro-organism populations was detected (table 15). Table 14. Coefficients of multifactor regression correlation in equations of micro-organism interactions in tvarogs packed into PA/PE Type of interactions between micro-organisms in tvarogs packed into PA/PE

r

2 r

Enterococcus sp. E. coli

Yeast Psychrotrophs

-0.202 -0.153

0.235 0.066

Staphylococcus aureus CP

Yeast Mould Mould Psychrotrophs Staphylococcus aureus Enterococcus sp. Staphylococcus aureus Psychrotrophs

0.257 -0.155 -0.117 0.190 -0.155 -0.175 0.215 0.214

0.013 0.024 0.036 0.060 0.046 0.060 0.046 0.046

Yeast Mould

0.249 0.184

0.062 0.033

Staphylococcus aureus CN Mould Yeast

Psychrotrophs

Steinka 2003a, CP- coagulase-positive Staphylococcus aureus, CN- coagulase-negative Staphylococcus aureus.

Influence of Interactions Occurring Between Micro-Organisms…

195

Table 15. Coefficients of multifactor regression correlation in equations of micro-organism interactions in tvarogs packed into Cryovac Type of interactions between micro-organisms in tvarogs packed into Cryovac

r

2 r

Enterococcus sp.

Staphylococcus aureus CP E.coli Enterococcus sp. Staphylococcus aureus CN Yeast Mould

0.146 0.753 0.767 0.409 0.411 0.020

0.021 0567 0.589 0.167 0.169 0.000

Staphylococcus aureus CN

Enterococcus sp. Yeast Escherichia coli

0.294 0.252 -0.120

0.086 0.063 0.014

Mould Yeast

Psychrotrophs Staphylococcus aureus CP Staphylococcus aureus CN Mould

0.678 0.329 -0.206 0.668

0.460 0.108 0.042 0.447

E. coli Staphylococcus aureus CP

Psychrotrophs

Steinka 2003a, CP- coagulase-positive Staphylococcus aureus, CN- coagulase-negative Staphylococcus aureus.

Equations presented in this paper describing variability of surface micro-flora of tvarogs, depending on storage conditions suggest the existence of factors other than time that influence changeability of micro-organism populations. Coefficients of linear correlation determined for equations describing population of staphylococci in both packing systems show that the behaviour of these micro-organisms is probably dependant not only on atmosphere occurring inside the packaging in both packing system, but also on other factors requiring further research. The above-mentioned observations are the reasons for which it is difficult to evaluate unequivocally the behaviour of staphylococci and to compare it with predictions obtained with the help of a predictive computer program called Pathogen Modelling Program. From predictions obtained as a result of simulations of Pathogen Modelling Program for model conditions at pH 4.7, it results that time necessary for achieving reduction in staphylococci count by only 1 logarithmic cycle at temperatures 4, 6 and 8°C is equal to 11.3, 13 and 14 days respectively. Data obtained through simulation of Pathogen Modelling Program show a low rate of variability in these micro-organism count in model conditions, what is also consistent with tendencies observed in tvarogs (Steinka 2003a). According to Kimur, the behaviour of staphylococci and Escherichia coli is dependant on the level of carbon dioxide and oxygen in the environment (Kimura et al. 1998). The tendencies of staphylococcus growth observed in tested cheese coming from Cryovac and PA/PE laminates show a significant meaning of atmosphere composition within the space under packaging surface for the growth of this group of micro-organisms. Temperature is not a factor that limits absolutely the growth of staphylococci. Their slow growth below the temperature of 10°C is observed in different types of food (Lee Wong et al. 2002). In tested tvarogs, the significant reduction in number of Escherichia coli rods during storage is observed. From literature, it results that this pehnomena is closely connected with sensitivity of rods to action of organic acids (Hsiao et al. 1999).

196

Izabela Steinka

Adams et al. (1995) proves that E. coli bacteria are sensitive to low pH values of the environment. However, from model research it results that during production and storage of a product at room temperature conditions, the reduction of Escherichia coli cells occurs not before the acidity reaches the value of pH 3.4. Although Buchanan et al. (1992) did not observe the growth of E. coli at the temperature of 4°C, Jordan et al. (1999) detected the ability of non-toxicogenic strains of E. coli O157:H7 to survive in the environment of pH 3.0, at the temperature of 37°C. This research showed that even after 3 days, it was possible to identify the presence of a significant number of cells that survived in such conditions. This allowed assuming that the growth of Escherichia coli in a product was not influenced by the packing system, but by a low temperature and reciprocal antagonistic interactions occurring among the micro-organisms. The size of enterococci population detected in tvarogs can be related to the literature information determining the level of contamination of unripened cheese (Baumgartner et al. 2001, Centeno et al. 1999). In the literature, there are no models available describing the growth of fungi in vacuumpacked unripened cheese such as tvarog. However, the issue of determining the number of these micro-organisms during storage of unripened cheese in conditions of modified atmosphere was undertaken in several papers (Fedio et al. 1994, Maniar et al. 1994, Westal et al. 1998). Yeast constituted quantitatively predominant micro-flora in tvarogs packed with both systems. Changes in populations showed high linear correlation with time of storage in refrigerating conditions. The dynamics of growth of yeast in vacuum-packed products confirmed tendencies observed by Westal (Westal et al. 1998). From data obtained by Westal, it resulted that as much as 25% of tested cottage cheese with additives packed in modified 6 atmosphere showed the significant increase in yeast count in a product ranging from 10 up 8 to 10 cfu/g after 2-5 days. The qualitative and quantitative composition of yeast in cottage cheese, as well as production of spores remained in a strong dependence on percentage composition of modified atmosphere. The tendencies of changes of psychrotropic micro-organisms observed in tvarogs are hard to be related to the models accessible in literature. The survival rate of Pseudomonas sp. during storage of cottage cheese was presented by Brocklehurst and Lund (Brocklehurst and Lund 1988). There are only a few publications mentioning changes of psychrotroph populations during tvarog storage. Tests conducted on cottage cheese aiming at determination of this micro-flora were performed by Fedio et al. 1994, Maniar et al. 1994, Neugebauer et al. 2005. Evaluation of changes of psychrotrophs populations in tvarogs packed with a hermetic and vacuum system was conducted by Steinka et al. 1999b. Packing tvarogs into Cryovac laminates is often preceded by blowing nitrogen through the product surface, what causes that the initial concentration of carbon dioxide at tvarog nonvacuum packing is different in comparison with its content in vacuum packaging. Complete modification of gaseous atmosphere involving addition of 100% N2 during packing of unripened cheese is commonly applied in many countries (Adams et al. 2000). Interpretation of change in micro-organism count in hermetically-packed tvarogs gives rise to many difficulties. In tested tvarogs packed with vacuum system, the existence of interactions were detected among micro-organisms that are typical in model conditions and difficult to precise unequivocally.

Influence of Interactions Occurring Between Micro-Organisms…

197

CHARACTERISTICS OF INTERACTIONS OCCURRING AMONG MICRO-ORGANISMS IN HERMETICALLY-PACKED TVAROGS The results of tests conducted on tvarogs (Steinka 2003a) allow saying that in vacuumpacking conditions, a major part of interactions occurring among micro-organisms had antagonistic character. It was indicated by negative values of correlation coefficients. In these tvarogs, the antagonistic interactions between yeast and enterococci, yeast and staphylococci, Escherichia coli rods and psychrotrophs as well as fungi and staphylococci were detected. The interactions between bacteria and fungi are more often observed in mould cheese. The antagonistic influence of Penicillium roqueforti on such pathogens as Escherichia coli or Staphylococcus aureus was described by Larsen (1997). The antagonism of fungi in relation to pathogens was conditioned by proteolytic and lipolytic action of fungi. High activity of enzymes decomposing proteins and fats results in high activity in inhibiting bacteria cells (Larssen 1997). It turns out that metabolites of fungi such as acetaldehyde and benzaldehyde play a significant role in antagonism occurring between fungi and pathogenic bacteria. Fungi of Penicillium type show the ability to inhibit Staphylococcus aureus and Listeria monocytogenes at the temperature of 150C as soon as after 6 days. Mould occurring in tvarogs include most of all Penicillium expansum and Aspergillus flavus. Their metabolic activity could contribute to inhibition of growth of staphylococci cells, however this inhibition was not effective. In tested lactic acid cheese, the growth stimulation occurred in the case of yeast and staphylococci, as well as yeast, mould and psychrotrophs. The direction of changes indicated reciprocal actions stimulating the growth of one population by the other. In laboratory conditions, the antagonistic action of Escherichia coli described in literature concerned staphylococci. In tested tvarogs, this type of interactions was observed in the case of Cryovac packaging. The synergistic interactions between coagulase-negative staphylococci population and Escherichia coli rods was detected in tvarogs packed into cryovac. The results of multifactor analysis did not confirm such a strong antagonistic interaction between staphylococci and psychrotrophs as were observed in the previous testing (Steinka 2001b) of vacuum-packed tvarogs. In model conditions of growth, the interactions between Escherichia coli and Enterococcus faecalis both of synergistic and antagonistic character are observed (Usajewicz 1995). In tested tvarogs, the interactions of synergistic character occurring between these bacteria reflect the tendency occurring in model conditions of mixed culture described by Usajewicz (1995). The synergism between these bacteria was detected in tvarogs packed into cryovac. From conducted model research concerning growth of facultative anaerobic bacteria in lactic acid curd, it results that the behaviour of some micro-organisms determined in a tvarog differs from the one detected in curds (Steinka 2003a). Low temperature and the atmosphere occurring inside the vacuum packaging lower the dynamics of growth of populations and the metabolic activity of lactic acid bacteria. The observed changes in count of lactic acid streptococci in curds produced in model conditions and in stored tvarogs could be described with the help of polynomial equations of

198

Izabela Steinka

the third and fourth order. Significant differences in the course of curves illustrating those changes were indicated in the case of non-hermetic and vacuum packaging (Steinka 2003a). Antagonistic influence of lactic acid streptococci on micro-organisms present in tvarogs could be significantly lowered, due to conditions occurring inside the packaging. Despite the acknowledged antagonistic properties of lactic acid bacteria against pathogenic bacteria, lactic acid cheese can also constitute the medium wherein the growth of such micro-flora as staphylococci occurs (Ingham 1996, Belickova et al. 2001, Masa-Calpe 1996, Steinka 2001c, Steinka 2004). From the research of Kornacki (2002), it results than even at correctly conducted process of souring the milk with application of active lactic acid cultures, the reduction in count of micro-flora originating from postproduction contamination is not always obtained. It concerns e.g. coli rods, which are sensitive to action of lactic acid bacteria, and which number was not reduced during industrial production of tvarog, using butter cultures. Changes of staphylococci population observed in tvarogs during product storage probably do not result from the antagonism of Escherichia coli, because the count of faecal bacteria was too low to inhibit Staphylococcus aureus. In order to inhibit the growth of staphylococci effectively, more than 100-fold number of rods in relation to number of staphylococci, as well as temperature higher than 150C are necessary (Steinka 2001 b). The insignificant inhibition of growth of staphylococci population observed in tested tvarogs was probably caused by the growth of mould. It was stated that reduction in number of cells belonging to Staphylococcus aureus species could not result from antagonistic action of yeast in relation to these staphylococci. The correlation coefficients showed the stimulating influence of yeast on staphylococci in both packing system. It was confirmed by model tests conducted on lactic acid curds. In tvarogs, no inhibiting influence of yeast on majority of micro-organisms groups was detected, despite the fact that yeast constituted predominant micro-flora in hermetically-packed tvarogs. Slow reduction of Staphylococcus aureus in products did not occur similarly to the experiments performed in laboratory conditions. However, in tvarogs packed into cryovac, a strong synergistic relations among populations of yeast, mould and psychrotrophs was observed. The character of interactions occurring among micro-organisms in tvarogs could result from variability of gaseous atmosphere inside the packaging. Research of Westal proved a small inhibiting influence of modified atmosphere on the growth of yeast in products (Westal 1998). Test results obtained by Westal were controversial in relation to the theory of Fedio and Alves, who confirmed the increase in tvarog stability depending on the level of atmosphere modification in MAP system. In conditions of tvarog hermetic packing, it is hard to indicate unequivocally the synergism, antagonism or antibiosis between two micro-organisms, because tvarogs presented in mentioned cases constitute the bases for growth of multi-component population composed of many species of fungi and bacteria. It is hard to separate the influence of one group on the others, if it is considered that the growth of certain micro-organism is influenced by metabolites of 7 groups of microorganisms at the same time. It is also difficult to separate their common influence on chemical and physical characteristics of a products as well as basic packaging properties.

Influence of Interactions Occurring Between Micro-Organisms…

199

Application of response surface models as well as equations describing them in order to evaluate the microbiological quality allowed defining changes occurring in tvarogs, taking at least three groups of micro-organisms into account (Steinka 2003a). The advantage of applied method was the possibility to predict magnitude of population changes, depending on variability of two other species during storage. The exact knowledge of these interactions is necessary for conducting observations of changes in microbiological quality and safety, in order to compare obtained values with valid microbiological standards for tvarogs. The obtained surface response models allowed defining changes in time, depending on the initial size of tested populations. In this regard, mathematical equations describing this variability allowed predicting strictly defined interactions occurring in a products. However, surface response models have some certain limitations, which concern among others a small number of relationships that they can illustrate. Despite the above-mentioned limitations, the surface response models constitute an assumption for interpretation of observed phenomena. In order to illustrate the interactions occurring in both types of hermetically-packed tvarogs, the dynamic model should be developed, which will consider all interactions occurring in a given time both among the micro-organisms and between the products and packaging.

DEVELOPMENT OF MATHEMATICAL MODEL FOR EVALUATION OF QUALITY OF LACTIC ACID CHEESE Development of a model was based on an assumption that interactions occurring among micro-flora present on the tvarog surface and the interactions between them and the packaging constituted a base for determining a mathematical model of dynamic interaction among micro-organisms. Calculations were started from searching for the relationship between variables

X i (t ), i = 1,2,..., m, of the form:

X i (t ) = a i 0 (t ) + a i1 (t ) X 1 (t ) + a i 2 (t ) X 2 (t ) + ... + a ii −1 (t ) X i −1 (t ) + a ii +1 (t ) X i +1 (t ) + ... + a im (t ) X m (t )

(6)

during time t , t ∈< 0, T >, for i = 1,2,..., m. Unknown coefficients a ik (t ), at defined i, i = 1,2,..., m, for k = 0,1, ,..., m, k ≠ i, are determined using the least squares method, minimising sums of squares of deviations between empirical values given in table 16 and calculated according to the following formula (6)

200

Izabela Steinka n

Δi = ∑ [ xij (t ) − a i 0 (t ) − a i1 (t ) x1 j (t ) − a i 2 (t ) x 2 j (t ) − ... − a ii −1 (t ) x1−1 j (t ) j =1

− a ii +1 (t ) x1+1 j (t ) − ... − a im (t ) x mj (t )] 2 , i = 1,2,..., m,

(7)

Table 16. Empirical values used for model development Variable

X 1 (t )

X 2 (t )

X 3 (t )

X m −1 (t )

X m (t )

Realisation number j

Variable realisation

1

x11 (t )

x 21 (t )

x31 (t )

x m −11 (t )

x m1 (t )

2

x12 (t )

x 22 (t )

x 32 (t )

x m −12 (t )

x m 2 (t )

3

x13 (t )

x 23 (t )

x33 (t )

x m −13 (t )

x m 3 (t )

n −1

x1n −1 (t ) x 2 n −1 (t ) x 3n −1 (t ) x m −1n −1 (t )

x mn −1 (t )

n

x1n (t )

x mn (t )

x 2 n (t )

x3n (t )

x m −1n (t )

Steinka 2003a.

From necessary condition of existence of function extreme Δi , i = 1,2,..., m,

∂Δi = 0, k = 0,1, ,..., m, k ≠ i, ∂a ik For each defined i, i = 1,2,..., m, the following system of equations was obtained n

n

n

n

j =1

j =1

j =1

na i 0 (t ) + ∑ x1 j (t ) a i1 (t ) + ∑ x 2 j (t ) a i 2 (t ) + ... + ∑ xi −1 j (t ) a ii−1 (t ) + ∑ xi +1 j (t ) a ii+1 (t ) j =1

n

n

j =1

j =1

+ ... + ∑ x mj (t ) a im (t ) = ∑ xij (t ) n

n

n

n

j =1

j =1

j =1

j =1

∑ x1 j (t ) a i 0 (t ) + ∑ x1 j (t )x1 j (t ) a i1 (t ) + ∑ x 2 j (t )x1 j (t ) a i 2 (t ) + ... + ∑ xi −1 j (t )x1 j (t )

aii−1 (t ) n

n

n

j =1

j =1

j =1

+ ∑ xi +1 j (t )x1 j (t ) a ii+1 (t ) + ... + ∑ x mj (t )x1 j (t ) a im (t ) = ∑ xij (t )x1 j (t ) ...

Influence of Interactions Occurring Between Micro-Organisms… n

n

n

j =1

j =1

201

∑ xi −1 j (t ) a i 0 (t ) + ∑ x1 j (t )xi −1 j (t ) a i1 (t ) + ∑ x 2 j (t )x i −1 j (t ) a i 2 (t ) + ... j =1

n

+ ∑ xi −1 j (t )xi −1 j (t ) a ii−1 (t ) j =1 n

n

n

j =1

j =1

j =1

+ ∑ xi +1 j (t )xi −1 j (t ) a ii+1 (t ) + ... + ∑ x mj (t )x i −1 j (t ) a im (t ) = ∑ xij (t )xi −1 j (t ) ... n

n

n

j =1

j =1

∑ xi +1 j (t ) a i 0 (t ) + ∑ x1 j (t )xi +1 j (t ) a i1 (t ) + ∑ x 2 j (t )xi +1 j (t ) a i 2 (t ) + ... j =1

n

+ ∑ xi −1 j (t )xi +1 j (t ) a ii−1 (t ) j =1 n

n

n

j =1

j =1

j =1

+ ∑ xi +1 j (t )xi +1 j (t ) a ii+1 (t ) + ... + ∑ x mj (t )xi +1 j (t ) a im (t ) = ∑ xij (t )xi +1 j (t ) ... n

n

n

n

j =1

j =1

j =1

j =1

∑ x mj (t ) a i 0 (t ) + ∑ x1 j (t )x mj (t ) a i1 (t ) + ∑ x 2 j (t )x mj (t ) a i 2 (t ) + ... + ∑ x i −1 j (t )x mj (t )

a ii−1 (t ) n

n

n

j =1

j =1

j =1

+ ∑ xi +1 j (t )x mj (t ) a ii+1 (t ) + ... + ∑ x mj (t )x mj (t ) a im (t ) = ∑ xij (t )x mj (t )

i = 1,2,..., m, from which the unknown equation coefficients can be determined assuming that for each defined i, i = 1,2,..., m, :

⎤ ⎡bi 00 (t ), bi 01 (t ),..., bi 0i −1 (t ), bi 0i +1 (t ),..., bi 0 m (t ) ⎡a i 0 (t ) ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢bi10 (t ), bi11 (t ),..., bi1i −1 (t ), bi1i +1 (t ),..., bi1m (t ) ⎢a i1 (t ) ⎥ ⎥ ⎢ . ⎥ ⎢. ⎥ ⎢ ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢ ⎥ ⎢. . ⎥ ⎢ ⎥ ⎢ B t b t b t b t b t b t ( ), ( ),..., ( ), ( ),..., ( ) = ( ) ⎥ ⎢ Ai (t ) = ⎢a ii −1 (t ) ⎥ i ii −10 ii −11 ii −1i −1 ii −1i +1 ii −1m ⎥ ⎢ ⎢a (t )⎥ ⎢bii +10 (t ), bii +11( t ) (t ),..., bii +1i −1 (t ), bii +1i +1 (t ),..., bii +1m (t )⎥ ⎢ ii +1 ⎥ , ⎥, ⎢. ⎥ ⎢. ⎥ ⎢ ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢ ⎥ ⎢. . ⎥ ⎢ ⎥ ⎢ ⎥⎦ ⎢ b ( t ), b ( t ),..., b ( t ), b ( t ),..., b ( t ) a ( t ) im1 imi −1 imi +1 imm ⎣ im 0 ⎦⎥ ⎣⎢ im

(8)

202

Izabela Steinka ⎡c i 0 (t ) ⎤ ⎢ ⎥ ⎢c i1 (t ) ⎥ ⎢. ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢ ⎥ C i (t ) = ⎢c ii −1 (t ) ⎥ ⎢c (t )⎥ ⎢ ii +1 ⎥ , ⎢. ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢ ⎥ ⎣⎢c im (t ) ⎦⎥

(9)

where: n

bi 00 = n, bi 0l = ∑ x lj (t ), l = 1,2,..., m, l ≠ i, j =1

(9a)

n

bik 0 = ∑ x kj (t ), k = 1,2,..., m, k ≠ i, j =1 n

bikl = ∑ x lj (t )x kj (t ), k = 1,2,..., m, k ≠ i, l = 1,2,..., m, l ≠ i, j =1 n

c i 0 = ∑ x ij (t ), j =1 n

c ik = ∑ x ij (t )x kj (t ), k = 1,2,..., m, j =1

systems of equations (8) can be expressed in a matrix form:

Bi (t ) Ai (t ) =C i (t ) for i = 1,2,..., m.

(10)

Hence, if matrix determinants Bi (t ), i = 1,2,..., m, are different from zero, then for each defined i, i = 1,2,..., m, we determine unknown coefficients

a i 0 (t ), a i1 (t ), a i 2 (t ), a ii −1 (t ), a ii+1 (t ), a im (t ) of models from matrix equations

Ai (t ) = [ Bi (t )] −1 C i (t ) during time t , t ∈< 0, T >, where [ Bi (t )] −1 are inverse matrixes of a matrix Bi (t ).

(11)

Influence of Interactions Occurring Between Micro-Organisms…

203

By this, we identify approximate relationships between variables X i (t ), i = 1,2,..., m, in defined time t , t ∈< 0, T > . Figure 14 presents the algorithm of determining the parameters of mathematical model of dynamic interactions between tvarog microbiological parameters and the packaging. The obtained polynomial equation served for developing a computer program called TWAROGI JMTPH, which takes the dynamic changes of micro-flora in time into account. In developed model, the predictions concerning changes in properties of applied packaging were also considered. The predictions applied to micro-biological parameters of products packed with a vacuum system and using the technology of shrink-wrapping of product with cryovac. The obtained predictions differed from each other as regards the absolute terms and values of constant coefficients. Below, the exemplary prediction of yeast population for day 11 of tvarog storage is presented: YPA/PE=126032.78+5.82x1+436.02x2-97.19x3+12.12x4-306.39x5+4.19x7

(12)

Yc=118770003.92-2.69x1+215.21x2+45.98.19x3+14.04x4-281.14x5+3.39x7

(13)

where x1-enterococci, x2-E. coli, x3-coagulase positive Staphylococcus aureus, x4-coagulase negative Staphylococcus aureus, x5 – mould, x7 –psychrotrophs Developed computer program allows quick prediction of magnitude and type of changes occurring in hermetically-packed tvarogs depending on packing technology of products. Although multi-parameter models used for predicting microbiological quality of products are criticised, they reflect conditions occurring in food to the much greater extent than other models (Baranyi 1996, Steinka 2003a). In predicting quality and safety of products, the mathematical models of a small number of parameters are most commonly acknowledged and applied. However, the disadvantage of the latter is that it omits many environmental factors responsible for the dynamics of observed changes. Up till now, one of a few available linear multi-parameter models was the model of Reinchart and Mohasci-Farkas (Ko³o¿yn – Krajewska 2003). The above-mentioned model took the influence of four environmental factors on inactivation of pathogenic bacteria into consideration. The presented evaluation of changes in quality of products packed with different technologies proves that the element of reciprocal interactions among micro-organisms, often omitted in predicting, can significantly influence the predicting quality. It should be also taken into consideration that in literature there are no models available connected with predicting the quality of tvarogs. Therefore, the presented program can constitute complementation of prediction models connected with evaluation of survival rate of micro-organisms in products of animal origin and can be useful in optimising the quality of hermetically packed tvarogs.

204

Izabela Steinka

START

v=1

i=1

Read in data Determine matrixes Bi(t), Ci(t) Determine matrix Ai(t)

i=m?

NO

i=i+1

YES v=w?

NO

v=v+1

YES Print matrix elements Ai(t), i = 1,…,m

i=1 Determine matrixes E, Fik

Determine matrix Dik

i=m?

NO

i=i+1

YES Print matrix elements Dik, i = 1,…,m

Is it end ?

NO

YES STOP

Figure 14. Algorithm of determining parameters of mathematical model of dynamic interactions occurring among microbiological parameters of tvarogs.

Influence of Interactions Occurring Between Micro-Organisms…

205

THE INFLUENCE OF INTERACTIONS AMONG MICRO-ORGANISMS ON PHYSICO-CHEMICAL PROPERTIES OF LACTIC ACID CHEESE STORED IN REFRIGERATING CONDITIONS Multidirectional interactions cause that tvarogs packed with different systems (vacuum, MAP, atmosphere) differ from each other in physico-chemical (water content) and chemical (concentration of hydrogen ions) properties. Regardless of the applied packing system and the packaging hermetic properties, the loss in water content was observed in tvarogs relating to the packing system and product storage time. As soon as after two days of tvarog storage, the decrease in water content from 0.6 g up to 0.8 g could be observed in products packed into cryovac, depending on the packaging tightness (figures 15 and 16). In tvarogs hermetically packed with vacuum system, the drop in water content was equal to 0.9 g. Tvarogs coming from depressurised packaging indicated 1.2 g less water in a product, in comparison with control sample. The significant loss in weight by drying of surface of tvarog packed into Cryovac coming from both hermetic and non-tight packaging was detected after 7 days of product storage. Between day 7 and day 14 of storing the product packed into cryovac, the insignificant decrease in water content was observed in non-tight packaging and the insignificant increase in hermetic packaging.

75 74,5 74 y= 0,1164x2 - 0,9956x+ 75,246

73,5

R2 = 0,9857

%

73 72,5 72 71,5

y= 72,78x-0,0075 R2 = 0,2858

71 70,5 70 0

2

4

7

14

Time (days) Vacuum

Non-vacuum

polynom.

Figure 15. Changes in water content in tvarog packed hermetically with different system

power

206

Izabela Steinka

After 4 days, loss of water content in vacuum-packed products was equal to 1.3 g and 1.2g for tight and non-tight packaging respectively, in comparison with control samples. In vacuum packaging, the increase of water content in product was observed after 7 days of storage resulting probably from falling of water present on the internal packaging surface. After 14 days, water loss equal to 1.1g was detected in tvarogs coming from Cryovac laminate, and it was higher by 0.5 g than the change observed on day 2 of product storage. Changes in water phase of a product after 14 days of storage were equal to 1.3g for tvarogs packed into PA/PE laminates and 1.1g for tvarogs packed into Cryovac. The dynamics of changes of water content during storage of tvarogs were different in PA/PE and Cryovac packaging. However, in the final period of storage, the magnitude of changes in water phase of products packed hermetically with both systems were comparable, despite the fact that during initial period of storage the higher loss in water content was observed in vacuum packed tvarogs. Changes in water content during storage of tvarogs packed into Cryovac could be 2

described by the equation y = - ax + bx + c, whereas the tendency of changes in water phase n

of tvarogs packed into hermetic PA/PE laminates was illustrated by a power function y=ax . However, the above-mentioned equation described only 28.6% of water phase variability in a function of time. Unfortunately, coefficients of determination of other functions showed even lower values. This fact evidences the existence of complex packaging-product interactions observed in this packing system of tvarogs.

75 74,5 74 73,5

y= 74,225x-0,0108 R2 = 0,9541

73 Log cfu/g 72,5 72 y= 0,2257x2 - 1,4023x+ 74,066 R2 = 0,7407

71,5 71 70,5 0

2

4

7

14

Time (days) Vacuum

Non-vacuum

power

polynom.

Figure 16. Changes of water content in tvarogs packed with different systems coming from depressurised packaging.

Influence of Interactions Occurring Between Micro-Organisms…

207

In this research, the average water content on the day of purchase of tvarogs packed into Cryovac was equal to 74.3%. Water content in vacuum-packed tvarogs was at the level of 73.0% during that time, what is consistent with values contained within the Standard for this type of cheese. However, as soon as after two days of storage, the differences connected with packaging hermetic properties were observed. Changes in water phase of tvarogs manufactured in experimental conditions presented by other authors showed significant fluctuations during refrigerating storage (Œmietana et al. 1997). From tests performed on lactic acid cheese coming from trade and subjected to storage (Steinka 2004), it resulted that after 7 and 14 days of storage, tvarogs in vacuum packaging showed a similar tendency as the one quoted by Œmietana. Whereas the tendency of changes in water content in tvarogs packed into Cryovac was different from the one presented by Œmietana. Conducted numerous research showed that at significant level of contamination of tvarogs with micro-flora, the biological factors should be taken into account for mathematical description of the water fluctuation process. Water fluctuation between product surface and the packaging is determined not only by breathing of a product and its barrier properties, but also by micro-organism metabolism (Steinka 2005b). Equations of trend lines observed during changes in water content within time periods Ä2, Ä4, Ä7, Ä14 during tvarog storage are presented below. Water fluctuations were tested in vacuum-packed tvarogs containing level of micro-flora taken into account while developing the computer program (Steinka 2003a). The trend of changes was described by quadratic equation of the following form Y = 3.6x2 + 8.9333x – 6.9 r2 0.999

(14)

The obtained results proved the existence of the average linear correlation between initial water content in tvarogs and its level after 14 days, what is showed by the equation: Y14 = 67.651 + 0.0707• x0 r 0.4320 where: y- water content (15) At significant contamination with micro-flora, during storage of tvarogs, the insignificant influence of micro-organisms on fluctuation of water phase between packaging and product surface was observed. Table 17 below presents equations of linear correlation reflecting the influence of microorganism count on changes of water content during storage of tvarogs. Table 17. The influence of water on populations occupying tvarog surface Type of micro-organism

Equation

Enterococcus sp. Yeast Psychrotrophs

Y=11003E-148e3x Y=8593E3-104e3x Y=1157E4-150e3x

Steinka 2003a, x-water.

2 r -0,268 -0,244 -0,185

208

Izabela Steinka

As a result of analysis of multifactor regression of data obtained during tests on tvarogs showing a high level of contamination with micro-flora, it was observed that the growth of chosen types of micro-flora could contribute to the water fluctuation. The analysis of coefficient values of semi-partial and partial correlations showed that in vacuum-packed tvarogs, the populations of enterococci, yeast and psychrotrophs inter-reacted with other micro-organisms and influenced the water fluctuation in tvarogs in 12.9%, 9% and 4.6% respectively (Steinka 2003a). From data analysis, it resulted that water phase fluctuation in tvarogs after 14 days of storage was mostly influenced by interactions of yeast and psychrotrophs with other groups of micro-organisms.

SAFETY OF LACTIC ACID CHEESE Many years of microbiological research on lactic acid cheese (Steinka 1998-2006) showed the presence of coagulase-positive and coagulase-negative staphylococci both in tvarogs packed in a traditional way (parchment paper) and into hermetic packaging. From literature it results that the growth of staphylococci in food is determined by numerous factors (Neumeyer et al. 1990, Taub et al. 2003, Castillo-Rodriquez et al. 2002, Pereira et al. 1991, Feehery et al. 2004, Inhram et al. 2004, Schaffener et al. 2001, VernozyRozynand et al. 1998, Post et al. 1988, Zurea-Cosano et al. 2004, Lindquist et al. 2002, Sameshima et al. 1998, McCann et al. 2003, Pepe et al. 2006, Schaffener et al. 2001). Application of different types of additives in food should limit staphylococci growth in products. Among other, Zurera–Cosano et al. (2004) tested the combined influence of temperature o

7-19 C, acidity equal to 4.5-8.5 pH, content of NaCl equal to 0-8% and 0-200 ppm of nitrates on the growth and lag phase of staphylococci in aerobic and anaerobic conditions. Authors estimated parameters, comparing suitability of a surface response model and Davey model. Two kinetic parameters were evaluated, and it turned out that using surface response model allowed achieving better prediction as regards the bacteria growth in comparison with the second model. It was evidenced by values of bias and accuracy factors equal to bf1=1.06 and bf2=1.31 and af1= 1.17 and af2=1.37 respectively for aerobic and anaerobic conditions. In lag phase, Davey model indicated higher values of coefficients for these conditions. Stewart et al. (2002) investigated the influence of humidity, pH 4.5-7.0 and sorbate concentration on the growth of staphylococci. Tests results showed that saccharose and fructose constituted factors inhibiting the growth of staphylococci populations at neutral pH value. Sodium chloride turned out to be a significant inhibitor of bacteria only at lower pH values. Authors also presented that the addition of potassium sorbate contributed to inhibition of growth, especially when pH value was lower than 6.0.

Influence of Interactions Occurring Between Micro-Organisms…

209

Table 18. The influence of bacteria on the behaviour of facultative anaerobic bacteria populations in tvarogs depending on gaseous conditions Type of micro-organism Staphylococcus aureus

Vacuum conditions

Variable atmosphere, micro-aerophilic conditions

N=5.941+1.5789NL

N=22.202-3.269NL r -0.8053

r 0.9045 Escherichia coli

Yeast

r 0.2026

N=-1.706 +0.6677NL

N=25.407-4.137NL r -0.6378

N=7.6124-0.6321NL

N=-10.39 + 2.5435NL

r -0.3622

r 0.2382

Steinka et al. 2003b, N-population count, Nbfm – LAB count.

Tests conducted on tvarogs indicate the significant meaning of the environment on type and intensity of interactions occurring among micro-organisms. Inside the tvarog packaging and above the product surface, various conditions are formed depending on packing system. These could be micro-aerophilic or aerobic conditions, or modified atmosphere (MAP). This is important not only for technical micro-flora and micro-flora re-infecting tvarogs. Table 18 presents interactions occurring among micro-organisms present on the surface of lactic acid cheese in environment of different proportions of oxygen and carbon dioxide. From presented data it results that the growth of facultative anaerobic micro-organisms is dependent on the level of lactic acid bacteria, however the type and magnitude of this influence is dependent on environment wherein these interactions are observed (Steinka 2003a). In the case of staphylococci, values of linear correlation coefficients were equal to r 0.904 for vacuum-packed tvarogs and r -0.805 for tvarogs packed into parchment paper. An important issue is different direction of changes of staphylococci population under the influence of lactic acid bacteria in both types of packaging. The size of staphylococci population was not large in tested products. It was at the level of 2 log jtk/g. No statistically significant differences were observed between sizes of staphylococci populations in vacuum-packed tvarogs (hermetically) and tvarogs packed in a traditional way (non-hermetically). In traditionally packed tvarogs, the growth of lactic acid bacteria populations was corresponding to the decrease in number of staphylococci, whereas in vacuum-packed tvarogs – the growth of streptococci population count was correlated with the growth of staphylococci. The obtained results indicate high importance of packaging hermetic properties in determining the dynamics of changes of population re-infecting lactic acid cheese. Due to their physiology, staphylococci constitute the greatest problem among facultative anaerobic micro-flora. They have a strong ability to repair sub-lethal damages resulting from the process of product manufacture, what can be confirmed by research of many authors (Steinka 1999a, 2003a). Moreover, from conducted research it results that their cluster distribution in products is also the reason for improper evaluation of food.

210

Izabela Steinka Table 19. Presence of staphylococci in tvarog samples and on the packaging during 7 days of storage Tvarogs before storage

Tvarogs after 7 days of storage (40C)

Surface layer of a product

Packaging 2 [cm ]

Surface layer of a product

Packaging 2 [cm ]

Presence of staphylococcus

23.1%

30.1%

61.5%

61.5%

Absence of staphylococcus

66.9%

69.9%

38.5%

38.5%

Percentage contribution of tvarogs with presence and absence of staphylococcus

Steinka et al. 1999a.

From data presented by many authors (Castillo-Rodriqueza et al. 2002, Walls et al. 1996, Schaffener et al. 2001), it results that staphylococci give rise to difficulties in predicting food safety and the predictive microbiology is moderately useful for determining safety of products, in which these bacteria can occur. From conducted observations it results that products of metabolism of these bacteria can appear not before refrigerating storage of the products. Staphylococci can appear in tvarogs after 7 days or later, although during postproduction period their presence was not detectable (table 19). Nevertheless, tvarog composition can have the significant influence on the behaviour of staphylococci populations. From conducted research it results that after dropping 3

2

staphylococci in amount of 1 cm on 1cm of tvarog surface, and then vacuum-packing of the products into PA/PE packaging, the behaviour of these micro-organisms differed depending on the fat content. Lactic acid cheese manufactured by the same producer and originating from one lot contained 15 and 30% of fat. Products after addition of Staphylococcus aureus of 0

inoculum 3 log cfu/mL were stored after repacking at the temperature of 6±2 C for the period of 14 days. Analyses were carried out after 2, 4, 7 and 14 days. The tendency of changes of staphylococci population count in fat tvarogs could be expressed with a quadratic equation. In low-fat tvarogs, the decrease in lactic acid bacteria (LAB) count was accompanied by the growth of staphylococci population. Variability in number of these micro-organisms could be also described with a quadratic equation (figure 16). In the case of fat tvarogs, the growth of lactic acid bacteria was accompanied by the decrease of staphylococci count. From research it resulted that variability of lactic bacteria count in both types of tvarogs was not significantly dependant on storage time and temperature in 18.7% and 40% respectively (table 20). In tested fat tvarogs a weak linear correlation (r 0.235) between storage time and staphylococci count was observed. This can evidence the influence of lactic acid bacteria, other micro-flora not defined in this research and the fat content in products. Similar relationships were observed in the case of low-fat tvarogs (r 0.283).

Influence of Interactions Occurring Between Micro-Organisms…

6 y = -0,1733x3 + 1,4386x2 - 3,2981x + 6,38

5

R2 = 0,8138

4

y = -0.2658x4 + 3.1433x3 - 12.834x2 + 20.957x - 7.7 R2 = 1

Log cfu/g 3

2

1

0 0

2

4

7

14

Tim e (days) LAB

Staphylococcus aureus

polynom.

polynom.

Figure 17. The behaviour of staphylococci and lactic acid bacteria in fat tvarogs.

6 y = -0,277x + 5,075 R2 = 0,8113

5

4

y = -0,255x4 + 3,0267x3 - 12,17x2 + 19,148x - 5,98

Log 3 cfu/g

R2 = 1

2

1

0 0 LAB

2

4

Tim e (days) Staphylococcus aureus

7 linear

Figure 18. The behaviour of staphylococci and lactic acid bacteria in low-fat tvarogs.

14 polynom.

211

212

Izabela Steinka

Table 20 below presents equations of linear correlation, as well as equations describing the tendency of changes in both types of tvarogs. Table 20. Trend equations of changes of LAB and staphylococci in stored tvarog of different fat content Type tvarog

of

Type of micro-organisms Staphylococcus aureus

Fat

Low-fat

4 3 2 Y = -0.1808x + 2.2083x - 9.3492x + 15.772x – 5.15 2 r 0.999 Y = -0.098x + 3.586; 2 r 0.235 3 2 Y = 0.0383x - 0.385x + 1.0467x + 2.662 2 r 0.3416 Y = 3.5854e-0.0297x 2 r 0.2419 Y = 3.4786x-0.0614 2 r 0.1673 Y = -0.255x4 + 3.0267x3 - 12.17x2 + 19.148x 5.98; 2 r 1 Y = 0.195x + 3.281 2 r 0.2839 Y = 0.3612Ln(x) + 3.5201 2 r 0.1574 3 2 Y = -0.0333x + 0.4707x - 1.616x + 5.036 2 r 0.6005 Y = 3.3189e0.0477x 2 r 0.2307

Starter cultures (Lactococcus spp + Leuconostoc lactis) 3 2 y = -0.1733x + 1.4386x - 3.2981x + 6.38 2 r 0.8138 y = 0.2092Ln(x) + 4.3097 2 r 0.0846 y = 4.3287e0.0124x 2 r 0.0414 y = 0.064x + 4.318 2 r 0.049

y = -0.277x + 5.075 2 r 0.8113 y = -0.7409Ln(x) + 4.9534 2 r 0.9376 y = 4.962x-0.1685 2 r 0.938

Own study.

Table 21. Size of Staphylococcus aureus population simulated with Pathogen Modelling Program Concentration of lactic acid

0 0.3 0.5 0.7 0.8 1

Magnitude of reduction of Staphylococcus areus count Log10cfu 4 Reduction time (days) 92.87 61.65 52.21 48.17 47.78 50.12

Developed on the basis of PMP v 5.1.

5

6

116.09 77.06 65.26 60.21 59.72 62.65

139.3 92.47 78.31 72.25 71.67 75.18

Influence of Interactions Occurring Between Micro-Organisms…

213

Computer simulation developed with Pathogen Modelling Program shows that in conditions corresponding to different magnitudes of changes connected with action of alkalising micro-flora, the level of staphylococci is varied (table 21). From data obtained by Pathogen Modelling Program, it results that time of reduction of 0 staphylococci count by 1 logarithmic cycle at temp. 4 C and pH 4.7 is equal to 11.3 days. In tested tvarog, no reduction in count of these bacteria was observed after 14 days. In low-fat tvarog the increase in bacteria number by 1 logarithmic cycle during that time was detected. It evidences that in case of infection of tvarogs, starter cultures present therein will not inhibit the growth of staphylococci in products of low fat content. From previous research it also results that reciprocal interaction between lactic acid bacteria and staphylococci present at this level in fat tvarogs will be dependent on the stage of hermetic properties and type of applied packaging (Steinka et al. 2003c). In literature, there are a few predictive models available, which allow evaluation of the growth of these bacteria in food (table 22). Table 22. Predicting changes in staphylococci count in model conditions and in food Type of product Model conditions Bread

Sterile food

Model conditions Model conditions Tvarogs

Type of described changes Davey and surface response model of the growth at several environmental conditions Kinetics of dying out, death in variable environmental conditions, quasi-chemical model was compared with Gompertz model, and probabilistic model integrated with quasichemical was applied Defined environmental conditions. Gompertz model and comparison of obtained data with PMP and FMM predictions Environmental conditions. Gompertz model – growth of staphylococci. Kinetic models, environmental factor influence on „growth/no growth”

Source Zurera-Cosano 2004

Polynomial survival rate

Steinka 2003

Taub 2003

Walls 1996

McCann 2003 Stewart 2002

Own study.

An attempt of developing such a model was also made by Steinka (2003a), Steinka et al. (2005a).

DEVELOPMENT OF THE PROGRAM FOR EVALUATION OF STAPHYLOCOCCI GROWTH The program was developed in Borland Delhi 2.0. language and serves for evaluation of microbiological quality of lactic acid cheese vacuum-packed into PA/PE as well as shrinkwrapped with Cryovac packaging. Values of differences in predictions for tvarogs in both types of packaging showed the higher dynamics of changes in bacteria count in tvarogs

214

Izabela Steinka

packed into Cryovac. It was observed that JMTPH computer program was useful for predicting changes of staphylococci count in stored tvarogs. Type of packaging and the packing system had the influence on evaluation of tvarog safety. In the computer program used for predicting changes of these bacteria populations in tvarogs packed into PA/PE laminates, 4 microbiological parameters were applied up to 7 days of storage and 5 parameters during further storage period. In the case of prediction regarding tvarogs packed into Cryovac, 6 microbiological parameters were applied. In conditions of shrink-wrapping the product with Cryovac, no influence of yeast on the level of determined staphylococci was observed in the second week of storage (table 23). From predictions obtained for vacuum-packed tvarogs, it resulted that at maximum product contamination equal to 3.32 log cfu/g, the level of staphylococci equal to 4 log10 cfu in one gram of tvarogs remained between day 3 and day 21 of storage. The prediction of change of coagulase-positive staphylococci population in tvarog during storage is presented in table 23. Table 23. Predictions of staphylococci growth in tvarog depending on packing system Day of storage

Parameters of mathematical model

Prediction result Log cfu/g

TVAROGS PACKED INTO PA/PE 3

Y=-3353.77+0.04 x1 +5.84 x2-0.11 x4+1.07 x5

4.11

7

Y=-1768.74+0.02x1+4.78x2-0.1x4+0.94x5

4.09

13

Y=608.79-0.01x1-3.17x2 +0.09x4+0.75x5+0.01x7

4.08

15

Y=1040.13-0.02x1+2.64x2-0.09x4+0.62x5+0.01x7

4.31

21

Y=3778.84-0.04 x1+1.04x2-0.08x4+0.49x5+0.02x7

4.06

TVAROGS PACKED INTO CRYOVAC 3

Y= 257.67 +0.01x1-+93.60x2-0.73x4-1.33x5+0.03x6-0.13x7

3.91

7

Y= 70.78-0.05x1+746.33x2-0.99x4-2.27x5+0.03x6+0.09x7

5.03

9

Y=-22.66+0.08x1+1072x2-0.06x4-2.271 213x5+0.01x6+0.76x7

5.14

15

Y=-303-0.16x1-2051.8x2-0.85x4+22.92x5-1.45x7

5.31

21

Y=-583.33-0.24x1+3030.90x2+1.64x4+33.72x5-0.01x6-2.11x7

5.48

From presented data the significant differences resulted in count of staphylococci population in tvarogs, depending on packing way and type of applied packaging. Differences between sizes of staphylococci populations determined in tvarogs packed into both types of packaging could be observed as soon as from the day 3 of product storage. Difference values in predictions obtained with the help of JMTPH computer program indicated the higher dynamics of changes in tvarogs packed into Cryovac.

215

Influence of Interactions Occurring Between Micro-Organisms…

In optimum model conditions and at temperature of 220C, the growth of staphylococci population shows significant dynamics. The increase in count of these bacteria by 3 logarithmic cycles can occur during 72 minutes. Simulation carried out with the Pathogen Modelling Program for the temperature 40C and pH 4.7 showed that staphylococci reduction time by 1 logarithmic cycle was equal to 11.3 days (6). This evidences the significant resistance of these bacteria to the influence of low temperature and acid in the environment. Computer simulations conducted with Pathogen Modelling Program showed that other microorganisms e.g. Salmonella spp., were characterised by different dying-out dynamics in comparison with staphylococci population at the temperature of 60C and pH 4.7. Reduction in number of rods by 1 and 2 logarithmic cycles occurred from 7 up to 14 hours respectively. It also should be noticed that presented predictions developed using Pathogen Modelling Program concerned the individual growth of micro-organisms without taking the presence of other micro-organisms into account. Predicting the bacteria growth in model conditions in many cases assumes the existence of microbiological rules obligatory for monocultures. From our previous research it results that the behaviour of both staphylococci and Listeria spp. rods in hermetically packed tvarogs was different than expected (Steinka et al. 1999b). Also the growth of staphylococci in conditions occurring in food can proceed with different dynamics than the one observed in model conditions, what was proved by research of Wallas et al. (1997) and Smittle et al. (1994). From data obtained by other researchers it resulted that temperature also did not constitute the factor that limited absolutely the growth of staphylococci. The slow increase in staphylococci count below temperature of 10oC was observed in different types of food by Lee Wong et al. (2002). The number of staphylococci cells determined in tvarogs with JMTPH computer program presented in this paper was similar to sizes of populations obtained by e.g. Belickov et al. (2001). The mentioned-above 1

4

authors isolated staphylococci from tvarogs at the level from 9x10 cfu/g up to 1.07x10 cfu/g. Research conducted by those authors proved the presence of staphylococci in other fermented dairy products such as sheep cheese, buttermilk and yoghurt, what can evidence the resistance of staphylococci to antagonistic influence of lactic acid bacteria. The above-menioned factors show that presented prediction of staphylococci changes obtained with the help of JMTPH computer program can be actual and can reflect the behaviour of population in conditions of hermetically packed tvarogs.

SURVIVAL RATE OF STAPHYLOCOCCI ON THE SURFACE OF TVAROGS AND THE SYNTHESIS OF ENTEROTOXIN DURING STORAGE One of the determinants of food safety, apart from the absence of vegetative forms of pathogenic bacteria, is the absence of toxins released into food. The hazard of uncontrolled synthesis is created by mostly such bacteria as Staphylococcus aureus. The intensity of enterotoxin production by Staphylococcus aureus strains is dependent on environmental conditions, among which the most significant are: temperature, environment pH value, redox potential and gaseous atmosphere (Adams 1995). The synthesis of staphylococcal enterotoxin is dependant on many factors such as: environment in which staphylococci population occurs, compositions of a medium, micro-organism count and the presence of inhibiting substance.

216

Izabela Steinka

As it results from the literature, despite the antagonistic influence of other micro-flora present in the environment (even lactic acid bacteria), in certain conditions the toxicogenesis can occur. In order to evaluate food safety, it is possible to apply microbiological predicting that enables approximate assessment of population growth. The existing computer programs help to determine lag phase, magnitude of population count reduction or the rate of its growth, however they not always take all environmental conditions into account. Therefore, it seems to be important to compare the growth and survival rate of populations in model conditions and in monoculture with data obtained in food. From research of Neumayer et al. (1990), it results that in a medium enriched with ingredients occurring in such vegetables as pea or bean, the bacteria growth occurs with significant dynamics in model conditions, however the concentration of produced enterotoxin is highest in a medium where pea is present. Enterotoxin accumulated therein in amount of 14-15 ng per mL of a medium. The presence of high concentrations of valine, cistine and arginine contributes to the synthesis of SEA by staphylococcus aureus in amount smaller than SEB, SEC (Bergdol et al. 1989). Moreover, the same research also showed that glucose had inhibiting influence on the production of SEB and SEC. Opinions as regards number of staphylococci necessary for enterotoxin production are varied. Halin–Dohnalek et al. (1989) indicated the level of 106 cfu/g in food environment of high fat content. In sterile milk, this number is equal to 10 6,5 cfu/mL (Fukijawa et al. 2006). Detectability of enterotoxin is not only dependant on the level of cells. Otero (1988) did not detect the presence of enterotoxin in cheese even at the level of 108 cfu. Whereas Delbar et al. (2006) detected enterotoxin after 24 hours in cheese, where number of staphylococci was equal to 5.55-5.6 log10cfu/g. This confirms our observations (Steinka 2004). While testing ready-to-serve food, Bahk et al. (2006) observed that as much as 29.73% of samples showed the presence of staphylococci at the level > 105 cfu/g, what could favour enterotoxin synthesis. From tests conducted on hermetically packed tvarogs and tvarog coming from unsealed packaging, it resulted that enterotoxin was formed more often in the latter. Research showed that a small supply of oxygen to the product favoured the enterotoxin synthesis (Steinka 2004). The presence of enterotoxic strains in food is observed in many different food products (table 24). Table 24. Contribution of toxicogenic strains among staphylococci present in dairy products Type of food Milk, cheese Milk from cows with mastitis

Data on the basis of Loir et al. 2003.

Contribution of toxicogenic strains 15.9 % 43% 72.8%

Source Rosec 1997 Cordobo 1999 Akineden 2001

Influence of Interactions Occurring Between Micro-Organisms…

217

From research of Otero et al. it results that aerobic conditions cause the increase of kinetics of staphylococci population growth, however they are not the reason for occurrence of enterotoxin in tested cheese. The significant factor influencing the production of enterotoxin is temperature. Literature data evidence that the lowest temperature in which enterotoxin synthesis is observed is equal to 70C (Adams et al. 1995). According to Halin-Dohnalek et al. (1989), most of staphylococci strains present in sour cream at the temperature of 370C synthesised enterotoxin as soon as after 18 hours. At 220C this time was prolonged up to 52 hours. From research of these authors, it resulted that no staphylococcus strain produced enterotoxin at 40C during 14 days. According to some authors, heating chicken products and then storing them at the temperature of 40C guaranteed elimination of enterotoxin (Pepe et al. 2006). In lactic acid cheese tested by Steinka et al. (2001c, 2004), enterotoxin should not occur due to the presence of lactic acid bacteria. From data obtained by Loir et al., it results that in laboratory conditions – the acetic acid added to the medium has the greater inhibiting influence on enterotoxin production than e.g. lactic acid. Whereas, basic pH constitutes a very significant factor determining the production of toxins SEB, SEC and SED (Loir 2003). This can be confirmed by research of Delbar et al. (2006), who observed the release of enterotoxin by bacteria present in cheese at the increase of pH values. The enterotoxin synthesis by Staphylococcus aureus is dependant on the presence of accompanying micro-flora in the environment (Oter et al. 1988, Noleto et al. 1987, Sameshima et al. 1998, Steinka 2004). Sameshima (1998) tested the influence of Lactobacillus cultures on staphylococci in fermented sausages. Inoculum was equal to 104 cfu for staphylococci and 107 cfu for Lactobacillus. Enterotoxin was detected during fermentation at each temperature, if the certain strain of lactic acid bacteria e.g. Lactobacillus acidophilus FERM P-15119 occurred in the presence of staphylococci. In the presence of L. rhamnosus and L. paracasei strains, the inhibition of toxin release was also satisfactory. Lactic acid bacteria belonging to starter cultures do not always show the identical ability to inhibit enterotoxin synthesis. It depends both on type of enterotoxin and the inhibiting strain. The influence of commercial starters on the growth of Staphylococcus aureus and the production of C1 and C2 enterotoxins in model conditions was investigated by Otero et al. 1988. Staphylococcus aureus FRI 137 strain producing enterotoxin C1 as well as FRI 361 and L2 strains producing enterotoxin C2 grew well both individually and in the presence of starters. This starter contained standard Lactococcus lactics spp. strains. Commercial starters showed a weak inhibiting influence on S. aureus only in late phases of growth of these bacteria. In contrast, the enterotoxin synthesis was strongly inhibited after 18 hours in as much as 89% for Staphylococcus aureus FRI 137. Presence of the starter inhibited release of toxin in 80% by Staphylococcus aureus FRI 3 and in 69% by staphylococci L2 strain. Enterotoxin C1 was both synthesised and accumulated during all phases of growth both in monoculture and in mixed population. Unfortunately, the growth of other strains caused reduction of its concentration after 24-36 hours (Otero et al. 1988).

218

Izabela Steinka

Extremely important research for understanding the character of staphylococci toxicogenesis was conducted by Noleto et al. 1987, who evaluated production of staphylococcal enterotoxin by Staphylococcus aureus in the presence of other pathogenic bacteria such as: Bacillus cereus and Escherichia coli, Streptococcus faecalis, Pseudomonas aeruginosa. All staphylococci strains showed the growth and production of enterotoxin in the presence of enterococci. In tvarogs tested by Steinka (2004), count of enterococci was significant, what could be the reason for stimulation of staphylococci to synthesise toxin. From research it resulted that in model conditions, the metabolism ability of staphylococci was also determined by the type of medium. Application of different medium types allowed observing that this phenomenon occurred only when preponderance of staphylococci over enterococci count was detected in the environment. Other behaviour of staphylococci was observed in the presence of Bacillus sp. Staphylococci showed growth in the presence of rods in both types of medium, however enterotoxin was produced only when their inoculum was 10 versus 104 in relation to Bacillus sp. Quantitive ratio of both populations necessary for enterotoxin production was dependent on type of staphylococci strain used for medium inoculation. And so, e.g. FRI 196E strain produced toxin in both types of applied medium even when inoculums of both bacteria were equal. Staphylococci did not produce enterotoxin only when Escherichia coli remained preponderant in number in both medium types. In order to observe enterotoxin synthesis, the count of staphylococci had to be significantly higher than number of rods (10 versus 104). Other types of relationships were observed in the presence of Pseudomonas aeruginos. Staphylococci did not produce enterotoxin when staphylococci inoculum exceeded 10 versus 3 4 10 or 10 . From the research of Noleto, it resulted that enterotoxin was produced only when staphylococci number was equal to or greater than number of accompanying species (Noleto 1987). A very important condition favouring enterotoxin synthesis is also the presence of inhibiting substances in the environment (Gonzales –Fandos et al. 1994). Epidemiologic data suggest that the enterotoxin B (SEB) is seldom observed in food, whereas enterotoxin of type A (SEA) and C (SEC) are predominant, and they are responsible for more than 70% of contamination of food samples. Staphylococcal enterotoxins SEA and SED are more often produced in food of basic pH, even at low number of cells. However, even among staphylococci isolated from fermented milk, the significant amount belongs to toxicogenic strains. From research of Steinka (2004), it results that concentration of determined enterotoxin was varied, and was higher in tvarogs packed with vacuum system then in tvarogs packed into Cryovac films. Lack of packaging hermetic properties favoured the enterotoxin synthesis. In samples of vacuum-packed tvarog coming from depressurised packaging, the 2-3-fold higher enterotoxin concentration was observed in comparison with hermetic packaging. 0 0 Enterotoxin was detected in tvarogs stored in the temperature from 6 to 8 C for the period of 7 and 14 days. Enterotoxin presence was not observed in samples taken for tests on the day of introducing a product into the market.

Influence of Interactions Occurring Between Micro-Organisms…

219

However, the observed case of the increase in enterotoxin concentration in samples coming from the same lot between day 7 and day 14 of product storage can suggest the probable existence of this toxin synthesis. The possibility of growth of Staphylococcus aureus strains producing enterotoxin in fermented dairy products arouses controversy. As it results from the literature, despite the antagonistic influence of other micro-flora present in the environment (even lactic acid bacteria), in certain conditions the toxicogenesis can occur. The necessity to evaluate the probability of staphylococci survival and the staphylococcal enterotoxin synthesis in hermetically-packed products during storage at low temperature is the reason for developing models determining the risk. For instance, the growth of staphylococci after 14 days in tvarogs stored in hermetic packaging could not be described with linear models, because they showed a low degree of equation matching. NPA/PE = 214.86 + 1.3269•N7 r2

0.057

(16)

NCryovac = 236.63 – 0.0068•N7 r2

0.131

(17)

where NPA/PE14 – staphylococci count in tvarogs packed into PA/PE laminated after 14 days NCryovac 14 – staphylococci count in tvarogs packed into Cryovac laminated after 14 days N7 – staphylococci count in tvarogs after 7 days of storage Staphylococcal enterotoxin synthesis depending on storage time, hermetic properties and packaging type as well as the ability of coagulase synthesis is presented in table 25. Table 25. Models of staphylococcal enterotoxin synthesis in dependence on the ability of coagulase synthesis and number of staphylococci in tvarogs Equation form Ep7=-84.7853+0.425N0

r2 0.865

Ep7=79.4059+0.3342N0+56.6643K7 Erp7=211.122-0.134925N0

0.867

2 Erp14=300.907-230.4(K14) Eb14=38.7367+74.0549K0-250.317K14+257.751K7-0.3713N14-0.0165N7

0.460 0.920

Erb14=-21.2696+3.21196N14 +1.74943N7 – 17002.1K14

0.856

0.176

Steinka 2004, N- staphylococci count; 0,7,14 tvarog storage time E- staphylococcal enterotoxin; K- coagulase; Indexes: p - vacuum, b – non-vacuum, r – depressurised.

As a result of conducted research, staphylococcal enterotoxin was observed in the insignificant percentage (4.5%) of tested samples coming both from tvarogs packed with

220

Izabela Steinka

vacuum system and those packed into Cryovac. Application of multifactor regression analysis as well as data transformation allowed expressing the probability of occurrence of enterotoxin in hermetically packed tvarogs with the help of linear and polynomial equations. When enterotoxin was present in tvarogs packed into Cryovac, the statistically significant relationships was also observed between the probability of enterotoxin occurrence and the staphylococci population count during storage, as well as synthesis of coagulase by these bacteria (table 25). Equation describing occurrence of enterotoxin after unsealing the nonvacuum packaging had quadratic form, what significantly evidences the influence of environment on enterotoxin synthesis. Equation describing this relationship showed the existence of weak connection between synthesis of enterotoxin and coagulase by Staphylococcus aureus in these conditions. This can indicate the crucial importance in evaluation of risk. In optimum model conditions, the growth of staphylococci count and the production of enterotoxin show significant dynamics. At temperature of 220C, the growth of staphylococci by 3 logarithmic cycles should occur after 72 minutes. According to Pathogen Modelling Program, in aerobic conditions, at pH change and temperature drop to 10-120 C, changes in staphylococci population count by 1 logarithmic cycle were not observed before 10-21 days. Shorter time (by 11 days) of staphylococci breeding predicted with this program for anaerobic conditions of growth show a significant meaning of redox potential for the growth of these bacteria. Vernozy-Rozynand et al. (1998) suggest that enterotoxin production is a slow process. In ripening cheese, the presence of enterotoxin was not observed in fresh curd, but only after 21 days of ripening. The ability of staphylococcal enterotoxin synthesis does not have to be connected with synthesis of coagulase, because there exist enterotoxic coagulase-negative Staphylococcus aureus strains that produce enterotoxin, as well as other species of enterotoxic staphylococci such as: Staphylococcus haemolyticus, Staphylococcus warneri, Staphylococcus saprophyticus or Staphylococcus epidermidis.

METHODS OF OPTIMISING QUALITY OF TVAROGS Before implementation of HACCP system into food production, tvarogs were characterised by low microbiological quality before storage. The traditional packing system connected with non-automated technological line can still arouse quality problems. The conducted numerous attempts to optimise the quality of cottage cheese and tvarogs (Kornacki et al. 1999, Kornacki et al. 2002 Molska 1992, Rosenthal et al. 1996 Herve et al. 1998, Stanton et al. 1998, Neugebauer et al. 2005) indicated not only the problem of obtaining lactic acid cheese of high stability level, but they also emphasised the significant influence of micro-flora on achieving desired sensory features of these products. Microwaves and ionising radiation are the directions of optimising microbiological quality of cottage cheese proposed by Hevre et al. 1998 and Rosenthal et al. 1995. Kornacki et al. 1999 suggested enriching starters used for tvarog production with addition of such bacteria as: Streptococcus salivarius spp. termophilus, Lactobacillus delbrueckii bulgaricus, Bifidobacterium bifidum, Lactobacillus acidophilus. Similar suggestions were made by Neugebauer et al. in relation to cottage cheese (Neugebauer et al. 2005).

Influence of Interactions Occurring Between Micro-Organisms…

221

Kornacki et al. (2002) showed that application of traditional butter starters used for production of tvarog made of raw material of low microbiological quality did not result in reduction in number of coliforms and psychrotrophs, and even the growth of proteolytic micro-organisms count in ready product was observed. Therefore, it is necessary to find a method for optimising the quality of these products. Optimisation of tvarog quality can be carried out with several methods: • • •

Application of additives of biocide character Modification of packing system Application of safety predictions for instance with the help of a computer program

Optimisation with Biocides Plant additives are often added to dairy products such as yoghurts, cottage cheese or ripening cheese (Ahmed et al. 2002, Beckmang et al. 1996, Grega et al. 1999, 2001). They include pieces of fruit, chives, onion, garlic, paprika, tomatoes, cucumbers, horseradish and herbs. However, the data prove that some of the spices and herbs modify the metabolism of starter cultures. This is the reason for changes in organoleptic properties of products manufactured using those starters (Arora et al. 1999). Various research concerning the behaviour of staphylococci in the presence of different substances of plant origin has been conducted until now. For instance, some authors evaluated the possibility of enterotoxin synthesis at variable concentration of garlic in the environment (Gonzales-Fandos et al. 1994). Enterotoxins SEA and SEB were detectable at garlic concentration not exceeding 1%, whereas SED was produced by staphylococci even at the level of 2%. Generally, no additives are applied in tvarogs. Some additives of flavour character are used as a supplement in cottage cheese. Some of them have also biostatic properties. This concerns for example garlic, onion and herbs. However, from our research (Steinka 2006a) it results that the presence of garlic in these products gives them a specific bitter taste. Searching for other plant additives e.g. mixture of rowan and aloe in model testing performed on semi-products (lactic acid curds) showed varied influence on the facultative anaerobic bacteria and fungi, which can be present in tvarogs (Steinka 2005c). Table 26 presents changes in count of staphylococci, enterococci and yeast, depending on plant additive. Table 26. The influence of plant additives on facultative anaerobic micro-flora Micro-organisms Staphylococcus aureus

Tvarog curd Aloe arborescens Y=0.13x+4.3

Sorbus aucuparia Y=-0.2x+4.63

Enterococcus faecalis Candida sp.

Y=3.11x-0.34 Y=0.94x+5.29

Y=-0.3x+3.07 Y=-0.76x+6.99

Steinka 2005d, x-value of control sample.

222

Izabela Steinka

The effort to find the appropriate conditions of adding aloe during production of lactic acid curds encountered significant difficulties (Steinka 20002b, 2003b, 2003d). The attempts to optimise the quality of ready lactic acid cheese with aloe aerosol were also made (Steinka 2003c). In tvarog samples taken for tests during refrigerating storage, the differences were noticed between sizes of micro-organism populations in products with aloe extract additive and those where aloe aerosol was not added. The average values of population counts indicated the stimulating action of aloe aerosol in relation to enterococci and yeast. From the fourth day of storage of tvarogs with aloe additive, the increase in staphylococci count was observed. The growth of yeast in tvarogs treated with aloe aerosol was observed during the entire period of lactic acid cheese storage. Whereas, the effectiveness of aerosol action was observed in relation to mould and staphylococci. During storage of tested products, the inhibition of mould and staphylococci counts were detected in samples with aloe extract additive. It was also observed that the presence of aloe resulted in the inhibition of growth of Lactococcus sp. population during the first days of product storage. From day 4 until day 7 of product storage, the growth of lactic acid bacteria in tvarogs with additive was observed, and then the reduction in number of these bacteria was noticed between day 7 and day 14 of tvarog storage. The results of statistical analysis showed a high correlation (r 0.9697) between the enterococci contamination level present in tvarogs not subjected to action of aerosol and stored in packaging and the addition of aloe extract (table 27). Table 27. Linear correlation between micro-organism populations present in control tvarogs and in tvarogs with aloe aerosol additive Type of micro-organisms Enterococcus sp.

Equation of linear correlation Ea= 13811+1.0894E

r

2 r

0.969

0.940

Staphylococcus aureus

Sa=13.199+0.79701S

0.792

0.627

Yeast

Da=154600+0.71205D

0.708

0.500

Mould

GSa=191500+0164523GS

0.385

0.148

Lactococcus sp.

La =2971000+0.03452L

0.114

0.012

Steinka 2003b, Ea - Enterococcus sp. in tvarogs with aloe additive; Sa- Staphylococcus aureus in tvarogs with aloe additive; Da- yeast in tvarogs with aloe additive; GSa – staphylococci in tvarogs with aloe additive; La - Lactococcus sp. in tvarogs with aloe additive; E- Enterococcus sp; S- Staphylococcus aureus; D- yeast; L- Lactococcus sp.

High coefficient of determination r2 0.9403 showed that enterococci count in tvarogs without additive was similar to number observed in tvarogs with aerosol additive. From statistic analysis it resulted that the variance of enterococci count only in 6% could be determined by the presence of aloe. Whereas, 37% of variance of staphylococci count in tvarogs with aloe additive could result from the influence of aloe.

223

Influence of Interactions Occurring Between Micro-Organisms…

Basing on coefficient of determination, it was also proved that mould and yeast showed varied susceptibility to aloe influence. In the case of yeast, more than 50% of variance of these fungi count resulted from the action of aloe aerosol, whereas 85% of variance of mould could result from the presence of aloe aerosol and the influence of this additive on fungi populations. Changes in number of bacteria and fungi in tvarogs stored with aloe additive, depending on storage time and the behaviour of other micro-organism populations present in tvarogs could be expressed with polynomial equations of the following form: Y = a1x1 + a2x2 + a3x3 + a4x4 …

(18)

which are presented in table 28. Table 28. The influence of storage time and the interactions among micro-organisms on the population of micro-organisms in tvarogs with aloe aerosol additive Type of microorganisms

Equation of linear correlation

r

R2

Enterococcus sp.

Ea= 29479.82-2466.85t+1.1E-9.05S0.09D+0.16GS-0.01L

0.983

0.962

Staphylococcus aureus

Sa=164.8455-14.2526t+0.0019E +0.7548S0.0002D-0.0001GS

0.802

0.593

Yeast

Da=134169.7-3827.7t-0.3E+3.3S+D-0.1GS

0.735

0.476

Mould

GSa=64808.98-1018.67t1.55E+36.38S+0.09D +0.88 GS-0.02L

0.807

0.603

Steinka et al. 2003b.

The presented coefficients of determination of equations describing changes of enterococci, staphylococci and yeast populations, taking interactions among micro-organisms and action of aloe into account, were similar in both types of tested tvarogs (tables 27 and 28). Coefficients of determination defined for those equations differed from each other by 2.18%, 3.41% and 2.55% respectively, indicating the insignificant influence of factors other than aloe on changes of these micro-organisms populations (tables 27 and 28). In the case of mould, differences in coefficients of determination of derived equations showed a great influence of interactions occurring among micro-organisms present in products and the time of tvarog storage on the level of fungi in tvarogs treated with aloe.

224

Izabela Steinka

In the case of mould population in tvarogs with aloe additive, the inhibition of its growth was the combined effect of application of aloe, hermetic packaging, low temperature as well as interactions occurring among the micro-organisms present in the product. Table 29 presents the influence of lactic acid bacteria on the growth of individual groups of micro-organisms in stored tvarogs as well as the influence of interactions among the microorganisms and the aloe aerosol on growth of these populations. These relationships were expressed with quadratic equations (Second Order Polynomial). Table 29. The influence of storage time, additive of aloe and lactic acid bacteria on changes of secondary micro-flora population in stored tvarogs

Type of microorganisms

Enterococcus sp

Equation form Tvarogs stored without aloe aerosol additive E=29195+4361,14t-0,002L180,297 t2+3,203e-4tL+1,037e10L2

Staphylococcus aureus

S=

1377,034-248,149t-8,123e5L+11,44t2+1,755e-5tL-1,059e12L2

Yeast

D=-35801,83+1,001e5t +0,005L-5082,894t2+0,008tL6,035e-10L2

Mould

GS=22261,7+71775,96t-0,006L1966,29t2+0,002tL+2,338e-10L2

Tvarogs stored with aloe aerosol additive Ea= 24412,87-36566,1t +0,03La+2653,61t2+0,017tLa -3,923e-9La2 Sa= 1202,67-232,937t-5,122e-5La +11,263t2 +1,223e-5tLa-1,014e12L 2 a Da=72940,75+1,006e5t+0,085La -6253,274t2+0,025tLa-7,192e-9 La2 GSa=-7391,507+35196,96t +0,037La-702,675t2+0,011t La -3,179e-9 La2

Steinka 2003b Ea - Enterococcus sp. in tvarogs with sloe additive; Sa- Staphylococcus aureus in tvarogs with sloe additive; Da- yeast in tvarogs with sloe additive; GSa – staphylococci in tvarogs with sloe additive; La - Lactococcus sp. in tvarogs with sloe additive; E- Enterococcus sp; S- Staphylococcus aureus; D- yeast; L- Lactococcus sp.; t- storage time.

From obtained data, it also results that the influence of lactic acid bacteria on the growth of secondary micro-flora was influenced by aloe additive (table 29).

Influence of Interactions Occurring Between Micro-Organisms…

225

Second order polynomial equations describing the growth of staphylococci, enterococci and mould in stored tvarogs differed among each other, if tvarogs were sprayed with aloe aerosol. Only surface response models determining the influence of lactic acid bacteria on yeast showed a very similar shape and direction of action in the case of tvarogs both without and with aloe aerosol additive. Changes of Lactococcus sp. populations under the influence of aloe could be expressed by the equation of the following form: La= -1.248e6 + 1.008e6t + 1.194L – 64381.85t2 – 0.043tL – 3.768e-9L2

(19)

where: La – number of Lactococcus sp. in tvarogs with aloe additive L – number of Lactococcus sp. in control tvarogs t - storage time of tvarogs Negative value of coefficient a in the equation indicated the direction of changes of Lactococcus sp. population count under the influence of aloe, suggesting inhibition of growth of lactic acid bacteria populations in tvarogs. The bactericidal properties of aloe in relation to many bacteria are commonly known. In food products its application is limited due to the presence of significant amount of aloin, which excessive amount creates the hazard of excretory system hyperaemia and nephropathogenesis. While aloin level in food products is not advantageous (special regulations control the level), the small amount of aloe additive to food influences favourably the intestine peristaltic motion as well as functioning of immunological system. There exist data indicating the possibility to lower the adverse effect of aloin in the presence of lactic acid bacteria (Steinka 2002b). Addition of small aloe amount can be applied in fermented food with the help of lactic acid cultures. From previously conducted research (Steinka 2001d,e 2002b), it resulted that the presence of aloe in the form of a pulp influenced significantly the growth of certain types of bacteria and fungi in lactic acid curd, as well as it affected animal organisms. The influence on micro-organisms was varied, depending on type of micro-organisms, bacteria species, presence of other micro-organisms and their count in tested environment (Steinka 2001d,f). It was showed that the additive of aloe in the form of aerosol into tvarogs of low microbiological quality lowered the staphylococci count during 14 days of product storage in hermetic packaging. Moreover, the aerosol stabilises the level of mould in these products. However, this method of aloe application shows stimulation influence on the growth of enterococci and yeast population in stored tvarogs.

226

Izabela Steinka

Aloe aerosol also cannot be applied for optimising the quality of tvarogs of high contamination level with enterococci and yeast. To sum up, optimising the quality of tvarogs of a signification level of contamination with facultative anaerobic bacteria, using aloe aerosol and vacuum re-packing is possible, but it requires more research to be conducted.

Optimising the Quality through Modification of the Packing System This type of optimisation involves packing in clean air atmosphere. From various research, it results that air in dairy plant can be the reason for contamination of ready products. The literature data report that department of fermented drink and cheese production can show a higher level of contamination with micro-organisms in comparison with other departments (Ren et al. 1992, Salustiano et al. 2003). Salustiano et al. detected the presence of staphylococci in dairy plants (table 30). Table 30. Contamination of air in different departments of dairy plant Processing area

Mesophilic Bacteria CFU/m3 110-600

Aerobic

Yeast and moulds

Total coliform CFU/m3

70-160

0.00-0.66

Milk acceptance section Cheese

100-920

90-610

0.33-1.66

Yoghurts

100-320

100-940

0.00-0.66

Own study on the basis of Salustiano et al. 2003.

The reasons for product contamination can be both traditional production technology, in which pressing and draining can constitute the critical control points. In automated production technologies, these stages do not create any hazard. According to FIL IDF, the air quality of the highest purity class should be characterised by OLD value not greater than 10 cfu/m3 while both fungi and yeast as well as pathogenic bacteria should not be present at all. The lowest air class (i.e. D) allows the presence of ca. 10000 aerobic mesophilic bacteria and more than 100 fungi and 10 pathogenic bacteria. Research conducted by Olbromska et al. 2005 showed that contact with personnel, machines, air and most of all with packaging is often the reason for secondary contamination determining the safety of dairy products. However, research of Steinka et al. 1998 did not show the high contamination level of packaging materials applied for packing lactic acid cheese and cottage cheese (table 31).

Influence of Interactions Occurring Between Micro-Organisms…

227

Table 31. Microbiological quality of materials for packing tvarogs Packaging type

Mould

Yeast

cfu/25cm2 Parchment paper

11

0

PA/PE laminate

30

0

Frischaltenfoliae PE

44

22

Aluminium foil

33

0

Styrofoam tray

70

0

Steinka et al. 1998.

In the case of tvarogs, modification of the packing system is hard due to the consistency and delicate structure of these products. Applying modification of traditional packing system and substituting it with Styrofoam trays does not guarantee high quality of tvarogs as well. Products packed in this way and wrapped with thin frischaltenfolie showed high count of 6 5 fungi. After storage, the level of yeast reached 1.1•10 cfu/g, while mould 4.3•10 cfu/g and these counts were higher than in the case of tvarogs packed into other types of packaging.

Photo 1. Tvarogs packed onto Styrofoam trays wrapped with thin PA/PE foil.

It is possible to protect the product against changes of sensory properties resulting from significant fungi count by avoiding the non-hermetic packaging, or storing the product for a short period of time - not longer than 4 days. There also exists the possibility to modify packaging materials. At the present, the project of creating laminate of modified composition is at development stage. Among modifying elements the followings are proposed: starch and two biocides.

228

Izabela Steinka

Optimisation with the Help of Prediction Models Among the possibilities of optimising the quality and safety of tvarogs, there is also microbiological predicting. The behaviour of multi-species populations in stored tvarogs as well as change of basic packaging properties can be evaluated with Twarogi JMTPH computer program (Steinka 2003a). For evaluation of staphylococcal enterotoxin synthesis, the probabilistic model was developed taking storage time, staphylococci and yeast populations counts in tvarogs into account. Development of computer program called TEG required application of Boolean expression, which helped to precise the series of conditions that have to be fulfilled in tvarogs for the occurrence of enterotoxin synthesis.

E = [(GN = 0) ∧ (GP ≥ 4)] ∨ {((GN + GP ) ≥ 5) ∧ [(GP > 3.5) ∨ (GN > 3.4 + 0.1t )

∨ (GP / D > 0.6) ∨ (GN / D > 0.7) ∨ ( GP − GN /(GP + GN ) ≤ 0.2)]}

(20)

where: GN - coagulase negative Staphylococcus aureus count, GP - coagulase positive Staphylococcus aureus count, D - yeast count, t - time In a computer program developed in Delhi 7.1, the staphylococci count, storage time and size of predominant population in tvarogs were also taken into account. The result of program operation is obtaining the answer that negates or confirms the presence of staphylococcal enterotoxin in a products (present-absent). The example of computer simulation is presented in table 32 below. Table 32. Predicting the presence of staphylococcal enterotoxin with TEG computer program Staphylococcus aureus CP count 2.78

Staphylococcus aureus CN count

Yeast count

Storage time of tvarogs

3.44

5.58

14

Result of simulation of enterotoxin presence Present

2.32

2.11

3.89

7

Absent

Steinka et al. 2007.

Influence of Interactions Occurring Between Micro-Organisms…

229

CONCLUSION Tvarogs constitute the important of many nutrients. Their nutritious and taste values evidence that they are significant diet components for both adults and children. The microbiological quality, taking safety of products into account, has been constantly improving. The interactions occurring among tvarog allochthonous micro-flora influence the growth of all micro-organisms present in a product. The dynamics of growth of individual types of these micro-organisms can be evaluated using JMPTH computer program. The packaging hermetic properties determines significantly the direction of changes of allochthonous micro-flora under the influence of lactic acid bacteria. The packing system as well as hermetic properties and type of packaging influence the growth of staphylococci count in a product. The probabilistic model is suitable for assessing the presence of enterotoxin in products, depending on the level of staphylococci and yeast. There also exists a possibility to optimise the quality of lactic acid cheese, using biostatic plant additives or through modification of the composition of packaging material.

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[114] Steinka I. (2006c). Ocena jakości twarogów dostępnych w handlu przed nowelizacją wymagań mikrobiologicznych. Jakość towarów i usług w obrocie. Wydawnictwo WSG Bydgoszcz , Volume II, 167-174. [115] Steinka I. Blokus A. (2007). Szacowanie wielkości populacji zdolnej do syntezy enterotoksyny gronkowcowej w twarogach, unpublished data [116] Steinka I., Parisi S. (2006b). The influence of cottage cheese manufacturing technology and packing method on the behaviour of micro-flora. Joint Proceedings, AM Gdynia, Hochschule Bremerhaven, 19, 30-37. [117] Stewart C. M., Cole M.B., Legan J.D., Slade L., Vanden M.H., Schaffener D.W. (2002). Staphylococcus aureus growth boundaries: moving towards mechanistic predictive models based on solute-specific effects, Appl. Environ. Microbiol., 68,4, 1864-1871. [118] Tamagnini L.M., de Sousa G.B., Gonzales R.D., Reveill J., Budde C.E. (2005). Behaviour of Yersinai enterocolitica and Salmonella typhimurium in Crottin goat’s cheese. Int. J. Food Microbiol., 99,2, 129-134. [119] Taub I. A., Feeherry F.E., Ross E.W., Kustin K., Doona C.J. (2003). A quasi –chemical kinetics model for the growth and Heath of Staphylococcus aureus In intermediate moisture bread. J. Food Sci., 68, 8, 2530-2537. [120] Turhan M., Kaletunc G. (1992). Modeling of salt diffusion In white cheese during longterm brining. J. Food Sci., 57,5, 1992. [121] Tyszkiewicz I. (1992) Przechowywanie mięsa w atmosferze gazów ochronnych, Gospodarka Mięsna 8, 20-22. [122] Usajewicz I. (1995) Oddziaływanie paciorkowców rodzaju Enterococcus na wzrost oraz aktywność metaboliczną bakterii grupy coli i Clostridium spp. Zeszyty Naukowe Akademii Rolniczo Technicznej. Olsztyn [123] Usajewicz I. (2000). Przeżywalność S. ureus, S. galinorum i E.coli w kefirze oraz w mleku ukwaszonym przez kultury L. plantarum i L. casei SHIROTA, Materiały Naukowe Szkoła letnia ‘Bakterie fermentacji mlekowej- klasyfikacja, metabolizm, genetyka, wykorzystanie’. Kazimierz Dolny, 59. [124] Vernozy-Rozynand C., Meyrand A., Mazuy A., Mazuy C., Delignette-Muller M.L., Joubert G., Perrins G., Lapeyere C., Richard Y. (1998). Behaviour and enterotoxin production by Staphylococcus aureus during the manufacture and ripening raw goats milk lactic cheeses. J. Dairy Res. 65, 273-281. [125] Walls I., Scott V.N. (1997). Validation of Predictive Mathematical Models Describing the Growth of Listeria monocytogenes. J. Food Protect. 60, (9), 1142-1145. [126] Walls I., Scott V.N.,Dane B. T. (1996). Validation of predictive mathematical models describing growth of Staphylococcus aureus. J. Food Prot., 59, 1, 11-15(5). [127] Westal S., Filtenborg O. (1998). Spoilage Yeasts of Decorated Soft Cheese Packed in Modfied Atmosphere. Food Microbiology 15, 243-249. [128] Zagory D. (1995). Principles and practice of modified atmosphere packaging of horticultural commodities. Lancaster, PA, Technomic Publishing Co Inc. 175-204. [129] Ziółkowski T., Staniewska K., Panfil-Kuncewicz H. (2003). Metody pakowania a bezpieczeństwo i trwałość twarogów i serów dojrzewających. Przegl. Mlecz., 7, 269272. [130] Zurera –Cosano G., Castillejo-Rodriguez A.M., Garcia-Gimeno R. M., Rincon –Leon F. (2004). Performanc e of response surface and Davey model for prediction of

Influence of Interactions Occurring Between Micro-Organisms…

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Staphylococcus aureus growth parameters under different experimental conditions. J. Food Prot., 67, 6, 1138-1145.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 239-305 © 2007 Nova Science Publishers, Inc.

Chapter 3

THE DEVELOPMENT OF ENGINEERING TECHNOLOGY TO IMPROVE THE QUALITY OF PRODUCTION OF TROPICAL FRUIT IN DEVELOPING COUNTRIES B. Jarimopas1, P. Sirisomboon2, R. Sothornwit3 and A. Terdwongworakul4 1,3,4

Faculty of Engineering at Kamphaengsaen, Kasetsart University, Kamphaengsaen, Nakohn Pathom, Thailand 2 Department of Agricultural Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

ABSTRACT Many developing countries are rich in agricultural and food resources but are unable to maximize the export income they earn from them because they lack value-adding technology. In other words, developing countries typically must sell their products in cheap unfinished form to nations which possess the technology that adds profitability to these goods. Accordingly, if developing countries wish to earn more revenue for the improvement of their people’s employment and education, they must develop food engineering technology alongside other food science technologies. These efforts at technological self-improvement should be supported by the developed countries as the reduction of the knowledge and income gaps between the industrialized and developing worlds will do much to further global peace and happiness. The desired trend for food engineering research is to focus on developing engineering technology that will help to improve tropical fresh produce quality. This chapter discusses three facets of this trend. The first aspect concerns the physical properties of tropical fruit and vegetables, which consist of post-harvest loss, physical characteristics, mechanical properties, firmness, friction, and non-destructive quality grading techniques relating to mangoes, mangosteen, durian, sweet tamarind, guava, tangerines, snake egg plants, white long radish and lime. The second aspect concerns innovations in machinery and devices used with mangosteen, durian, young coconut, dry

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B. Jarimopas, P. Sirisomboon, R. Sothornwit et al. over-mature coconut and baby corn. State of the art design, operating principles and key performance tests of tropical fruit machinery and inventions will be reviewed. The third aspect concerns packaging technology, particularly that which is directed towards the extension of the shelf life of the aforementioned tropical fresh produce. There are three current realities which inform this book. They are as follows: that there is a high incidence of post-harvest loss and a corresponding magnitude of shortage in research and development work on tropical fresh produce; that the global flow of information is increasing while agricultural labor is becoming scarcer and more expensive; and that tropical produce engineering technology must be thoroughly understood. Accordingly, we make two recommendations: for producer countries to instigate a dramatic increase in the research and development that they conduct into tropical fresh produce, and in the support that they provide for this research; and that the research trend should cover all economic tropical fruit and vegetable goods grown in the producer countries and all aspects of engineering technology that they use, with a particular emphasis on developing computerized non-destructive techniques for quality assurance.

INTRODUCTION If the task of science is to understand the composition of nature, the goal of engineering is to employ that scientific understanding in the quest to create new things. In other words, engineers who wish to improve the production of tropical fresh fruit and vegetables first must understand the natural behavior of tropical fresh produce. Only when they possess this knowledge can they develop innovations in production processes, devices and machinery. The next goal for engineers is to improve the distribution and preservation processes; that is, they must seek to improve the packaging that holds produce together and protects it from the adverse conditions of handling, transport and environment. Indeed, the drive to improve packaging has become of paramount importance due to the modern recognition of its capacity to secure standard qualities of freshness, uniformity, flawlessness and attractive appearance. Great opportunities exist today to increase export sales of tropical fruit and vegetables. This is implied by the comparatively low level of sales that currently exist. To clarify, the United States Department of Agriculture (2004) reports that exports to the world market of four temperate region fresh fruit and vegetables (apples, pears, potatoes, tomatoes) in 2002 were valued at more than USD5250 m., while exports by Thailand in 2006 of tropical fresh fruit and vegetables produced only USD172 m. in income (Customs Department, 2007). Thus, in effect, the huge markets of the US, the EU, Japan and China are challenging developing countries to improve their fruit and vegetable production technology, especially their engineering technology. As noted before, improvements in engineering technology should include developments in knowledge of the natural behavior of tropical fresh produce and the creation of innovative packaging technology for this produce. The natural behavior of tropical fresh produce relates to its physical properties. Innovations in this area occur when developments are made in primary processing and postharvest machinery, and in devices which facilitate the production and consumption of tropical fresh produce. Packaging technology relates to packaging for distribution and for extension of shelf life of produce.

Development of Engineering Technology to Improve the Quality of Production… 241 However, at present, the investment into the research and development of tropical fresh produce is comparatively very low. A Google Scholar search in April 2007 found that 79% more research (≅ 233) is conducted into apples, peaches and pears than the leading tropical fresh fruit (durian, longan, mangosteen, mango, young coconut, pineapple, pumelo, rambutan, rose apple, dragon fruit) in terms of their physical properties and associated nondestructive techniques. Meanwhile, 92.3% more research in the same areas is conducted into tomatoes, potatoes and carrots (≅ 298) than into the dominant tropical fresh vegetables (egg plant, snake egg plant, Chinese radish, chilli, Chinese cabbage, cabbage, kale, water morning glory). Accordingly, there is a serious shortage of knowledge in these aspects of engineering technology with regard to tropical fruit and vegetables. This chapter reviews and discusses the current body of knowledge relating to the natural properties of tropical produce and engineering innovations associated with these products, and then suggests a future trend for research and development which it is hoped will help to redress the serious imbalances described above.

1. PHYSICAL PROPERTIES OF TROPICAL FRESH PRODUCE IN DEVELOPING COUNTRIES 1.1. Post-Harvest Loss and Physical Characteristics of Selected Tropical Fresh Fruit Longan is one of Thailand’s major export fruits. The fruit are usually presented in bunches and, after manual harvesting, undergo sorting, handling, packaging and transportation. The first step of this post-harvest process is to separate twin fruit and broken branches and leaves from the gross product, a procedure which typically results in the retention of 78.5-87.0% of marketable fruit (Jarimopas, 1985). The usual effect of handling, packaging and transportation of the fruit is that some mechanical damage is caused in the form of fruit rupture and berry-dropping, with average losses running at 9.0% in two stacking containers and 14.8% in four stacking containers. The containers are cylindrical bamboo baskets which are piled directly on top of each other without cushioning. Mechanical damage is lowest in containers placed at the tops of stacks and is highest in containers placed at the lowest levels, possibly due to the higher compressive loads exerted by the upper containers. Another major export fruit in Thailand is mangosteen. This fruit has been investigated for post-harvest loss occurring at grading-buying points in the orchards and at retailer locations. Table 1 shows the various kinds of loss recorded from samples taken at the grading-buying points. The relevant fruit were stored at ambient temperatures until ripe, and were inspected and analyzed. The sampling survey was carried out in two provinces - Chantaburi in the east, and Chumpon in the south - both of which are major mangosteen production areas in Thailand. Samples were collected three times: at the beginning, the middle, and the end of the harvesting season. The greatest loss (48.6%) was due to rough surface, which greatly reduces the value of the fruit. The second greatest loss (33.9%) was due to internal defects (although it should be noted that mangosteen might have more than one defect (Pushpariksha and Jarimopas, 2006a)).

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To determine the post-harvest loss at the retailing stage, sampling was performed at representative sites of the most typical vending locations: at supermarkets, open markets, and mobile retailers (those who sell fruit from utility vehicles such as pick-up trucks). The sampled fruit was kept at room temperature for 3-4 days before being inspected and analyzed. Losses recorded in rank of frequency were due to rough surface (83.8%), hard rind (33.4%), translucent flesh and gummosis (26.9%), and decay (6.9%). The incidence of hard rind fruit, which is caused by mechanical injury, increased tremendously from the wholesaler to the retailer stages due to unsatisfactory packaging and inefficient handling and transit procedures. To clarify, mangosteen in Thailand which are intended for local consumption (rough fruit typically are not exported) usually are packed with minimal protection and care into paperlined reusable plastic containers before being transported by small trucks from the eastern and southern provinces to Bangkok. However, despite the high quantity of rough fruit found at local retailers, cracked fruit are generally absent because they have been culled out by the wholesalers. The quantity of fruit with internal disorders (translucent flesh and gummosis) tends also to be relatively low at retail point of sale because fruit with these defects tend to decay after they leave the wholesalers and accordingly are sorted out by the retailers (Pushpariksha et al., 2006). Post-harvest loss of rose apple fruit was ascertained with respect to two variables: variety (the Thongsamsri and Toonklao strains) and transport destination (retailers and wholesalers). Samples from retail locations were drawn from three mobile vendors, large open markets, and popular supermarkets around Bangkok and provincial cities. Wholesale samples were taken from three large fruit markets in Bangkok. The rose apples were manually harvested, packed and transported by trucks to all the above vending locations. Post-harvest loss was quantified in terms of abrasion and bruising damage. Two parameters of the damage evaluation were: Average damage per fruit (D x ) =

Total area of each damage type on fruit surface Total fruit of the package

(1)

Number of damaged fruit in a package ×100 Total fruit of the package

(2)

Average damage percentage per package ( D y ) =

With regard to post-harvest damage of the premium Thongsamsri variety found at wholesalers, the average damage due to bruising was 0.45 cm2/fruit (Dx) or 23.3% (Dy), while that due to abrasion was 0.66 cm2/fruit (Dx) or 72.2% (Dy). At the retailers, the average damage due to bruising was 1.45 cm2/fruit (Dx) or 56.8% (Dy), while abrasion was 0.95 cm2/fruit (Dx) or 78.1% (Dy). With regard to the Toonklao variety (a popular variety which is cheaper and less sweet than the Thongsamsri strain), damage was found to be higher at retail point of sale locations than at wholesalers. The major damage was bruising and abrasion. The average bruising and abrasion were 0.22 cm2/fruit (Dx) or 26.7% (Dy) , and 0.45 cm2/fruit (Dx) or 58.3% (Dy), respectively at the wholesalers while at retailers, average bruising and abrasion were 0.61 cm2/fruit (Dx) or 61.7% (Dy), and 1.21 cm2/fruit (Dx) or 90.0% (Dy), respectively (Toomsaengthong et al., 2006). We turn now to studies conducted into maturity grading and sizing of fresh tropical fruit. To evaluate the maturity of two Thai mango cultivars (Nam Dokmai and Chok Anan), Kittawee and Jarimopas (2006) attempted to measure the specific gravity (SG) of samples every two days for 40 days starting from when the fruit were immature until they were over-

Development of Engineering Technology to Improve the Quality of Production… 243 ripe by using the technique suggested by Mohsenin (1996). Good correlation was found between SG and maturity (based on time T after fruit set) for the Nam Dokmai variety while poor correlation occurred with reference to the Chok Anan strain. The regression equation for the Nam Dokmai analysis was SG = 0.87+0.0016T (R2 = 0.90). Sizing of produce is in general based on its dimensions. Sizing is not only useful for packaging, but also adds value to the produce (Jarimopas, 2006). Pushpariksha and Jarimopas (2006) studied physical characteristics of mangosteen by measuring the following variables of newly harvested fruit of four sizes (large, medium, small and undersize): weight, maximum diameter Dmax, minimum diameter, volume, diameter of calyx circumscribing circle Dc, and height with and without calyx. Table 2 shows the physical characteristics of fresh mangosteen related to size. The dimension ratio (DR), which was the ratio between Dc and Dmax, was proposed as a parameter to identify undersize fruit. It was found that the DR of the marketable mangosteen (excluding those that were undersized) was greater than 1. Therefore, DR could be a suitable parameter for undersize sorting. Since the weight of mangosteen stored at ambient temperatures dropped 14% in 2 weeks while their diameter decreased only 0.3%, mangosteen sizing by dimension would probably return more consistent results than sorting by weight.

1.2. Mechanical and Textural Properties Studies have been conducted into the mechanical and textual properties of tropical fruit such as mango and sweet tamarind and vegetables including snake egg plant and white long radish. Parameters of these properties includes rupture force, rupture deformation, the slope of the force-deformation curve, the Poisson’s ratio, modulus of elasticity, and firmness. Friction properties will also be reviewed and discussed in this section. A discussion of the technical experience of testing and measurement of the physical properties is also included. Table 3 (Chaiyapong and Jarimopas, 2006) shows the mechanical properties with respect to the rupture force FR, rupture deformation DR, and firmness expressed by the slope of the force-deformation curve S (Jarimopas and Kittawee, 2007) of the Thai popular mature mango “Nam Dokmai”. The experiment was performed with slow compression of the fruit by a 4mm plunger driven by the universal testing machine UTM (INSTRON 5569) at a loading rate of 20 mm/min. The compression test was controlled by ripeness (unripe, ripe) and fruit orientation (5 points of loading application – figure 1). FR, DR and S were significantly affected by ripeness and fruit orientation at the significance level of 5%. The top edge and the bottom edge of the mature and unripe mango statistically exhibited the highest and the lowest FR, respectively. Specifically, the FR of the bottom edge and the head of the mature and unripe mango were insignificantly different, with the greatest and the lowest slope S recorded at the top edge and the tail of the fruit, respectively. This implies the maximum firmness of mango occurs at the top edge and the minimum firmness at the tail. When mango is ripe, the ripening process has changed flesh cells to totally soluble solids, which results in reduction of the strength of the maximum FR of the ripe fruit to one-eleventh of that of the unripe fruit. The maximum FR of the ripe mango in this experiment occurred at the cheek (the most convex part of the fruit) while the FR of every point of load application was statistically indifferent, except at the fruit head where FR was the least. Firmness in terms of the slope of the ripe mango dropped to about one eleventh of that of the unripe fruit, while

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firmness of every load application point was statistically indifferent. However, the slope could not differentiate the maturity of Nam Dokmai and Chok Anan mangoes (Jarimopas and Kittawee, 2007). The Poisson’s ratio μ and modulus of elasticity E of the mango were also determined by means of uniaxial compression with the INSTRON 5569 until 50% of rupture force. The sample was cylindrical mature Nam Dokmai flesh measuring 15 mm in diameter by 30 mm in length. The resulting μ and E was 0.24±0.05 and 3.39±0.3 MPa, respectively (Chaiyapong and Jarimopas, 2006). Sirisomboon et al. (in press) have conducted a preliminary study which intended to add to the progress towards the design of a firmness tester suitable for mango maturity classification. They have designed an experiment which consisted of two parts: probe selection, followed by evaluation of the selected probe in mango maturity classification using a texture analyzer. In the first part, mango samples (Mangifera indica L. variety Namdokmai) at three different stages of maturity (120 fruit each at 60, 70 and 80% maturity, comprising a total sample size of 360 fruit) were tested. These different maturity stages were classified by farmers through sensory evaluation. A texture analyzer (TA-XT2i, Stable Micro System, UK) with 4 probes including a 5, 10, and 15 mm diameter spherical stainless steel probe and a 75- mm diameter circular flat aluminum plate probe were used to measure the firmness of the mango samples. Thirty mango fruit at each stage of maturity were subjected to a compression test at a maximum force of 3 N and probe speed of 0.2 mm/s (as no bioyield point appeared during the procedure, this can be considered a non-destructive test). Each probe was compressed on the cheek of one side of the fruit. The result showed that the 5-mm diameter spherical stainless steel probe provided the best performance due to its minimum value of standard deviation, coefficient of variation and variance. According to the Duncan’s test the means of the firmness values tested by the probe at different stages of maturity were significantly different (p<0.05). This indicated that the three different maturity stages (60, 70, and 80% of full ripeness) could be classified by firmness. The second stage of the experiment again involved the use of 360 mangoes of the same variety selected according to the three different stages of maturity described above. Each of the 360 fruit samples was measured for firmness and classified into three firmness categories: greater than 13.0 N/mm; 13.0 - 11.6 N/mm; and less than 11.6 N/mm. A texture analyzer with a 5 mm diameter spherical stainless steel probe was used to measure the firmness of the samples. A total of 80 fruit of each firmness category were randomly sampled and were divided into 4 groups (20 fruit each). The first group was immediately measured for soluble solids while the other three groups were ripened through exposure to calcium carbide (CaC2) at approximately 25°C (room temperature) for one, two and three days respectively. The soluble solids content was measured each day by a digital refractometer (Atago, PR-32, Japan) and expressed in % Brix. The experiment was conducted because the soluble solids content is one of the indices of the maturity of mango fruit. Therefore, if the mangoes, which were classified into different firmness categories by the probe, had corresponding differences in soluble solids content, then the maturity of mangoes could also be classified by firmness. The authors concluded that sugar developed better in the softer mangoes than in the harder ones; in other words, they ripened faster.

Development of Engineering Technology to Improve the Quality of Production… 245 The results showed that lower firmness indicated higher levels of soluble solids at each ripening stage. The soluble solids at day 1, 2 and 3 of the ripening stage were significantly different between different firmness categories. The first category (>13.0 N) and the third category (<11.6 N) had different soluble solids but they were not different from the second category (13.0-11.6 N). This indicated that a texture analyzer with a 5-mm spherical probe could classify the maturity into two different stages: namely, the 60% and 80% stages of full ripeness. However, the technique could not detect the difference between 60% and 70% of full ripeness or between 70% and 80% of full ripeness, which are distinctions needed by growers in determining fruit maturity. The author indicated that further study is needed to develop a firmness tester that can work nondestructively online with high resolution in classifying the maturity of mangoes during harvesting time. Also required is the development of a firmness tester based on the direct measurement of force and deformation at a force level not higher than the bioyield force of the fruit; in other words, nondestructive measurement may be more appropriate for a nondestructive system than other indirect measurements such as acoustic firmness testers. An additional point in favor of a direct measurement tester is that it can be adapted to an online system and made to be independent of the effects of operatorinstrument interaction. Our attention now is focused on experimental work concerning sweet tamarind (Tamarindus Indica L.) This is a favorite fruit of both Thais and foreigners, as can be seen from its export value in 2000 of roughly USD4 million. Tamarind has a sweet and sour flavor and is rich in nutrients (figure 2). The mature sweet tamarind presents as a pod, which contains a seed enclosed by flesh and a shell. A distinctive feature of the tamarind is that the shell cleanly separates from the flesh; however, tamarind currently suffers a considerable amount of mechanical damage during post-harvest handling, packaging and distribution due to lack of information about the physical and mechanical properties of the fruit. Of recent interest is research conducted into the mechanical properties of two commercial cultivars of sweet tamarind (the “Sitong” and “Srichompoo” cultivars). The research was motivated by concern about the prevalence of pod shell cracking, which usually occurs during the packaging process. Thirty pods of each cultivar, newly harvested and of uniformly medium size, were compressed with a 3mm spherical plunger that was driven by a UTM at 25 mm/min. The drop impact tester developed by Chen et al. (1996a) was also used. The experiment included three control factors: cultivar, maturity (immature and mature) and pod surface profile (convex and concave). The mechanical properties included weight, dimension, specific gravity, slope of force deformation curve and firmness index (peak acceleration divided by the corresponding contact impact time) (Jarimopas et al., 2005a). The Sitong pods were physically bigger, heavier, and denser than the Srichompoo fruit (table 4). The strength of each pod shell was investigated by means of two properties: firstly, the slope of the force deformation curve S under plunger slow compression; secondly, the application of firmness index FI through the impact set-up. Table 5 shows that the slope and the firmness index of the Sitong shells were higher than those of the Srichompoo variety at the same level of maturity. Additionally, it was found that the S and FI of the shells of immature sweet tamarind were higher than those of mature fruit from the same cultivar. It is speculated that this is because tamarind flesh tends to separate from its shell when it approaches maturity. Moreover, the pods that were tested had been hung out to dry for several

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weeks. Accordingly, the shells were drier and thus were likely to be more fragile and more prone to cracking. The surface pod profile significantly influenced the slope of the force-deformation response of the pod shell at a significance level of 5% (table 6). The convex shell had a higher slope than the concave shell in both cultivars. Accordingly, it can be deduced from plate and shell theory that yield stress due to compression on the shell of the convex side was higher than that of the concave side (Timoshenko and Goodier, 1970), which in turn caused the S of the convex shells of sweet tamarind pods to be greater than the S of the concave shells. As mentioned in our introduction to this section, work has also been conducted into the mechanical and textural properties of vegetables such as snake eggplant and long white radishes. These vegetables have been of great significance to the local Thai market for a considerable time and are usually consumed fresh. In recent times, a strong export market has developed for the vegetables, and thus production requirements and standards have risen. A higher standard implies standardization, which in turn requires knowledge of physical and mechanical properties which to date have been only sketchily understood. Jarimopas et al. (2006a) and Jarimopas et al. (2006b) note that the most significant problem facing consumers and sellers of stored vegetables is that the vegetables quickly lose their quality after harvesting. A few days after being collected, snake eggplant are softer and their skin wilts, while the texture of the radish flesh changes from firm to spongy. Accordingly, the modulus of elasticity E of each vegetable was determined with respect to storage time as well as Poisson’s ratio μ. An experiment was conducted in which cylindrical samples taken from the intact vegetables were subjected to two kinds of contact loading tests (radial compression and die loading) with regard to two control factors (orientation and storage time). The radial compression method used 15 mm x 30 mm cylindrical samples while the die loading method used a 3.2 mm cylindrical probe. The testing instrument was the UTM (INSTRON 5569) with a loading rate of 25 mm/min (ASAE Standard, 1998). It was found that the storage time t significantly affected (p<0.05) modulus of elasticity of the white long radish and the snake egg plant. The modulus of elasticity decreased exponentially with t. The equation of relationship between modulus of elasticity and t is shown in table 7. The average μ along the fruit axis was 0.252±0.082 while the μ along the transverse axis was 0.166±0.047. The μ of the axial and the transverse samples of the long white radish was 0.444±0.07 and 0.384±0.08, respectively. The position of snake egg-plant sample along its body did not significantly affect E. It was also ascertained that the firmness of snake egg-plant reduced with respect to storage time. Orientation of preparation of sample of snake egg plant and white long radish (axial and transverse) significantly affected μ at the significance level of 5%. Difference of μ of a certain vegetable suggests that the assumption of fresh produce as an elastic material ought to be carefully considered because the produce does not, in reality, behave isotropically. However, in the case of this particular experience, the elasticity modulus decreased by 30% with respect to the corresponding drop in weight. This might explain that new fresh vegetables initially feature cells full of moisture. However, after storage in ambient air for a week, the lost moisture causes the cells to deform, resulting in wilted skin and reduced firmness.

Development of Engineering Technology to Improve the Quality of Production… 247 With regard to the variable of friction, it can be said that it is an important mechanical property of fresh produce. Conveying and processing machines are designed with regard to friction (Wang, 1963; Mohsenin, 1996). Jarimopas et al. (2005b) developed the simple electro-mechanical type and measured the coefficient of friction C and rolling angles A of fruits. A typical device consists of a steel frame (300 mm wide by 500 mm long) with an adjustable inclined surface. The angle of inclination θ of the surface of the machine discussed in this context was computer-controlled and C and A results were displayed. The static friction coefficient CS was the tangent of θ when the fruit started sliding, while the kinetic friction coefficient Ck was the tangent of θ when the fruit uniformly slide along the inclined plane after minor disruptive contact. The θ corresponding to the start of rolling of the fruit was the rolling angle. The experiment described here included three variables: produce (lime, tangerine, guava), contact surface (galvanized sheet metal, plywood, plastics), and fruit orientation (opposite stem side, stem side, cheek), which controlled the variation of Cs, Ck and A. The produce and contacting surface significantly affected Cs and Ck (p<0.01) while the produce and the orientation significantly influenced A (tables 8, and 9). Ck was less than Cs with regard to identical forms of produce and contact surfaces, which corresponds with the claims made by Mohsenin (1996). The highest coefficient of friction for lime and tangerine was returned when the experiment was conducted on plastic surfaces, while the results for guava were maximized on plywood. The lowest coefficient of friction of all the produce was recorded when galvanized steel was used. Depending on the contact surface, lime and guava exhibited respectively the smallest and the greatest friction coefficient. For all produce, the orientation of opposite stem side gave the greatest A because this orientation presented the greatest contact area to the inclined surface. Rolling started when the line of produce weight passing through controid was out of the contact area. The cheek contacted the inclined surface least. Pushpariksha et al (2006) replicated the experiment concerning the static coefficient of friction of mangosteen using the device developed by Jarimopas et al (2005b). In this experiment, measurement was controlled by mangosteen type (normal surface, rough surface), contact surface (plexiglass, plywood, galvanized steel sheet) and direction of mangosteen slide (longitudinal, transverse to the fruit axis). All the fruit used in this experiment was newly-harvested, uniform mangosteen. It was found that the mangosteen type, contact surface and fruit sliding direction significantly affected Cs at a significance level of 5% (table 10) and that the Cs of the mangosteen sliding along the transverse axis was greater that that recorded by that moving in a longitudinal direction. Furthermore, the Cs of normal mangosteen moving along plexiglass was higher than that recorded by rough mangosteen, which probably results from the fact that mangosteen typically has a smooth, waxed surface (and the cohesion of the surface of smooth fruit is likely to be greater than that of rough fruit.)

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1.3. Testing and Measurement 1.3.1. Contact Loading Technique Contact loading is an important means of determining the mechanical and textural properties of agricultural and food material. To attain efficient and successful testing and measurement the following factors should be considered. (A) Sample support: when a fruit sample is subjected to contact loading -- even when the phenomenon of concern is at the upper contact point -- the support of the fruit sample which creates the lower contact point must be considered (figure 3). The fruit, assumedly an elastic body, stands at rest on a rigid support while its uppermost point is compressed with a flat plate or a plunger. The lower contact point at the bottom of the fruit meanwhile is also supported and compressed but the fruit is deformed because of greater support rigidity. Theoretically, the lower contact point is considered to be stationary but, in reality, it is not because of the fruit’s deformation. Thus, this affects the movement of the area of interest – the upper contact point -- and, consequently, produces error in the deformation reading. In order to produce an insignificant deformation of the fruit at the lower contact point, the fruit should be supported with a large enough contact area so that compression is distributed in a way that imparts very small contact stress and therefore also minimal deformation. To facilitate the desired stability, moistened sand has been used by several researchers (Chaiyapong and Jarimopas, 2007; Sarakan, 2006; Meeklangsaen et al., 2007; Vanichang and Jarimopas, 2007) to support fruit including mango, rose apple, young coconut and dragon fruit (figure 4). The chief advantages of this method are that moistened sand not only improves stability but also can be manipulated to fit any fruit shape. (B) Proper compression head. Questions often arise about which kind of contact loading test and which compression head size should be used. With regard to determining the mechanical properties of dragon fruit, the equipment that historically been deployed include a flat plate, the die and the plunger probes. However, in helping to determine loading rate, compression probes also significantly affect rupture force FR, rupture deformation, modulus of elasticity and firmness. For example, the modulus of elasticity of the flat plate EFP at the loading rate of 250 mm/min is 10.3% higher than when it is applied at 25 mm/min and is 23.7% higher than at a rate of 2.5 mm/min. Ultimately, the change of modulus of elasticity of the cylindrical and the spherical probes due to loading rate change is close to 100%. On the other hand, FR of fresh produce as a result of using die and ball heads is customarily low because of the small contact area required. This lower contact area provides greater opportunities for repeat testing of produce, which enables the orientation effect or homogeneity or consistency of produce to be investigated, while the use of a flat plate results in a large contact area and deformation at rupture force. Hence, with regard to flat plate testing, it is often difficult to perform repeat tests using the same produce. According to the ASAE Standard (1998), the modulus of elasticity of a curved material subjected to a flat plate and a spherical indenter is

E FP

0.338 K 3 / 2 F (1 − μ 2 ) ⎡ 1 1⎤ = + ⎥ ⎢ D3 2 ⎣ R1 R1′ ⎦

1

2

(1)

Development of Engineering Technology to Improve the Quality of Production… 249

0.338K 3 / 2 F (1 − μ 2 ) ⎡ 1 1 4⎤ Eb = ⎢ + ′+ ⎥ 32 D ⎣ R1 R1 d ⎦

1

2

(2)

In order to more easily use a ball head, the ball size should be manufactured so that d << R1 (about 10 times or more). Eb is then simplified to

Eb = 0.676

(1 − μ ). 2

d

1

2

F D

3

(3) 2

Eb is dependent on the FD graph while μ and d (ball diameter) are kept constant.

Ed =

(1 − μ ) F 2

dd

(4)

D

(Mohsenin, 1996)

d d = die diameter (m) Due to the comparatively greater flexibility of the design of the die and ball head, they are considered to be highly compatible with produce size and structure. Conversely, the flat plates that typically accompany UTM are usually too big for use with small stone fruit, especially in mechanical damage studies. However, in cases where the use of flat plate is mandated, the radial compression method may be an efficient approach. Jindal and Techasena (1985) suggested the model of radial compression estimating modulus of elasticity of potato as follows:

(

)

1.927 1 − μ 2 d10.2 F ER = L D1.2 when ER μ d1 L

(5)

= modulus of elasticity by radial compression test (MPa) = Poisson’s ratio = sample diameter (m) = sample length (m)

The radial compression test requires the preparation of a sample of a cylindrical shape as well as the measurement of d1 and L. The modulus of elasticity also is dependent on the force-deformation response of the sample. However, if consistent sample preparation occurs, reliable ER can be expected.

The low mass plunger impacter developed by Chen et al. (1996a) is widely applicable in research which attempts to determine the mechanical and bruising properties of fruit (Chen et

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al., 1996b; Jarimopas and Kittawee, 2007; Sarakan, 2006; Jaren et al., 1992; Corea et al., 1992). Jarimopas and Kittawee (2007) enumerate its desirable features as follows:- i) the measured acceleration signal increases, ii) both the magnitude of the calculated firmness index and the rate of change of firmness index with respect to fruit firmness increase, iii) the error due to movement of the fruit during the impact is minimized, iv) fruit damage caused by the impact is minimized, and v) high speed sensing is applicable. With regard to the determination of modulus of elasticity, a die seems to be the best head for producing less complication and variation in E. This is because E depends directly on the force-deformation ratio while μ and dd, are constant (see equation 3). A good alternative if the condition to apply equation 3 holds is the low mass plunger probe. Determining the elasticity modulus by means of FP and plunger head requires the measurement of radius of curvature in two orthogonal planes, while radial compression necessitates good sample preparation and measurement of sample dimension. It should be recalled that greater measurement and replication requirements are likely to produce greater errors and testing and analyzing times. (C) Effect of the size of the compression head. Jarimopas and Srirungruaug (2006) investigated the relationship between impact and bruising in young coconut fruit by the use of varying plungers (12, 24 ,48, and 96 gm/head) dropping on intact mature young coconut of medium size (≅ 1.35 kg/fruit). Strong linear correlations were obtained between bruise volume V and impact energy U (R2 > 0.99). The regression equations for different impact heads were:V V V V

=1161.3U-21.0 =1261.1U-20.6 =1333.1U-33.4 =1936.6U-94.2

(12 g impactor) (24 g impactor) (48 g impactor) (96 g impactor)

The regression equations indicated the impact head variation caused variation in bruise susceptibility (coefficient of U).

1.3.2. The Effect of Mechanical Loading upon Varying Mechanical Properties of Fruit and Vegetables (Orientation Effect) Mechanical loading applied to different points on the surface of a fruit or a vegetable can cause different mechanical responses. For example, testing shows that a popular Thai papaya (“Khagdum”) which is packed in the typical reusable plastic container in a vertical orientation demonstrates 31% more bruising than if it is packed in a horizontal orientation (Chonhenchob and Singh, 2005). This may be because the power spectrum density of package acceleration is higher in a vertical direction than in a horizontal direction (Jarimopas et al., 2005c). In another example, the abrasions to rose apple which occur because of handling and transit vibration and which are the dominant form of damage to this fruit more often occur in the section close to the stem (tail) than that in the head (Toomsaengtong et al., 2006). This phenomenon was experimentally explained with reference to rose apples by means of slow compression using a 4 mm plunger, which showed that the rupture force FR of the tail and head were 5.50 and 8.89 N respectively. The smaller FR of the tail seems to indicate that there is less mechanical resistance in this section than in the head, which leaves the tail more vulnerable to damage. Similarly, with regard to “Nam Dokmai” mango, fruit orientation has been described to influence FR and S of the fruit response (Chaiyapong and Jarimopas, 2007).

Development of Engineering Technology to Improve the Quality of Production… 251

1.4. Sound and Ultrasound Properties Sonic vibration occurs in the audible frequency range from about 20 Hz to about 15kHz. An object vibrates at sonic frequencies when excited either by means of free vibration or forced vibration. The object resonates at particular frequencies at which the amplitude is at peak. The resonant frequencies as vibration characteristics are governed by elasticity, mass, geometry and density. Fruit and vegetables also behave similarly when caused to vibrate. A number of tropical fruits are evaluated for their internal quality by tapping and listening to the generated sound. The ripeness or maturity level is judged from the pitch of the sound. Experienced graders generally are very accurate in relating sound to ripeness and maturity level. As a result, over the past 15 years, much research has been conducted into objectively using sound production to predict maturity, especially with regard to durian and pineapple. In such sound measurement experiments, fruit are caused to show a sonic (or acoustic) vibration. Initial research into modeling forced vibration in order to study vibration characteristics of fruit was initially conducted by Abbott et al. (1968). They subjected apples to an acoustic range of frequencies by a mechanical exciter. A series of resonant frequencies was displayed and the second resonant frequency was found to be related to flexural vibration. The apple mass and its firmness influence the second resonant frequency. The stiffness coefficient f2m (where f was the second resonant frequency and m was the mass) was formulated to compensate for the difference in mass. This stiffness coefficient was later corrected in terms of dimension by Cooke (1972) as f2m2/3. Following this, Yamamoto et al. (1980) modeled the free vibration mode of vibration for watermelons in the early 1980s. They excited the fruit to vibrate freely by hitting it with a wooden ball pendulum and recording the produced sound. Watermelon firmness indices expressed as functions of the resonant frequency, mass, and density were correlated with firmness and sensory scores. Comparing the forced vibration to the free vibration in terms of displayed series of resonant frequencies, the lowest resonant frequency of the forced vibration is found to represent the interaction between the fruit mass and the force induced by local deformation of the fruit on the vibrator. While this may not be the case for the free vibration where a series of resonant frequencies are detected (Chen, 1996), what is in common for both modes of vibration is that they represent the mechanical properties of the whole fruit (Abbott, 1999). Implementation of sonic vibration to detect tropical fruit qualities is mainly of the free vibration configuration, which closely replicates the way the graders test the firmness of fruit. Research into quality detection of tropical fruit thus focuses on the application of this sonic vibration configuration. For example, Terdwongworakul et al. (1997, 1998) found that the resonant frequency based function of durian decreases progressively in the days after full bloom (figure 6 and figure 7). They established this by measuring the sonic vibration of Thai durians developing on trees (all samples were allowed to continue growing on trees so that the small variation in samples was maintained (figure 5)). The size difference was compensated for by measuring the volume of each sample by the water replacement method and taking into account the density-based mass of representative samples of each stage of maturity. Meanwhile, research conducted in Indonesia by Haryanto et al. (2001) attempted to determine the acoustic properties of durian fruit which in daily farm practice are graded

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according to the evaluation of multiple parameters. In 2006, Terdwongworakul and Neamsorn studied the changes in stem strength related parameters and resonant frequency in association with durian maturity, which is referred to as pulp dry matter percentage. They indicated that the area under the force-deformation curve of the stem was most positively correlated with the dry matter percentage at a correlation coefficient of r = 0.808. On the other hand, a negative relationship was found between resonant frequency and dry matter percentage (r = 0.448). A multiple linear regression analysis indicated that the area under the forcedeformation curve and the resonant frequency could be used in linear combination at a multiple coefficient of correlation of r (0.844). Another fruit that is subject to acoustic measurement is pineapple. Boonmung et al. (2006) have fused resonant frequency, firmness and soluble solid measurement by employing artificial neural networks (ANNs) to evaluate pineapple internal quality. They successfully developed ANN model that is able to classify pineapple into three classes at accuracy of more than 83%. Nondestructive forced vibration has also been proposed as a method for determining maturity levels of durian. Kongrattanaprasert et al. (2001) applied mechanical oscillation to durian samples at regions between the prickles located at the middle of the fruit, after which they recorded the frequency response through the use of laser Doppler techniques. Classification of durians into immature and mature categories using pulp dry matter percentage as a reference index was achieved with 90% accuracy by means of finger printpattern matching of power spectral density. Another method involves the use of ultrasonic waves, which interact with materials by transmittance, reflectance, refraction or diffraction. Parameters which are successfully used to evaluate the quality of horticultural produce are wave propagation velocity, attenuation and reflection, although unsuccessful results have been reported when high-frequency 1 MHz ultrasound is used to distinguish between damaged and undamaged apple tissue (Upchurch et al., 1987). This is believed to be because the structure and air spaces in apple tissue block the ultrasonic wave transmission, which in turn makes it difficult to obtain useful information. The application of low frequency (50 kHz) ultrasonic on the other hand has been of some successes in determining acoustic properties of certain fruits and vegetables (Mizrach et al., 1989). Attempts to use ultrasonic measurement to determine durian quality was first reported by Budiastra et al. (1999), as shown in figure 8. Kongrattanaprasert et al. (2001) later performed a series of data transformations, including wavelet transformation. The transmission ultrasonic pattern eventually was matched with the reference finger print-pattern by correlation and 95% accuracy in terms of classifying durian into different maturity levels was achieved. Ultrasonic transmission measurement (figure 9) was also found to be useful in classifying mangosteen (Nasution et al., 2005)

1.5. Light Properties Appearance is the primary property of fruit and vegetables that consumers judge quality by. A fruit or vegetable’s “appearance” is actually the information that is contained in the light reflected back from the agricultural product. However, when a light beam illuminates an agricultural product, only about 4% of the incident light is reflected at the outer surface as

Development of Engineering Technology to Improve the Quality of Production… 253 regular or specular reflectance (figure 10). The remaining 96% of the incident energy is transmitted through the surface into the cellular structure of the product, where it experiences scattering in all direction at small interfaces and absorbance by cellular constituents (Birth, 1976). An optically dense product such as fruit or vegetable alters the path length traveled by the light, which makes it difficult to penetrate due to the complex physical structure of tissues. Thus, the majority of the light energy penetrates only to a very short distance before being scattered back to the surface and leaving the fruit at the vicinity of the point of incidence. This type of reflection is perceived as “color” and termed “body reflectance”. According to Birth (1976), light must be transmitted through the pigment within the cells in order to give a colored appearance. The remaining scattered light energy penetrates deeper into the fruit tissues, diffuses, and is then absorbed by various constituents of the fruit. This light energy eventually exits the fruit some distance away from the point of incidence and carries useful chemical-related information of the fruit. This type of reflection, which varies with the constituents and, the light wavelength and path length, is termed “diffuse reflectance”. The ripening of fruit is associated with color change. For example, bananas change from green to yellow as they ripen, which is caused by the reduction in chlorophyll and the resulting unmasking of carotenoid. An early experiment by Finney et al. (1967) using spectrophotometric techniques to measure color changes in banana peel showed that the color change is related to flesh firmness. Birth et al. in 1978 attempted to determine papaya maturity using a nondestructive optical technique but reported unsuccessful results when attempting to differentiate immature papaya from mature green papaya through evaluation of surface color (the degree of yellow-orange coloration in a papaya’s flesh determines its maturity, with papaya changing color from white to light yellow-orange to bright orange as it matures). This failure was because direct transmittance (with the fruit between the source and the detector) provides inadequate signals as a result of the characteristic shape of the fruit. However, by using a fiber optics system to measure the transmittance of the papaya, Birth et al. (1978) obtained useful transmittance and found high correlations between chlorophyll, carotene, and soluble solids concentration and the transmittance at different stages of maturity. A good example of optical characteristics application to evaluate firmness and yellowness of mango can be found in the investigation conducted by Jha et al. (2006). In this study, the authors measured the spectra of mango in the visual wavelength range 400–700 nm using a handheld colorimeter in order to predict the firmness and yellowness index with reference to maturity and ripening stages during growth and storage. The relative reflectance was analyzed using comparative calibration models encompassing partial least squares regression (PLS1 and PLS2), principal component regression (PCR) and multiple linear regression (MLR). They showed that the PLS2 model derived from smoothed and subsequently MSC treated spectra in the wavelength range of 530–550 nm provided the best results. This in turn proves that color information obtained from the whole range of 530-550 nm provides a better nondestructive prediction model than information ascertained from just a few wavelengths.

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1.6. Near Infrared Properties Matter molecules respond to near infrared (NIR) radiation in the wavelength range 700 or 770 to 2500 nm by vibrational absorption. They vibrate at fixed frequencies when energized by radiation and therefore absorb light energy of that particular frequency or wavelength (Mohsenin, 1984). Water, carbohydrates, fats and protein (i.e. functional groups that contain a hydrogen atom e.g. OH, CH, NH) absorb light in the NIR region. NIR spectra contain a large amount of useful physical and chemical information about molecules. Nevertheless, this information cannot be always extracted from the spectra in straightforward analysis. The difficulty of this information extraction is probably due to two major reasons. One is that an NIR spectrum comprises NIR bands, which have their origins in overtones and combinations of fundamentals. These bands strongly overlap with each other, resulting in strong multicollinearity. The other comes from the fact that NIR spectroscopy deals with real samples containing various components and thus yields poor signal-to-noise (SN) ratio, baseline fluctuations, and severe overlapping of bands (Ozaki et al., 2007). The NIR spectra are therefore normally broad and much weaker than IR bands. In addition, it is often difficult to assign the NIR bands because of the overlapping of bands and complicated combinations of vibrational modes. Hydrogen bonding also causes a band shift for particular bands. For example, the NIR band often experiences the problems of unwanted spectral variation and baseline shifts. The sources of the variation and the baseline shifts may originate from 1) light scattering, 2) path length variation causing poor reproducibility, 3) variations in temperature, density, and particle size of samples and 4) various kinds of noises from a detector, an amplifier, or an AD converter. The precise and proper spectral analysis of NIR spectra is needed in order to allow extraction of useful information from NIR spectra to prevent incorrect interpretation. Pretreatment methods of data are crucially important in NIR spectroscopy. Some of the pretreatment methods encompass noise reduction, baseline correction, resolution enhancement, and centering and normalization. Near infrared spectroscopy has been used as a non destructive method for determining the maturity degree of mango fruits Thai cultivars Nam Dokmai and Chok Anan (Mahayothee et al., 2002). The reflectance spectra were collected in the integrating sphere using FT-NIR spectrometer in the wavelength range from 650 to 2500 nm and the NIR spectra of the mango were subjected to multivariate calibration techniques using principal component analysis (PCA). The calibration model was validated using full cross-validation and it was found that the ripening time and weight loss of both mango cultivars were highly correlated with the reflectance of NIR illumination. NIR spectroscopy was also reported for the taste evaluation of the fruit, with the mango being graded into four different groups: sweet-sour, sweet, sour and tasteless (Saputra et al., 1995 and Susanto et al., 2000) Application of NIR spectroscopy to another important fruit, mangosteen, has also been investigated. The prediction of internal translucent flesh disorder in intact mangosteen fruit by using short wavelength near infrared (SW-NIR) transmittance spectroscopy was investigated by Teerachaichayut et al. (2007). In this experiment, the NIR absorption spectra of 193 mangosteen samples were obtained in the wavelength range from 640 to 980 nm on four sides of each sample. The best result from a discriminant analysis for leave-one-out crossvalidation was 92.0% classification accuracy, with the results showing that the hardening pericarp disorder influenced the accuracy of the classification.

Development of Engineering Technology to Improve the Quality of Production… 255

1.7. Image Properties Visual inspection by humans can be used to evaluate the quality of fruit and vegetables provided that the external image is related to the quality of the produce. Accordingly, effort has been invested in automating visual inspection in the form of a machine vision system, The external features that can be analyzed by the machine vision system are usually color, shape and defects, although in the past it was difficult to process the images and extract useful feature information at an acceptable speed. However, due to the declining cost and increasing speed of the hardware, the machine vision or image analyzing system has rapidly gained popularity as an automatic sorting system. Research has mostly been conducted into the area of optimum configuration in order to minimize unwanted features in the image, and also into developing the efficiency of algorithms that extract the required information for further analysis.

1.8. Recommended Future Research Directions The preceding literature review has attempted to illuminate the factors responsible for the high post-harvest loss of tropical fresh produce. We have argued as a result of this review that the goal of producing tropical fresh produce at the standard required by local and export markets demands greater understanding of the physical, mechanical, textural, electrical, light, electromagnetic and sound characteristics of the produce. Nevertheless, it appears that research into tropical fruit is still a relatively neglected area. A count of papers returned on the academic search engine Google Scholar (April 2007) shows that research into the physical properties of the dominant tropical fresh fruits (durian, longan, mangosteen, mango, young coconut, pineapple, pumelo, rambutan, rose apple, dragon fruit) and related nondestructive analysis techniques occurs at a level that is at 21% (≅ 233) of that conducted into fruits such as apples, peaches and pears. Similarly, research into dominant tropical vegetables such as egg plant, snake egg plant, Chinese radish, chilli, Chinese cabbage, cabbage, kale, water morning glory occurs at a much lower level (7.7%) that applied to tomatoes, potatoes and carrots (≅ 298). More research into tropical fruit and vegetables will help define mechanisms and thresholds with regard to the variety of produce, property, physical, physiological and breeding factors, which in turn will help lead to the development of new post-harvest techniques and machinery as well as new ways to make use of under-grade or culled produce. Accordingly, a desired-for trend in research into tropical fruit and vegetables is for an increase in knowledge of fundamentals relating to a) their physical, mechanical and bruising properties in order to support mechanical sizing, conveying, handling, packaging and associated machinery, b) non-destructive properties in order to support high precision sorting, sizing and the associated machinery, c) textural properties in order to support breeding development, and the food product related to the fresh produce out of the export marketing quality. Research into the non-destructive properties should be informed by grower experience (for example, the identification of watermelon and pineapple maturity by sound (Boonmung et al., 2006; Kawano, 1994) which has led to the commercial development of a watermelon maturity sorting device), while the bulk of future inquiry should be directed

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towards those tropical fresh fruit and vegetables which are well known and accepted by foreign markets (namely baby corn, okra, egg plant and chilli). Table 1. Post-harvest loss of mangosteen fruit at wholesale level Post-harvest Loss (%) Fruit cracking Hard rind Rough surface Internal defects Translucent flesh Gummosis Translucent flesh and Gummosis Decay

Chantaburi 1.2 0.8 37.2

Chumpon 1.3 3.3 59.9

average 1.3 2.1 48.6

14.6 10.0 4.6 1.6

15.0 12.0 8.5 1.3

14.8 11.0 6.6 1.5

Table 2. Physical characteristics of large, medium, small and undersize mangosteen fruit Physical characteristics Weight (gm) Volume (cm3) Maximum diameter of fruit (mm) Minimum diameter of fruit (mm) Diameter of calyx circumscribing circle (mm) Height of fruit with stem end (mm) Height of fruit without stem end (mm) Dimension ratio

Large 102 105.2 60.3 57.9 51.8

Medium 81 80.3 56.0 54.4 47.8

Small 65 66.9 51.4 49.9 49.6

Undersize 44 44.6 44.4 43.3 47.4

65.5 51.7 0.86

63.6 48.3 0.85

59.6 43.1 0.96

55.0 37.2 1.06

Table 3. Statistical mechanical properties of Nam Dokmai Mango with the variation of maturity and point of loading application Point of loading application 1 2 3 4 5 •

Rupture force (N) 70.43± 8.72 b* 60.77± 10.88 c 65.56± 12.31 cb 59.66± 4.47 c 82.35± 5.86 a

Mature Rupture deformation (mm) 5.64± 0.59 c 7.05± 1.04 b 8.80± 1.21 a 5.33± 0.81 c 5.76± 0.77 c

Slope

(N/mm) 14.42± 1.32 b 10.78± 1.09 c 9.62± 1.52 c 12.90± 2.10 c 16.01± 0.39 a

Rupture force (N) 7.14± 0.65 a 5.97± 0.84 b 6.52± 0.91 ab 6.96± 1.00 a 6.87± 1.06 a

Ripe Rupture deformation (mm) 5.00± 0.58 ab 4.23± 0.65 c 5.35± 0.90 a 4.68± 0.69 cb 4.29± 0.73 c

Slope

(N/mm) 1.34± 0.14 a 1.42± 0.32 a 1.38± 0.18 a 1.41± 0.19 a 1.48± 0.20 a

Means followed by the same letter in the same column are insignificantly different at p < 0.05.

Development of Engineering Technology to Improve the Quality of Production… 257 Table 4. Physical characteristics of Sitong and Srichompoo sweet tamarind pod Cultivar

Sitong Srichompoo

Physical characteristics Weight (g) Width (cm)

Length (cm)

25.14 ± 4.88 16.48 ± 2.72

16.08 ± 2.62 11.80 ± 2.65

2.43 ± 0.15 2.16 ± 0.11

Thickness (cm) 2.09 ± 0.12 1.76 ± 0.09

Specific gravity 1.0093 ± 0.0120 0.9980 ± 0.0033

Table 5. Statistics of slope and firmness index of Sitong and Srichompoo sweet tamarind pod with maturity variation Cultivar Sitong

Firmness Index (m/s3) 0.242a 0.217a 0.224a 0.169b

Maturity Immature Mature Immature Mature

Srichompoo

Slope (N/mm) 1.095a 1.074a 0.764b 0.689b

* Means followed by the same letter in the same column of a particular cultivar are insignificantly different at p < 0.05.

Table 6. Statistics of slope of sweet tamarind pod with different surface profile (N/mm) Cultivar Sitong Concave 17.04b

Convex 22.93c

Srichompoo Concave 10.92a

Convex 12.23a

* Means followed by the same letter in the same row are insignificantly different at p < 0.05.

Table 7. Poisson’s ratio and Young’s modulus of selected tropical vegetables Vegetables

Snake Egg plant White Long radish

Poisson’s ratio Axial sample 0.252±0.082

Transverse sample 0.166±0.047

0.444±0.07

0.384±0.08

Young’s modulus (E) (Equation of E with respect to storage time t) (MPa) Radial compression Die loading Fruit axis Transverse axis Fruit axis ER,T=0.531e-0.04t ED=0.524e-0.07t ER,a=0.476e-0.03t 2 2 (R =0.81) (R =0.95) (R2=0.99) ER,a=9.05e-0.08t ER,a=8.66e-0.08t ER,a=3.07e-0.084t (R2=0.99) (R2=0.95) (R2=0.94)

Table 8. Statistics of coefficient of friction for each combination of produce and contacting surface Produce

Lime Tangerine Guava

Friction coefficient Cs Ck Cs Ck Cs Ck

Contacting surface Galvanized sheet metal 0.39± 0.021a* 0.37± 0.020a 0.60± 0.054a 0.51± 0.047a 0.85± 0.12a 0.79± 0.120a

Plywood

Plastics

0.44± 0.062a 0.41± 0.078ab 0.61± 0.086a 0.56± 0.083a 0.92± 0.084a 0.87± 0.070a

0.55± 0.049b 0.50± 0.030b 0.91± 0.12b 0.89± 0.150b 0.91± 0.092a 0.83± 0.081a

* Means followed by the same letter in the same row of the same friction coefficent type are insignificantly different at p < 0.01.

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B. Jarimopas, P. Sirisomboon, R. Sothornwit et al. Table 9. Average values of rolling angle (degree) for each different combination of produce and orientation Produce

Orientation Opposite stem side 21.30± 0.79c* 20.88± 0.59c 20.81± 0.85c

Lime Tangerine Guava

Stem side 13.44± 0.54b 13.36± 0.77b 16.01± 0.19b

Cheek 6.90± 0.85a 8.22± 0.15a 11.54± 1.51a

* Means followed by the same letter in the same row are insignificantly different at p < 0.01.

Table 10. Coefficients of static friction of normal and rough surface mangosteen fruits Description Normal surface fruits Longitudinal axis Transverse axis Rough surface fruits Longitudinal axis Transverse axis

Plexiglass

Plywood

Galvanized steel

0.4553f 0.4553f

0.3663c 0.3772cd

0.3340b 0.3371b

0.3058a 0.3324b

0.3804cde 0.3915de

0.3762cd 0.3965e

* Means followed by the same letter are not significantly different at probability < 0.05 according to Duncan’s multiple range test.

2

4

1

5

3 Figure 1. Illustration of “Nam Dokmai” mango with points of loading application ( 1- Cheek, 2-Head, 3-Tail, 4-Bottom edge, 5- Top edge).

Development of Engineering Technology to Improve the Quality of Production… 259

a) Srichompoo

b) Sitong

Figure 2. Sweet Tamarind.

Figure 3. Theoretical contact loading.

Moistened sand

Figure 4. Plunger compression test with moistened sand supporting fruit sample.

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FFT ANALYZING RECORDER

SOUND LEVEL METER

DURIAN Figure 5. Instrumentation configuration for measuring the acoustic vibration of durian.

Exponentia Index

3000000

2500000

2000000 M1 M2

1500000

M3 1000000

M4 M5

500000

M6

0 40

50

60

70

80

90

100

110

120

130

140

Number of days after blossom Figure 6. Change of resonant frequency index or exponential index, 2/3f2ln(m) (where f is the resonant frequency and m is the mass) with number of days after blossom.

Development of Engineering Technology to Improve the Quality of Production… 261

Stiffness coefficient

60000000 50000000 40000000 30000000 M1 20000000

M2 M3

10000000

M4 0 M5 40

50

60

70

80

90

100

110

120

130

140

M6

Number of days after blossom Figure 7. Change of stiffness coefficient f2m2/3 (where f is the resonant frequency and m is the mass) with number of days after blossom.

Ultrasonic tester

Digitizing oscilloscope

PC Lab. card

Personal computer T

R

Figure 8. Ultrasonic instrumentation setup for durian ultrasonic measurement (Budiastra et al., 1999).

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Ultrasonic tester

Digitizing oscilloscope

PC Lab. card

Personal computer

Figure 9. Experimental setup for nondestructive evaluation for mangosteen quality using ultrasonic wave (Nasution et al., 2005).

Incident light Regular reflectance Body reflectance Absorbance

Transmittance Figure 10. A simplified schematic representation showing the interaction between light and a fruit.

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2. INNOVATIONS OF TROPICAL FRESH PRODUCE MACHINERY 2.1. Durian Maturity Sorting Device Production of fresh durian always encountered the problem of fruit contaminant, i.e. matured fruit contaminated with the immature fruit intentionally or unintentionally. The production of quality fruit for export requires detection and segregation of the immature out of the mixture. Recently, Chawapradit et al. (2000) developed the durian maturity sorting device.

2.1.1. Concept of the Design Three points of view supported the design of the durian maturity sorting device, i.e. a) the natural frequency fn of durian decreased as the fruit was getting matured, b) consistency of fn was dependent on the consistency of the knocking area of durian surface, c) double knock was undesired, only one knock was required for a test. 2.1.2. Design and Operation Figure 11 showed the durian maturity sorting device comprising 50 cm x 70 cm steel frame with two floors and the knocking mechanism. The knocking mechanism consisting of teflon head and pneumatic solenoid valve was installed in the second floor in the middle of the durian maturity sorting device. The teflon head hit the durian from the bottom. The teflon head restored to its original position by gravity after switch was shut to prevent double knocks. The durian fruit was placed on top of the frame on A and B stands, resulting in the contact c and d. The fruit weight deformed stand spring so that the fruit lowered and contacted metallic plates beside at a and b, giving rise to consistent knocking area regardless of durian size. The knock was controlled by pneumatic solenoid valve. The knocking sound was transmitted through microphone, converted to electrical signal, processed through the signal conditioning circuit and analyzed by computer program. The natural frequency of each durian fruit was recorded and implemented for maturity separation. fn of the mature durian of 120 days after pollination ranged from 600 to 720 Hz. 2.1.3. Performance Evaluation Performance test indicated that the durian of maturity difference less than 7 days contributed the sorting error of about 15%. For the durian of maturity difference more than 7 to 14 days, the sorting error was zero. The practical unit consists of 2 durian maturity sorting devices connected to conveyor and controlled by 2 operators to place and remove durian fruit interchangeably and continuously. The system capacity amounted to 1,500 fruit or 6 tons per hour.

2.2. Mangosteen Sizing Machine Mangosteen is a locally prized tropical fruit. Despite its importance, sorting of the fruit for local and export consumption has largely been manual and inefficient. There are two main sizing systems currently being used. The first is a tangerine mechanical sizer that uses

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perforated cylinders to sort the fruit. However, this method is not appropriate for sizing mangosteens because of the fruit’s large calyxes, which puts the fruit at risk of getting stuck in circular holes. Due to the absence of appropriate commercially available sizing machinery, mangosteens therefore have been historically sized by weight, not by dimensions. One commercially available mangosteen sizing machine of this type features 10 kg load cells and microcomputer control and weighs fruit dynamically in a continuous packing line. Nevertheless, there are many disadvantages in this approach, including high initial and operating costs and complicated maintenance procedures.

2.2.1. Concept of the Design A desirable mangosteen sizing machine is one that uses the diameter of the fruit body as sizing parameter. This is because mangosteen does not have a spherical appearance despite having a large calyx, and thus rather spherical bodies. The basis of design features a rotating conical-shape disk and a metering board with gaps of increasing size arranged along the periphery of the disk (figure 12). Mangosteens are fed onto one section of the rotating disk and the combined centrifugal and gravitational force moves the fruit toward the periphery until contact with the metering board is attained. The tangential force then rolls the fruit along the metering board, where they are sized and allowed to drop through gaps according to their dimensions. 2.2.2. Design and Operation The mangosteen sizing machine (figure 13) comprises a rotating disk, a sizing board, a feeding tray, receiving trays, and a power drive, all attached to a steel frame, which rides on four small wheels. The frame is 820 mm wide by 820 mm long by 960 mm high and made of 40 mm by 40 mm steel L-beams. The rotating disk is made of 9.5-mm thick steel plate and is 600 mm in diameter. The top surface is formed into a conical shape with a 10 degree slope, which allows the fruit to roll down to the sizing gaps by gravity. The center of the disk is connected to a 50-mm diameter steel shaft which is driven by a 187 W, 220 V electric motor through a 1:40 gear reducer and pulleys. Above the edge of the rotating disk is a 50 mm wide by 6 mm thick vertically adjustable steel sizing board curved along the disk circumference. The feeding and receiving trays are made of 1.0-mm thick steel sheet while the rotating disk, the sizing board and the feeding and receiving trays are all lined with 3 mm thick rubber foam to protect the mangosteen from mechanical damage. The factory prototpe contained a 400 mm feed opening. In operation, mangosteens are continuously poured onto the feeding tray and then rolled down onto the rotating disk in clusters of 6 to 10 fruit at a time. Each fruit is then brought into contact with the sizing board and the rim of the rotating disk through gravitational and centrifugal forces. The fruit move along the sizing board and drop down to the receiving tray whenever the diameter of the fruit is less than the sizing gap. Small fruit thus will be sized before big fruit. 2.2.3. Performance Evaluation The disk diameter, disk speed and type of metering gap significantly affected the sizing performance. The most efficient configuration of the factory prototype was a rotating disk speed of 21 rpm using a step-type metering gap, resulting in sizing error of 22.8%, and capacity of 1,026 kg/hr with minimal fruit damage (0.48%). The tested machine showed better performance than currently existing commercial models (sizing error of 44% and

Development of Engineering Technology to Improve the Quality of Production… 265 capacity of 500 kg/h) and manual sizing (153.4 kg/h with error of 33.4%). An engineering economic analysis showed that the break even point and pay back period for the factory model would be 46,020 kg/yr and 6 ½ months, respectively. (Jarimopas et al., 2007a)

2.3. Semi-Automatic Young Coconut Fruit Trimming Machine Young coconut is one of the most popular fruit varieties. The edible parts include the sweet juice at the core of the fruit and the soft flesh attached to the inner surface of the shell. The juice contains glucose, vitamins, hormones and minerals, and is widely considered to be a refreshing drink. The flesh contains carbohydrates, calcium and phosphorus, and is commonly used in desserts (Pechsmai, 2002). Young coconut is the immature fruit of coconut palms, which can be easily grown in South East Asia. The trimming process is manually done which, required skilled labor, and is extremely hazardous. Currently, workers must shear the husk off the green fruit with a long sharp knife. The inner white husk is then finely sheared to form a conical shaped top, a slightly tapered cylindrical body, and a flat base. The final shape has a pentagonal profile (figure 14) After it has been trimmed, the fruit is dipped into a sodium metabisulfide solution to prevent surface browning. The shortage of experienced labor and the high production cost has created an urgent need for mechanical trimming machines.

2.3.1. Concept of the Design The prototype is based on the lathe cutting machine mechanism. The principles underlying the design were: i) the final product must have a pentagonal shape ii) the trimming mechanism must be based on the lathe cutting technique iii) the construction must be rugged, compact and transportable, iv) the mechanism must be uncomplicated, strong and must resist vibration, v) it must be capable of being operated by one person. 2.3.2. Design and Operation The machine comprised: a) power transmission, b) control, c) shoulder and body knives, d) base cutter, e) fruit holder, f) crest holder and, g) shaft holding pedicel (figure 15). In operation, a young fruit was first firmly mounted between the crest holder and the shaft holding pedicel, so that fruit axis was aligned with horizontal line. The fruit was rotated and then trimmed by the body knife. Next, the fruit holder was manually fastened to the trimmed body, while the crest holder was pulled away from the fruit to its retracted position. The shoulder knife then trimmed the fruit until a sharp crest was achieved. A foam rubber pad covered the crest, and the crest holder again held the fruit by pressing against the padded crest. Next, the fruit holder was opened and the trimmed fruit rotated. Finally, the base knife descended to cut the fruit base, producing the complete trimmed fruit. The finished fruit was then dipped in sodium metabisulfide solution to prevent browning. 2.3.3. Performance Evaluation The two angles in orthogonal planes of shoulder knife (γ,α1), the two angles in orthogonal planes of body knife (α2,β), the trimmed fruit speed significantly influenced the trimming performance. The appropriate operating conditions were: shoulder knife γ = 560, α1

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= 610, body knife β = 760, α2 = 610, trimmed fruit speed = 300 rpm. The corresponding trimming performance was 21 fruit/h, untrimmed green area of 1.1% , remaining fibrous area of 0.2%. The sample to be trimmed had to be uniform size and shape and newly-harvested (Jarimopas et al., 2007b).

2.4. Semi-Automatic Young Coconut Fruit Opening Machine The young coconut fruit that was already trimmed needed opening for consumption because the edible part of both flesh and juice was inside the fruit shell. Opening of the trimmed coconut is traditionally obtained by manual impact cutting with big sharp knife. The cutting process was much likely to endanger the operator and juice was usually spilled off. Besides, the appearance of opening looks unclean and not neat. Hence, the semi-automatic young coconut fruit opening machine was developed to achieve safe, nice, clean and fast opening with minimum loss.

2.4.1. Concept of the Design Jarimopas et al. (1989) comparatively investigated three mechanism of opening the trimmed young coconut, i.e. impact, shearing and cutting. The cutting was the proper means mechanically opening the coconut because easy and complete opening with little spilled-off juice was obtainable. Based on this mechanism, the trimmed young coconut can be cut open by moving two rigid sharp knives in the same horizontal plane toward each other cutting the stationary coconut placed in between. 2.4.2. Design and Operation The machine comprised two triangular stainless steel sharp knives (figure 16). Each knife was fixed to the two shafts with thread on their surface at the left and the right sides. The threads of each half of each shaft were in opposite direction to drive the knives traveling toward each other. The shafts were driven by gears and ¼ hp 220 v electric motor with switch to turn on, off the machine and to control forward and rearward travel of the knives. There was an adjustable platform in the middle of the machine to support the varying size of trimmed young coconut. In operation, the trimmed young coconut was first placed on the adjustable platform. The operator would adjust the platform vertically so that the coconut could be cut open with sufficiently wide opening. The machine was then switched on to move the knives forward to completely cut open the coconut and to move the knives backward. The machine was turned off and the open coconut was removed. 2.4.3. Performance Evaluation The opening machine could open the trimmed young coconut including the big, medium, small sizes (117 mm ≤ fruit height ≤ 137 mm) at the average opening time of 8.5 sec/fruit with the spilled-off juice about 4.7% and opening width of about 80 mm. Juice and flesh was clean and well acceptable (Jarimopas and Pechsmai, 2001). It was not commercialized because it was noisy. Besides, the cutting knives sometimes got stuck during cutting and the design did not look safe enough for operator.

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2.5. Young Coconut Fruit Opening Device As mentioned earlier of the necessity of opening the trimmed young coconut, Supsomboon (1998) invented the young coconut fruit opening device.

2.5.1. Concept of the Design In this case, the device was so designed to be handy and for one usage to support personal consumption. Opening mechanism is sawing. 2.5.2. Design and Operation The device comprised a metallic rectangular strip one long side of which featured lots of teeth of saw; plastic holder to hold the strip ; and a plastic knife (figure 17). To open the trimmed young coconut fruit (figure 14), the plastic holder with the strip underneath was put over the crest of the fruit, manually pressed and turned around to and fro. The teeth of the strip sheared the mesocarp and shell till rupture, the young coconut was then open. The plastic knife facilitated opening in case of incomplete cut. 2.5.3. Performance Evaluation Testing the opening device revealed that it took 41.4 seconds to open a trimmed coconut with spilled-off juice of 3.8%. Some sawdust of mesocarp and shell was found in the juice (Jarimopas and Pechsmai, 2001). The device has already been patented.

2.6. General Purpose Coconut Dehusking Machine Ripe coconut fruit is extensively used for fresh consumption and cooking of various dishes in many countries. In order to utilize the coconut it must be first dehusked to get the fruit enclosed with shell (figure 18). Then, the shell of the fruit will be so cracked that the white firm flesh can be scratched into small pieces. Next, the small pieces of white flesh with some water will be squeezed to get coconut milk for further cooking. The dehusking was traditionally practiced with sharp knife and strong labor. Nature of the work is dangerous and indeed requires the replacement of machine. Kwangwaropas and Sukjareon (1997) recognized the problem and developed the general purpose coconut dehusking machine.

2.6.1. Concept of the Design Coconut growers manually dehusk the ripe coconut mesocarp by tearing. A knife was impact cut first longitudinally to partition the mesocarp, then the partitioned mesocarp was removed by tearing. The developed machine featured the mechanism imitating the manual tearing. 2.6.2. Design and Operation Figure 19 showed the general purpose coconut dehusking machine consisting of frame, two 105 mm dehusking rollers, polishing brushes, power and transmission system. The frame was made of welded steel construction. The dehusking rollers featured 6 fins welded to roller surface and inclined to roller axis like spiral. The roller rotated at 49 rpm. The polishing set consisted of two shafts each of which had eleven 175 mm diameter by 25 mm thick steel

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brushes. The general purpose coconut dehusking machine was powered by a 2 hp, 220 v electric motor. The power was further transmitted by chain driving the dehusking rollers and brushes. In operation, the dry ripe coconut was first put onto the operating dehusking rollers with the lid closed. Vertical compression at the lid plus friction force of the roller fins caused pressing and pulling a portion of husk (or dry mesocarp) out of the shell. The rotating rollers turned the coconut as simultaneously as they repeated tearing other portions of husk. It took 5-8 seconds to complete dehusking. The dehusked fruit further rolled by gravity due to slightly inclined setting of the machine to polishing brushes. It took 8 seconds to get the dehusked coconut completely polished.

2.6.3. Performance Evaluation Mechanical dehusking process could be averagely fulfilled in 4.8 seconds/fruit. Power consumption for dehusking and dehusking plus polishing was 1.77 and 0.82 hp respectively. The machine could make complete dehusking. The machine could further dehusk fresh coconut satisfactorily and spent dehusking time as similarly as the machine did with the dry ripe coconut. The machine has already been patented.

2.7. Unripe Durian Cutting Machine Exporting fresh durian is nowadays performed under standard (Office of Thai Agricultural Commodity and Food Standard, 2003). The mature durian fruit that does not meet size, shape and internal defect standard is a lot. Since its internal quality is good, an alternative to make value added to the under grade durian is needed, at least to save the income of the fruit growers. The durian, normally harvested mature and unripe, is very firm and tough. Moreover, its surface is full of sharp thorns. A strong and skillful man with sharp knife can cut and open it. The fried raw flesh of durian has been successfully accepted in several markets.

2.7.1. Concept of the Design Opening durian fruit was accomplished by hydraulic cutting in vertical plane with alignment to fruit axis and section wall. 2.7.2. Design and Operation Figure 20 showed the unripe durian cutting machine comprising 38 mm x 38 mm x 3 mm rectangular steel frame welded construction, 9x18x0.3 cm typical stainless steel knife, 2direction hydraulic system with 35 cm stroke, 0.75 kw 220 V electric motor. The stem of an unripe mature durian fruit was first cut off. Then, the durian fruit was mounted to the unripe durian cutting machine at its center with the fruit stem pointing upward. The knife was hydraulically moved down to touch the fruit axis while the knife edge was aligned along a section wall. Next, the knife was switched on to move through to cut the fruit and then the knife went up to repeat cutting the next section. The operator controlled the cutting at his foot on the paddle switch. Cutting control was attained semi-automatically by the hydraulic system with the operator manually rotating the fruit to set alignment.

Development of Engineering Technology to Improve the Quality of Production… 269 2.7.3. Performance Evaluation The 95 kg unripe durian cutting machine, requiring one operator, was capable of cutting the unripe, mature “Hmontong” durian fruit at the capacity of 350 kg/hr with flesh loss of 1.6%. A strong and experienced man could succeed durian cutting of 80 kg/hr. Electrical power consumption was 540 watt-hour. Economic analysis indicated the cost of mechanical cutting durian was approximately 1.5 USD/ton with the payback period of 0.63 year.

2.8. Baby Corn Husking and Flesh Separating Machine In the early 1970s, Thailand was the first country to seriously start cultivating baby corn. Since then Thailand has become known as an exporter of baby corn. Production of baby corn is on a steadily increasing trend and thus making Thailand among the world leaders in baby corn production. At present, exporters are faced with the problem of insufficient supply of fresh baby corn cob when demand is high. Lack of skilled workers to husk the corn is a major factor. There have been a number of researches that attempted to develop the baby corn husking machine. The recent development of baby corn husking and flesh separating machine is reported as follows.

2.8.1. Concept of the Design The baby corn husking machine was designed based on the following physical property relationships. Terdwongworakul and Suppattakul (2005) investigated the relationship between the ear thickness and the ear diameter with the purpose to obtain the basis for the design. The relationship was such that as the diameter of the ear increases from the silk end (starting at the flesh tip inside), the ear husk is thinner. The relationship is consistent up to the point where the ear diameter is greatest. Then from the greatest diameter point to the stem end, the relationship is reversed as depicted in figure 21. That is the ear diameter relates positively with the ear husk thickness. This relationship governs the design of the cutting mechanism. The cutter of the mechanism has to be designed to cut through the silk end and just makes slice trace along the husk contour up to the stem end. The cut at the silk end opens the husk and such initiates the cut path from which the subsequent husking operation will be able to remove the husk. The husk will be torn aside by the husking rollers subsequently. The other useful relationship depicts that the greater the maximum ear diameter the greater the ear thickness (figure 22). This means the baby corns must be graded according to their biggest ear diameter prior to being loaded into the husking machine to control the consistency of the thickness.

2.8.2. Design and Operation The husking machine consists of four main components (figure 23): a pair of feeding rollers, a cutting mechanism, a pair of conveying rollers, and a pair of threaded husking rollers. The feeding rollers have V-shaped groove at the middle to accommodate for varying ear size. An ear is fed manually through the V-shaped groove and the rotating action of the rollers forces and feed the ear to enter the cutting mechanism. The four-bar linkage cutter is designed based on three coupler position synthesis. The coupler of the linkage is the cutter of the mechanism. The ear is cut as it moves forcibly by the feeding rollers. The ear silk end is

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cut through whereas along the ear contour, the husk is sliced open by the controlled action of the four-bar linkage cutter. The conveying rollers ((C) in figure 23) pulls the cut ear from the cutting mechanism and at the same time acts to feed the ear into the husking rollers ((D) in figure 23) as showed in figure 24. The husking action of the husking rollers is controlled by a cam operation. The threaded husking rollers comprise the first and second husking rollers, each of which is made left-hand threaded and right-hand threaded to the center, respectively. The two-way thread on the roller, when rotating, will grip the husk and silk and take them from the center towards both ends of the roller promoting the tearing and separating actions. In action, the first roller rotates statically whereas the second roller will epicycle around a shaft of the first roller. Figure 25 illustrates the sequential action of the threaded husking rollers. The plane of the husking rollers initially is vertical making the first roller be on top of the second roller in order to be ready for receiving the silk end into the gap between the rollers. As soon as the silk end is captured by the teeth of the husking rollers, the cam will make the plane of the rollers turn to the horizontal position. The husk and silk are not only pulled towards ends of the roller but also pulled through the gap or downwards as presented in figure 25. The flesh is forced to move ahead over the rollers and thus separated from the husk and silk simultaneously. When finished, the timer of the controller will terminate the cam action and let the husking roller be back to the original position or the upright position. The machine is therefore ready for the next ear to be fed and separated from the husk.

2.8.3. Performance Evaluation The tests performed to evaluate the performance of the machine showed that the separation efficiency is 84.0% with the obtained flesh without husk attached to the flesh (Terdwongworakul and Suppattakul, 2005). The silk remaining on the flesh accounts to approximately 1.7%. However, each ear takes about 30 seconds for the complete operation. The husking time could be reduced by improvement on the husking operation.

2.9. Recommended Research Trend So far there has been serious shortage of several appropriate machinery a) supporting certain postharvest operations to produce standard tropical fresh perishables, b) facilitating eating the fresh produce, and c) supporting production of the undergrade produce to get value added product. Durian, mangosteen, longan, pumelo, pineapple, mango, rambutan, rose apple are either well-known tropical fruit or are gaining popularity in the world market. Hundreds of million people in this planet are suffering nutritional deficiencies that the nutrition ingredients they need prevail in the tropical fresh produce. Table 11 listed the unavailability of machinery relating the postharvest process and fruit. Trend of the research should direct mostly to the development of the sorting and sizing machine. The machines are rather selective for fruit. Mechanical sizer, characterized by 3 grade sizing (small, medium, large), approximately 20% error, and 1-4 ton/hr capacity, is primarily recommended to be developed to support those tropical fruit. The mechanical sizer is basically rugged, easily repaired, has low cost that suits grower income in the developing and under developed countries. The mechanical sizer can well support the production of tropical fresh product to local markets and supermarkets. This engineering means will

Development of Engineering Technology to Improve the Quality of Production… 271 perhaps lift up the quantity and standard of production to a certain extent. The perforated cylindrical sizer for tangerine in Thailand is a distinct example. More than 600 perforated cylindrical sizers widely support the tangerine becoming the basic fruit distributed in every local market and supermarket (Jarimopas, 2006). However, the development of hi-technology sorting machine with built-in sizer should be developed in parallel because exporting production required high standard (error 5-10%) that the mechanical sizer cannot perform. Besides, the hi-technology sorting machine will use less space in the packing line, rendering the shorter line and the lower cost for exporting production. Development of proper packing line integrating the sizing, sorting machines to other conventional postharvest machinery like, washing, surface drying, weighing machines is also required for cooperatives to support the quality fruit production for local market and export. Introduction of the packing line featuring machinery and a small number of experienced operators to replace that featuring a lot of labour is essential. This is because the problem of worker shortage and expensiveness is invading the countries producing fresh produce. There are several tropical fresh fruits that are strange to foreigners. The edible part is inside the fruit whose peel is too hard to manually open. To promote eating and marketing the fruit the device or machine facilitating eating tropical fresh fruit must be developed. Appropriate machinery should be based on ease of use, inexpensiveness, sufficient efficiency, easy maintenance, and minimum damage. Table 11. Recommended trend of research and development of postharvest machinery of tropical fresh fruit Fruit

Durian

Postharvest machinery of tropical fresh fruit Sorting Sizing Eating facility U

U

Longan Mangosteen

U (Device sorting internal defects is unavailable) U U

U A

Not necessary U

Mango Pineapple Young coconut

U U U

A U U

Not necessary U A

Pumelo

U

U

U

Rambuttan

U

U

U

Rose apple Dragon fruit

U U

U U

Not necessary Not necessary

Note : U = Unavailable ; A = Available ; Uf = undergrade fruit.

Management of Undergrade fruit There is unripe durian cutting machine Dryer Undefined process of Uf Local market Feed stuff factory Undefined process of Uf Undefined process of Uf Undefined process of Uf Local market Local market

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Fruit

Figure 11. Durian maturity sorting device.

Figure 12. Sizing concept.

Figure 13. Mangosteen sizing machine (1 frame ; 2 feed opening ; 3 rotating disk ; 4 receiving tray ; 5 metering gap).

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Figure 14. Young coconut fruit: (I) untrimmed, (II) trimmed.

Figure 15. Semi-automatic young coconut fruit trimming machine; (1 fruit holder ; 2 shoulder knife ; 3 body knife ; 4 base cutter ; 5 control switch ; 6 electric motor drive).

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Figure 16. Semi-automatic young coconut fruit opening machine.

a) 1 = metallic strip with plastic holder 2 = plastic knife

Figure 17. Young coconut fruit opening device.

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Figure 18. Intact dry mature coconut fruit (left) with husk, (right) dehusked fruit.

Figure 19. General purpose coconut dehusking machine.

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Figure 20. Unripe durian cutting machine.

Figure 21. The relationship between ear diameter and ear husk thickness. The starting end of the arrow represents the silk end of the ear being pertinent to the flesh tip inside.

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Figure 22. The relationship between average ear husk thickness and maximum ear diameter.

Figure 23. Baby corn husking and flesh separting machine consisting of (A) a pair of feeding rollers, (B) a cutting mechanism, (C) a pair of conveying rollers, and (D) a pair of threaded husking rollers.

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Figure 24. The operation of the baby corn husking and flesh separating machine consisting of a) feeding rollers forces the ear into the cutting mechanism, b) the ear is cut along the contour by the cutting mechanism and the conveying rollers feeds the cut ear into the husking rollers, and c) the ear husk is being separated by the duhusking rollers.

Figure 25. The sequential action of the threaded husking rollers.

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3. PACKAGING OF TROPICAL FRESH PRODUCE Packaging of tropical fresh produce covers packaging for distribution and for extension of the produce shelf life. The content of this aspect is divided into three topics, namely the present status, interior packaging management, and edible film development.

3.1. Present Status Packaging for distribution accounts for wholesale and retail packaging. For a certain developing country like Thailand, typical wholesale packaging for fresh produce was kheng, plastic bag, crate and corrugated box (Jarimopas, 2006). Figure 26 showed kheng which was made of either bamboo or plastics. The bamboo kheng could carry load of 4 to 200 kg, depending on size, design and quantity of bamboo used. Kheng is cheap, irreusable and easily deformable when loaded. Because of its shape like frustum, stacking is impractical because the upper kheng will damage the produce of the lower kheng. This renders inefficient utilization of the truck space during transit and transportation cost cannot be reduced. Related weight, capacity and compressive resistance were given in table 12. Due to wide availability and inexpensiveness, plastic bag is popularly used for packaging fresh fruit and vegetables. Instead of protection, the plastic bag rather collect produces in a place to ease handling and shipping because its structure is thin plastic laminar which is not rigid. The produce contacting the bag could be easily exerted by external force and consequently damaged. Besides, the produce inside the bag could be further pressed in chain through contacts of neighboring produces resulting additional damages. Based on plastics design, the plastics of a bag was either uniform laminar or net with holes or without holes (figure 27). Based on construction material, the plastic bag is made of linear low density polyethylene (LLDPE), low density polyethylene (LDPE), high density polyethylene (HDPE), polypropylene (PP) and polystyrene (PS). Crate is made of HDPE plastics of two designs, i.e. 45 litre rectangle and 45 litre trapezoid (figure 28). The compressive resistance of crate was as high as 840 kg. The structure of crate is more rigid than plastic bag and kheng. Consequently, crate was found to protect mango bruising better than kheng did (Chonhenchob and Singh, 2003). The full rectangular crate occupied the truck space as equally as the empty crate because such a crate is rigid and not collaspsible. Even though the empty trapezoidal crate could be piled up and occupied less truck space, this increased the transportation cost of returning the empty crate. Crate is reusable and well ventilated but limitedly printed. Each crate costing about 4.5 USD may last as long as 10 years. The crate made of recyclable plastics costing about 3 USD may be out of order in 5 years. The corrugated paper CP box is popularly used for transporting fresh fruit and vegetable to both local and exporting markets. This is because the CP box has smooth surface facilitating printing and not damaging the produce. The corrugated paper was light, degradable, recyclable and needs little space before box assembly. The box assembly is easy and sufficiently strong to protect the produce inside. Disadvantage of the CP box is low ventilation and the paper absorbs moisture which reduces its strength. Table 13 presented the specifications of corrugated paper box for exporting fresh fruit and vegetable recommended by Thailand Packaging Center. According to US Standard the CP box for fresh produce must

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bear a) the compressive stress of 19.3 kg/cm2, b) 210-220 cm stacking height. The CP box was so designed to well meet OECD standard for palletization, i.e. 80 cm x 120 cm or 100 cm x 120 cm. Generally, CP box capacity was 12 kg for typical produce and 5 kg for bruise susceptible produce. The recommended design of CP box was for packaging banana, rambutan, durian, fresh vegetable, guava, tomato, egg plant, papaya, mangosteen, longan, pomelo, grape. Amongst corrugated box, plastic crate, and kheng, corrugated box with foam net provided the best protection. Mangoes packaged in kheng showed the highest mechanical damage and weight loss (Chonhenchob and Singh, 2004). The typical retail packagings for fresh produce included bag, foam tray, kheng and box. Bags are the most popular retail packaging because they are cheap but their performance was the poorest among packagings (Peleg, 1985). Bags are mostly plastic in the form of either laminar or net. The net is often used to package tangerine and sweet tamarind. The netted tangerines came in a corrugated paper box from the producer to facilitate retailing whenever the box is opened. Disadvantages of the bags are a) the produce inside the bags were injured because of mutual impact among produce during handling and transport, b) absence of individual produce separation, c) low package density and d) since the produces inside the bag could not be clearly seen, this urged the buyer to manually explore the bag which added produce damage. Foam tray, made of polystyrene, was applicable for intact and peeled fruit. Foam tray formed a retail packaging with stretch film. Transparency of the stretch film provided clear vision attracting buyers. They could also inspect the packaged produce. Besides, the stretch film tightened the produce to the tray not to move when loaded. This not only collected the produce together for easy carriage but also prevented bruising due to mutual impact. Another advantage of the stretch film was to reduce transpiration of the produce. Kheng was available for the capacity of 1 to 2 kg/container and was applicable limitedly to local grape. Box was divided into two categories, i.e. paper box and plastics box. The paper box was made of paperboard coated with wax and structured with a plastic top window providing visual observation to buyers. The plastic box was made of PVC (polyvinyl choride) with holes for ventilation. The paper box and plastic box were available for sweet tamarind and strawberry respectively. For better protection of fruit, cushioning material like foam net or air bubble plastic sheet was used to wrap the fruit before packaging (figure 29). Chinese apple, dragon fruit, papaya were primarily wrapped with foam net and then packaged in the wholesale packaging like corrugated box before shipping. This not only reduced transportation bruising due to fruit contact impact but also facilitated retailing without repackaging. Popularity of the air bubble plastic sheet to wrap the fruit was less as compared with foam net. Thongkamthamachat and Timinkul (2000) found that performance of bruise protection of Chinese apple and guava by commercial foam net to impact was better than that by commercial air bubble sheet. Degree of bruise protection of the apple under impact varied as the size of foam net thread. No bruise was found in an apple wrapped with 5 mm thread foam net while bruising was detected in an apple wrapped with 1.5 mm thread foam net under the impact energy of 1.1 J or less (Jarimopas et al., 2003).

Development of Engineering Technology to Improve the Quality of Production… 281

3.2. Interior Packaging Management Rose apple, mangosteen, mango, rambutan, longan and sweet tamarind were nowadays packed in the aforementioned wholesale packagings. Considerable damages of the packaged fruit remained which indicated that only the wholesale container was insufficient to protect the produces from mechanical damage. Management of the produces inside the packages with cushioning materials was additionally required. One way to implement this was the application of existing cushioning material with different design of interior packaging. Design criteria was a packaged fruit a) must not contact its neighboring fruit and package body, b) must not move in side the package, c) must not bear vertical load, d) can be wrapped with cushioning material to reduce damage (Peleg, 1985; Jarimopas, 2006). The sweet tamarind in corrugated box shipped from the orchard to the wholesaler depicted the total damage of 22 and 29.5% for Sitong and Srichompoo cultivars. Rachanukroa et al. (2006) demonstrated the concept of mechanical damage reduction of the packaged sweet tamarind by means of mixing small foam ball to the sweet tamarind at varying mixing ratio and packing them in the corrugated box. The more the proportion of foam ball was, the more the amount of damage decreased. In parallel, packing density also reduced. Further research should be made to find the appropriate proportion between sweet tamarind and foam ball while maintaining proper packing density in a wholesale container. Rachanukroa et al. (2007) utilized the above concept with the single face corrugated paper to design a retail packaging of 15 cm diameter by 20 cm height sleeve. The 1 kg sleeve was filled with sweet tamarind mixed with 5 mm foam ball at 30% mixing ratio. Drop test evaluated that the sleeve could produce damage of sweet tamarind of Sitong and Srichompoo cultivar at the level of 1 and 1 5

3

of the maximum damage in typical retail packaging. Chaiyapong and Jarimopas (2007) attempted to utilize foam net, single wall corrugated paper, shredded paper with fruit stacking variation to improve packaging of intact Nam Dokmai mango for export. Typically, 24 flawless mature mangoes were packed in 24.5 cm x 35 cm x 25 cm, double wall, full telescope corrugated box in two layers with some shredded paper distributing on the bottom of the box. Twelve fruits were placed on each layer with their cheeks turning downwards. The improved packaging used the same size container packaged with mango cushioned with foam net. The cushioned fruit was placed diagonally on shredded paper at the bottom of the box. The shredded paper was spreaded over the cushioned fruit and a piece of single wall corrugated paper followed. The second, the third and the fourth layer was obtained by repeating the previous arrangement. Each layer comprised 8 cushioned mangoes. The 4-layer box of wrapped mangoes weighed 17.5 kg while the typical box of bare mango weighed 10 kg. Performance test by means of vibration simulation was applied to the typical export and the improved packaging. Table 14 showed that the improved 4-layer container exhibited potential packaging over the current container because it could carry about 33 %more mango with less than half of the bruising that the current box gave. Another management concerned with mangosteen packaging. Pushpariksha et al. (2006) made up 8 different interior designs with 2 kinds of wholesale container (corrugated fiberboard box and reusable crate). The corrugated fiberboard boxes (CFB) were packages for export while reusable plastic container (RPC) was used for domestic market. The experimented design were

282

B. Jarimopas, P. Sirisomboon, R. Sothornwit et al. 1.CFB-1T Corrugated fiberboard box/Pattern-packed/Tray pack/1 layer 2.CFB-1C Corrugated fiberboard box/Pattern-packed/Cell pack/1 layer 3.CFB-2CP Corrugated fiberboard box/Pattern-packed/Cell pack/2 layers with Shredded paper 4.CFB-3T Corrugated fiberboard box/Pattern-packed/Tray pack/3 layer 5.CFB-F Corrugated fiberboard box/Pattern-packed/Foam net 6.CFB-FP Corrugated fiberboard box/Pattern-packed/Foam net and Shredded paper 7.RPC-R Reusable crate/Random-packed with paper liner 8.RPC-FH Reusable crate/Pattern-packed with paper liner/Foam net/Horizontal 9.RPC-FV Reusable crate/Pattern-packed with paper liner/Foam net/Vertical

No. 1, 2 and 7 were the current packagings. No. 3,4,5,6,8,9 were the developed packagings. The various packaging systems shown in figure 30-33 were tested by simulated vibration. All mangosteen samples for the tests were maunally harvested and carefully handled from orchards. After tests, the mangosteens were stored at ambient temperature for 24 hours before the mechanical damages were evaluated. Four types of mechanical damages on mangosteen were torn or detached calyx, scratched stem-end, hard rind and skin abrasion. The amount of hard rind was less when fruit was isolated even in tray, cell or foam net compared with the RPC-R. Abrasion was low in tray pack but high in cell pack. One layer pack has excessive space resulting in tremendous damages on calyx and stem-end. Damages were high in packages of CFB-F, RPC-FH, RPC-FV. This was speculated that foam net could not well protect mangosteen because foam net slipped of the fruit when top layer fruits bounced in head space. Using shredded paper in the head space would perhaps reduce this problem. Packaging performance included not only mechanical damages but also packing density. Overall packing density of CFB types were quite low (18-25%) as compared with RPC types (40-50%) except CFB-F and CFB-FP (≅ 37%). Using foam net instead of internal partition or separator in CFB-F and CFB-FP packages could improve their packing density. By the way, foam net application as cushioning material in RPC type would decline its packing density about 10%. The corrugated fiberboard box with three layers of expanded polystyrene tray exhibited the best protection, yielding the lowest overall percentage of damages, i.e. hard rind 5.6%, skin abrasion 9.3%, calyx’s damage 23.3%, and scratch on stem-end 2.2%. Even though foam net has been extensively used as fruit cushion, its very long degradable life threatens human life. An attempt to determine proper cushioning material out of agricultural waste and recyclable material to substitute foam net was recently made. Jarimopas et al. (2006) compared the cushioning performance of dry banana string, dry water hyacinth, single face corrugated paper, new and used double wall corrugated paper to protect bruising of the stored “Fuji” Chinese apple. The cushioning materials of concern were wrapped up the apple samples and impacted by a simple ballistic pendulum. Results of the impact test indicated that, based on the lowest bruise susceptibility and the highest bruise threshold, the single face corrugated paper with corrugators facing out of the fruit could best protect the apple sample. The bruise threshold of the single face corrugated paper and the foam net was 0.75 and 0.475 J respectively. The banana string and water hyacinth featured several hitches according to their structure of netting which caused many small bruising. This phenomenon was undesired.

Development of Engineering Technology to Improve the Quality of Production… 283 Shredded (s) paper is often used below and top of a package, especially corrugated box, as cushion. The knowledge about applying the s-paper as cushion to wrap up a fruit is limitedly available. At present, the use of foam net is widely spreaded because of acceptable impact protectionability and operation convenience. However, its difficult degradability becomes increasingly problematic. An effort to find the s-paper which is recyclable, cheap and easily degradable as an alternative cushioning material was executed. The s-paper was set into three conditions of width (3 and 6 mm), and type (used A4 office paper and used newspaper paper), the s-paper density (36, 48, 60, 72 kg/m3). The set s-paper was applied to cushion the Chinese stored apple of Red Fuji cultivar. The cushioned fruits were impacted at the constant energy of 2 J by a ballistic pendulum and the resulting impact bruising was analyzed. Results showed that the s-paper made of 3 mm wide, used A4 office paper, with 60 kg/m3 density in a cloth sack could protect apple bruising under the threshold impact energy of 1.4 J which is 87% higher than that of the foam net. The s-paper could practically make good protection on rose apple fruit with damage 2.4 times less than that of the foam net.

3.3. Edible Films and Coatings for Fresh Produce 3.3.1. Definition and History of Edibl Films and Coatings Edible film is defined as a stand-alone film used for food separation layer, wrap or casing and pouch. On the other hand, edible coating is an edible film formed as coating such as coating on surface of oranges and other fruits. Edible films and coatings have a potential to extend the shelf life and quality of fresh produce, adding value to the edible film-forming polymer, and reducing synthetic packaging materials. Edible films and coatings provide barriers to oxygen and carbon dioxide, aroma, oil and moisture, depending on the nature of the edible film-forming materials (Donhowe and Fennema, 1993; Krochta, 1997). In addition, they enhance the appearance and integrity of food, carrier of antioxidant, flavor, color and antimicrobial. Several materials were used to form edible films, such as protein, polysaccharide and lipids. Coating fruits and vegetables has been performed since the 12th century in China by waxing of oranges and lemons to prolong the storage time (Hardenburg, 1967). Edible films and coatings have been used to reduce the moisture loss and improve appearance of whole fresh produce; for example, citrus and apple (Baldwin and Baker, 2002). Moreover, the behavior of consumers is aware of the healthy eating habits and less time to prepare food. This is a driving force for fresh-cut fruits in the market. However, fresh-cut fruits are subject to undesirable physiological changes such as color, texture, aroma, and overall appearance that cause a short fruit shelf life and quality loss. Therefore, edible films and coatings can delay ripening, undesirable changes and thus extend fresh produce shelf life. The prolonged shelf life will expand both the shipping distance of fresh produce and selling in the market. 3.3.2. Edible Films and Coatings for Fresh Produce Generally, the purpose of using edible coatings for fresh produce is to enhance the natural barrier, if existing, or to restore it in cases where washing and handling has detached it. Recently, it is emphasized the use of edible coatings in terms of creating an internal modified atmosphere of fresh fruits which will delay ripening and senescence (Baldwin, 1994). Selected the appropriate coating permeability to gases helps decrease internal O2 and increase

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internal CO2 between coated fresh produce and the environment; thus, retarding the metabolism. High CO2 concentration inside fruit tissues decreases the ethylene synthesis and essential hormone for ripening resulting to delay ripening (Saltveit, 2003). Reviews related to edible coatings for fresh produce can be found in Baldwin (1994), Wong et al. (1994), Baldwin and Baker (2002) and Olivas and Barbosa-Canovas (2005).

3.3.3. Composition of Edible Coatings for Fresh Produce Several materials such as lipids, resins, carbohydrates and proteins or hydrocolloids can use as edible coatings for fresh produce. There are advantages and disadvantages in each material; therefore, most commercial fresh produce coatings contain a mixture of components so called composite coatings (Kester and Fennema, 1986). The characteristics of some commercially coatings for fruits and vegetables are shown in table 15. For example, SemperFresh coatings on different varieties of whole mangoes only retarded ripening in some varieties at the concentration studied (Carrillo Lopez et al., 1995) 3.3.4. Lipids and Resins Generally, lipid-based coatings provide good water vapor barrier and reduce the moisture loss, shriveling and shrinkage of coated fresh produce. Lipid coatings are relatively permeable to gases resulting in less modification of fresh produce internal atmosphere. These cause anaerobic conditions and off-flavor finally (Baldwin and Baker, 2002). In general, lipids can be included in the formulation of edible coatings in a single layer of lipid-based film, or lipids dispersed in a network formed by hydrocolloids (protein or polysaccharide) or a secondary layer over a hydrocolloid (Olivas and Barbosa-Canovas, 2005). Unfortunately, lipid coatings alone cannot form good structural integrity so they require the use of hydrocolloids to form the film matrix (Baldwin et al., 1997). Besides, the appearances of lipid films are generally opaque, rigid and present waxy taste or may not adhere well to the hydrophilic surfaces (Baldwin et al., 1995; Perez-Gago and Krochta, 2001). Beeswax, acetylated monoglycerides, fatty alcohols and fatty acids are lipids used incorporated with proteins or polysaccharides to elicit the best practice for fresh produce, especially fresh-cut fruits. Carnauba, candelilla or polyethylene waxes can offer quite high gloss coating for a high shine consumer satisfaction such as apples and citrus fruits (Baldwin et al., 1997). Resin-based coatings provide very glossy, high shine appearance on fresh produce and appropriate used on apples and citrus. Resin materials are shellac and wood rosin (Hernandez, 1994). Resin coatings offer fairly effective in reducing in moisture loss but excellent barriers to gases (Hagenmaier and Shaw, 1992). Be aware of the anaerobic respiration and flavor changes which are usually undesirable can occur. 3.3.5. Hydrocolloids (Proteins and Polysaccharides) Protein and polysaccharide (hydrocolloid) coatings are excellent gas barriers. Therefore, they are more effective for ripening control by creating a gentle modified atmosphere and a gloss on fresh produce. However, they generally provide limited water vapor barrier due to their hydrophilic nature so they are not effective in reducing moisture loss (Kester and Fennema, 1986; Gennadios and Weller, 1990; Park and Chinnan, 1990; Gennadios et al., 1994). The common approach to reduce the moisture loss of hydrocolloid coatings is to add lipids in the formulation.

Development of Engineering Technology to Improve the Quality of Production… 285 Proteins used in coatings are zein, soy protein, wheat gluten, whey protein, casein, gelatin and collagen. Protein films and coatings have a potential to some fruits such as whey protein coatings for apples (Cisneros-Zevellos and Krochta, 2003a; Cisneros-Zevellos and Krochta, 2003b), whey protein coatings on fresh-cut mangoes (Plotto et al., 2004), whey protein coatings on oranges (Sothornvit, 2005), whey protein based composite coatings on fresh-cut apples (Perez-Gago et al., 2005; Perez-Gago et al., 2006) and wheat gluten-based films and coatings on refrigerated strawberry (Tanada-Palmu and Grosso, 2005). Furthermore, it has been used with vegetables such as sodium caseinate/stearic acid emulsion coatings on peeled carrots (Avena-Bustillos et al., 1994a), calcium caseinate-acetylated monoglyceride aqueous emulsions coating on zucchini (Avena-Bustillos et al., 1994b), whey protein isolate, sodium caseinate or sodium caseinate/beeswax emulsion coatings on green bell pepper (Lerdthanangkul and Krochta, 1996) and zein films on fresh broccoli (Rakotonirainy et al., 2001). The reason to use protein-based coatings on fresh-cut fruits is that it can provide nutritional value in the fresh produce. It was recommended to label on the marketing display to inform the type of the coatings such as animal-, resin-, or beeswax-based. The reason is they might cause the allergic reaction or intolerances to certain proteins such as lactose or gluten intolerances for some consumers (Baldwin and Baker, 2002). Moreover, the vegetarians would not accept to consume animal-derived protein coatings such as gelatin and milk protein. One approach to overcome this problem is using the natural materials such as banana flour to form banana films (Sothornvit and Pitak, 2007) which can apply on the fresh produce and no allergen concern. Banana flour films possessed good mechanical properties and oxygen barrier. In addition, fruit and vegetable purees such as peach, strawberry, apricot, apple, pear, carrot and broccoli are an alternative for edible films and coatings (McHugh et al., 1996; McHugh and Olsen 2004). Fruit and vegetable films under certain relative humidity (RH) and temperature imparted good gas barriers but poor water vapor barrier. Moreover, fruit films apply on the same fresh-cut fruits might benefit both quality and shelf life such as apple film incorporated with beeswax, pectin, glycerol, ascorbic acid and citric acid showed a significant reduction of moisture loss and browning in fresh-cut apples (McHugh and Senesi, 2000). This may solve the problem of allergic reaction and more attractive to vegetarian consumers. Polysaccharides used in coatings are carrageenan, maltodextrin, methylcellulose, carboxymethyl cellulose, pectin, alginate and starch. Polysaccharide films and coatings also have a potential to some fruits such as chitosan films covered in the carton boxes of fresh mangoes (Srinivasa et al., 2002), potato starch-based edible coatings on guava (Quezada Gallo et al., 2003), hydroxypropyl methylcellulose-lipid edible composite coatings on plum (Perez-Gago et al., 2003), carboxymethycellulose, chitosan and starch coatings on fresh-cut mangoes (Plotto et al.,2004), hydroxypropyl methylcellulose-lipid edible composite coatings on fresh-cut apples (Perez-Gago et al., 2005), chitosan coatings on sliced mangoes (Chien et al., 2007), alginate coatings on fresh-cut apples (Olivas et al., 2007).

3.3.6. Composite and Bilayer Films and Coatings Combination of hydrocolloids and lipids can be applied in forms of a composite films and coatings where all components are mixed into one homogeneous coating layer. Alternatively, the combination forms two layers, one with hydrocolloids and the other with lipids which is called bilayer films and coatings. The goal is to improve the barrier characteristics of composite and bilayer films and coatings by taking advantage of good moisture barrier

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property of lipids and good gas barrier properties of hydrocolloids. Bilayer films have more effective than composite films. However, composite films are easy to apply on fresh produce because it requires one application and one drying step and adhere better to a larger number of surfaces of polar and non-polar characteristics (Perez-Gago and Krochta, 2001). The important issue is the orientation of lipid molecules within the coating. For fresh-cut fruits coated with hydrocolloid-lipid emulsion, the lipid fraction of the coatings tends to orient towards the outside to the environment which is lower humidity which hydrocolloids are generally more affinity to fruit surfaces than lipids. It was shown that coated apple cylinders with a mixture of polysaccharides (pectin, carrageenan, alginate and microcrystalline cellulose) and the second layer of acetylated monoglyceride, reduced the moisture loss 12-14 times compared to uncoated apples (Wong et al., 1994).

3.3.7. Additives Additives such as antibrowning agents, antimicrobial agents, firming agents, plasticizers, nutraceutical, volatile precursor, flavors and color can be helped to improve functional, nutritional, organoleptic and mechanical properties of films and coatings (Olivas and Barbosa-Canovas, 2005). Mostly, additives are necessarily used for fresh-cut produce to prevent browning reaction, loss of firmness and microbial growth. Combination of antibrowning and firming agent to the edible coatings effectively prolonged shelf life of apple slices (Lee et al., 2003). However, the usual additive used to improve mechanical properties of edible films and coatings is a plasticizer, which is a low molecular weight nonvolatile substance, to reduce biopolymer chain-to-chain interaction, resulting in film flexibility (Sothornvit and Krochta, 2000; Sothornvit and Krochta, 2001). Plasticizers increase not only flexibility but also water vapor permeability of film. Fruits and vegetables are often stored under high RH (85-99%) to reduce the water vapor gradient compared to inside the fruits (100% RH) to prevent moisture loss from transpiration (Baldwin and Baker, 2002). The high storage RH can increase fruit coating permeability (Lerdthanangkul and Krochta, 1996); therefore, reducing modified atmosphere ability of edible coatings within fruits and vegetables. Some researches applied different compositions of edible coating on fresh-cut fruits and the results as shown in table 16.

3.4. Refrigerated Storage and Packaging Controlled temperature is the key factor to maintain the quality of fresh produce. Maintenance of the cold chain or refrigerated storage is the best way to minimize all forms of deterioration after harvesting, such as moisture loss, softening, bruising, unwanted ripening, texture degradation, color change, mold or rot growth. The rate of deterioration of fresh produce is related to the rate of respiration. Good refrigerated storage and appropriate packaging are the significant management to keep fresh quality of produce, leading to greater satisfaction and increased demand. Generally, the export of fresh produce often involves long journey times. Therefore, refrigerated storage is required from production to retail sale. The breakdown in temperature control at any stage will impact on the final quality of fresh produce; although, the visible effect may not appear until several days later. In general, maintenance of fresh produce at optimum low temperatures is necessary and some scientific

Development of Engineering Technology to Improve the Quality of Production… 287 postharvest investigations on citrus fruits can be viewed from Schiffmann-Nadel et al. (1971), Nordby et al. (1987), Paull (1999) and Henriod (2006). The use of air freight allows perishable produce to be rapidly transported around the world. Using the right combination of packaging, refrigerated storage and handling procedures can maintain the quality of fresh produce. Fresh fruit and vegetable continue respiration which produces carbon dioxide, water and heat. The heat produced by respiration results in warming of the produce and needs to keep it cool by refrigeration. The refrigerated storage will lower the rate of respiration with limited heat accumulation resulting in lower rate of deterioration. Different types of produce have different rates of respiration. The higher rate of respiration is more highly perishable and temperature control is very critical. Ethylene causes fresh produce ripening and deterioration in some produce. Keeping fresh produce cool also reduces the production of ethylene (https://www.business.vic.gov.au/busvicwr/_assets/ main/lib60167/maintainingcoldchain_airfreight_perish.pdf). During non-refrigerated transport stage, the simple cooling agents such as wet or dry ice or gel packs are needed to maintain the correct produce temperature. When using refrigerants, it is needed to be aware of the freezing or chilling injury to fresh produce so the location and amount of refrigerant used is the critical points. Ensure refrigerants and packagings used are matched. For example, wet ice requires packaging which will hold liquids and ensure that the form of packaging used such as chipped, flaked, block, will not damage to the produce during packing, handling or transportation. Dry ice requires sufficient ventilation to prevent an accumulation of CO2 which will harmful to most fresh produce. Conventional refrigerated storage room needs to concern three factors which are temperature, relative humidity and air movement.

3.4.1. Temperature Temperature control is based on tight, well-insulated structure, sufficient refrigeration capacity for maximum demand, and control of refrigerant flow through the system by means of thermostats and /or pressure-controlled expansion valves. 3.4.2. Relative Humidity Relative humidity (RH) is the percentage of saturation of air with water vapor at a given temperature. As the temperature of air increases, its water-holding capacity increases as well. As RH of air decreases, its vapor pressure (VP) decreases. As VP decreases, the capacity of the air for removing water from moist sources increases. Therefore, it is important to maintain a high VP and as small a VP differential between the stored product and the storage air as possible. 3.4.3. Air Movement Air movement must be in sufficient volume to remove respiratory heat and heat entering the room through exterior surfaces and door ways and has a uniform flow of air. 3.4.4. Storage for Fresh Produce Proper storage of fresh produce can affect both quality and safety. To maintain quality of fresh produce, certain perishable fresh fruits and vegetables should be stored in a clean refrigerator at a temperature of 40 oF or below. The purposes are to minimize growth of microorganisms and reduce enzyme activity. Packaging or storage also helps control

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respiration rate and ripening. Most of the leaf, stem, bud and root vegetables are stored at a few degrees above their freezing point. Storage life of most of the leafy vegetables is only a few weeks, even in optimum environments. However, those root crops, such as carrots, horseradish can store for several months held in ventilated storage. Refrigerated storage is used each year for a part of each of these crops, that either to be stored for prolonged periods or held in areas where outside temperature is not favorable for ventilation. Other produces, which for reasons of economy are usually stored under ventilation, may be placed in refrigerated storage during the later holding period for marketing beyond the season feasible from ventilated storage such as potatoes, carrots, sweet potatoes, dry onions and cabbage. For a long term storage, it is necessary to maintenance of as low temperature as possible (close to 0 oC), high air relative humidity up to 85-95% and % CO2 in air related to the vegetable species (Dauthy, 1995). This will minimize respiration and transpiration of fresh produce. Leafy vegetables should be promptly cooled as close to 0 oC (32 oF) as possible and held there throughout marketing. Wilting, diseases and disorders increase in severity as temperature increases. Vegetables have to be handled and transported as soon as possible in the storage conditions.

3.5. Recommended Research Trend 3.5.1. Research of Interior Packaging Management of Tropical Fresh Produce Should Be Conducted into 2 Directions One is to develop new design of interior packaging based on utilizing the available cushioning materials to get minimum damage, high packing density at reasonable packaging cost. The other is to develop new cushioning material protecting fresh fruit and vegetable. The research trend should include development of related application technique of the agricultural wastes as well as recyclable materials for efficient produce cushioning. One successful example on application of the treated waste water to irrigate the plants in dessert was recently shown. The developing countries which produces a lot of tropical fresh produce are rich of produce waste. The countries may take this similar problem as challenge to make use of the produce waste especially as an alternative packaging cushioning material. 3.5.2. It Needs To Be Taken into Account that the Maximum Storage Life of a Harvested Crop Depends on Its Production History and Quality and Maturity at Harvest Refrigerated storage adds cost to the produce and requires storage method. The correct storage technique is governed by the types of produce (its temperature from harvest and its respiration rate as well as produce quality), the storage temperature and humidity best suited to the produce and intended storage life, without chilly injury or microbial spoilage. Refrigerated storage rooms are well established for designing and installing and common use for fresh produce. However, the unsuccessful operation in the developing countries was due to the significant common problems which are untrained or unmotivated supervision, deterioration of produce quality during storage, improper use of facilities and underutilization of refrigeration space (Choi, 2002). These problems could be attributed to inadequate planning and management performance. The successful operation of cold storage is dependent on some knowledge of costing, specific commodity requirements, refrigeration

Development of Engineering Technology to Improve the Quality of Production… 289 technology, and produce marketing. Good management of refrigerated storage needs knowledge and experience of storage conditions of the commodities, directions for loading of the rooms and maintaining a hygiene, management, control and maintenance of refrigerated equipment and staff training (Choi, 2002).

3.5.3. The Use of Edible Coating Should Be Used in Combination with other Methods Such as Low Temperature Storage, Controlled Atmosphere or Modified Atmosphere Packaging to Enhance Stability of Fresh-Cut Produce Tropical and subtropical fruits are subject to chilling injury; thus, application of edible coatings is awareness. Fresh whole and fresh-cut produce have different characteristics which need to further study the effect of each edible film and coatings for each commodity. To overcome the allergen problem and vegetarian concern, fruit and vegetable coatings itself can open the good opportunity to investigate and can develop as commercial coatings in the future. Furthermore, the natural color and flavor of each fruit and vegetable itself might be attractive to consumers, especially children and teenager. However, consumer perception and safety issue from microbial growth of edible films and coatings wait for future studies prior to commercialize. Finally, edible films can provide convenient for use as wrappers and it is still under investigation. Table 12. General characteristics of wholesaling container Wholesaling container Wide mouth kheng Cylindrical kheng Vegetable kheng Wooden crate Plastic crate Corrugated box

Capacity (litre) 11-237 40-188 40-82 40 45 22-60

Weight (kg) 0.25-4.65 0.7-0.9 0.18-0.58 6.0-7.0 2.33 0.45-1.5

Compressive resistance (kg) 80-200 67 4-43 >5,000 840 400-1,000

Source: Jarimopas (2006).

Table 13. Corrugated paper box recommended by Thailand Packaging Center for exporting fresh fruit and vegetable Produce

Transportation type

Banana Rambutan without foam tray Rambutan with foam tray Durian

Fresh vegetable Guava Tomato Mango

Carrying weight (kg) 125 5

Compressive resistance (kg)

Ship Airplane

Outside dimension (cm) 50x40x23 40x30x10

890 600

Maximum stacking layers 9 15

Airplane

40x30x10

4

600

15

Short distance airplane Long distance airplane Airplane Airplane Air conditioned truck Airplane

48x45x23

12

460

6

48x45x3

12

800

6

45x35x20 40x30x10 40x30x12

3-10 5 5-6

650 600 570

8 15 18

50x30x10

5

700

15

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B. Jarimopas, P. Sirisomboon, R. Sothornwit et al. Table 13. (Continued)

Produce

Transportation type

Outside dimension(cm)

Carrying weight (kg)

Compressive resistance (kg)

Papaya (0.5-0.7 kg) (1.2-1.5 kg) (1.2-1.5 kg) Mangosteen without foam tray Mangosteen with foam tray Longan Pumelo (38-46 cm circumference)

Airplane

45x35x10

5

800

Maximum stacking layers 15

Airplane Ship Airplane

40x30x35 40x30x35 40x30x10

12 12 5

490 600 600

4 6 15

Airplane

40x30x10

4.5

600

15

Airplane Ship

40x30x10 50x40x25

5 18

600 860

15 10

Airplane Airplane

45x35x20 40x30x10

6 5

650 600

8 15

Grape

Source : Amornrat (1988).

Table 14. Performance comparison between the current and the developed wholesale packaging for exporting mango Performance Parameter Bruising (%) Packing density (%) Packaging cost (USD/kg mango) Count number Packing weight (kg)

•

Mango without cushion (export) 15.0±5.6c 37.8d 0.05b

Cushioned mango with stacking layers 2 3 2.50±3.07a* 5.83±2.35ab 25.22b 28.25c 0.070c 0.023a

4 7.22±1.89b 23.14a 0.027a

24 10

16 7.5

36 17.5

24 11.5

Means followed by the same number in the same row are insignificantly different at p < 0.05.

Table 15. Some commercially coatings for fruits Edible coating (Company) Semperfresh (Surface System Intl. Ltd.) PacRite products (American Machinery Corp.)

Vector 7, Apl-Brite 300C, Citrus-Brite 300C (Solutec Corp.) Primafresh Wax (S.C. Johnson)

Major ingredients sucrose esters of fatty acids, sodium carboxymethyl cellulose water-based, carnauba-shellac emulsions, shellac and resin water emulsions, water based mineral oil fatty acid emulsions shellac-based with morpholine; AplBrite and Citrus-Brite are carnaubabased carnauba-wax emulsion

Shield-Brite products (Pace Intl. Shield-Brite) Sta-Fresh products (Food Machinery Corp.)

shellac/ carnauba-based, natural wax vegetable oil/wax and xanthan gum natural, modified naturaland synthetic resin and combination

adapted from USFDA, 2001

Applications most fruits and vegetables, nectarines apples, citrus, peaches, plums, nectarines

apples and citrus

apples, citrus and other firm surfaced fruit citrus, pears, stone fruit citrus, apples, stone fruits, pomegranates, pineapple and cantaloupes

Development of Engineering Technology to Improve the Quality of Production… 291 Table 15. (Continued) Edible coating (Company) Fresh Wax products (Fresh Mark Corp.) Apple Britex 559, Melon Wax 551, Banana Wax 509, Pineapple Wax 510 and other fruit coating products (Brogdex Co.)

Fresh SealTM (CPG Technologies of Agway, Inc. to produce) Nature SealTM, AgriCoat (Mantrose Bradshaw Zinsser Group)

Major ingredients shellac and wood resin, oxidized polyethylene wax, white oil/paraffin wax carnauba wax emulsions with or without fungicides, high shine resinbased, carnauba-based emulsion, vegetable oil, resin-based and concentrated polyethylene emulsion wax cellulose derivatives and emulsifiers composite polysaccharide

Applications citrus, cantaloupes, pineapples and apples apples, melon, bananas, avocado, papaya, mango, pineapple, stone fruits and lemons

avocado, cantaloupe, mangoes and papaya sliced apples, pears avocados, and bananas

Table 16. Different edible coatings on fresh-cut fruits Fruit apple slices1

Edible coating carrageenan

apple slices1

WPC + CMC

cut apple2

WPI + beeswax

apple slices3

calcium caseinate + CMC, WPC + CMC apple puree + beeswax or vegetable oil Nature SealTM, CMC and soy protein CMC

cut apple4 apple slices5 sliced mango6 apple cylinders7

double coating: carrageenan/AMG Pectin/AMG Alginate/AMG MCC/AMG

Additives AA, OA, CA, Gly, PEG AA, OA, CaCl2, Gly Gly CaCl2, Gly AA, CA, Gly AA, PS, soy oil, CaCl2 Lecithin, PEG, BA, CA AA, CA, CaCl2 and NaCl

Results prolong shelf life by 2 weeks in packed trays at 3 oC 20% decrease in initial respiration rate, firm and good sensory inhibit browning, no significant reduce weight loss delay browning effective of wraps over the coatings on moisture loss prolong shelf life by 1 week in overwrapped trays at 4 oC retard color change reduce the rate of carbon dioxide and ethylene accumulation of 50-70% and 90%, respectively. Alginate films presented the best water vapor barrier.

WPC = whey protein concentrate, CMC = carboxymethyl cellulose, WPI = whey protein isolate, AMG = acetylated monoglyceride, MCC = microcrystalline cellulose, AA = ascorbic acid, OA = oxalic acid, CA = citric acid, Gly = glycerol, PEG = polyethylene glycol, PS = potassium sorbate, BA = benzoic acid. 1 Lee et al. (2003), 2 Perez-Gago et al. (2003), 3 Le Tien et al. (2001), 4 McHugh and Senesi (2000), 5 Baldwin et al. (1996), 6 Nisperos-Carriedo and Baldwin (1994), 7 Wong et al. (1994).

292

B. Jarimopas, P. Sirisomboon, R. Sothornwit et al.

a) Bamboo kheng for banana

for longan

for garlic

for cabbage

for lettuce

b) Bamboo kheng

c) Ratan made kheng for pumpkin Figure 26. Different design of wholesale packaging “Kheng”.

d) Plastic kheng for durian

Development of Engineering Technology to Improve the Quality of Production… 293

a) Plastic bag for green onion

b) Netted plastic bag for red onion

c) Plastic without holes containing lime Figure 27. Plastic bag.

a) Trapezoidal

294

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b) Rectangular Figure 28. Plastic crate.

a) Foam net Figure 29. Cushioning material.

b) Air bubble plastic sheet

Development of Engineering Technology to Improve the Quality of Production… 295

CFB-1T (Thermoformed foamed polyethylene tray)

CFB-3T (Expanded polystyrene tray)

Figure 30. Tray pack in corrugated fiberboard box

CFB-1C

CFB-2CP

Figure 31. Cell pack in corrugated fiberboard box

CFB-F Figure 32. Packaging systems using foam net with and wihout shredded paper

CFB-FP

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RPC-R

RPC-FH

RPC-FV

Figure 33. Reusable plastic containers

CONCLUSION A review of 129 relevant publications has been conducted. Several terms have been clarified; “engineering technology of tropical fresh produce”, which concerns physical properties, innovations and packaging; and “physical properties”, which – with regard to mango, sweet tamarind, guava, tangerine, mangosteen, durian, snake egg plant, white long radish and lime -- consist of post-harvest loss, physical characteristics, mechanical properties, firmness, friction, and associated non-destructive techniques. The discussion of innovations concerned inventions of machinery and devices associated with durian, mangosteen, young coconut fruit, dry over-mature coconut and baby corn. Finally, the discussion of packaging technology related to developments in packaging which improves the distribution and extension of shelf life of selected tropical fresh produce. Owing to the current high postharvest loss and comparative underinvestment in tropical fresh produce research and development, it is the position of the leaders that the countries which produce tropical fresh produce should dramatically increase their funding of research and development in all the engineering technology aspects discussed in the review.

Development of Engineering Technology to Improve the Quality of Production… 297

ACKNOWLEDGEMENT The authors gratefully acknowledge Professor Paul Chen, Professor Emeritus, Department of Biological and Agricultural Engineering, University of California, Davis to technically review majority of the chapter.

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Perez-Gago, M.B., Serra, M., Alonso, M., Mateos, M. and del Rio, M.A. (2005). Effect of whey protein- and hydroxypropyl methylcellulose-based edible composite coatings on color change of fresh-cut apples. Postharvest Biol. Technol., 36, 77-85. Plotto, A., Goodner, K.L. and Baldwin, E.A. (2004). Effect of polysaccharide coatings on quality of fresh cut mangoes (Mangifera indica). Proc. Fla. State Hort. Soc., 117, 382-388. Pushapriksha, P., Singh. S. P., and Jarimopas, B. (2006). Post-harvest loss and performance comparison of wholesale packaging of mangosteen fruit under simulated vibration. Proceedings of the 15th IAPRI World Conference on Packaging, Oct 3-5, 2006, Tokyo., 285-289. Pushpariksha, P., and Jarimopas, B. (2006). Post-harvest damages and some mechanical properties of mangosteen fruits. Agricultural Science Journal,. 37(2) Supplement , 242-245. Quezada Gallo, J.A., Diaz Amaro, M.R., Gutierrez Cabrera, D.M.B., Castaneda Alvarez, M.A., Debeaufort, F. and Voilley, A. (2003). Application of edible coatings to improve shelf-life of Mexican guava. Acta Hort., 599, 589-594. Rachanukroa, D. Singh, S. P. and Jarimopas, B. (2006). Postharvest damage and performance comparison of sweet tamarind wholesale packaging. Proceedings of the 15th IAPRI World Conference on Packaging, October 4-5, 2006, Tokyo. p 209-213. Rachanukroa, D. Singh, S. P., and Jarimopas, B. (2007). Development of sweet tamarind pod retail packaging. Proceeding of the International Conference on Agricultural, Food and Biological Engineering and Post Harvest/Production Technology, organized by Thai Society of Agricultural Engineering, American Society of Agricultural and Biological Engineers, and Faculty of Engineering, Khon Kaen University, 21-24 January, Sofitel Raja Orchid Hotel, Khon Kaen, Thailand. Rachanukroa, D., Singh, S. P., and Jarimopas, B. (2006). Postharvest damage and performance comparison of sweet tamarind wholesale packaging. Proceedings of the 15th IAPRI World Conference on Packaging, Oct 3-5, 2006, Tokyo., 209-213. Rakotonirainy, A.M., Wang, Q. and Padua, G.W. (2001). Evaluation of zein films as modified atmosphere packaging for fresh broccoli. J. Food Sci., 66, 1108-1111. Ryall, A.L. and Lipton, W.J. (1979). Handling, Transporatation and Storage of Fruits and Vegetables, Vegetables and Melons, (Vol. 1, pp 293-306). Westport, CT: AVI Publishing Company, Inc. Salveit, M.E. (2003). Is it possible to find an optimal controlled atmosphere? Postharvest Biol. Technol., 27, 3-13. Saputra, D., Budiastra, I.W., and Purwadaria, H.K. (1995). Classification of mango by near infrared diffuse reflectance. Proceedings of Food Processing Automation IV, Chicago, ILL, USA, 3-5 November, 1995, ASAE. Sarakan, S. (2006). Mechanical Properties of Thai Rose Apple Fruit. Unpublished Master Engineering Thesis, Department of Agricultural Engineering, Graduate School, Kasetsart University, Kamphaengsaen, Nakohnpatom, Thailand 184 p. Schiffmann-Nadel, M., Lattar, F.S. and Waks, J. (1971). The response of grapefruit to different storage temperatures. J. Am. Soc. Hortic. Sci., 96, 87-90. Sihawong, S. (2005). Design and testing of a machine cutting and opening umpire durian for durian chip processing using hydraulic system. Kasetsart Journal, 36(5-6) supplement, 133-136.

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[107] Sirisomboon, P., Boonmung, S., Pornchaloempong P., and Pithuncharurnlap, M. (in press). A Preliminary Study on Classification of Mango Maturity by Compression Test. International Journal of Food Properties. [108] Sothornvit, R. and Krochta, J.M. (2000). Plasticizer effect on oxygen permeability of beta-lactoglobulin films. J. Agri. Food Chem., 48, 6298-6302. [109] Sothornvit, R. and Krochta, J.M. (2001). Plasticizer effect on mechanical properties of beta-lactoglobulin films. J. Food Eng., 50, 149-155. [110] Sothornvit, R. and Pitak, N. (2007). Oxygen permeability and mechanical properties of banana films. Food Research International, 40, 365-370. [111] Sothornvit, R. (2005). Edible film formation and properties from different protein sources and orange coating application. Acta Hort,. 682, 1731-1738. [112] Srinivasa, P.C., Baskaran, R., Ramesh, M.N., Harish Prashanth, K.V. and Tharanathan, R.N. (2002). Storage studies of mango packed using biodegradable chitosan film. Eur. Food Res.Technol., 215, 504-508. [113] Supsomboon, T. (1998). Young coconut fruit opening device. National Research Council of Thailand Report. p 80-83. Ministry of Science, Technology and Environment. [114] Susanto, Suroso, Budiastra, I.W., and Purwadaria, H.K. (2000). Classification of mango by artificial neural network based on Near Infrared Diffuse Reflectance. Prodeedings of 2nd IFAC/CIGR International Workshop on Bio-Robotics II, Sakai, Osaka, Japan, 25-26 November, 2000, Pergamon Press. [115] Tanada-Palmu, P.S. and Grosso, C.R.F. (2005). Effect of edible wheat gluten-based films and coatings on refrigerated strawberry (Fragaria ananassa) quality. Postharvest Biol. Technol., 36, 199-208. [116] Teerachaichayut, S., Kil, K.Y., Terdwongworakul, A., Thanapase, W., and Nakanishi, Y. (2007). Non-destructive prediction of translucent flesh disorder in intact mangosteen by short wavelength near infrared spectroscopy. Post-harvest Biology and Technology, 43 (2), 202-206. [117] Terdwongworakul, A., and Neamsorn, N. (2006). Non-destructive maturity measurement of “Montong” durian using stem strength and resonant frequency. KKU Engineering Journal, 33(5) 555-563. (in Thai). [118] Terdwongworakul, A., and Suppattakul, P. (2005). Development of baby corn husking machine using rollers, Proceeding of NSTDA Annual Conference 2005, 28-30 March 2005, Science park of Thailand, National Science and Technology Development Agency, Thailand (in Thai). [119] Terdwongworakul, A., Srichonpet, S., Poniyom, K., and Sukchareon, A. (1997). Investigation of change of exponential frequency index as related to durian maturity. Proceedings of 5th International Symposium on Fruit, Nut, and Vegetable Production Engineering, 3-10 September 1997, UC Davis, California USA. [120] Terdwongworakul, A., Srichonpet, S., Poniyom, K., and Sukchareon, A. (1998). Frequency index as related to maturity stage of 'Montong' durian. Journal of the National Research Council, 30(1-2), 25-38. (in Thai). [121] Thongkamthamachat, S. and Timinkul, A. (2000). Comparative study on the performance of foam net and air bubble plastic sheet protecting fruit under impact. M. Eng. Special Problem Report. Department of Agricultural Engineering, Graduate School, Kasetsart University, Kamphaengsaen, Nakohnpathom, Thailand. 25 p.

Development of Engineering Technology to Improve the Quality of Production… 305 [122] Timoshenko, S. P., and Goodier, J.N. (1970). Theory of Elasticity. McGraw-Hill Kogakusha, Ltd. [123] Toomsaengthong, S., Singh. S. P, and Jarimopas, B. (2006). Post-harvest loss and development of wholesale packaging of fresh rose apple. Proceeding of the 15th IAPRI World Conference on Packaging, Oct 3-5, 2006, Tokyo, 280-284. [124] Upchurch, B.L., Miles, G.E., Stroshine, R.L., Furguson, E.S., and Emerson, F.H. (1987). Ultrasonic measurement for detecting apple bruises. Transactions of the ASAE, 30(3), 803-809. [125] USFDA. (2001). Microbiological safety of controlled and modified atmosphere packaging of fresh and fresh-cut produce. In Analysis And Evaluation Of Preventive Control Measures For The Control And Reduction/Elimination Of Microbial Hazards On Fresh And Fresh-Cut Produce. 2 April 2007. Available from: http://www.cfsan.fda.gov/~comm/ift3-toc.html [126] Vanichang, J., and Jarimopas, B. (2007). Some mechanical, physiological and light properties of dragon fruit. Proceedings of of the 7th National Seminar on “Research Enhancing the Potential of Thai Agricultural Commodities in the World Market”, Organized by Thai Society of Agricultural Engineering and Faculty of Engineering, Mahasarakhanm University, 23-24 January 2006, Mahasarakhanm, Thailand. [127] Wang, J.K. (1963). Design of a ground berry husking machine. Transactions of the ASAE, 6(4), 211-213. [128] Wong, D.W.S., Camirand, W.M. and Pavlath, A.E. (1994). Development of Edible Coatings for Minimally Processed Fruits and Vegetables. In J.M. Krochta, E.A. Baldwin and M.O. Nisperos-Carriedo (Eds.), Edible Coatings And Films To Improve Food Quality. (pp. 65-88). Lancaster, PA, Technomic. [129] Yamamoto, H., Iwamoto, M., and Haginuma, S. (1980). Acoustic impulse response method for measuring natural frequency of intact fruits and preliminary applications to internal quality evaluation of apples and watermelons. Journal of Texture Studies, 11, 117-136.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 307-337 © 2007 Nova Science Publishers, Inc.

Chapter 4

DEVELOPMENT OF GEL PRODUCTS CONTAINING FRUIT PIECES USING OSMOTIC TREATMENTS WITHOUT BY-PRODUCT GENERATION N. Martínez-Navarrete*, M.M. Camacho*, E. García-Martínez* and M.E. Martín-Esparza* Polytechnic University. Food Technology Department – Institute of Food Engineering for Development. Camino de Vera s/n. 46022. Valencia, Spain

ABSTRACT Fruits are products of a very important nutritional interest. Nevertheless, and mainly due to their relatively short shelf-life and modern-day eating habits, the level of consumption is below that recommended by the World Health Organization. In this sense, the development of foods with a high fresh or processed fruit content, that maintain the nutritional and sensorial properties of the fresh fruit, may contribute to stimulating the interest of the consumer, thus increasing the product consumption. Osmotic dehydration (OD) techniques have been widely applied in fruit processing, since they require little energy and allow us to obtain high quality products. However, its industrial use may be limited by the management of the osmotic solution (OS). To solve this problem, the re-use of the OS in more than one OD cycle, with or without a previous re-concentration stage, may be considered. When there is no re-concentration, the re-use will be limited by the possible microbiological contamination and by the progressive dilution that takes place after each OD cycle, which may affect the kinetics of the osmotic process. On the other hand, as some native hydrosoluble compounds, such as volatiles, acids, minerals, vitamins and phytochemicals, will be released together with water into the OS during OD, its management as an ingredient in some product formulation seems *

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N. Martínez-Navarrete, M.M. Camacho, E. García-Martínez et al to be an interesting alternative. To this end, this work analyses the viability of formulating a fruit-gel product with the osmodehydrated fruit (strawberry, kiwi or grapefruit) and the re-used OS obtained from the dehydration step, in order to diminish the loss of flavour, aroma and functional components of the fruit and avoid the generation of by-products in the process. In this study, the number of OS re-use cycles has been optimized, on the basis of its microbial recounts, the dilution level, the solution enrichment in fruit bioactive compounds, the fruit-solution ratio used during the dehydration step and the fruit-gel ratio in the final product. The kind and concentration of gelling agents, which best favour the properties of aspect (transparency) and texture of the gels, taking the peculiar composition of the re-used OS used as gelling medium into account, have been identified. The conditions in which the fruit pieces are mixed with the gelling solution have also been studied and defined. Finally, the fruit-gel product formulation conditions have been optimized, on the basis of its sensory acceptance and its compositional stability during storage, ensuring the thermodynamic equilibrium between the fruit and the gel when mixed. The microbiological stability of the product was of at least 15 days in refrigerated storage. During this time, the evolution of some properties such as phytochemicals, vitamins, acids, volatile compounds, colour and texture was studied.

INTEREST OF DEVELOPMENT OF GEL PRODUCTS CONTAINING FRUIT PIECES Fruits are an important part of the human diet, contributing some micro-nutrients, such as fibre, minerals, acids and vitamins. They also contain other non-nutritive substances, called phytochemicals, that mainly include terpenes and phenolic compounds, some of which are important from a sensorial point of view (King and Young, 1999; Belnstein, 2001). All these compounds may be included in the group of bioactive substances, as their intake seems to have a significant impact on the prevention of some diseases such as different kinds of cancer, cerebro and cardiovascular diseases, (Klein and Kurilich, 2000; Wargovich, 2000; Kaur and Kapoor, 2001; Hertog, Hollman, and Van de Putte, 1993), strokes, Alzheimer's disease, cataracts and some of the functional declines associated with ageing (Rui-Hai-Liu, 2003). Its biological effects include antioxidant activity (Ming-Liu et al., 2002; Prior and Guohua-Cao, 2000; Wolfe et al., 2003), antimutagenic (Wargovich, 2000), antibacterial and angioprotective properties (Venant et al., 1989). Nevertheless, in general, fruits are products with a relatively short post-harvest life-span, mainly due to their high water content. This fact, together with modern-day eating habits (easy, quick-to-eat food, food purchased with a relatively long life utility), have led to a drop in the consumption of fresh fruit, especially among the young, which has been replaced by juices, dairy products with added fresh or processed fruits, preserves, confectioneries, etc. (Torreggiani and Bertolo, 2001). Although there is an ample supply of this type of products, it is important to point out that many of them have minimum fruit content, which is replaced, in many cases, by a great number of additives. In this sense, developing another kind of food with a high content in fresh or minimally processed fruit seems to be of great interest. The latter will lead to a longer product shelf-life while maintaining the nutritional and sensorial properties (aroma, flavour, colour and texture) of the fresh fruit, thus trying to stimulate the interest of the consumer and to contribute to the acceptance of the product.

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The possibility of developing fruit-gel products has been studied since the 1980s. In fact, in a conducted bibliographical revision, numerous patents have been found, some of them related to the selection or preparation method of the gelling agents (Gliksman and Farkas, 1974; Shank, 1985; Prest and Buckley, 1986; Musson and Prest 1988) and others that describe diverse products. Among these can mainly be found products like “artificial fruit”. These are made with either texturized fruit-flavoured gels (Cheney et al., 1984) or with added fruit juice or crushed fruit (Fleck and Schindler, 1991; Jensen, 1991) or both and, in some cases, they are shaped like a fruit (Trilling and Smadar, 1984; Elisabelar and Albelda, 1985; Okonogi et al., 1989; Kaletunc et al., 1990). Dehydrated fruit-gels have also been described (Arnstein et al., 1974; Wust et al., 1985) that are sometimes used to simulate the presence of pieces of fruit in the elaboration of products like ice creams, cereals, jams, etc. (Walter and Funk, 1998; Aoki et al., 2000; Gordon et al., 2001). Nevertheless, no references have been found describing a gel product that includes an important proportion of fresh or processed fruit portions. If the hydrocolloids are selected carefully, the resulting food could be highly attractive to the eyes of the consumer, as long as it is possible to formulate a gel with not only a high level of transparency, thus allowing the content and type of fruit to be identify, but also with a high nutritious value and one that is easy to consume. A reasonable shelf-life must be contemplated by theses products, one which offers suitable commercialization margins, as much in terms of its security as of any changes in its physical and sensorial properties. From this point of view, it is more advisable to manufacture the product with partially dehydrated fruit. However, it is crucial that this product maintains, as far as possible, the highest quality characteristics of the fresh fruit. Osmotic dehydration (OD) with sugared solutions has been widely applied for minimal fruit processing (Fito et al., 2001, Alzamora et al., 1997; Tapia de Daza et al., 1999). Its application is technologically simple, since it consists of submerging the sample into a high osmotic pressure solution at temperatures that can be moderate, so that a two-way mass transfer is established: water and some natural soluble substances flow out of the fruit into the osmotic solution (OS), and in the opposite direction, soluble solutes may be transferred from the solution to the fruit. This method has received considerable attention due to the low energy requirements (Taiwo et al., 2001) and fruit quality improvement (Heng, Guilbert and Cup, 1990; Panagiotou, Karathanos and Maroulis, 1998), compared to alternative processes. As fruits are not submitted to high temperatures, sensory attribute changes, such as colour, aroma, flavour and texture, are minimised (Heng et al., 1990; Raoult-Wack, 1994; Fito et al., 1995). Moreover, in comparison with other traditional drying treatments, OD only slightly affects the food structure because water elimination does not involve phase changes (Forni et al., 1987; Pinnavaia et al., 1988; Giangiacomo et al., 1994). Despite the above mentioned advantages, one limitation to the industrial application of OD may be the management of the osmotic solution (Torreggiani and Bertolo, 2001; Dalla-Rosa and Giroux, 2001). During OD, it must be taken into account that the fruit releases some of its natural components into the external solution, together with water. Some of these components, like soluble fibre, organic acids, mineral salts, vitamins and phytochemicals, are interesting from the nutrition and health point of view. For this reason, the use of OS as an ingredient in new product formulation is an interesting alternative for its final management. In this case, the previous re-usability of OS in several OD cycles would suppose, in addition to a decrease in generated by-products, that more and more of these bioactive compounds are accumulated. There are authors who propose that it be used as a syrup for fruit canning or as a component of carbonated fruity soft drinks (Dalla-

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Rosa and Giroux, 2001). However, the development of products that not only use all the dehydrated fruit but also all the OS necessary for its dehydration would be kinder on the environment and would allow for a greater supply of fruit products in the market. In the face of modern-day eating habits, this would be of great interest as it would both encourage fruit consumption and also make it easier, thus promoting a healthier diet. The use of OS in the formulation would not only contribute to improving the aroma and flavour of the fruit, but also to recovering some of the micro-nutrients and other bioactive substances lost by it during the osmotic processing. In this sense, some investigations reflect, for example, the possibility of elaborating stable jams from OD fruit and the OS, without the use of a heat treatment. These jams would be of a higher quality than conventional ones, in which the high cooking temperatures entail undesirable changes in the sensorial (colour, texture, flavour) and nutritional (mainly loss of vitamin C) properties (Shi et al., 1996; García-Martinez et al., 2002b). As the objective is to extend the supply of this type of foods, this chapter describes the methodology followed in order to develop a fruit-gel product, formulated from dehydrated fruit and the actual osmotic solution used in the dehydration operation. The intention is to obtain a product with a relatively long shelf-life (at least 15 days in refrigeration) by means of a manufacturing process which is viable, economically profitable and that does not, as far as possible, generate any by-products. Therefore, re-using the osmotic solution in successive OD cycles before its final use in the formulation of the product has been considered. The optimisation of the number of re-use cycles depends on the dilution level reached by the OS after each OD cycle, related to the dehydration level of the obtained fruit, the microbiological aspects, the fruit-solution ratio in the dehydration step and the fruit-gel ratio in the final product. The presence in the OS of bioactive compounds that come from the fruit will constitute an additional advantage, from the nutritional point of view of the developed product. By using the osmotic solution as gelling medium, the kind of gelling agents and their concentration which provide the best properties of aspect and texture to the gels, also have to be considered. The operating conditions of the step when the fruit pieces are mixed with the gelling solution (mixing step) are defined. Another aspect of interest relating to the quality of the product and its changes during storage, and which is considered in this chapter, is the fruit-gel interaction, which defines the transport of components (mainly water), if neither phase is near the thermodynamic equilibrium. This transport may alter the properties of the gel and the fruit, changing the quality of the product to a remarkable extent. It is therefore necessary to adjust the water activity of the fruit and gel phases when designing this type of products. All these questions are developed in the following sections.

THE USE OF OSMOTIC DEHYDRATION FOR THE FORMULATION OF FRUIT-GEL PRODUCT’S Osmosis is a phenomenon that occurs when two liquids or gases with different concentrations of a certain substance are separated by a membrane permeable only to some components. The differences in chemical potential of the components on both sides of the membrane will promote the flow of those to which this membrane is permeable, in an attempt by the system to reach thermodynamic equilibrium. The characteristic tissue of the fruits is cellular in structure, so that most of its liquid fraction finds bulkhead by membranes that are

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selectively permeable to the water. Thus, when a fruit is submerged in a concentrated sugar solution, an osmotic dehydration takes place. However, simultaneous to the osmotic mechanism, capillary and diffusion mechanisms promote a solute outflow from the fruit towards the external solution and a solute inflow from the solution towards the fruit. The interchange of all these components will continue until the system reaches thermodynamic equilibrium. Normally, however, the rate of water flow in the osmotic processes is high during the first 2-3 hours and the interchange of solutes takes place during the first 30 min of treatment (Conway et al., 1983; Giangiacomo et al., 1987; Martínez-Monzó, 2002). As the interchange of matter from these levels of concentration occurs much more slowly, in practical terms (with a reasonable processing time), OD is not advisable to obtain reductions in water content of over 50%. This means foods with an aw>0.90. Mass transfer rates during OD operations depend on factors such as type, size and geometry of the sample, type and concentration of the osmotic medium, degree of agitation of the solution, working pressure, temperature and sample to solution ratio (Taiwo et al., 2001). At a macroscopic level, the OD operation has been modelled for many foods in general, in particular for many fruits, under different process conditions (Fito et al., 2001; Fito and Chiralt, 1997; Talens, 2002). This allows us to know how much process time is needed to reach a desired concentration level in the product. The fruits selected for the development of the gel product were kiwifruit (Actinidia chinensis P. var. Hayward), grapefruit (Citrus paradise var. Star Ruby), pineapple (Ananas comosus) and strawberry (Fragaria ananassa var. Camarosa). In the first three cases, the fruits were cut into 1 cm thick slices, the kiwifruit had an external diameter of 4,4 cm and the pineapple of 8.5 cm, and they were dehydrated until a final water content of 75%. The strawberry was cut in half and dehydrated until reaching a water content of 80%. The osmotic treatment was carried out with a 55 ºBrix sucrose solution, at 30 ºC while being stirred continuously. In every case but one it was processed at atmospheric pressure. The exception was grapefruit, in which a 10 min vacuum pulse (50 mbar) was applied at the beginning of the process, in order to accelerate the fruit’s dehydration. The ratios of fruit:osmotic solution were 1:4 for strawberry, 1:5 for grapefruit, 1:5, 1:10 and 1:20 for kiwifruit and 1:20 for pineapple. Under these conditions, the total process times were 1 h for kiwifruit, 3 h for grapefruit and strawberry and 2 h for pineapple. The previously mentioned operating conditions were established on the basis of prior experiments of dehydration kinetics carried out on each fruit (García-Pinchi, 1998; Talens, 2002; Valero, 2003; Moraga, 2007) and the results obtained from the study of different factors, like the temperature or the ratio of fruit:OS applied in the osmotic treatment. Thus, in the case of the strawberry, the effect of temperature both on the process time and on some quality characteristics of the obtained fruit, such as the citric and ascorbic acid contents, texture, colour and aroma, was evaluated. An experiment was performed under the same conditions as mentioned above, but at 20 ºC, which meant that twice the process time without significantly improving the quality of the strawberry (Penagos, 2006). The study to evaluate the influence of the ratio of fruit:OS was carried out on kiwifruit. Some aspects of this study are mentioned in the following paragraphs, although the final conclusion obtained was that, under the tested conditions, working with the smallest amount of OS, does not affect the dehydration kinetics of the fruit. However, it has to be pointed out that all the fruit pieces have to be totally submerged in the OS, which also conditions the required OS volume. This was the reason why it was necessary to work with a ratio of 1:20 in the case of pineapple.

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One of the aims of the designed product was to avoid by-product generation. In OD processes, the by-product is the osmotic solution used for fruit dehydration. When this must be discharged as wastewater, the main problem to be considered is related to the high biochemical oxygen demand (BOD5). For this reason, prior to being drained into the public sewer, a preliminary sanitation must be carried out by using ion exchange resins, reverse osmosis, micro- and ultra-filtration, etc. (Dalla-Rosa and Giroux, 2001), which increases the process cost. As an alternative to OS management, the possibility of re-using it in successive OD cycles and its final use in food formulations may be considered. When re-using it, with or without a previous re-concentration stage, the possible microbiological contamination must be considered. The re-concentration stage and hygienization would also suppose an additional cost to the process, whereas its re-usability without previous treatments will be mainly limited by the changes that take place during the process. This is because the progressive OS dilution after each OD cycle could cause a slowing down of the mass transfer rate and the consequent extension of the osmotic process. Thus, the first aspect to be tackled in this study, prior to developing the fruit-gel product, was to determine the viability of the direct re-use of the OS in further OD processes, achieving the same efficacy with respect to the dehydration level reached by the fruit without affecting its microbiological quality. An exhaustive parallel analysis of the effect of the osmotic dehydration on the composition of the fruits, as well as on the changes suffered by the OS during its re-usability, was carried out. The aim of this study was to know if the final use of OS in the elaboration of the product proposed in this chapter was of interest, on the basis of an enrichment in micro-nutrients and other bioactive substances that come from the fruit. Successive OD operations or cycles were programmed for every fruit being tested, so that in each cycle the fruit was renewed but the OS was re-used in the subsequent cycles, with no previous re-concentration treatment. In each cycle, the soluble solids (ºBrix) and aw of the fruits and OS before and after the OD were analyzed, as well as the water content (xw) of the fruit before and after the OD. In the case of kiwifruit, the influence of the fruit:OS ratio on the characteristics of the obtained dehydrated fruit was verified. As to the characteristics of the fresh fruit, the analysis of variance (ANOVA) carried out by studying the ºBrix, xw and aw of different batches of each fruit used in the study showed significant differences (α<0.05) in xw and ºBrix. Nevertheless, this compositional variability, that seems to be normal in this kind of raw material, was not reflected by significant differences in the aw of the pieces (α>0.05). Table 1 shows the mean values of these properties in the fruits under study. Table 1. Water content (xw), soluble solid content (ºBrix) and water activity (aw) of different batches of the fresh fruits implied in all the OD cycles Fruit

ºBrix

aw

Kiwifruit Grapefruit Pineapple Strawberry

14.9±0.6 12.5±0.6 12±1 8,0±1,4

0.987±0.006 0.9895±0.0012 0.989±0.001 0,994±0,002

Values expressed as mean±standard deviation.

xw (g water/100g sample) 83.6±0.6 86.4±0.6 87±2 -

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Figure 1 and tables 2 and 3 show the results obtained with kiwifruit, with which 10 successive OD cycles were programmed working with three fruit:OS ratios: 1:20, 1:10 and 1:5, and with the other three fruits under study. Figure 1 shows the decrease in the soluble solid content and the increase in the water activity observed in the OS after each of the different OD cycles considered and, in the case of kiwifruit, for the three fruit:OS ratios considered. As expected, the greater the number of OS re-uses and the higher the dehydrated fruit ratio (1:5), the greater the syrup dilution.

Figure 1. Mean ºBrix (full symbols) and aw (hollow symbols) values of osmotic solution (OS) at different osmotic dehydration cycles and fruit:OS ratios.

■ Kiwifruit (1:20), ■ Kiwifruit (1:10), ■ Kiwifruit (1:5)

♦Grapefruit (1:5), ▲ Pineapple (1:20), • Strawberry (1:4). Lines correspond to predicted values from correlation equations fitted between both variables (table 4): (—) ºBrix; (---) aw..

Table 2 shows ºBrix, xw and aw of dehydrated kiwifruit. If the progressive OS dilution observed when re-using it had a significant effect on the osmotic dehydration kinetics, a different dehydration level in the fruit processed in each OD cycle could be expected. The statistical study carried out using an ANOVA, taking the cycle factor into account for each fruit:OS ratio, only showed a significantly (α<0.05) greater soluble solid content in the 1:20 fruit:OS ratio and for the first two cycles, thus confirming a greater effectiveness of the OD process in these cases. In all the other cases, the observed differences of these two properties may be related more to differences in the raw matter than to the diluting effect of the OS, as no clear cycle-related tendencies can be pointed out. Nevertheless, aw was not significantly affected by the re-use of the OS. Similar results were obtained with grapefruit, pineapple and strawberry during 8, 15 and 7 consecutive OD cycles with the same OS, respectively (Table 3). From this point of view, an OD process consisting of a ratio of fruit:OS of around 1:5 and the re-usal of the OS for different subsequent OD cycles, can be recommended. Under these conditions, and in the programmed time, it is possible to obtain fruit with the same stability

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(aw), while reducing the generated waste, which is important in order to make the OD process more profitable. Table 2. ºBrix, aw and xw (g/100 g dehydrated fruit) of osmodehydrated kiwifruit obtained when re-using the same osmotic solution for different cycles of osmotic dehydration Cycles 1

2

3

6

10

Fruit:OS 1:20 1:10 1:5 1:20 1:10 1:5 1:20 1:10 1:5 1:20 1:10 1:5 1:20 1:10 1:5

ºBrixa 27.90 23.13 23.40 27.27 22.60 22.57 23.83 23.00 22.57 21.57 21.83 22.10 21.67 22.40 21.60

awb 0.979 0.981 0.981 0.980 0.978 0.980 0.981 0.981 0.979 0.979 0.980 0.983 0.982 0.978 0.979

xwc 69.9 76.5 74.3 74.1 75.4 75.9 70.7 75.3 75.2 76.8 76.2 76.3 75.5 75.7 77.2

a

Reproducibility lower than 0.15 Reproducibility lower than 0.005 c Reproducibility lower than 0.2. b

Table 3. ºBrix, aw and xw (g/100 g dehydrated fruit) of osmodehydrated grapefruit, pineapple and strawberry obtained when re-using the same osmotic solution for different cycles of osmotic dehydration

Cycles 1 2 3 4 5 6 7 8 9 12 15

Grapefruit (1:5) ºBrixa awb 23.0 0.981 23.1 0.979 24.3 0.976 21.4 0.986 22.3 20.0 0.983 22.5 21.7 0.980

xwc 76 74 76 78 80 78

Pineapple (1:20) ºBrixa awb 23.5 0.978

xwc 77

24.3

0.978

76

23.0

0.980

75

22.5

0.979

76

22.0 21.0 23.0

0.980 0.981 0.978

77 78 76

Strawberry (1:4) ºBrixa awb 14.4 0.981 13.2 0.979 13.4 0.981 11.6 0.986 13.6 0.979 11.6 0.988 11.6 0.984

a

Reproducibility lower than 0.3 Reproducibility lower than 0.005 c Reproducibility lower than 3. b

For all the above-mentioned properties, it was possible to propose simple empirical equations (linear or polynomic) that allow us to predict their value based on the number of OS uses (Table 4). These equations could be useful for determining the number of OD cycles that can be carried out depending on which final water or soluble solid content is desired in the OS, taking any possible final use into account.

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Table 4. Parameters of the model (y=a+bx+cx2) fitted to predict ºBrix and water activity (aw) of osmotic solution as a function of osmotic dehydration cycles. R2: determination coefficient Propiedad

Fruit a b (*) Kiwifruit 53.894 -1.156 Grapefruit 55.096 -2.4116 ºBrix Pineapple 54.967 -0.4435 Strawberry 55.392 -4.0413 Kiwifruit(*) 0.922 0.0053 Grapefruit 0.9186 0.0089 aw Pineapple 0.915 0.0022 Strawberry 0.9185 0.01 (*) The fruit:OS ratio of 1:5 was the only one considered for kiwifruit.

c 0.0998 0.1669 -0.0005 -0.00004 -0.003

R2 0.9775 0.9982 0.9650 0.9972 0.993 0.9801 0.9650 0.9972

As concerns the microbiological aspects of the OS, which could also limit its re-use, it is well known that osmotolerant or osmophilic micro-organisms are particularly tolerant to high concentrations of sucrose and cause numerous problems in industrial activities where large amounts of sugar are used. In this case, the presence in the syrup of viable micro-organisms (counts in Plate Count Agar for 48–72 h at 30 ºC) and yeasts and moulds (counts in Sabouraud Chloramphenicol Agar for 3–5 days at 30 ºC) were considered in the studies carried out on kiwifruit (García-Martínez et al., 2002a) and grapefruit (Moraga et al., 2005). The number of colony forming units (CFU) per ml of syrup was negligible in both cases (less than 10 CFU for yeasts and moulds and ranging between <25 and <100 for total viable microorganisms). In general, the number of micro-organisms increased with the syrup uses, but it did not reach a critical value that could not guarantee the microbiological stability of the process (counts were always lower than 102 UFC/ml for yeasts and moulds and 104 for viable micro-organisms, the legal limit for fruit and derivate products according to the Microbiological Norm, Real Decree 670/1990 of May 25, B.O.E. 31-5-90), thus confirming the possibility of OS re-usage. The pH of the OS after fruit OD, lower than 3.5 in every case, probably contributes to this fact. In any case, in order to ensure a greater stability of the osmodehydrated fruit in the gel formulated products considered in this study, a mild thermal treatment of the OS before each cycle (heating to 72ºC for 30 s) has been proven to reduce micro-organism counts (Moraga et al., 2005). As regards the effect of OD on the micro-nutrients of the different fruits under consideration, analyses of majority minerals (calcium and magnesium by atomic absorption spectrometry, potassium and sodium by atomic emission spectrometry and phosphorus by spectrophotometry, according to the method described by de la Fuente et al., 2003), ascorbic acid (AA) (AOAC 985.33, 1997), citric acid (CA) (AOAC 985.33, 1997) and total pectin (determined from the galacturonic acid, AGU, present in the fruit as described by Contreras et al., 2005) were performed. These analyses allowed us to confirm the loss of hydrosoluble components of the fruits as a result of the osmotic dehydration. Quantitatively, these are small losses, yet since the compounds in question are also present in small amounts in the fruits, they turn out to be important from the point of view of the nutritious value, as can be observed in table 5. Thus, for the different fruits, average losses in the order of 25% of their ascorbic acid and citric acid, 43% of their minerals and 20 % of the total pectin have been

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quantified. In general, these losses depend neither on the number of re-uses of the OS nor on the fruit:OS ratio (García-Martínez et al., 2002a; Peiró et al., 2006; Peiró-Mena et al., 2007; Penagos, 2006). Table 5. Citric acid (CA), ascorbic acid (AA), galacturonic acid (AGU) and minerals lost by the different fruits during osmotic dehydration. Values correspond to mg of compound loss/100 mg of the compound present in the fresh fruit Compound CA AA AGU Ca Mg K P Na

Grapefruit 25±5 12±7 18±7 28±5 29±9 57±10 40±9 59±10

Pinneaple 34±8 non detected 21±9 59±21 26±10 44±13 30±12 41±13

Kiwifruit 22±5 30±5 9±3 23±17 11±7 20±7 55±12 10±1

Strawberry 12 35 -

Values expressed as mean±standard deviation.

In the case of grapefruit and strawberry, the flow of phytochemicals was also studied. In strawberry the antocyanins, referred to pelargonidine – 3 glucoside, the main one present in this fruit (Torreggiani et al., 1998;), were quantified, following the method described by Alarcao-E-Silva et al. (2001). The amount present in the fresh fruit was found to be 19 mg/100 g. The osmotic treatment caused an antocyanin loss of 32%, part of which was incorporated into the OS, thus conferring the reddish tonality particular to the strawberry. In grapefruit, on the other hand, the analysis by reverse-HPLC of a methanol-water extract, allowed us to identify and quantify 7 flavonoids: naringin, narirutin, hesperidin, neohesperidin, poncirin, didymin and naringenin. Of these, the first three were the major ones, being present in amounts of 103, 44 and 14.8 mg/100g, respectively whereas the rest appear in quantities of between 5 and 8 mg/100 g. Losses in the order of 20%, referred to the component present in the fresh fruit, were quantified for all of them, during the dehydration. The incorporation into the OS of the components lost by kiwifruit, grapefruit and pineapple during their dehydration, and which depends on the number of re-use cycles, was studied (figures 2 and 3). In every case, a progressive compound enrichment in the osmotic solution was observed, with a practically linear evolution. The increase in the citric acid promoted a decrease in the pH of the OS, changing from around 6.5 when prepared, to nearly 3.5 just after the first OD cycle, with no greater changes in the rest of the OD cycles. As AC is a weak acid, the observed change in its concentration from cycle 1 does not affect the pH. Similar behaviour has been observed by Valdez-Fragoso et al., (1998) who studied changes in 60 ºBrix OS when used successively in apple dehydration after a reconcentration process. As for the phytochemicals, some of the major flavonoids lost by grapefruit were also partially recovered in the OS, naringin being the most abundant (9.6 mg/100 g OS re-used in eight OD cycles).

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Figure 2. Mean values (numerical values also appear in the table) of citric acid (CA), ascorbic acid (AA) and galacturonic acid (AGU) analysed in the osmotic solution (OS) when re-used for different osmotic dehydration cycles of kiwifruit (fruit:OS ratio of 1:5), grapefruit and pineapple.

Figure 3. Mean values (numerical values also appear in the table) of different minerals analysed in the osmotic solution (OS) when re-used for different osmotic dehydration cycles of kiwifruit (fruit:OS ratio of 1:5), grapefruit and pineapple.

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Additionally, it was wanted to know the effect that the incorporation of these compounds had on OS electrical conductivity and viscosity, as these two physical properties are relevant for the characteristics of the gel that is going to be formulated to obtain the product. The electrical conductivity (EC) of the syrup depends on its sugar concentration, temperature, the chemical composition of the water used in its preparation and, probably, on the type and concentration of the soluble components coming from the fruit. The distilled water used to prepare OS had an EC = 4.13 ± 0.02 μS/cm which changed to 12.38 ± 0.05 μS/cm when sucrose was added to obtain the 55 ºBrix osmotic solution. For all the fruits under study, compositional changes of OS during the OD process promoted an increase in EC over the course of the OD cycles, as is shown in figure 4. When the EC of the OS was compared to that of sucrose solutions which had the ºBrix of OS obtained after each OD cycle (data shown in figure 1), much greater values were observed in the former (figure 4). From these results, the observed progressive enrichment in soluble components coming from the fruits may be confirmed as being mainly responsible for the observed EC increase. By means of a stepwise multiple regression analysis procedure, Ca, Mg, P, AA and AC were found to be the main components responsible for this EC increase in grapefruit (Peiró et al., 2006) and pineapple (Peiró-Mena et al., 2007). The smaller amount of these compounds present in the OS re-used for pineapple dehydration (figures 2 and 3), probably due to the lower fruit:OS ratio used in the osmotic step in this case, may be what is responsible for the much lower EC values obtained in this OS.

Figure 4. Mean values of electrical conductivity (EC) of the osmotic solution (OS) when re-used for different osmotic dehydration (OD) cycles of kiwifruit with a fruit:OS ratio of 1:5 (■), grapefruit (♦) and pineapple (▲). EC of a sucrose solution with the same ºBrix as the OS after different OD cycles of kiwifruit (□), grapefruit (◊) and pineapple () also appears. Lines correspond to predicted values from correlation equations fitted between both variables (table 6).

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From the point of view of the viscosity studies, the OS obtained from the different OD cycles of every fruit under consideration showed Newtonian behaviour when the flow-curve was obtained in a Physica Rheolab MC1 rheometer at 25 ºC (shear rate sweep from 0 to 150 s-1 in 90 s) using a concentric cylinder sensor (internal radius=12.5 mm, external radius=13.56 mm, height=37.5 mm). As observed in figure 5, with the re-use of OS a sharp decrease in viscosity (μ) was observed, due to the fact that it becomes more diluted. For pineapple OS, the lower fruit ratio dehydrated in each OD cycle led to a much less dilution and so, much greater viscosity values. In this case, when the results obtained were compared with the viscosity of sucrose solutions with the same ºBrix as the osmotic solutions obtained after different OD cycles (data shown in figure 1; Pancoast and Junk, 1980), higher viscosity values of the re-used OS were observed (figure 5). The pectin content detected in the OS may be responsible for the observed differences, as this component contributes to an increase in the viscosity. From these results, it can be deduced that both the sugar and pectin content, are what is responsible for the OS viscosity.

Figure 5. Mean viscosity values (μ) of the osmotic solution (OS) when re-used for different osmotic dehydration (OD) cycles of kiwifruit with a fruit:OS ratio of 1:5 (■), grapefruit (♦) and pineapple (▲). Viscosity of a sucrose solution with the same ºBrix as OS after different OD cycles of kiwifruit (□), grapefruit (◊) and pineapple () also appears. Lines correspond to predicted values from correlation equations fitted between both variables (table 6).

Both EC and μ, were well correlated with the number of syrup uses by means of linear or second grade polynomic relationships (Table 6). Lines in figures 4 and 5 show the predicted values using these equations. These equations could be useful for determining the number of OD cycles that can be carried out depending on what the OS is to be used for (in certain food formulations) and so, which are its desired final properties. On the other hand, practical equations were found to correlate EC and μ with OS ºBrix for all the cycles being studied (table 7). Despite the close correlation obtained, it has to be remembered that, as commented

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on above, μ and, especially, EC, are related to the presence of other fruit soluble solutes in the OS, apart from sugar. So, the equations are only useful for predicting these two properties in re-used osmotic solutions. Table 6. Parameters of the model (y=a+bx+cx2) fitted to predict osmotic solution electrical conductivity (EC) and viscosity (μ), depending on the cycles of re-use. R2: determination coefficient Property EC

Fruit Kiwifruit(*) Grapefruit Pineapple Kiwifruit(*) Grapefruit Pineapple

a -0.0144 0.9186 6.6309 0.0339 20.402 24.005

b 0.0514 51.679 8.8851 -0.0043 -3.1306 -0.9555

c 0 0 0 0.0002 0.1931 0.019

R2 0.9915 0.9949 0.9910 0.9708 0.9698 0.9885

(*)

The fruit:OS ratio of 1:5 was the only one considered for kiwifruit.

Table 7. Parameters of the model (y=a+bx+cx2) fitted to predict EC and μ as a function of ºBrix of re-used osmotic solutions. R2: determination coefficient Fruit Kiwifruit(*) Grapefruit Pineapple

Property EC (mS/cm) (mPa*s) EC (mS/cm) (mPa*s) EC (mS/cm) (mPa*s)

a 0.0025 368.3 5294.3 4175.5 3741.7 127.73

b -0.2792 -16.712 -80.65 -157.78 -122.3 -5.7532

c 7.8876 0.1993 1.5418 1.4963 0.991 0.0703

R2 0.9998 0.9873 0.9927 0.9986 0.9899 0.975

(*)

The fruit:OS ratio of 1:5 was the only one considered for kiwifruit.

From the results commented on above, the direct re-use of the OS in successive OD cycles may be recommended, as there were no stability problems detected in the obtained fruit, from either the point of view of the water activity or the microbiological counts. Nevertheless, this implies that changes in the composition of the OS, related to the incorporation of water and fruit soluble solutes, become more and more significant. These affect some physical characteristics, such as EC and μ, which may be relevant when using the spent osmotic solution for a gel formulation.

OPTIMIZATION OF PRODUCT FORMULATION IN A PROCESS WITHOUT BY-PRODUCT GENERATION Once the possibility of OS re-use was verified, both from the point of view of the characteristics of the obtained fruit as well as from the interest of its use related to the presence of fruit bioactive substances, the key to formulating the gel product from all the dehydrated fruit and with the greatest quantity of the OS used in the osmotic process were considered in order to avoid wastewater. To find out some of the product’s possible problems, some first formulations were carried out with and without dehydrated kiwifruit (obtained in the conditions described in the

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previous chapter and using a fruit:OS ratio of 1:5), working with different solvent means and gelling agents. As solvent means, sucrose solutions of 55ºBrix and pH=4 (SS1), simulating the OS after one OD cycle, sucrose solutions of 40ºBrix and pH=3.5 (SS10), simulating the OS after ten OD cycles, and finally the actual OS obtained after 10 OD cycles (OS10) were tested. To select the gelling agents, food gelatine was dissolved in the OS10 according to the manufacturer’s specifications. The fact that this system does not gel is probably due to the presence of actinidine, a proteolytic enzyme characteristic of kiwifruit (Préstamo, 1995). This enzyme may be liberated into the osmotic solution during the OD process, interfering with the helicoidal three-dimensional net formation that characterizes the gelling process of this collagen derivative (Imeson, 1997). For this reason, and because of the cultural problems of using it in some diets due to its animal origin, gelatine was discarded for the formulation of the product. Carrageenan was then tested, at concentrations of 0.5, 0.6 and 0.75 %, dissolved in warm SS1 and SS10. Gels with different characteristics were obtained depending on the hydrocolloid concentration (Ribé et al., 2001). From this study, 0.6% carrageenan concentration was selected as providing the best consistency. Nevertheless, in OS10 no gel was obtained at this concentration, probably due to the fact that sucrose, ions (as deduced from the mineral analysis carried out, figure 3) and carrageenan all compete for the water. Nevertheless, an interesting characteristic of the gels formulated with the OS10 was the absence of syneresis, evident when SS was used. In order to favour carrageenan gel formation, other tests were carried out adding locust bean gum, due to the synergic character described for both gelling agents (Camacho et al., 1999). Nevertheless, gels obtained in this case showed lower consistency and greater syneresis than the ones formulated only with carrageenan. This can be a consequence of the pH and ionic strength of the OS10 and also of the temperatures used for the hydrocolloids dissolution. At pH <4.5 and temperatures over 80ºC, the hydrolysis of the glycoside chains of the locust bean gum takes place, due to acid catalysis and thermal degradation processes and so, maximum viscosity was not reached (Imeson, 1997). For all these reasons, it was decided to use only carrageenan but at an increased concentration of 0.75 %. Once the hydrocolloid and its concentration were optimized, the process of dosing and mixing the gel with the dehydrated fruit was standardized. First, the ratio of the product’s gel:fruit phase was optimized. To this end, ratios of 70:30, 65:35, 60:40 and 50:50 were tested. Of these, 65:35 and 60:40 were the ones that allowed us to obtain a homogeneous product, with a balanced content of each component and one that behaved satisfactorily when it was turned out of the tub, not breaking the gel. As regards the mixing step, it was tested by previously combining all the gel phase and the fruit and pouring the set into polystyrene tubs. This leads to a heterogeneous product, with the kiwifruit in the top half of the product, which was broken while turning it out of the tub. Flotation of the kiwifruit prior to the gel of the system was a consequence of the low viscosity of the gel phase, due to its high temperature when mixing with the fruit. These disadvantages were avoided by alternating layers of gel phase, gel phase with fruit and gel phase again, in a ratio of 30:40:30. In order to avoid the gel layers separating after the product is turned out, the temperature of the mixing process was optimized, so that, after placing the first gel layer in the tub at 50ºC, it was introduced in a bath with water at 20ºC for a few seconds until the walls were observed to gel. At this moment the gel:kiwifruit layer was added at 50ºC, placed again in the bath at 20ºC and, finally, the last gel layer was added. The product prepared in this way was allowed to gel for at least 24 h at 8ºC before turning it out for analysis.

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Following this methodology, and as it has been described in chapter 2, a batch of gel products was prepared from OD kiwifruit and the OS obtained after 10 consecutive cycles of fruit dehydration. Before proceeding to mix the gel phase with the dehydrated fruit, both were analyzed separately, as to soluble solids and water activity. In addition, fruit water content was also controlled. The same analyses were repeated 24 h after the product’s elaboration and after one and two weeks of chilled storage (8 ºC), during which time syneresis was also controlled. Results of aw and ºBrix analysis revealed that, initially, the product was not in thermodynamic equilibrium, as the initial aw of the dehydrated kiwifruit was greater than that of the gel (0.985 ± 0.001 and 0.947 ± 0.001, respectively). This means that the kiwifruit, with an initial ºBrix of 18.4 ± 0.5 and humidity of 80.29 ± 0.04 %, is still dehydrated inside the gel, which leads to a simultaneous increase of the gel’s aw. The product equilibrium was reached after between 7 and 14 days of chilled storage (final gel and kiwifruit aw = 0.966 ± 0.001). Syneresis was not observed during the period of time being studied, thus indicating that all the water lost by the kiwifruit was incorporated by the structure of the gel. These results suggest the need to formulate a thermodynamically stable product, in order to avoid major fruit dehydration during storage. This will need the OS to be diluted until it reaches the same aw as the dehydrated fruit. To know the microbiological stability of the product total viable micro-organism, yeast and mould counts were carried out at 24 h, 7, 14 and 30 days. Up to 15 days, counts were lower than the permitted maximums established by the Microbiological Norm, Real Decree 670/1990 of May 25 (B.O.E. 31-5-90), as 105 UFC/g product for total viable and 104 UFC/g product for moulds and yeasts. After one month, visual symptoms of contamination appeared and the counts were over the allowed limit. When formulating the product, the way of presenting the fruit inside the gel was taken into account. Some tests were carried out with the fruit pieces obtained directly after the OD (whole slices of kiwifruit, half slices of grapefruit and strawberry halves) and also with these pieces cut into different sizes. Sensory evaluations did not show marked preferences, as, although visually whole fruit is preferable, it was easier to consume when cut into pieces. Nevertheless, this aspect concerns neither the previously described stages of product formulation, nor the product’s stability which will be described later. To evaluate the sensory acceptance of the formulated product, the colour, flavour, aroma and texture of the gel, the dehydrated kiwifruit and the whole product, were tested. A consumption test was also carried out and the results indicated that 80% of the panellists would buy it, which shows that the formulated product was well accepted. Nevertheless, it was suggested that the gel should be more consistent and not as sweet. To achieve this, the dilution of the OS may be recommended, which, in turn, would improve the carrageenan chain extension, thus increasing the gelling power. In addition, as has been shown in previous paragraphs, this OS dilution is necessary in order to increase its aw until near that of the dehydrated fruit. In fact, other subsequent sensory tests confirmed that the sweetness of the gel formulated with the OS that has been diluted until reaching thermodynamic equilibrium with the fruit, was suitable. In order to know the quantity of water to be added to OS, the use of Norrish’ equation for a sucrose-water solution (eq. 1) is proposed, which relates the molar water fraction (xw) and molar soluble solid fraction (xs) with the water activity of the solution (aw). aw = xwexp(-6.47xs2) (1)

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In order to apply eq. (1), it is necessary to know the aw of the OS which will be equal to that of the dehydrated fruit. On the other hand, as it is assumed that the solution is only made up of water and sucrose, which are the major components, xw is equal to (1-xs). Therefore, both of them can be determined. Transforming molar fractions to mass fractions allows us to obtain the ºBrix of the diluted OS. From this data and the ºBrix of the OS obtained after the dehydration process, the quantity of water that is necessary to add, in order to obtain the same water activity as the dehydrated fruit (thermodynamic equilibrium), must be calculated. On the other hand, as one of the objectives of the proposed product was to avoid byproduct generation, the number of re-uses of the OS in successive OD cycles must be optimized, so that all the dehydrated fruit obtained after the different OD cycles and the spent solution from the last usage are used in the formulation, while always taking into account the required dilution level. To this end, different experiments were programmed with grapefruit, kiwifruit and strawberry. For each fruit, a set of consecutive OD cycles were programmed under the conditions described in section 2, with the only difference being that the spent OS in one cycle was directly re-used in further ones, without any re-concentration treatment. The weight, ºBrix and water activity of both dehydrated fruit and OS, were measured after each OD cycle. From these data, the calculated amount of water that is necessary to add to the OS for the product to reach thermodynamic stability and the selected gel:fruit ratio in the final formulation, it is possible to determine the number of OD cycles that must be carried out to design a closed process. As an example, table 8 shows data from the strawberry experiment. In this case, seven consecutive OD cycles (fruit:OS ratio of 1:4) were programmed. The mean value of osmodehydrated strawberry aw obtained from all the cycles was 0.983. Applying eq. 1 with this aw value allowed us to calculate the amount of water that needed to be added to the OS depending on the number of re-uses, to obtain the weight of equilibrated OS shown in table 8. To formulate the product a gel:fruit ratio of 60:40 was considered in this case. The fruit and OS quantities necessary were calculated taking into account that 250 ml PET tubs were to be used, in which 200 g of fruit-gel product can fit. The amount of equilibrated OS available after each OD cycle and the accumulated osmodehydrated strawberry allowed us to calculate the number of tubs that could be prepared by using either all the obtained dehydrated fruit or all the OS. As can be observed in table 8, the optimum re-use of the OS would be five consecutive OD cycles, as by-product generation is the smallest. In this case, a total of 26 tubs will be obtained and just 24 g of strawberry (0.7 %) and 467 g of equilibrated OS (13 %) will be left over. Following the same procedure but using kiwifruit and grapefruit (dehydrated at a fruit:OS ratio of 1:5) and with a final gel:fruit ratio of 65:35, it was possible to estimate the optimum number of OS re-use cycles in order to minimize by-product generation as 4.

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Figure 6. Scheme showing the optimized process for fruit-gel product formulation, using osmotic treatments, without by-product generation.

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Table 8. Weight and water activity of osmodehydrated strawberry (mODS and awODS) and weight and ºBrix of osmotic solution (mOS and ºBrixOS) obtained after each OD cycle. Number of gel fruit product tubs that could be obtained by using all the equilibrated OS (TOSeq) or all the osmodehydrated strawberry (TODS) obtained after the corresponding OD cycles

(1)

Cycle

mODS (g)

aw ODS

mOS (g)

ºBrixOS

mOSeq(1) (g)

TOSeq

TODS

1

410

0.981

2257

52.2

5034.9

42.0

5.1

2

394

0.979

2230

48.1

4583.9

38.2

10.1

3

421

0.981

2216

45.0

4261.5

35.5

15.3

4

450

0.986

2160

41.5

3830.8

31.9

20.9

5

430

0.979

2141

39.2

3586.6

29.9

26.3

6

485

0.988

2077

37.1

3293.0

27.4

32.4

7

462

0.984

2034

35.5

3085.8

25.7

38.2

Weight of equilibrated OS obtained when diluted to reach the same water activity as osmodehydrated strawberry, calculated as explained in the text.

Therefore, with the results obtained from this part of the study, it is possible to propose the gel product formulation process which contains the highest quantity of osmodehydrated fruit pieces and which does not generate by-products. Figure 6 schematizes the corresponding process.

CHARACTERIZATION AND STABILITY OF THE PRODUCT In order to evaluate product changes during refrigerated storage (14 days), at 8ºC, two different experimental designs were programmed on grapefruit and strawberry gel products. In both cases, the aim was to achieve thermodynamic equilibrium between the dehydrated fruit and the gel matrix. On the one hand, tubs were prepared with just one fruit piece inside the gel matrix. These were used to study, under more controlled conditions, changes in colour, mechanical properties and volatile profile in both the fruit and the gel matrix. On the other hand, tubs prepared following the optimized formulation process, thus ensuring the adequate gel:fruit ratio, were used to analyze compositional changes in the fruit and the gel matrix, and to evaluate sensorial acceptance of the product. Colour changes in the product were observed to be dependent on the pigment content of the fruit included in the gel. The visual aspect of the fruits was not perceived to change during product storage, although significant differences in CIEL*a*b* colour-coordinates (obtained from the fruit surface with a spectrocolorimeter Minolta CM 3600D, D65-10 º) were detected. As an example, table 9 shows the L*a*b* evolution of the osmodehydrated strawberry inside the gel matrix at different storage times, as well as hue angle (h*ab=arctg b*/a*) and chrome or saturation index (C*ab=(a*2+b*2)0.5) attributes. An increase in luminosity and a decrease in a* and b* was observed. As a consequence of these changes, hue angle and chrome decreased throughout storage, leading to a redder but less pure strawberry colour. As regards the gel, the

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colour was not affected in the case of grapefruit and kiwifruit, but with strawberry it immediately turns red. Transparency of gel was not lost with any of the fruits, which is a desired characteristic as it helps to identify the fruit pieces inside the product. Table 9. Values of CIEL*a*b* coordinates, chrome (C*ab) and hue (h*ab) measured on osmodehydrated strawberry during storage of fruit-gel product. ∆E: colour difference, related to sample at t=0, due to storage time Time (days) 0

L*

a*

b*

C*ab

h*ab

∆E

29±2a

23.4±1.5a

10.7±1.3a

26±a

24±3a

---

2

33.9±0.4b

20.6±1.2ab

6.72±0.07b

22±2b

18.1±1.9b

6.64±1.17a

14

31.8±1.8

ab

12.1±0.5

c

3.5±0.5

b

12.6±0.4

c

16±2

b

13.66±0.12b

Values expressed as mean±standard deviation. Within column, values with the same following letter do not differ significantly from each other (p>0.05).

Several factors may contribute to the colour changes observed in the strawberry product. In the case of the fruit, these may be physical changes on the surface that may affect its reflectance, pigment losses as a result of lixiviation, oxidation or enzymatic action, or further fruit dehydration (Contreras, 2006). Changes in the gel phase will more probably only be due to the incorporation of anthocyanins coming from the strawberry. In this sense, the evolution of the pelargonidine-3-glucoside was controlled during chilled storage of the product. Measurements were taken both in the fruit and in three concentric zones in the gel matrix around the fruit position. For fruit analysis, the anthocyanin concentration was referred to the osmodehydrated fruit weight at storage time t=0. A significant pigment decrease was observed in the first 2 storage days, changing from 14.9 to 7.2 mg/100g. No additional decrease was observed after 14 storage days (7.4 mg/100g). Nevertheless, no correlation was observed between fruit pigment concentration and colour coordinates, as the degradation of free anthocyanins does not always have an immediate effect on visual colour changes (Skrede et al., 1992) and because of the possible contribution of changes in the other factors mentioned above. As regards the gel phase, figure 7 shows the average values for pigment concentration in the three different zones considered in the gel matrix, during storage time. As expected, significantly higher values were obtained for zone A (closer to the fruit position), followed by zone B and zone C. In all of them, it can be observed that the longer the storage time, the greater the anthocyanin content. This behaviour is coherent with a pigment migration from the fruit to the gel matrix and within the matrix itself, as a consequence of its hydrosoluble character. Both the temperature of the gel at the moment of introducing the fruit (40ºC), together with the cellular damage caused by osmotic treatment, could be responsible for the intense pigment diffusion from the vacuoles of the external cells to the gel matrix in the first moments of storage. In this case, there was a close correlation observed between the a* colour coordinate and the hue angle with the anthocyanin content (figure 8).

327

Development of Gel Products Containing Fruit Pieces Using Osmotic…

mg pgn 3 g/100g sample

1.4 zone A

1.2

zone B

1

zone C

0.8 0.6 0.4 0.2 0 0

2

15

storage time (days)

y = 0.124x + 0.207 R² = 0.747

1.4

pgn 3 g (mg/100 g sample)

pelargonidine 3-glucoside (mg/100 g sample)

Figure 7. Evolution of the pelargonidine-3-glucoside (pgn 3 g) content, during product storage, in three concentric zones of the gel matrix around the strawberry piece (zone A, the closest to the fruit and C, the furthest away).

1.2 1.0 0.8 0.6 0.4 0.2 Zone A

Zone B

y = -0.018x + 1,519 R2 = 0.744

1.4 1.2 1.0 0.8 0.6 0.4 0.2 Zone A

Zone C

Zone B

Zone C

0.0

0.0 0

2

4 a*

6

8

0

50 h*ab

100

Figure 8. Relationship between pelargonidine-3-glucoside (pgn 3 g) and a* colour coordinate and hue angle (h*ab).

Texture is one of the main quality attributes for fruit and fruit based products, mainly when the fruits in question are especially fragile and perishable (Sanz et al., 1999). In this sense, when the stability of the formulated gel product is considered, possible changes in the mechanical properties of the gel matrix and the fruit during its refrigerated storage have to be taken into account. Different mechanical tests (puncture and shear) were carried out to evaluate this aspect in the product. In every case a small although significant decrease of the fruit’s resistance to fracture was observed after two storage days, but there were no other

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significant changes during the rest of the storage time (Table 10 shows the results for the strawberry gel product). Nevertheless, this decrease in the fruit’s resistance does not seem to be a consequence of the gel being present, as the observed changes were of the same order as those shown by the osmodehydrated fruit when not included in the gel matrix (Penagos, 2006). There were no observed changes in the mechanical properties of the gel matrix during storage. Table 10. Fracture force (Ff) and deformation at fracture (Df) of osmodehydrated strawberry included in the gel matrix at different storage times Ff (N) Time (days) 0 2 14

Df (mm)

Shear test

Puncture test

Shear test

Puncture test

6.8±0.3a 2.0±0.2b 2.4±0.3b, c

6.4±1.6a 2.6±0.4b 2.5±0.3c

13.8±0.9a 5.3±0.8b 9.1±0.9c

0.32±0.03a 0.40±0.02b 0.41±0.05b

Values expressed as mean±standard deviation. Within column, values with the same following letter do not differ significantly from each other (p≤0.05).

The volatile profile of the strawberry gel product was analysed by using the “purge and trap” method for the extraction and using a GC/MS for compound identification and quantification (Otson and Chan, 1987). A total of 28 compounds could be identified and quantified (relative area of each compound referred to the internal standard is shown in figure 9): sixteen esters, seven alcohols, four aldehydes and one foranone (furaneol). In dehydrated strawberry, the family of ester compounds was the major fraction, representing about 57% of the total quantified volatile compounds. These “key compounds” (ester family) are not only important from the quantitative point of view, but have also been reported to be relevant components of the original strawberry aroma (Schreier, 1980; Dirinck, Schreyen and Schamp, 1981; Douillard and Guichard, 1989; Talens et al., 2003). Migration of alcohols and furaneol compounds into the osmotic solution may be responsible for the smaller concentration observed in the osmodehydrated fruit, when compared to fresh fruit (Talens et al., 2003; Penagos, 2006). The highest relative areas (related to concentration) in dehydrated strawberry were shown by 2 hexenal E (2.286), butanoic acid 3 methyl ethyl ester (2.124) and butanoic acid ethyl ester (2.112). Individual compound analysis during storage showed a small transfer of some volatile components from osmodehydrated strawberry to the gel matrix, such as methylacetate, butanoic acid 3 methyl ethyl ester, or butanoic acid octyl ester. In general, a decrease in all the quantified compounds was observed during product storage, except for ethanol, which may be due to sugar fermentation.

Development of Gel Products Containing Fruit Pieces Using Osmotic… 7.5

Relative Area

6.5 5.5 4.5 3.5

329

Fruit 0 days Fruit 2 days Fruit 15 days Gel 0 days Gel 2 days Gel 15 days

2.5

Relative Area

1.5

1

methyl acetate butanoic acid ethyl ester ethylacetate propanoic acid ethyl ester acetic acid prophyl ester butanoic acid methyl ester butanoic acid 3 methyl ethyl ester butanoic acid 2 methyl ethyl ester butanoic acid octyl ester butanol acetate 2 butenoic acid ethyl ester acetic acid penthyl ester hexanoic acid methyl ester acetic acid octyl ester benzonoic acid ethyl ester acetic acid fenylethyl ester 1 pentan 3 ol ethanol 1 butanol 1 pentanol 2 hexen 1 ol E 3 hexenol Z dodecanol methyl 2 butenal hexanal 2 hexenal E nonanal 3(2H) foranone 5 ethyl

0

Volatile compounds

Figure 9. Strawberry and gel volatile profile obtained at different storage times of the product.

All the fruits showed similar results when comparing the product’s compositional evolution during storage. To illustrate this, the results for the grapefruit gel product are shown in the following paragraphs. In the fruit included inside the gel, a slight dehydration was observed during product storage, more accused during the first 48 h (Table 11). This could be related to an effect of the carrageenan, which decreases the gel´s aw, thus removing the pretended thermodynamic equlibrium (equal water activities) with the dehydrated fruit, as can also be observed in table 11. In this sense, it would be necessary to consider the presence of carrageenan in eq. 1 calculations. Nevertheless, despite the variations observed in the water

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content of the dehydrated fruit and the initial difference in aw between the fruit and the gel at t=0, these are so small that no significant changes, either in the fruit or in the gel’s aw over storage time, were appreciated. Table 11. Water content (xw, g water/100g sample), ºBrix (g soluble solids /100 g liquid fraction), water activity (aw) ascorbic acid (AA, g /100g sample) and citric acid (CA, g / 100 g sample) of the osmodehydrated grapefruit included in the gel structure (ODG) at different storage times. ºBrix and aw of the gel matrix (GEL)

0

30.2

GE L 20.1

0.976

0.970

xwa FD O 80.9

42

4.5

1497

168

1

28.8

20.3

0.975

0.966

78.4

29

7.3

1140

328

2 4 7 10 14

30.5 28.5 29.6 28.1 29.1

20.1 19.5 22.2 21.6 23.6

0.974 0.979 0.977 0.980 0.975

0.963 0.976 0.972 0.974 0.976

77.8 77.7 75.8 76.6 74.6

32 31 28 27 22

9.0 8.2 9.3 6.7 6.7

1211 1269 1181 1122 998

365 425 484 471 542

t (days)

awb

ºBrixa ODG

ODG

GEL

AAc (mg/100g)

CAd (mg/100g)

ODG

GEL

ODG

GEL

Values expressed as mean±standard deviation. aReproducibility lower than 0.6. bReproducibility lower than 0.005. cReproducibility lower than 3 in ODG and 1 in GEL. dReproducibility lower than 100 in ODG and 30 in GEL.

In all the products under study, whereas the ascorbic and citric acid contents of the gel increased, those of the fruit decreased (Table 11). This, together with the increase of the electrical conductivity of the gel detected until the seventh day (from 822 to 963 6 μS/cm in the grapefruit product, for example) and the decrease in the pH (from 3.15 before introducing the grapefruit to 2.80 after 24 h of product storage), would indicate that, together with the water liberated by the fruit into the gel during the storage of the product, as commented on above, a flow of soluble water substances takes place (ascorbic acid, mineral salts and organic acids). The evolution of galacturonic acid, minerals and flavonoids during storage was only analyzed in the grapefruit gel product. Although a decrease was observed in the AGU of the dehydrated fruit (from 1590 to 1258 mg AGU/100g), which was more significant in the first four days, no changes in the gel’s AGU content were detected (mean value of 259 mg AGU/100g). This can be due to the enzymatic action of poligalacturonase, which is abundant in the grapefruit (Riov, 1975). As for Na and Ca content during storage (figure 10), there was a slight decrease observed in the gels and yet a progressive enrichment in the grapefruit. This would indicate a certain reincorporation of both minerals into the fruit. The opposite trend was found for P evolution (figure 10). Dehydrated fruit showed a significant increase in Mg and K during the first 24 h, with no additional changes, either in the fruit or in the gel, observed during the rest of storage. During this time, the fruit showed mean values of 6 ± 1mg Mg/100g and 109 ± 10 mg K/100g, whilst in gel they were 2,5 ± 0,2 mg Mg/ 100g and 95 ± 3mg K/ 100g. In the case of flavonoids, there was no detected evolution of these compounds in the osmodehydrated grapefruit included in the gel. An analysis of these compounds in the gel matrix revealed the presence of only naringin, poncirin and naringenin. Naringin, the most abundant in the OS, was present in smaller quantities in the gel (10

Development of Gel Products Containing Fruit Pieces Using Osmotic…

331

mg/100g OS vs. 3.7 mg/100 g gel). The other minor phenols present in the OS, with the exception of naringenin, were not detected in the gel matrix. This was as expected, as the OS is diluted prior to formulating the gel product. The presence of naringenin in the gel matrix may be explained as it is a hydrolysis product of naringin, which is the main phenol present in the OS, but also the one that most decreases in gel. ODG

GEL

mg Na/100g sample

5 4 3 2 1 0

mg Ca/100g sample

20

15

10

5

0 12

mg P/100g sample

10 8 6 4 2 0 0

1

2

4 t (days)

7

10

14

Figure 10. Evolution of Na, Ca and P content in osmodehydrated grapefruit (ODG) and in gel during storage.

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From these results, if any changes are observed in the fruit and gel compounds during chilled storage, they are so small that the compositional stability of the product seems to be guaranteed. A panel made up of 30 untrained tasters took part in a test to determine the level of acceptance of the colour, aroma, flavour, texture and fruit content of a gel product formulated with strawberry. A hedonic scale of 5 points was used. As observed in figure 11, the attributes of colour and aroma were the most widely accepted, followed by strawberry content and flavour. The gel texture, however, turned out to be slightly soft. On the other hand, before the product was tasted and only from a visual point of view, it was asked if they would buy the product and 90 % of the panellists answered affirmatively. The same question was repeated once the tasting was over, but this time the percentage fell to 77 %, due to the low consistency of the gel which may easily be improved by slightly increasing the concentration of carrageenan.

AROMA 2 1 0 FRUIT CONTENT

COLOUR

-1 -2

TEXTURE

TASTE

Figure 11. Results of the sensory acceptance evaluation of strawberry gel product.

CONCLUSIONS During the osmotic dehydration of fruits with sucrose solutions, compositional changes take place, both in the fruit and in the OS. On one hand, fruit water loss leads to a dilution of the OS which, nevertheless, does not prevent it from being re-used in successive dehydration cycles, without affecting the prearranged dehydration level of the fruit or causing considerable microbiological contamination. At the same time, changes observed in the fruit allow us to recommend a fruit:OS ratio of 1:4 or 1:5 for the dehydration operation, as using a greater quantity of OS would only contribute to increasing the by-product generation. Osmotic dehydration of fruits leads to a loss in micro-nutrients and other bioactive substances, which has been verified and quantified. Nevertheless, a progressive enrichment in the osmotic solution when it is re-used during successive cycles of fruit dehydration has been observed, which turns it into an attractive ingredient for food formulation. This, together with

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the optimization of the fruit:OS ratio, would help to make the osmotic process more profitable, both from an economic and environmental point of view. With this in mind, this work proposes the methodology to formulate a gel product from osmodehydrated fruit and the actual osmotic solution used for its dehydration, with a good sensory acceptance and a microbiological stability of at least 15 days of chilled storage. The process has been optimized to avoid by-product generation. To this end, the re-use of OS for 4-5 consecutive fruit OD cycles is recommended, depending on the fruit:OS ratio during the dehydration operation and on the fruit:gel ratio in the final product, which may range between 65:35 and 60:40. In order to achieve compositional stability of the gel product during its storage, the OS must be diluted before the gelling agent is incorporated, so that its water activity is the same as that of the dehydrated fruit. Although carrageenan looks like the most appropriate gelling agent due to the compositional characteristics of the OS, its concentration must be adapted, since gels formulated from the OS present less consistency than expected. The dosing and mixing of the ingredients must be done by layers and under controlled temperature conditions, to obtain a homogeneous product that is easy to turn out.

ACKNOWLEDGEMENTS Authors thank the Ministerio de Educación y Ciencia and the Fondo Europeo de Desarrollo Regional (FEDER) for the financial support throughout the projects AGL200201793 and AGL 2005-05994. Also, our most sincere thanks to Rebeca Peiró Mena and Lucas Penagos Vélez, who have contributed to data shown in this chapter with results obtained while doing their PhD Thesis.

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[54] Riov, J. 1975. Polygalacturonase activity in citrus fruit. Journal of Food Science, 40(1), 201-202. [55] Rui-Hai-Liu. (2003). Protective role of phytochemicals in whole foods: implications for chronic disease prevention. Applied Biotechnology, Food Science and Policy, 1(1), 3946. [56] Sanz, C.; Pérez, A.G.; Olias, R. and Olias, J.M. (1999). Quality of strawberries packed with perforated polypropylene. Journal of Food Science, 55, 1049-1053. [57] Schreier, P. (1980). Quantitative composition of volatile constituents in cultivated strawberries Fragaria x ananassa c.v. Senga Sengara, Senga Litessa and Senga Gourmella. Journal of Science and Food Agriculture, 31, 487-494. [58] Shank, J.L. (1985). A method of preparing a gelled food product. European Patent. EP 0 073 907 B1 (EP0073907B1) [59] Shi, X.Q.; Chiralt, A.; Fito, P.; Serra, J.; Escoin, C. and Gasque, L. (1996). Application of Osmotic Dehydration Technology on Jam Processing. Drying Technology, 14: 841847. [60] Skrede,G.; Wrolstad,,R.E.; Lea, P. and Enersen, G. (1992). Color stability of strawberry and blackcurrant syrups. Journal of Food Science, 57(1): 172-177. [61] Taiwo, K.A.; Angersbach, A.; Ade-Omowaye, B.I.O. and Knorr, D. (2001). Effects of pretreatments on the diffusion kinetics and some quality parameters of osmotically dehydrated apple slices. Journal of Agricultural and Food Chemistry, 49 (6): 28042811. [62] Talens, P. (2002). Tratamientos osmóticos en la crioprotección de fresa y kiwi. PhD Thesis. Universidad Politécnica de Valencia (Spain). [63] Talens, P.; Escriche, I.; Martínez-Navarrete, N. and Chiralt, A. (2003). Influence of osmotic dehydration and freezing on the volatile profile of kiwi fruit. Food Research International, 36, 635-642. [64] Tapia de Daza, M.S., López-Malo, A., Consuegra, R., Corte P. and Welti-Chanes, J. (1999). Minimally processed papaya by vacuum osmotic dehydration techniques. Food Science and Technology International, 5(1), 43-52. [65] Torreggiani, D. and Bertolo, G. (2001). Osmotic pre-treatment in fruit processing: chemical, physical and structural effects. Journal of Food Engineering, 49: 247-253. [66] Torreggiani, D.; Forni, E.; Maestrelli, A. and Quadri, F. (1998). Influence of osmotic dehydration on texture and pectic composition of kiwifruit slices. Proceedings of the 11th International Drying Symposium, A: 930-937. [67] Trilling, D.M. and Smadar, Y. (1984). Production of shaped food products. United States Patent. US 4 436 759 (US4436759) [68] Valdez-Fragoso, A.; Welti-Chanes, J. and Giroux, F. (1998). Properties of sucrose solution reused in osmotic dehydration of apples. Drying Technology, 6 (7) 1429-1445. [69] Valero, M.J. (2003). Evaluación de la reutilización de la disolución osmótica en la deshidratación osmótica de piña (Ananas comosus). Proyecto Final de Carrera. Universidad Politécnica de Valencia (Spain). [70] Venant,A.; Borrel, S.; Mallet, J. and Neste, E-van (1989). Gel permeation chromatography as a method for the simultaneous clean up of organochlorine, organophosphate and polychlorinated biphenyl residues in fat extracts. Analusis, 17(1/2): 64-66

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[71] Walter, D.L. and Funk, D.F. (1998). Fabricated fruit pieces and method of preparation. United States Patent. US 5 718 931 (US5718931) [72] Wargovich, M.J. (2000). Anticancer properties of fruits and vegetables. HortScience, 35(4), 573-575. [73] Wolfe, K; Xianzhong, W. and Rui-Hai-Liu. (2003). Antioxidant activity of apple peles. Journal of Agricultural and Food Chemistry, 51(3), 609-614. [74] Wust, U.; Zehle, G. and Huenecke, H. (1985). Manufacture of dried fruit-like products. German Democratic Republic Patent. DD 220 495 (DD220495).

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 339-360 © 2007 Nova Science Publishers, Inc.

Chapter 5

QUALITY ASPECTS OF DEHYDRATED AND REHYDRATED FRUIT IN RELATION TO DRYING METHOD C. Contreras, M.E. Martín-Esparza, A. Chiralt and N. Martínez-Navarrete Food Technology Department, Institute of Food Engineering for Development. Polytechnic University. P.O. Box 22012, 46071. Valencia, Spain

ABSTRACT The development of new attractive dehydrated fruit-based products, to be consumed as dried or rehydrated, with high quality and reasonable shelf-life, will increase and diversify its availability in the market. In this sense, it is necessary to optimize the dehydration operation conditions to achieve not only the maximum process efficiency and control, but also various characteristics in the final product in relation to colour, texture, water activity, nutritive value, etc. Air drying has been the most frequently selected process for industrial food dehydration, due to its efficiency, versatility and easy management. However, it is known that it provokes considerable changes in sensory and nutritional quality. Some research works refer to the advantages of applying microwaves to convective drying associated with the fast volumetric heating of the product due to its high penetration power. On the other hand, the application of certain pre-treatments before drying operation, such as vacuum impregnation or vacuum pulsed osmotic dehydration, could help to enhance the stability and quality attributes, as high temperatures are not employed and specific solutes can be incorporated into the porous structure. In this chapter the advantages of microwave application to convective drying of apple and strawberry are pointed out. These are related to the great reduction in process time and to the fact that they allow obtaining a dehydrated product with a greater resistance to deformation and fracture and a greater stability during commercialization. Nevertheless, its use is not recommendable when the product has to be used or eaten after its rehydration, as the structural damage caused by microwaves decreases the mechanical resistance and the retention capacity of the incorporated liquid phase. The colour of dehydrated or rehydrated product is more affected by microwave treatments when the fruit pigment content is relevant, as occurs with strawberry anthocyanins. Application of

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C. Contreras, M.E. Martín-Esparza, A. Charalt et al a previous vacuum impregnation/osmotic dehydration step with sugared solutions is always recommended.

DEHYDRATED AND REHYDRATED FRUITS, A TRADITIONAL MARKET Most fruits are consumed when fresh, but their market availability is commonly limited by the seasonal production throughout the year and its short shelf life, mainly due to fungal attack and mechanical damage during distribution. Nowadays, the research and development of preservation techniques which lead to high nutritional quality products with the most similar sensorial characteristics to those of fresh food, is of special interest. In this sense, preservation methods that enlarge the shelf-life of the fruits, increasing their market availability, such as freezing, freeze-drying, dehydration, etc., are very useful [1,2]. Nevertheless, all of them induce modification of fruit quality attributes (texture, colour, and flavour) to a different extent. Dehydration is a widely used traditional preservation technique for processing plantbased foods despite the possible existence, in some cases, of adverse effects on nutritional and organoleptic qualities. Several references distinguish between drying and dehydration techniques. Drying refers to leaving food in the sun to reduce water content while dehydration is an artificial drying process [3]. In either case, final water content of about 2 % in the food may be achieved. The advantage of dried or dehydrated fruits is that they are natural foods available at any time, with a longer preservation time than fresh fruit and easier to consume. They are widely consumed as a dried product (powder or snack) or as a semi-moist ingredient in prepared foods and may also be rehydrated before their final use. Traditionally, dried fruits are used in a lot of industrial areas such as pastry, lacteous, hostelry and restoration, dietetics, ecologic, artisan and sports foods, etc. Their several utilities make them very useful in our daily diet, alone or mixed, with milk, chocolate, in cookies, in jams or confitures, etc. Despite good prospects, the trade market of this kind of product is still modest. Nevertheless, if the drying process has been well optimized, the consumption of dried/rehydrated fruit may help to increase the daily intake contributing to a recommended healthy diet. From this point of view, the development of new, consumer-attractive and high quality dried fruit products is desirable in order to widen product availability and diversify the market, particularly as nowadays fresh fruit consumption is below that recommended in the consumers’ diet. Good raw materials are needed to obtain high quality dried products. Nevertheless, the drying conditions must also be optimized to obtain not only economic processes but also the desired product characteristics related to color, texture, water activity, nutritional value, etc. This includes the possible application of some pre-treatments prior to drying itself. Finally, good storage and distribution conditions must be ensured.

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FOOD DRYING PROCESS. APPLICATION OF MICROWAVE TECHNOLOGY The drying process consists of removing part of the water in the product up to a certain threshold value. This allows the product to be stored for a longer period of time, since the activities of the micro-organisms, enzymes and some chemical reactions are slowed down [4]. Different techniques are used to reach this purpose. However hot-air drying is the most common process in the case of fruits and vegetables and implies elimination of water by evaporation. The kinetics of this drying process is limited by the rate at which water diffuses from the interior to the surface of the product from which it is evaporated. The longer or more difficult the diffusion path the slower the drying. Increasing the ambient temperature, thereby evaporating surface water faster, it is possible in some cases to speed up drying. However, this is also limited by the rate at which the interior water can reach the surface [5]. Conventional heat also has a particularly difficult time reaching the wet inner areas, because the dry external surface layers have low thermal conductivity [6]. This may imply the continuous heating of the surface. As a consequence, high temperatures or long drying times with this method may cause serious damage to the product’s flavour, colour and nutrients, reducing bulk density and rehydration capacity of the dried product [7]. Microwave processing has been successfully applied on a commercial scale in some food processes such as cooking (meat and poultry), precooking (bacon), tempering (meat, poultry, fish, butter, fruit), baking (dough), pasteurizing (ready meals and pasta) or drying (pasta, snacks, fruits and vegetables) and results in a substantially reduced processing time leading to increased production capacity [5,7]. In this case, the energy of the electromagnetic waves interacts with water molecules, ions, and other food components (some solutes such as sugar and salt) to generate heat. Weaker interactions occur with other food components, such as fat [8]. The conversion of microwave energy into heat is achieved by dipole rotation and ionic conduction. The relative importance of these two mechanisms of heat transfer depends to a large extent on the temperature, due to its effect on ion mobility and characteristic dielectric relaxation times. At the commonly used frequencies for microwave heating, it is mostly the water component in food that makes heating possible. Microwaves interact with water molecules, which allow them to penetrate the food, although with attenuated energy. Thus heat will be generated not only at the food surface, but within the food as well [8]. This supposes an internal evaporation of water and therefore an internal pressure gradient, which effectively pumps water to the surface. From this point of view, the heating process may be non uniform at lower water contents. Nevertheless, the volumetric heating of the product when microwaves are applied implies a faster drying than in a conventional process. Combined convective-microwave heating may be of interest, as microwaves will selectively heat the wet internal areas, thus increasing moisture transport from inside toward the surface of the product, and the superficial water may be easily removed by hot air. In any case, heat transfer in microwave processes is more difficult to study due to the complex interaction of the microwaves with the food, and a complete engineering description of this process, that includes physical, chemical, biological and sensory aspects, is simply not available. In fact, conventional heating produces a simpler more intuitive and predictable heating pattern than microwave heating [8]. In this sense, this chapter tries to contribute to the study of the effect of microwave application to conventional hot air drying, considering heat

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transfer mechanisms and drying kinetics of the process as well as quality aspects of the products. To this end different drying experiences were programmed with Granny Smith apple and Camarosa strawberry. The mean fresh apple composition was: water content (xw) 0.858±0.005 g/g sample, soluble solids (xs) 0.12±0.04 g/g sample and water activity (aw) 0.991±0.002. Slices (7 mm thick) were obtained perpendicular to the apple axis; these were not peeled but the core was taken out with a cylindrical 20 mm diameter core borer. The mean fresh strawberry composition was: xw 0.912±0.013 g/g sample, xs 0.08±0.23 g/g sample and aw 0.991±0.003. The strawberries were cut in half. The fruits were dried by air drying (AD) and by combined air-microwave technique (A/MWD), using a modified household microwave oven coupled to an analytical balance to control the weight of the sample during the process, and connected to a computer [9]. The equipment allows us to control air temperature, air velocity and microwave power. The air velocity was kept at a constant value of 2.5 m/s inside the cavity and relative humidity ranged between 35-45 %. Apple slices were placed suspended from a support perpendicular to the airflow. Air temperature was 30 ºC and microwave incident power 0.5 W/g. Strawberry halves were placed cut side up on the dryer grid to favour the mass transfer. In this case, air temperature was 40 ºC and microwave incident power 0.2 W/g. All fruit samples were dried to 10 g water/100 g sample and each drying treatment was done in triplicate.

PRE-DRYING TREATMENTS During drying, changes in the quality of foodstuffs occurs, the greater the water elimination or process time, the greater the changes. Nevertheless, the obtained product must satisfy the consumer’s expectations in various quality aspects such as flavour, colour, texture and nutritional value, among others. Several works [10-14] suggest application of pretreatments such as vacuum impregnation and/or osmotic dehydration prior to drying process in order to contribute to the preservation of colour and texture. Vacuum impregnation (VI) of a porous product consists of exchanging the internal gas or liquid occluded in open pores for an external liquid phase, due to the action of hydrodynamic mechanisms promoted by pressure changes [15,16]. The operation is carried out in two steps after the product immersion in the liquid phase. In the first step, vacuum pressure (50–100 mbar) is imposed on the system for a short time (5–15 min), thus promoting the expansion and outflow of the product’s internal gas. In the second step the atmospheric pressure is restored in the system and compression leads to a great volume reduction of the remaining gas in the pores and so to the subsequent inflow of the external liquid in the porous structure. Compression can also reduce the pore size depending on the mechanical resistance of the solid matrix. Coupling of deformation and impregnation in the sample depends on the characteristic times of each phenomenon, which are respectively defined by the sample mechanical properties and by the pressure drop during the flow of the liquid into the pores [12]. In the last few years, the application of VI has been claimed as a useful way of introducing liquids into the porous structure of some foods [17,18]. In this way, it allows the embedding of a solution with some specific solutes into the porous product in order to adapt

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its composition to certain quality or stability requirements (acids, criopreservative agents, vitamins, minerals, browning substances, sugar, salt, etc.). With this procedure, not only the product composition but also its physical and chemical properties may be changed in order to improve some food characteristics [19]. In this sense it has been used to introduce calcium salts into peeled apples, tomatoes or potatoes in order to increase parenchimatic tissue firmness [20,21], extend the shelf-life [22] and prevent structure collapse [23]. Besides, mass transfer is improved without modifications of the original cellular structure, leading to shorter process times such as in cheese salting [24], cod salting and desalting [25,26] or ham curing [27]. The osmotic dehydration (OD) of fruits is based on the principle of the natural cell wall acting as a semi-permeable membrane. In OD, a cellular tissue is immersed in a concentrated solution of sugars or salts in order to promote intra-cellular water loss, due to differences in the water chemical potential established between the external solution and the internal liquid phase of the cells. Nevertheless, due to the open structure of the tissue in the intercellular spaces and to the presence of possible damaged external cells during product manipulation, diffusion of internal and external solutes also occurs. This contributes to a net opposite flux of water and solutes that allow the tissue to become concentrated with a determined ratio solute gain/water loss, depending on process conditions. In addition to mass fluxes in the tissue, structural changes and cell alteration also occur. These phenomena provoke changes not only in the macroscopic properties of the sample, such as optical and mechanical properties, which are related to product appearance and texture, respectively, but also in cell physiology and biochemical reactions, which in turn can provoke several chemical modifications in the tissue [28]. Hydrodynamic mechanisms are promoted to a great extent when pressure changes are imposed on the system such as in VI processes. Vacuum treatments have an important effect on water transfer during the OD. When VI is applied at the beginning of an OD treatment, the process is called pulsed vacuum osmotic dehydration (PVOD). OD under vacuum makes it possible to obtain a higher diffusional rate of water transfer. Several researches could be consulted to increase knowledge of VI/DO/PVOD processes and their mechanism transport, mass transfer, modelling or the response of some properties of fruits to these processes [1517,29,30]. In recent years, OD/PVOD of fruits, as an alternative intermediate step or as a pretreatment technology, has received increasing attention in the field of fruit preservation processes in order to reduce energy consumption and improve the quality of the fruit product [31,32]. Different studies have been carried out related to the use of osmotic processes to obtain several kinds of fruit products or food ingredients such as minimally processed or intermediate moisture fruits [33], or to their application as a pre-treatment in air drying [3437] or freezing [38,39]. When applied prior to drying, the osmotic step has been found to reduce the degree of structural collapse which occurs during air drying, as reflected by a 2530 % increase in the final sample volume. As some examples, the work of [36,37], carried out with peach cubes pre-treated by using 60 % sorbitol osmotic solution supplemented with 1 % ascorbic acid and 0.5 % NaCl showed a lower structural collapse, retaining better surface smoothness. Colour stability during air dehydration was also improved by the osmotic step with sorbitol showing the highest protective effect. On the other hand, when studying air drying and the combination osmotic-air drying of strawberry, the pre-dried samples showed a much better tissue organization than the strawberries frozen without pre-treatment with a greatest texture improvement of the fruit after thawing [40].

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In this study, the influence of VI/OD was also considered when applied prior to AD and A/MWD processes. To this end, some additional experiments were carried out with apple and strawberry. Apple slices were impregnated with a commercial and isotonic apple juice (same aw as fresh apple) by applying vacuum pressure (50 mbar) for 5 minutes and then restoring the atmospheric pressure, keeping the samples immersed in the isotonic solution for 10 more minutes (VI samples). Strawberry halves were pre-treated with a osmotic dehydration step, carried out with 55 ºBrix sucrose solution at 20 ºC, under stirring conditions, by applying a vacuum pressure pulse (50 mbar) for 5 minutes and then restoring the atmospheric pressure, keeping the samples immersed for 3h more (PVOD samples). A mass ratio 1:20 for fruit sample:osmotic agent was used. After these processes, samples were taken out of the corresponding solutions and gently blotted (with a paper towel) to remove adhering solution. These pre-treated samples were submitted to the same drying processes as non pre-treated ones. The xw, xs and aw of samples were controlled before and after pre-treatments. As compared to raw material, VI of apple with isotonic solution did not change these properties significantly, being in this case xw (0.862±0.003 g/g VI sample), xs (0.12 ±0.02 g/g VI sample), and aw (0.992±0.002). Nevertheless, due to the osmodehydration, the xw and aw of strawberry decreased to 0.863±0.009 g/g PVOD sample and 0.985±0.002, respectively, and xs increased to 0.121±0.012 g/g PVOD sample.

HEAT TRANSFER MECHANISM AND DRYING KINETICS Microwave application has been shown to significantly reduce convective drying time of fruits and vegetables [41-43]. In this study, the time implied in combined convectivemicrowave drying treatments was around 75-85 % shorter than corresponding convective air drying times (table 1). Pre-treated samples increased the convective drying time process up to 10 % due to the increase in its liquid phase volume. Convective-microwave drying time was not influenced by VI or PVOD pre-treatments. A kinetic study was carried out in order to propose accurate models for both drying processes. To this end, registered continuous weight data and initial water content of pretreated or non pre-treated samples were used. Kinetic behaviour was dependent on drying method. In all AD samples a falling rate period was identified, as corresponds to an internal control of mass transfer, governed by intrinsic product properties and the internal resistance to water diffusion [44,45]. Nevertheless, all A/MWD samples showed two differentiated behaviours. Initially, a relatively constant period was observed. It can be justified if water evaporation on the product-air interface occurs at a similar rate to water diffusion from the inner to the product’s surface, due to the vapour partial pressure gradient generated by internal water evaporation. With the drying progress, the lower mobility of the remaining water molecules induces a decrease in the microwave power absorption. From this moment, air drying begins to play a relevant role and a second falling rate period is observed. The common and easy to interpret semi-empirical Page’s equation [46], described by Eq. 1 was used to describe the convective drying kinetics of apple slices and strawberry halves.

Quality Aspects of Dehydrated and Rehydrated Fruit...

(X tw − X ew ) (X ow

− X ew )

345

= exp (− k * t n )

(1)

where Xw is the water content (g/g dry matter) with superscripts: o (initial condition), t (at time t) or e (at equilibrium condition), t is time process (h), k is the drying constant (h-1) and n is a dimensionless exponent. As in drying experiences carried out the values of the equilibrium moisture content were much smaller than Xwo, Xwe may be assumed to be zero. The fitting of Ln (-Ln (Xwt/Xwo) versus Ln (t) allows obtaining k and n parameters (table 1). Related to A/MWD samples, the initial drying constant period was described through a simple linear equation (Xwt= Xwo-b*t). The falling rate period was also modelled by Page’s equation (table 1). Table 1. Process time (t), kinetics constants (k, n and b), and root mean square deviation (RMSD) for each drying treatment. Values are expressed as mean±standard deviation Strawberry

Apple VI AD

AD

PVOD AD

AD

t (h)

28.9±0.8

17.9±0.3

41.4±1.2

37±2

k (h-1)

0.121 ±0.016

0.20±0.03

0.09±0.02

0.108±0.009

1.02±0.04

1.03±0.02

0.98±0.02

0.99±0.05

0.0024

0.0027

0.0046

0.0074

n (1)

RMSD

VI A/MWD

A/MWD

PVOD A/MWD

A/MWD

t (h)

3.81±0.12

3.9±0.3

10.9±0.9

9.5±0.6

b (h-1)

2.6±0.3

2.5±0.3

1.16±0.06

2.05±0.14

k (h )

1.48±0.03

1.42±0.24

0.57±0.13

0.577±0.114

n

0.97±0.04

1.08±0.02

0.91±0.11

1.01±0.17

RMSD(1)

0.0076

0.0039

0.0026

0.0033

-1

(1)

RMSD =

(

1 z t t ∑ X w −exp, i − X w −pre, i z i=1

)

2

where Xwt-exp,i is the experimental water content, Xwt-

is the predicted water content and z is the observation number. Corresponds to the highest RMSD value, among replicates, for each drying treatment.

pre,i

During convective drying, pre-treatment significantly decreased k drying constant, related to the increase in drying time commented on above. This can be explained by the increase in the liquid phase volume promoted by pre-treatments, especially in apple where no simultaneous dehydration occurs. The increase on sugar concentration in the intercellular spaces of pre-treated samples during drying can also contribute to difficult water transport. No influence was observed on n parameter. For combined air-microwave drying, pre-

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treatment only affected significantly during the first strawberry drying period, resulting in lower b values (slower rates).

QUALITY OF DEHYDRATED AND REHYDRATED FRUIT Quality characteristics of dried fruits are very different to those of fresh ones. An important aspect that must be considered when optimizing the drying process is the nutritional value of the product. In the case of fruits, the maximal retention of organic acids, minerals, vitamins, fibre, phytochemicals, etc. has to be an objective in order to ensure its functional role in the human diet. Nevertheless, despite the interest of consumers in the healthy aspects of foods, other quality parameters are implied in the selection of the preferred ones. In this sense, product appearance is of great interest and customer depends mainly on shape, size and optical properties (colour, translucency and brightness). Optical properties of tissue predetermine our expectation of both flavour and quality, because they inform about another aspects of the product such as maturity, sanitary state, pigment’s concentration, etc. Thus, it is important not to underestimate the influence that these physical properties have on the consumer. When colour deterioration occurs extensively, it results in visually unacceptable products. From a physical point of view, optical properties depend on the way in which the food interacts with the visible electromagnetic radiation. Structural and compositional aspects are implicated in such interaction and so, despite the great influence of pigments presence on colour, they are not the only element responsible for visual perception of this property. Food texture is also a determinant factor in positive consumer opinion. The main factors that contribute to mechanical properties of plant tissue are cell turgor, which is one of the most important ones, cell bonding force through middle lamella, cell wall resistance to compression or tensile forces, density of cell packaging that defines the free spaces with gas or liquid, and various other factors common to other products, such as sample size and shape, temperature and strain rate [47]. Fruits and vegetables soften when heated partly due to the loss of turgor, but also due to a variety of enzymatic and chemical changes in the cell wall matrix polysaccharides [48]. In development of new products, colour and texture are important quality parameters because consumers have increased their expectations and requirements for attractive colours and different textures.

Optical Properties Optical properties of the fruit may change considerably during any drying process. The causes of these changes are of chemical and physical nature: (1) Water loss implies an increase in the effective pigment concentration, which could enhance selective light absorption [28]; (2) Degradation or loss of fruit pigments and development of browning during pre-treatments or drying process; (3) Exchange of internal gas in the pores for external liquid near the sample surface due to the action of hydrodynamic mechanisms which may occur during VI/PVOD pre-treatment. Changes in pigment concentration is mainly related to

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colour changes, whereas the partial substitution of gas by liquid induces a more homogenous refractive index in the tissue, which promote light absorption against scattering, the product thereby gaining transparency. Changes in product translucency induced by a process can be analysed by the spectral distribution of Kubelka Munk coefficients (ratio between light absorption (K) and scattering (S)), obtained from reflectance spectra when measured on black and white backgrounds [49]. When the mentioned spectra were obtained from the samples considered in this study, fresh apple and strawberry samples, as well as pre-treated strawberry, behave as an infinitely thick layer as no influence of the background was detected in reflectance measurements. Nevertheless, VI apple slices showed translucency, due to the commented effect of air exchange for impregnation liquid (figure 1). VI also affected fresh apple CIEL*a*b* colour co-ordinates (figure 2), promoting a significant L* (lightness) decrease due to the greater light penetration depth in the less opaque samples. Moreover, a* values increased to less negative values, thus decreasing the greenness of sample, and b* values decreased turning to less yellowness. This promoted a significant lower chrome of VI samples although hue angle was not affected. The global colour difference between fresh apple before and after VI pretreatment (ΔE= [Δa*2+Δb*2+ΔL*2]0.5) was ±31. In strawberry (figure 2) the previous osmotic dehydration supposed a significant decrease of a*, b* and chrome, whereas luminosity and hue angle were not affected. In this case ΔE was ±10.

Figure 1. Spectral distribution of Kubelka-Munk coefficients (K/S) of fresh and vacuum impregnated (VI) apple samples before and after drying treatment.

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Figure 2. L* values and a*,b* chromatic diagram of fresh (○) and dried samples (∆ AD, □ A/MWD). Hollow symbols pre-treated samples; full symbols non pre-treated samples.

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Drying treatments decreased translucency of pre-VI apple and promoted changes in measured colour co-ordinates (figures 1 and 2). AD or A/MWD dried apple slices showed higher L*, a* and b* values than initial fresh fruit. In samples without pre-treatment, the shorter process time needed when microwave application, provoked less global colour changes (ΔE= ±11) than in the convective process (ΔE= ±15). In pre-VI samples, the increase in sugar amount and a possible no enzymatic browning may be responsible for the observed highest ΔE value with respect to the fresh non pre-treated sample (±17 in A/MWD and ±22 in AD samples).This greater colour change could be a negative characteristic in the obtained product and so the use of this pre-treatment will be limited when the principal objective of the process is to obtain a final product with a similar appearance to raw material. For dried strawberry halves (figure 2), the clearest effect was the increase in sample lightness promoted by microwaves application (greater L* values), which could be related to the sample discoloration at surface level in line with the higher temperature reached in the sample during drying. Nevertheless, hue angle was not affected by drying treatment, which could be an advantage for the product’s acceptance. On the other hand, the colour of fruits is due to the presence of pigment groups, such as chlorophylls, carotenoids, anthocyanins, etc. They are susceptible to colour deterioration during heat treatment. In this sense, the thermal process optimisation may be desirable to prevent degradation of natural pigments. Stability of strawberry anthocyanin during drying process was studied, considering the pelargonidine-3-glucoside (Pgd) as the major anthocyanidin of this fruit [50]. Fresh strawberry showed 34±6 mg Pgd/100 g fresh sample. Neither PVOD nor AD treatments affected the content of the studied pigment, whereas microwave application induced a significant anthocyanin degradation (20±4 mg Pgd/100 g fresh sample), probably associated with the higher temperature reached by the samples in this case. No significant correlations were observed between CIEL*a*b* colour co-ordinates and anthocyanin concentration. This agrees with that reported by other authors [51] and can be explained by the heterogeneous distribution of pigments in the fruit and the influence of the surface structure on reflectance. Nevertheless, the higher L* values in air-microwave dried samples may be related to the suggested anthocyanin degradation caused by microwaves on the sample surface, where reflectance was measured.

Structural Aspects Thermal treatment of fruits may cause important structural modifications, which are related to alterations in cell wall and middle lamella components. These accumulative changes could result in different tissue texture. The mechanical properties are closely related to the developed structure as a result of induced deformations in cells (shrinkage/swelling) and intercellular spaces, ruptures of cellular bonds and changes in cell wall polymers taking place throughout the drying process. Also the glassy or rubbery physical state achieved by the product’s liquid phase will affect the mechanical response. On the other hand, behaviour of dried samples during rehydration will be closely related to structural damage occurred during drying. An approach in this sense has been carried out with the samples considered in this study, analyzing all these aspects before and after drying treatments.

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As to the structure and functional properties, pectin is very important when compared to other cell wall polymers because of its chemical sensitivity and thermal vulnerability. Pectin is an important component on the primary cell wall and middle lamella and it has a relevant structural role. The dominant feature of pectin is a linear chain of α-(1,4)-linked Dgalacturonic acid units in which varying proportions of the acid groups are present as methoxyl (methyl) esters. Pectin may be broken down into pectinic acid and finally pectic acid. Galacturonic acid units compose more than 65 % of pectin structure. Pectin may be separated into calcium sensitive and non calcium sensitive fractions. The former appears to have blocks of galacturonic acid which are de-esterified and are believed to come from the middle lamella region, whilst the non calcium sensitive pectin comes from the primary cell wall [52]. Two different pectin fractions, oxalate soluble (OSP) and water soluble (WSP) pectin, may be analysed by selective extraction procedures of the total pectin (TP), as described by [53], and respectively related to them. Galacturonic acid (GalA) content in the different fractions may be determined colorimetrically at 520 nm by using the mhydroxydiphenyl method [54]. The difference between TP and the sum of WSP and OSP, analyzed with mentioned procedures in samples under study, was taken into account to estimate the amount of non extractable pectin (NXP), which was the protopectin fraction. Different kinds of fruits contain different types and quantities of pectin. For example, apple or banana contain high total pectin content (in the range of 0.5 to 1.6 g/100 g), while soft fruits, such as cherries and strawberries contain lower amounts (0.2-0.7 g/100 g) [55]. The TP in apple and strawberry used in this study was 0.844±0.003 and 0.53±0.08 g GalA/100 g of fresh sample, respectively. Of the whole pectin content of apple, about 55 % corresponds to NXP, 37 % to WSP and 8 % to OSP. A different behaviour was observed in strawberry, for which the OSP represent the major pectic fraction in the fresh sample (about 53 %) and the NXP the lower one (about 19 %). The lower esterification degree of strawberry pectin is responsible for the greater amount of OSP. This pectin fraction can bind calcium forming a cross-link structure [42]. In this sense, calcium-treatments contribute to maintain structural integrity of fruits with a high OSP fraction [56]. After drying process pectin content was again analysed in samples with and without pretreatment and compared with initial raw material. The difference between the pectin content in dried and fresh samples, in both cases referring to the mass of the corresponding initial fresh sample, is shown in figure 3. Drying processes induced a pectin solubilisation, as a decrease in NXP was quantified. Also an effect on the calcium pectin bonding occurs, thus decreasing the OSP fraction. These changes contribute to the observed WSP increase. Both pre-treatments and microwave application enhance the observed changes. On the one hand this may be a consequence of the temperature effect [13], so that the greater heating reached in microwave treated samples will favour pectin solubilisation. On the other hand, as a result of the vacuum step during pre-treatment, intercellular spaces become full of an aqueous phase which will be in contact with middle lamellae, thus favouring the pectin solubilisation phenomenon during subsequent drying process. From these results it can be concluded that the pectin fractions of non pre-treated air dried samples will be the less affected, while the pre-treated ones dried with microwave application will change the most. Nevertheless, if the sugar content of the sample is increased during pre-treatment, as in the case of PVOD strawberry, no additional pectin solubilisation occurs during microwave treatment, probably due to a certain pectin-sugar interaction that will protect it from the changes induced by the

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high temperatures. In fact, it has been reported in a previous work [57] that cells protected by sugars exhibited less damage to the middle lamella and less severe shrinkage during drying.

Figure 3. Difference in diverse pectic fractions between dried and fresh samples. (■) WSP, water soluble pectin; (■) OSP, oxalate soluble pectin; and (□) NXP, non extractable pectin.

As commented above, both drying treatments and pre-treatments applied to studied samples promoted changes in native solutes, due to changes in pectin composition and increase in the sugar content of the samples. Changes in the average molecular weight of the solutes present in the fruits liquid phase may affect the glass transition temperature (Tg) that characterises the glassy to rubbery state transition [58,59]. Glassy and rubbery are amorphous states, characterized by a disorderly molecule state presenting a metastable configuration, achieved when imposed changes to the system occur faster than crystallization rate. In a

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drying process, when the solubility limit of solutes is achieved, its separation in the form of crystals is not frequent due to kinetic problems. In this situation, the remaining liquid phase will be in a rubbery or glassy state depending on the final temperature and water content of the product. As both are non equilibrium states, they may evolve to a crystalline stable state, the greater the temperature or the water content, the quicker the evolution. Nevertheless, on a kinetic level, the glassy state is considered to be much more stable than the rubbery one, which shows a greater molecular mobility. In fact, in practical terms related to foods, the glassy state may be considered as much stable as the crystalline one. Changes in molecular mobility associated with the glass transition are related to changes in mechanical and diffusional properties of foods [59]. In order to analyse the effect of pre-treatments and drying treatments applied to apple and strawberry on the physical state of the remaining liquid phase, the Tg of dried products was analysed by differential scanning calorimetry (DSC 5200Co, Seiko Instruments). Obtained results are shown in table 2. Significant differences were detected among samples dried with different treatments. As the final water content of the samples was of the same order, differences may be explained taking into account the final composition of the solutes present in dried samples. Table 2 shows the soluble solid fraction (xs) determined in the samples submitted to DSC analysis, as well as the WSP fraction (xWSP). Soluble solid fraction was determined from the soluble solid fraction in the liquid phase (ºBrix/100) of pre-treated or fresh samples and applying the corresponding mass balances for drying operation. As can be observed in table 2, osmotically pre-treated strawberry samples showed a significantly greater soluble solid content due to the external sucrose gain. Nevertheless, the ratio xWSP/xs was lower in pre-treated ones. From these data a lower average molecular weight seems to correspond to soluble compounds of liquid phase in PVOD pre-treated samples. This fact agrees with the lower Tg values observed for them. In non pre-treated apple samples, the Tg value was in the expected range taking into account the xWSP/xs value, related to strawberry ones. Nevertheless, in the case of vacuum impregnated apple, the Tg of samples was much greater. Other compounds with higher molecular weight comming from commercial apple juice used for sample impregnation, could contribute to the behaviour of aqueous phase in these cases. These samples were the only ones in a glassy state at normal storage temperature of the dried product. Table 2. Soluble solids (xs), water soluble pectin fraction (xWSP) and glass transition temperature (Tg) for dried samples. Values are expressed as mean±standard deviation Sample Apple VI AD VI A/MWD AD A/MWD Strawberry PVOD AD PVOD A/MWD AD A/MWD

xs xWSP (g/g dried sample)

xWSP/xs

Tg (ºC)

0.63±0.03 0.66±0.04 0.69±0.02 0.77±0.04

0.021±0.003 0.023±0.002 0.019±0.002 0.022±0.003

0.032±0.03 0.035±0.04 0.027±0.02 0.028±0.02

39.3±0.4 40.3±0.3 3.12±0.12 4.51±0.14

0.82±0.07 0.81±0.08 0.71±0.06 0.69±0.05

0.022±0.002 0.019±0.002 0.024±0.002 0.028±0.003

0.025±0.004 0.023±0.003 0.034±0.03 0.041±0.004

-1.0±0.3 -1.6±0.3 3.1±0.7 6.3±0.6

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As has been described, changes in pectin solubility occurring during drying may affect the cell bonding forces supporting the cellular structure and thus the mechanical behaviour of dried and rehydrated samples. Also the glassy or rubbery state will affect. Mechanical response may be interpreted by means of some physical parameters measured through a puncture test with a Universal Texture Analyzer (TA.XT2, Stable Micro Systems). For this purpose, a cylindrical 2 mm diameter punch was used at a penetration rate of 1.5 mm/s until total sample penetration. Temperature during the test was 25 ºC. Parameters obtained from the force-penetration depth curves were: the maximum force required to punch the sample (Fmax) and the slope (Si) of the curve in the linear zone prior to fracture point. The peak of maximum force is related to the product resistance to fracture or sample firmness [60] and the slope of the curve is related to sample resistance to deformation (rigidity). Fresh sample behaviour was considered in order to compare the mechanical response of dried samples with the initial fruit performance. The force to penetrate fresh apple increased to Fmax which kept relatively constant until the end of the test (7.8±0.9 N). For fresh strawberry halves, two fracture peaks, related with the resistance (firmness) offered by the strawberry epidermis (1.3±0.2 N at a penetration depth of 1.5±0.2 mm) and the pulp (2.3±0.6 N at 12.1±0.9 mm), to punch advance were observed. After drying process (figure 4), samples showed a viscoelastic behaviour with a greater deformability (lower Si) before fracture, and with only one fracture peak followed by an abrupt fall in force, regardless of kind of fruit and drying conditions. Higher values of slope and maximum force were obtained for pre-treated samples and also for A/MWD samples. Both process conditions promoted the dried sample mechanical rigidity and resistance. However, microwave influence was not significantly evident in VI samples. Gas-liquid interchange occurred during vacuum impregnation of samples, also as the increase in sugar content in osmodehydrated samples, could favour the generation of an extra compact cell matrix during drying. The greater pectin content detected in the aqueous phase in A/MWD samples will agree with the more rigid, less deformable observed structures.

Figure 4. Continued on next page.

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Figure 4. Force-penetration depth curves for air (a) and air-microwave (b) dried samples.

The different impact of treatments on cellular structure may also be observed in rehydrated samples (in distilled water at 20 ºC for 8h). To this end, the same mechanical test conditions were used but punch diameter was 4 mm. Microwave applications, in either pretreated or non pre-treated samples, implied a significant decrease in the mechanical parameters, in agreement with the observed changes in pectin fractions by microwave action. As has been commented, the microwave favours the WSP increase. This has been related to a greater mechanical resistance of dehydrated samples, due to the higher viscosity of the liquid residual fraction. Nevertheless, the water incorporated during rehydration may attenuate the compositional effect of the liquid phase. However, the OSP fraction, which decreased with this treatment, confers resistance to the middle lamella and cell wall. This could have a more relevant influence in the mechanical response of the rehydrated samples. A better mechanical response of rehydrated pre-treated samples than non pre-treated ones was observed, especially for strawberry halves, despite the fact that the decrease in OSP was greater in these cases. This seems to indicate that pre-treatment helped to preserve the fruit matrix structure much better whereas microwave application provokes a greater structural alteration. In rehydrated apple samples, the rehydration ratio (RR) and the capacity of liquid phase retention (CLPR in Eq. 2) were evaluated (table 3). The RR was expressed as the weight ratio between the rehydrated and dehydrated samples [46]. The rehydration study was not carried out with strawberry because an important colour loss was observed.

CLPR =

(

)

rh M rh * x rh w + xs − mL

(

M o * 1 − x ow

)

(2)

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where M is the sample weight (g), xw is the water content (g/g), xs is the soluble solid content (g/g), mL is the liquid phase weigh (g) after sample centrifugation (10 min at 4000 rpm). Superscript o corresponds to the sample prior to dehydration and rh to the rehydrated sample. Table 3. Rehydration ratio (RR) and capacity of liquid phase retention (CLPR) for dried apple samples. Values are expressed as mean±standard deviation Sample VI AD VI A/MWD AD A/MWD

RR 0.65±0.02 0.70±0.03 0.569±0.017 0.63±0.02

CLPR (g LF/g dry solid) 2.02±0.09 1.68±0.08 2.62±0.10 2.32±0.08

VI and A/MWD samples showed the greater RR and lower CLPR values, associated with promoted structural changes. Both the effect of vacuum pressure during pre-treatment and the porous structure generated when microwave is applied [61,62] will explain the greater water incorporation but also the lower capacity to retain the liquid incorporated.

CONCLUSION Microwave application to hot air drying provokes a great reduction in process time and changes in pectin solubility, resulting in an increase in the water soluble fraction and a decrease in both the oxalate soluble fraction and the residual pectins. As a consequence, when applying microwaves, dried strawberry and apple showed a higher mechanical resistance. However, the structural damage associated with dehydration is reflected in the lower mechanical resistance of the corresponding rehydrated samples and also in a lower capacity to retain the liquid phase. On the other hand, microwave application induces an increase in luminosity of the strawberry, which shows an anthocyanin content loss. Related to pretreatments, vacuum impregnation of apple increases translucency and highlights the colour changes that take place during drying, although the osmotic pre-treatment did not significantly affect the strawberry colour. Both kinds of pre-treatments implied higher process times when using convective drying but no significant differences were found when applying microwaves. Nevertheless, both promoted pectic solubilization which, together with the higher sugar content when dried, could explain the higher mechanical resistance observed in the pre-treated samples when dried. At the same time, the sugar added during the osmotic pretreatment of strawberry contributes to a better mechanical response of the tissue when rehydrated. Taking into account all these considerations, it is recommended to use these pretreatments and microwave application to hot air drying in order to obtain dried apple or strawberry with a high mechanical resistance, although colour changes would be greater in these cases, especially in apple. The higher the sugar introduced into the product with the pretreatment, the higher the resistance of the dried fruit to fracture. If the objective of the process is mainly to obtain a product with good mechanical response to further rehydration, pretreatments are recommended as well, but not microwave application to convective drying.

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ACKNOWLEDGEMENTS The authors would like to thank the Spanish Ministry of Education and Science and the European Regional Development Fund for financial support throughout the project AGL 2005-05994.

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In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 361-396 © 2007 Nova Science Publishers, Inc.

Chapter 6

PEST CONTROL USING HIGH PRESSURE CARBON DIOXIDE AS AN ADVANCED TECHNOLOGY Mustafa Bayram University of Gaziantep, Faculty of Engineering, Department of Food Engineering, 27310-Gaziantep-TURKEY

ABSTRACT Food products are always under the risk of infestation by pests. In view of the competitive markets, there has been increasing demand for quality in foods in terms of freedom from pest and pesticide contaminants. Also, it is very important for trade purpose suffer economic and quality losses. Zero tolerance of insect pest in foods has been adopted in some of countries and there is a tendency to achieve this goal in overall the world. The governments, the food industries and exporters are dependent on fumigation as a quick and effective tool for insect pest control in food commodities. Fumigants are widely used for pest elimination in these commodities. Toxic substances have therefore been used to destroy for example pests, as well as their eggs, larvae, cocoons and adults. Currently used substances, such as methyl bromide, hydrogen phosphide, ethylene dioxide, malathion etc., are characterized by more or less serious problems. In recent years, that fumigation technology based on the chemical control of products has been facing threats/constraints because of regulatory concerns, the development of resistance, handling hazards, residues, food safety, cost, carcinogenicity, involvement in ozone depletion, resurgence, environmental pollution and other factors. Reliance upon fumigation as an overall solution to infestation problems in food products has become questionable. The chemical action of fumigants upon commodities and the environment has necessitated the withdrawal of many fumigants from the market. Also, some of them are being phase out their uses at the international level. Due to becoming the target of increasing criticism of toxic substances, such concerns have led to the development of non-chemical methods for the control of insect pests that infest food commodities. One such method is the high pressure carbon dioxide application, which mainly involves the use of CO2 at high pressure (10-40 bar) for food fumigation. It is a new effective, non-chemical, non-residual, safe, fast and environmentally friendly method for the food industry. It has been generated and

362

Mustafa Bayram developed within last 20 years. Carbon dioxide is a fumigant and being used to

control pests in the food industry. After extensive testing, high pressure carbon dioxide fumigation can be accepted as the advanced pest control technology for the future. Nowadays, it is particularly indispensable for the gentle, safe, natural and organic food products. If operation time for the fumigation is constraint and non-chemical treatments are required, this technique is suitable for conventional products.

INTRODUCTION Storage of food in safe must maintain quality and quantity. In order to obtain this safe storage, food products should be protected from environmental effects, microorganisms, high moisture, high temperature, insects, rodents, animals, mites, odors and contaminations etc. Pests can cause structural damage and threaten food safety and employee health. Protecting consumers from pathogens that cause food poising, pneumonia and conjunctivitis is serious matter. The one of the most important defects caused during storage is insect damage. The destruction also begins before storage and then continues during storing with the rate of quality loss depending on storage conditions. If all contaminations is prevented, that rate is slowest when the food is driest and coolest, since growth rate of insect depends on moisture and temperature. Insects are a hazard to stored food products in several ways. For example in grain industry, some devour whole kernels, others consume broken kernels and dust. All cause rises in grain temperature and moisture. All contaminate the grain, and particles of insects may get into grain products where, though harmless, they are aesthetically objectionable (Bailey, 1974). Grain-infesting insects are very sensitive to temperatures. They multiply slowly or not at all below 15.56oC, and they cannot survive in temperature of 41.67oC or above. They appear to thrive best at about 29oC, and at that level their life cycles may be as short as 30 days. These insect may get into grain in the field, harvesting machinery, farm bins or trucks, country elevators, or during bin-site storage, so that after 80 days of storage at temperatures above 21.11oC any lot of stored grain is likely to show evidence of insects. Insects infesting stored products can be controlled by different ways (Bailey, 1974).

INSECTS Insects are a major cause of loss in stored food products. They not only consume these materials but also contaminate them with insect fragments, feces, webbing, ill-smelling metabolic products, and with a variety of microflora; they therefore constitute a major sanitation and quality-control problem (Cotton and Wilbur, 1974). Insects may be classified in different groups. However, it will be suitable to classify them within two groups such as insects that develop inside and outside food. For example, weevils deposit their eggs inside food; lesser grain borers and Angoumois grain moths deposit eggs outside of, and their newly hatched larvae promptly tunnel into, the kernels. Bran bugs, flour and bran beetles are insects that grow outside of the product. Their eggs usually are laid

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indiscriminately among the kernels or throughout the product. For the most part the larvae are free-living, though several species tunnel under the germ covering where the larvae develop as “hidden infestation” (Cotton and Wilbur, 1974). Insects require optimum condition to continue their life like other organism. However, they are very resistant organisms, and they exist over the world before human. In addition, their species are very much. The most important food-infesting insects live inside the foods during much of their lives, it is difficult for food handlers to determine the extent of infestation in their products. There are, however, various techniques and procedures that can be of great help in evaluating infestations. The simplest and most practical determination can be done using visual techniques (by eye, magnifier or microscope), stains, specific gravity separation methods, radiography (X-ray), ninhydrin-impregnated paper, measuring temperature, crackingflotation, measuring carbon dioxide production, measuring uric acid content, traps, ELISA, NMR, NIR, acoustic and aural etc. (Cotton and Wilbur, 1974; Rajendran, 2003). A few of more than 100 species of stored-product insects and mites found in product cause serious damage; the others are fungus feeders, scavengers, predators and parasites (Abramson et. al., 2001). Insects have four life stages: egg, larva, pupa and adult (figures 1 and 2).

Egg The eggs may be laid either in the crevices of foods or in the dust and refuse within storage areas. Some species, such as granary weevils, lay their eggs inside kernels.

Larva The larva is the only stage during which the insect grows. It consumes several times its own weight in food, and as the larval skin cannot stretch, it periodically moults allowing it to increase in size. Cast-off skins found in foods indicate that insects are, or were, present.

Pupa The pupa, which forms after the last larval molt, does not feed. In some species, the pupa is enclosed in a cell, or cocoon, constructed by the larva. During the pupal stage, the insect undergoes extreme internal and external changes that lead to the development of the adult.

Adult Adults of stored-product insects are between 0.1 and 1.7 cm long. They have three pairs of legs and their bodies are divided into three parts: head, thorax and abdomen. The head includes the mouthparts and sense organs; the thorax bears the legs and wings; and the

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abdomen contains the reproductive organs. Adults move in the spaces between food particles and can penetrate deeply into a bulk of products, with the exception of moths and spider beetles. Some stored-product insects can fly and are widely distributed. Beetles have poorly developed wings and some species are unable to fly, although the rusty grain beetle, the red flour beetle and the lesser grain borer fly well.

Figure 1. Life cycles of stored product insects: A, a beetle and B, a moth (Source: Abramson et. al., 2001).

Figure 2. Stages of vermin (egg, larva, pupa and adult, respectively) (DKSH, 2007).

Stored-product beetles often appear similar but have differing behaviour patterns and status as pests. It is important to determine which species are present before taking remedial action. A detailed identification guide is now available (Bousquet, 1990) to help determine which species are present. The characteristic features of the main beetle species occurring on stored products are as follows (Abramson et. al., 2001):

Rusty Grain Beetle This beetle (figure 3, Plate Ia, b) is the most serious pest of stored product. It usually feeds on the germ (embryo) part of a whole seed. Heavy infestations cause grain to spoil and heat. The adult is a flat, rectangular, shiny, reddish-brown beetle, 0.2 cm long and has long, bead-shaped antennae that project forward in a “V”. It moves rapidly in warm and can fly when the air temperature is above 23°C. Eggs are laid in the crevices of kernels and in grain

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dust. The tiny larvae penetrate and feed on the germ of damaged kernels. Eggs become adults in wheat in about 21 days at 14.5% moisture content and 31°C.

Sawtoothed Grain Beetle These beetles (figure 3, Plate Ic) are more common in oats than in wheat, barley or canola. The adult is brown, is about 0.3 cm long, and has six tooth-like projections on each side of the thorax. In warm grain it takes about 22 days to develop from egg to adult under optimal conditions of 31 to 34°C and 14 to 15% moisture content.

Indianmeal Moth This moth (figure 3, Plate Id) is common on corn and processed feeds and foods, and throughout the country in warehouses and stores.

Granary Weevil This weevil (figure 3, Plate Ie) is one of the most destructive pests of stored grain in the world. The adults have a distinctive snout, with which they bore into grain kernels. The female deposits a single egg in a hole in each kernel and then seals the opening with a gelatinous plug. The larvae feed on the endosperm and complete their development within the kernel. The pupae develop into adults that chew holes in the side of the kernels as they emerge. Development from egg to adult takes 25 to 35 days under optimal conditions of 26 to 30°C and 14% moisture content. The granary weevil adult is about 0.3-0.4 cm long and cannot fly. When disturbed, they fold their legs under their body and appear to be dead.

Rice Weevil This weevil (figure 3, Plate If) has been found in storage and in some prairie elevators in recent years. It is 0.2 to 0.4 cm long and has four distinct reddish orange spots on the wing covers, which are folded over the abdomen. It completes development from egg to adult in 28 days at 30°C and 14% moisture content. Adult rice weevils can fly, and attack a wide range of cereals other than rice; larvae develop and pupate within the kernel.

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Figure 3. Insects found in food products (Source: Sinha and Watters, 1985).

Yellow Mealworm These insects (figure 4, Plate IIa) are the largest found in stored grain. They are not common pests on farms. They first infest animal feeds and then move into stored grain that is going out of condition. The adults are black beetles about 1.5 cm long; the larvae are yellow

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and 0.2 to 2.8 cm long. Yellow mealworms prefer dark, damp places in a granary or a feed bin. The adults live for several months and the larvae may take 1 to 2 years to change into pupae under harsh conditions. Because of their relatively large size, they are easily visible and often appear to be more numerous than they actually are. Their presence indicates poor storage and sanitation conditions.

Figure 4. Insects found in food products (Source: Sinha and Watters, 1985).

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Cadelles Cadelles (figure 4, Plate IIb) are shining black or dark reddish-brown beetles about 1.25 cm long, which makes them the largest of the major stored grain damaging insects. Their larvae range between 1.58 and 2.54 cm long; they are creamy white with a black head, two black or dark plates on the upper part of the segment just behind the head, and a dark plate with two stout dark projections on the tip of the abdomen (Cotton and Wilbur, 1974).

Red Flour Beetle This pest (figure 4, Plate IIc, d) develops on stored grains and oilseeds on farms and in primary elevators. The adult is reddish brown and 0.4 cm long. Larvae and adults feed on broken kernels. Complete development from egg to adult occurs in about 28 days under optimal conditions of 31°C and 15% moisture content. Slower development occurs at moisture contents as low as 8%. Adults fly in warm weather or may be blown by the wind into farmhouses or other buildings.

Confused Flour Beetle The adult (figure 4, Plate IIe) resembles that of the red flour beetle and is difficult to distinguish without a microscope or magnifying glass. Larvae and adults feed on flour, animal feed and other ground material. Unlike the red flour beetle, the confused flour beetle is more common in flour mills than elsewhere, and the adults do not fly. The summarized information about pests is tabulated in table 1 and 2 for beetles, and moths.

Table 1. Stored-product beetles found in food products (Source: Sinha and Watters, 1985)

Table 2. Stored-product moths found in food products (Source: Sinha and Watters, 1985)

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PEST CONTROL IN GENERAL USES The insect pests of stored food product have certain temperature, moisture, air humidity, ambient air composition, and food requirements, which directly affect their abundance, and hence their ability to cause damage. By controlling these conditions, insects can be controlled and killed. However, absolute control requires advanced techniques. To prevent and control infestations it should be known where and when insects occur. Surveys have shown that most empty storages are infested with low numbers of insects and mites. Animal feeds, trucks and farm machinery are other sources of insect infestations. Some insects can fly as well as walk, which increases their ability to infest stored products. The insect control may be defined as the preventing contamination of adult insects to products (cross contamination), growth of insects, stopping incubation of eggs, sustainability of clearness of good products and killing eggs, larvas, pupas and adults using effective methods. Practically, there are a lot of techniques to kill them such as physical, chemical and biological. Some of them have still been used traditionally. Recently, high pressure carbon dioxide system has effectively been used in industry. In order to compare it with other techniques, they will be explained briefly. Cooling, heating, irradiation, diatomaceous earth, impact-pneumatic auger, controlled and modified atmospheres, chemicals (phosphine, carbon dioxide, malathion), biological control, cleaning, sanitation, drying and aeration are general techniques used widely to control insects. The most important methods selected before the explaining high pressure carbon dioxide method are given below.

Cooling and Heating Insects being poikilotherms are sensitive to large temperature changes in the environment. Increase or decrease in temperatures outside the optimum range of 25-32 oC results in developmental delay, drop in reproduction, and mortality at the end. Insects are killed rapidly by heat rather than by cold treatment. Tolerance to heat treatments varies depending on the insect species, stage, and age of the insect, and its physiological state. Product is also affected from heating e.g. losing germination ability, physicoshemical changes etc. Moreover, product after heating should be cooled immediately for safe storage (Rajendran, 2003). A method to control insect infestations in winter is to lower food temperature. This can be done by mixing and transferring infested products from one granary to another which will lower food temperatures about 10°C in the winter; or by transferring part of the product to a truck or small pile exposed to low air temperature, leaving it to cool for one or more days and then returning it to the granary. However, aeration systems are much more effective at lowering the food temperature. Insects do not develop or feed at temperatures below 10°C. At temperatures below 0°C, the insects will die eventually Abramson et. al. (2001). The effect of temperature on the insects can be summarized using figure 5.

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Temperature (oC)

40

Growth of insects is slower

30

Growth of insects is faster

20

Growth of insects is slower

10 Death of insects in weeks, months 0 -10

Death of insects in days, months

-20

Death of insects in minutes

-30 min.

max.

Effectiveness Figure 5. The effect of temperature on the growth of insects.

Irradiation Ionizing radiation for insect control using gamma-radiation emitted from cobalt-60 and cesium-137, or accelerated electrons of less than 10-V energy from a cathode is applied on stored products. The treatment causes mortality as well as sterility in food insect pests and the effect occurs at all temperatures (Rajendran, 2003).

Diatomaceous Earth Control of insects can be achieved by using a nontoxic dust made from prehistoric diatoms. Inert dusts, such as wood ash, paddy husk ash, kaolins, lime, and clay materials; have been traditionally used for grain preservation. They act as a desiccant, absorbing water from the insect body and may also have an abrasive action. They act slowly and take 20 or more days to cause insect mortality (Rajendran, 2003). When insects come in contact with this dust, the waxy covering on their skin is absorbed, leaving them prone to dehydration and death. The product is applied to grain as it is augered into the bin, and is most effective when applied to dry grain at harvest (Abramson et. al. 2001).

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Impact-Pneumatic Auger (Abramson et. al. 2001) Most free-living adult and larvae insect pests are killed during bin unloading by using a “grain-vac.” Insects are killed by abrasive contact and impact as the grain and insects are moved through the discharge tube. Better control is achieved when there is a 90° bend in the tube; this causes more contact of insects with the sidewalls of the tube.

Controlled and Modified Atmosphere Normal atmosphere consisting of 78 % nitrogen, 21 % oxygen, 0.03 % carbon dioxide, and the balance argon and other gases. The objective of modified atmosphere treatment is to attain a composition of atmospheric gases rich in CO2 and low in O2, or a combination of these two gases at normal or altered atmospheric pressure within the treatment enclosure, for the exposure time necessary to control the storage pests. Various terms used in reference to MA storage for the control of storage insect pests or the preservation of food have appeared in the literature to define the same method of treatment but using different means to attain the same scope of control without adversely affecting the environment (Navorro, 2006). In a CA treatment, the atmospheric composition in the treated enclosure is controlled or maintained at a level lethal to insects. The modified gas composition, usually produced artificially, is maintained unchanged by additionally generating the desired gases (CO2 or N2) or by further purging the storage with these gases, supplied from pressurized cylinders or otherwise (Navorro, 2006). The controlled atmospheres applied may be of either low-oxygen (0.5 % oxygen and 99.5 % nitrogen) or high-carbon dioxide atmosphere (40-80 % carbon dioxide, balance air) or burner gas (0.5 % oxygen, 13-21 % carbon dioxide, balance mainly nitrogen). The choice between the three depends on local availability of the gases, suitability of the structures, and afford-ability (Rajendran, 2003).

Chemicals Approved insecticides are selected largely on the basis of i) low toxicity to mammals and high toxicity to insects, ii) freedom from taint or odour on food, iii) non persistent environmental effects, iv) safe, economical and easy use and v) presence of negligible residues or toxic products in food. Some insecticides are more effective and longer lasting than others. Premium-grade malathion, cyfluthrin, pyrethrum with piperonyl butoxide are at present the insecticides registered for empty-bin treatments (Abramson et. al. 2001). Methyl bromide has been an effective and important tool for controlling pests in food processing facilities. However, the phaseout of methyl bromide has been an overshadowing concern for producers globally since 1992, when 160 countries signed an amendment to the Montreal Protocol environmental treaty that included methyl bromide as one of the several identified ozone-depleting substances to be phased out of production (Buckley 2004, Methyl Bromide Industry Government Working Group, 1998). Fumigants are chemicals available as gases, liquids, and in solid formulations, but act on the insect and other pests in gaseous state (Ranjendran, 2003). Fumigants generate toxic gases that are used to control insects in stored grain. They are available for farm use only as solid

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formulations. Fumigants are also toxic to humans and farm animals and, therefore, must be applied only by trained people. Avoid inhaling the vapours (Abramson et. al. 2001) . The gas fumigant sulfuryl fluoride is the probably the most anticipated new option for structural fumigations. Phosphine is another methyl bromide option. Available in many forms, it can be used for treating bulk grain storage, finished products, commodities in transport and also structural spaces, although care must be taken to prevent corrosion to metal and electronics. Phosphine tablets and pellets have been used for than 40 years. Aluminum phosphide pre-pacs, and magnesium phosphide plates are especially suited for situations where spent fumigant needs to be recovered. Cylinderized phosphine gas and phosphine generator systems ae the newest applications to come out in recent years, and have everal advantages over the pellets. Prime benefits are that flammability risks are reduced and there are no spent residues to remove and dispose. Personal safety is enhanced because it’s remote application eliminates the need to enter the fumigated area (Buckley, 2004).

Biological Control There has been a growing tendency in favor of nonchemical methods. There are situations in which biological control, which is residue-free but has species-specific action, will play an active role. Control may be manifested by the action of predators, parasitoids, or pathogens. Predators attack the immature stages of stored food insect pests. Parasitoids are very small hymenopteran insects, parasitizing egg or larva (Rajendran, 2003).

HIGH PRESSURE CARBON DIOXIDE PROCESS (HPCO2PR) FOR INSECT CONTROL Food products may always be under the risk of infestation to a greater or lesser extent (figure 6). Toxic substances have therefore been used to destroy for example insects, as well as their larvae and eggs, up to now. Also, insects, pests and other vermin are a common and very serious threat to commodities. However, these toxic substances were becoming the target of increasing criticism. Insects damage to stored plant products due to pest infestation by direct feeding damage (reduction in weight, destruction of seedlings, decrease in baking quality or food value), pollution, transfer of micro-organisms and damage to the health of humans and animals (allergies, poisoning). To avoid economic and reputation losses in the markets, it is absolutely necessary to take measures against infestation and contamination. However, the use of poison gas and insecticides can cause problem due to possible residues such as prophylactic extermination, processing or packaging products directly after the treatment, multiple treatments for long-term stored products, improper extermination of pesticide from producer, pesticide use during cultivation, environmental pollution, industrial safety and labor safety.

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Figure 6. Egg, larva, pupa and adult on different food products (a) dried apricot, b) beans, c) chickpea, d) lentils.

Biological treatment of products against all vermin (pest control) used to be a very expensive and time consuming process. High pressure carbon dioxide process (HPCO2Pr) is a new alternative method. Industrially, carbon dioxide is used as dry ice pellet, liquid and gas for carbonation, refrigeration, crude oil recovery, pH control, deflashing, industrial cleaning, electronics, preserving, baked goods manufacturing, inert blanketing and purging, shielding gas, solvent, metallurgy (steel making), textiles, pressurization, papermaking, plastics, agriculture, horticulture, food and chemical etc. It is a non-flammable, colorless, odorless gas, about 1.5 times as heavy as air. Carbon dioxide can be added from an external source to a sealed enclosure using either gas produced from a liquid supplied in pressurized cylinders or from solid "dry ice". It is a compound formed by the combination of carbon and oxygen atoms in a 1:2 ratio and proportioned by weight of about 27.3% carbon to 72.7% oxygen. It is present in the atmosphere at a concentration of 0.03 percent by volume. It is a normal product of human metabolism. It is a gas at normal atmospheric temperature and pressure. It is relatively non reactive, non toxic and a slightly acidic gas. Carbon dioxide treatment at atmospheric pressure as a fumigant remains slower-acting than phosphine or methyl bromide. However, chemical solutions are not popular in recent time. Therefore, high pressure with CO2 is an effective solution method for insects. Due to that one, the study on high pressure carbon dioxide process has been continued for twenty years. Carbon dioxide is not a residual gas due to its inert property. It is non-flammable, ecologically friend gas that is safe for residual case. It is also used during packaging of food product. In addition, it can be used repeatedly and prophylactically on the applications and

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resulting in an extremely rapid and 100% effective treatment. As no pesticides or any other undesirable chemicals are used, the process is suitable for HACCP, GMP, organic and conventional rules and procedures.

Researches and Developments on the HPCO2Pr Application of CO2 at atmospheric condition is long time request process. In order to decrease the operation time, high pressure was started to use in CO2-insect treatments (figure 7). The Stored-Product Laboratory at Bordeaux investigated the use of high pressure and carbon dioxide for insect/pest control at 1980’s. After extensive testing in the laboratory, a high pressure fumigation chamber was designed and built in collaboration with MG SIAC (Saint-Denis, France) (Fleurat-Lessard et. al. 1996). The first trials in the laboratory showed that raising the CO2 pressure from 1 to 20 atmospheres reduced the time to control the granary weevil (Sitophilus granarius) larvae from 18 days to 4 hours (Le Torc'h and FleuratLessard, 1991; Prozell and Reichmuth, 1991). Given the success of these first tests, the study was expanded to insects that are more common in processed foods, the likely commodity for this procedure. Eggs, larvae, and adults of the red flour beetle (Tribolium castaneum), the hide beetle (Dermestes maculatus) and the Indian meal moth (Plodia interpunctella) were placed in a pressurized chamber and sub-samples were removed at various times to check for survival. Pupae were not used, as in a pretest they were shown to be more susceptible than the other stages. Table 3 gives the minimum times needed to control insects. For the red flour beetle the eggs are the most resistant stage, requiring just over 4 hours at 10 atmospheres and 1 hour at 19 atmospheres. Other insects at other stages are more susceptible than red flour beetle eggs. Trials are also conducted with air in place of CO2. Hide beetles adults had 93% and Indian meal moth adults have 100% mortality at 19 atmospheres after 2 hours, but all three larval and egg stages are unaffected after 2 hours at 19 atmospheres (Fleurat-Lessard et. al. 1996). After laboratory studies, a pre-industrial pilot pressure chamber with a capacity of 0.75 m3 and the ability to simulate the complete pressure treatment with a rapid rise in pressure to 11 or 16 atmospheres, with a final stable pressure of 19 atmospheres was built and tested. The pressure chamber was attached to a suite of 11 cylinders with 160 atmospheres of CO2 and a heat exchanger for the injection of the gas into the chamber. The CO2 was injected into the lower part of the chamber and the bags of insect-infested pet food placed at different levels in the chamber to estimate the degree of gas stratification. The chamber was not purged of air before the injection of CO2, causing a slight reduction in CO2 concentration (approximately 1/19th). This may be the reason there were a few red flour beetle eggs that survived in the top most level. To avoid this, the industrial scale chamber was designed to be purged with one volume of CO2 before pressurization, and gas is introduced at several points in the chamber (Fleurat-Lessard et. al. 1996). The pilot trials also determined the rates of pressurization and depressurization that the packaging could tolerate. The initial pet food packaging was too gas tight and often tore during the rapid changes in pressure. Replacing the packaging with a more porous material, and the reducing the rates of pressure change solved this problem (Fleurat-Lessard et. al. 1996).

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Figure 7. Application time for CO2 at atmospheric condition (Carvex, 2007).

Table 3. The minimum duration necessary to completely control various insects at a given stage under high pressure CO2 fumigation (Fleurat-Lessard et. al. 1996)

Stahl and coworkers (Rau, 1985; Stahl and Rau, 1985; Stahl et al, 1985; Gerard et al, 1988a,b) tested carbon dioxide under high pressure to kill insects and microbes of compressed carbon dioxide against the cheese mite, Tyrophagus putrescentiae. Pohlen et al (1989), Prozell and Reichmuth (1991), Le Torc’h and Fleurat-Lessard (1991), Nakakita and Kawashima (1994), Reichmuth (1997), and Prozell et al (1997) also reported on the improved efficacy of carbon dioxide under high pressure. Developmental stages of L. serricorne, O.

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surinamensis, T. castaneum, T. confusum, Trogoderma granarium, Corcyra cephalonica (the rice moth), Ephestia elutella, E. cautella, P. interpunctella, and Sitotroga cerealella (Angoumois grain moth) were exposed (at a temperature of 20°C) to carbon dioxide at 37 bar for 20 min, 30 bar for 1 hr, and 20 bar for 3 hr. These treatments resulted in 100% mortality of all insects. Survivors of T. confusum were found after treatment with 10 bar for 20 hr. Therefore, Adler et al (2000) concluded that extrapolation of laboratory results for carbon dioxide and high pressures to field situations are risky. The rate of decompression of pressurized chambers also have an adverse impact on insect mortality (Ulrichs, 1994; Ulrichs et al, 1997a,b). Treatment with high-pressure carbon dioxide under different temperatures may result in different rates of mortality. For example, at 15°C, 95% mortality of L. serricorne was observed after 38.5 min of treatment, while the same level of control was achieved within 1 min at 45°C (Ulrichs, 1995). With high pressures (20–40 bar), all types of pests and their life stages can be killed within a short time. The relatively rapid control of pests in all stages of development is based, on one hand, on the narcotic and acidifying effect induced by the high solubility of carbon dioxide in cell fluid and, on the other hand, on the destruction of the cells following the carbon dioxide pressure treatment during depressurization (Adler et al, 2000) (Navarro, 2006). Extremely short exposure times (a few hours) are needed to control all stages of storage insects with carbon dioxide at pressures between 10 and 37 bar. Generally, increasing the pressure reduces the lethal exposure time. Stahl et al (1985) and Stahl and Rau (1985) were the first to report that pressurized carbon dioxide is lethal to insects (Navarro, 2006). CO2 under high pressure followed by quick decompression was also introduced by Stahl et al., (1985) and Gerald et al. (1988a). The greatest advantage of this method as a pest control measure is its short lethal exposure period. Several researchers who investigated the use of CO2 at high pressure on different insect pests of cereal grains have reported that the exposure period can be reduced to less than an hour, regardless of the species or the developmental stage (Prozell and Reichmuth, 1991; Reichmuth, 1991; Nakakita and Kawashima, 1994; Reichmuth and Wohlgenmuth, 1994; Locatelli et al., 1999; Song et al., 1999). In all these studies, the eggs were reported to be more tolerant than other developmental stages. However, regarding the difference in the tolerance to CO2 of insect eggs of different ages under high pressure, there is only one report, on Plodia interpunctella (Hübner) (Reichmuth and Wohlgenmuth, 1994). Hence, the aim of this study was to provide data on the effect of CO2 under high pressures on the mortality of C. maculatus egg of different ages. In the study of Shazalli et. al. (2004), their study was to provide empirical data on the effect of ambient carbon dioxide (CO2) under high pressure on the mortality of Callosobruchus maculatus (F.) eggs at different ages. The mortality of the eggs was assessed in combinations of four CO2 pressures (15, 20, 25, and 30 bar) and five egg-age groups (one, two, three, four and five days old) with 5, 10, 15, and 20 min exposure periods. The investigations were carried out in an automated pilot plant (volume: 1.1 lt) at 25°C and 70% R.H. After each exposure period the gas pressure was decreased to atmospheric level in one second. One-day-old eggs were found to be the most tolerant, requiring 30 bar and 20 min exposure for complete extermination. On the other hand, five-day-old eggs were the least tolerant, requiring only 20 bar and 10 min. The tolerance of the eggs declined with age and the mortality was influenced by the pressure level and the exposure period. To achieve 100% mortality, increasing pressure was more effective than increasing exposure time. Decisions

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regarding pressure and exposure time of CO2 should be made case by case, because the effect of CO2 appears to vary depending on the age of the egg. The operational values are given in table 4. The egg stage is more tolerant than other developmental stages, and in fact it is characterized by a low water content and very few cells, besides having the most stable form as a sphere (Gerald et al., 1988a; Reichmuth and Wohlgenmuth, 1994). Reichmuth and Wohlgenmuth (1994) studied the effect of egg age of P. interpunctella on mortality when exposed to CO2 under high pressure, and concluded that 100% mortality of young eggs (24 h) was achieved at 20 bar and 40 min. Locatelli et al. (1999) concluded that 20 bar and 15 min were required to prevent P. interpunctella eggs of mixed ages (2–4 days old) from hatching. These differences in the estimated 100% mortality levels show that the mortality of very young eggs must be examined when insect pests are eradicated using high-pressure CO2. Decisions regarding pressure and exposure time of CO2 should be made case by case, because the effect of CO2 appears to vary depending on the age of the egg. Table 4. Mean mortality of Callosobruchus maculatus eggs of different ages exposed to CO2 under high pressure Shazalli et. al. (2004)

Commercial Applications The stored-product pests laboratories at Bordeaux and Berlin investigated the use of carbon dioxide at high pressure (Fleurat-Lessard, 1990; Reichmuth and Wohlgemuth, 1994). After extensive testing in the laboratory, a high-pressure fumigation chamber was designed and built in collaboration with the French company MG SIAC. The chamber can hold the

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equivalent of the contents of one transport trailer. The unit is designed to recover at least 85% of the carbon dioxide used (Navarro, 2006). Gerard et. al. (1990) described a high-pressure chamber connected to a tank of liquid CO2 placed on a balance. This unit is commercially available and utilized in Germany (Navarro, 2006). The full scale pressure chamber of MG MIAG (Saint-Denis, France) has a working capacity of 80 m3, enough space to treat 32 pallets of pet food or the equivalent of the contents of one transport trailer. In a normal treatment cycle, pallets are loaded into the chamber; the chamber is sealed and purged with one volume of CO2. The CO2 is injected into the chamber, and it takes 90 minutes to obtain 19 atmospheres. This pressure is maintained for 60 minutes and it takes 30 minutes to depressurize the chamber. The entire cycle with loading, fumigation and unloading takes approximately four hours. The operation of pressurizing and depressurizing is complicated by the desire to minimize the amount of CO2 lost during each fumigation. This is obtained by a patented system using two CO2 holding tanks and a compressor. To verify the effectiveness of the unit, red flour beetle adults and Indian meal moth larvae were placed in bags of pet food, the commodity to be fumigated, and placed throughout the chamber. None of the 3200 red flour beetles or the 1600 Indian meal moth survived the fumigation (Fleurat-Lessard et. al. 1996). There is another unit built in Germany by another company that is used for fumigating spices. CARVEX is a system constructor for HPCO2Pr in Germany which their system is carried out by the Federal Biological Research Centre for Agriculture and Forestry of Germany. It is an independent supreme authority in the domain of the Federal Ministry of Consumer Protection, Food and Agriculture (Carvex, 2007). The study of Martin Bauer (MABA-PEX) started as long ago as 1986. The first major trials of the new MABA-PEX process took place in October 1987. All subsequent trials were conducted in close cooperation with the Federal Institute of Biology in Brunswick and the Department of Chemical Engineering at the University of Erlangen/Nuremberg (Martin Bauer, 2007). Door of vessel is opened from upper section. It supplies low press to hinge (figure8).

Figure 8.View from MABA-PEX system.

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Over the last 10 years, DKSH Inc. (Holland) has developed a unique process for the ecological and biological treatment of vermin, using a combination of CO2 and high pressure. DKSH Inc. also applies their own single horizontal and vertical (figure 9) systems for their raw materials used for pet feeds and others. Bulk commodities are treated in the vertical 70 m³ tank. Packaged products (when not airtight and/or vacuum packed) are treated in a horizontal tank with a capacity of 14 europallets. A different opening and closing mechanism is used on the gate mechanism of horizontal vessel. Frame at around the gate is rotated to lock tooth.

Figure 9. Horizontal (left) and vertical (right) HPCO2Pr of DKSH Inc. (DKSH, 2007).

Tariş Co. (İzmir-Türkiye) uses two horizontal vessel systems made by Buse-Gastek (Germany) to apply on dried grape and figure etc. The system contains a CO2 recovery system (figure 10).

Figure 10. Two pressure chambers made by Buse-Gastek Co. for dried fruits and CO2 recovery system used by Tariş Co.

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Davert Inc. (Germany) uses two horizontal vessel systems made by Carvex Co. (Germany) for organic products in packed form (figure 11). In this system similar to Tariş/ Buse-Gastek, the gate of horizontal vessel is open and closed by rotating gate on a bearing (figure12). It is also jointed on hinge at side. Frame is not rotating. Their gate system is different from DHSK Inc.

Figure 11. Double horizontal system with aspiration and heat exchanger components used by Davert Inc. for organic products.

Figure 12. Gate system and bearing used by Tariş/Buse-Gastek and Davert/Carvex.

Tiryaki Group (Türkiye) uses its own vertical system for bulk grains and legumes either for organic and conventional food products. It was developed by R and D team (Mustafa Bayram, Faruk Kutbay, Süleyman Tiryakioğlu, Ahmet Tiryakioğlu) that is one of the high capacity systems for the organic products (figure 13). Commercially, these systems are also used under sub-contract protocols for different companies.

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Figure 13. Bulk product application with HPCO2Pr.

Operations The influence of HPCO2Pr on the insects is (i) the effect of acidity of cell liquids and hemolymph of insects through the dissolution of CO2 in the body fluid is fortified under pressure (carbon dioxide is formed), (ii) effect of pressure, which is especially effective during tension release, (iii) deoxygenation, (iv) dehydration during trying respiration of insects, (v) sharp pressure change and explosion of insect egg, larva, pupa and body during loading and discharging of CO2. The disinfestation effect of high pressure CO2 system depends on various parameters, such as exposure time, species, phase of insects (figure 14), pressure (figure 15), pressure drop rate, temperature (figure 16), etc. The parameters depend on each other. According to studies of Carvex (2007), the increase in temperature reduces required exposure time, however high temperatures adversely affect product quality and low temperatures increase required exposure time. In addition, if temperature is between 0 and 15°C, required exposure time increases. Out of this range, required exposure time decreases. Also if pressure increases, the influence of the temperature decreases.

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Figure 14. Effect phase of insects (Carvex, 2007).

Figure 15. Effect of pressure level on the insects (Carvex, 2007).

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Figure 16. Effect of temperature for high pressure CO2 system (Carvex, 2007).

The treatment requirements include pressure-proof horizontal or vertical gas-tight vessels (autoclaves). The high pressure CO2 process involves exposing the product in its gaspermeable original packaging materials e.g. bags and boxes (figure 17) (for horizontal vessel) or bulk (for vertical vessel) to a pressurized carbon dioxide atmosphere. If the product is packed in bags, bales, bundles, etc. and also marshaled on pallets, measures must be implemented to ensure that the carbon dioxide can permeate the material completely. The product to be treated is left in its original packaging and is transported on pallets into the autoclave. Products are transported and loaded into vessel on a specially designed vehicle and conveyors (figure 18). Once loaded, the vessel (if two vessels are used) is flooded with gaseous carbon dioxide to the required final pressure. Liquid carbon dioxide in stock tank (figure 19), this tank is 80 bar, another type storage tank are also hold between 18-20 bar using conditioning system to decrease thickness of tank) is vaporized via a heat-exchange unit (figure 20), introduced into the autoclave in gaseous form, and raised to the treatment pressure. Over all processing time at each cycle is related to loading, discharging, processing (e.g. 2 hr at 20 bar) etc (figure 21). The subsequent pressure equalization between vessels I and II, which has been loaded in the meantime already represents a saving of 50% carbon dioxide. Additional carbon dioxide can be taken to build up the necessary final pressure following pressure equalization. If a single vessel is used instead of two vessels, the gas used is discharged to atmosphere or recycled using recycling system. Due to the investment and energy cost of recycling system (special compressor/pump for gas, coolers, regulators, tank, climatic system etc.), it is not used widely. For two vessels and recovering systems, there is also transfer risk of odors after applying gas and re-use it in subsequent operation. Therefore, if the product has specific volatile odor, a single use should be applied.

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Figure 17. Loading of packed products in vessel (Carvex, 2007).

Figure 18. Loading of products using conveyors and forklift (Carvex, 2007).

Figure 19. CO2 stock tank (on the left, Carvex, 2007) and high pressure CO2 fumigation with tanks for recycling gas (on the right, Fleurat-Lessard et. al., 1996).

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Figure 20. CO2 evaporator (heat exchanger) and fan system (Carvex, 2007).

Figure 21. Application time for high pressure CO2 system (Carvex, 2007).

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After the operation, the remaining carbon dioxide in the vessel is then discharged through a fan/compressor to atmosphere for the safety. Pressure drops back to ambient atmospheric pressure. The gas is exhausted into the atmosphere in a short time (10–15 min) with the use of a silencer (figure 22), to avoid a loud noise during release. The rate of decompression of pressurized storages may also have an adverse impact on insect mortality. Penetration depth and flow conditions during pressure build-up and relief were criteria that presented particularly difficult problems initially. The capacity of system depends on the number of cycle and vessel, size, chamber pressure limits and product bulk density etc. In order to help the loading and discharging of products, two gates of vessel (figure 23) can be used. However, its investment cost is higher than single one. If horizontal system is used for cubic packaged product, the sides of cylindrical vessel should be filled and closed using brick, cement or other bulking materials to decrease the cost of operation by decreasing head space for gas (figure 24).

Figure 22. Silencer during CO2 disharging.

Figure 23. Two gates of vessel for inlet and outlet of product (Carvex, 2007).

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Figure 24. Filling of side of cylindrical horizontal vessel by materials.

Table 5. The product applied with HPCO2Pr Dried fruits and berries Apples cubes, flakes, Apples pieces Figs Pears, cubes, slices Apricots cubes, wholes Banana slices, brokens Banana slices, wholes Coconut flakes Cranberries Elder berries Juniper berries Mountain-ash (rowan) berries Mango cubes Melon (Cantaloupe) berries Papaya cubes, red/orange Papaya cubes, green Pineapple cubes, slices Raisins Rose hips, wholes Tutti-frutti (fruit mix) Sour cherry

Dried vegetables and spices Carrot flakes, cubes Green beans, sliced Green cabbage, flakes White cabbage, flakes Green leek, flakes Onion flakes Onion powder Garlic flakes Garlic powder Garlic granules Red bell peppers (flakes) Parsley stems Potato cubes Red beets flakes Tomato flakes, wholes Pepper berries Paprika powder Mushroom Cinnamon Coriander Aniseed Eggplant Pepper

Others Dried chillies large (4-6 cm) stemless small (2-4 cm) Malawi Turkish delight (lokum) Seeds Pulse rice Lentil Chickpea Dried beans Beans Sesame seeds Pistachio nuts Coconuts Hazelnuts Other nuts Pet feeds Animal feeds Wood Pallet Furniture Historical materials Organic products Textile/fabric

Products Applied with High Pressure Carbon Dioxide System The products including dried fruits, berries, angelica root, valerian root, birch leaves, stinging nettle, fennel, rosehip, hibiscus, chamomile, peppermint, sage leaves, yarrow, and senna leaves) in various cutting sizes (whole product, concise product, finely cut product, pulvis subtilis) and various types of packaging (paper sacks, jute sacks, pressed bales, boxes

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and crates, drums and tape fabric sacks) can be applied using horizontal and vertical systems (Martin Bauer, 2007; DKSH, 2007). The most common product list is given in table 5.

THE EFFECT OF SYSTEM ON PRODUCT QUALITY According to the documentation of Martin Bauer (2007), apart from insecticidal efficacy, the subsequent laboratory experiments investigated such product specific parameters as appearance and organoleptic properties (smell and taste of the product and tea infusion), drying losses, extract content, color value, and constituent substances. GC and HPLC methods were used to test the constituent substances, as well as the methods described in the pharmacopoeias. The results indicate a 100% mortality rate and no differences in quality between treated and untreated drugs. The products were tested again after 6 weeks, 3 months, 6 months and 12 months. Here again, no differences are found between untreated and treated products. Other products have been tested and analyzed in order to verify the results obtained at that time. No changes are found that could have been attributed to the pressurized carbon dioxide treatment. However, the R and D Department of Tariş-Türkiye (Karabat, 2007) studies on the effect of moisture content of product on color change of products during treatment with HPCO2Pr. During discharging CO2 from the horizontal HPCO2Pr, if the discharging of gas is fast, the packages explore. Therefore, the discharging rate should be regulated and controlled.

Cost of HPCO2Pr The investment costs of horizontal and vertical system change around 300,000-500,000 € and 100,000-400,000 €, respectively, which depend on capacity, size, number and pressure. In general, horizontal system is more expensive than vertical one due to special gate of vessel. The CO2 suppliers rent their CO2 stock tanks to commodities. Therefore, the investment cost decreases. If CO2 recycling system is preferred, it highly increases the cost. Some companies manufacture their systems to decrease the investment cost. The operation time depends on capacity should be calculated very well. If the capacity is low and there is no time limit, the thickness of vessel can be selected thinner at low pressure long time. The processing cost of the system depends on the cost of CO2 supplied in country, pressure applied, space in vessel, bulk density of product, duration and specie of pest etc. Therefore, operational cost can change from one to the other. According to value of CARVEX Co., if the double horizontal chamber system (diameter: 2 m, length: 5.2 m, max. working pressure 32 bar) with cylinder lining is used for products on pallets, the processing cost of the system is 3.0, 4.7, 6.4 and 10.4 €/pallet at 10, 15, 20 and 30 bar, respectively. The dimension of one pallet is width of 1.2 m, length of0.85 m, height of 1.5 m and volume of 1.53 m³. The R and D Department of Tariş Co. investigated and compared the cost of CO2 with other systems. According to the results, the processing costs of 1, 1, 0.5 and 10 USD/ton of product on pallet are found for methyl bromide, atmospheric CO2 treatment, phosphine and HPCO2Pr (with 50% recovery), respectively (Karabat, 2007). This cost is less when vertical

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system with 50% recovery is used for bulk products e.g. 5.5-6 USD/ton. Both prices are valid at Türkiye (the unit bulk price of CO2 in Türkiye is 350-500 USD/ton of CO2). As seen, HPCO2Pr is expensive, therefore, a good working time chart should be prepared before the selection and investment of system. For that purpose figure 25 can be used. In this figure, the price factors of pressure, length and diameter of vessel are compared. According to the report of Fleurat-Lessard et. al. (1996) on MG SIAC system, currently the unit does six fumigations a day, at a cost of approximately $300 CAN to cover the electricity and 300-400 kg of CO2 needed for each fumigation. The unit cost approximately $850,000 CAN in 1995 to build, and the pet food company, Royal Canin, intends to build another two units, which will enable them to treat 15 truck loads of product a day. The one pressurized unit replaces eight fumigation units that used phosphine and took three days per fumigation. This system is appropriate for any packaged food or animal feed product that has a non airtight packaging, is a high value product, the manufacturer needs a rapid treatment and a high assurance that the product is free from infestation.

Figure 25. Price factors of parameters for HPCO2Pr vessel.

CHEMICAL AND PHYSICAL PROPERTIES OF CO2 (CITED FROM BOC) Food grade carbon dioxide is supplied as a liquid in pressurized cylinders. Liquid CO2 changes to a gas when released from the cylinder. The rate of release of gas from the cylinder is controlled by a regulator designed specifically for carbon dioxide. High pressure tubing is used to pass the gas from the regulator to the entry port located at the base of the storage silo or enclosure.

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Solid "dry ice" is also a useful source of gaseous CO2. At sub-zero temperatures, carbon dioxide changes directly from a solid to a gas. Dry ice is supplied as blocks, crushed ice or pellets. Crushed ice or pellets rapidly change to a gas and are best for initial gas addition. Blocks are useful to make up gas loss during treatment due to their slower release. Dry ice is best suited for use in gastight containers or low volume sheeted stacks. Place the dry ice on the commodity surface prior to sealing or at the base of the container or stack. Table 6. Properties of CO2 Properties Specific gravity Density, lbs/ft3

Values 1.5289 at 1 atm and 70 °F solid: -109.25 °F = 97.6 lb/cu. ft. liquid: +1.7 °F, 300 psig = 63.36 lb/cu. ft. liquid: 70 °F, 830 psig = 47.35 lb/cu. ft.

liquid: -69.8 °F = 73.5 lb/cu. ft. Specific volume, lbs/ft3 Normal boiling point Specific heat-gas-varies (at constant pressure of 1 atmosphere) Viscosity-gas at atmospheric pressure Heat of vaporization

Solubility of carbon dioxide

Carbon Dioxide is obtained as a by-product from one of several sources

Purification methods

8.57 at 1 atm and 60 °F 8.74 at 1 atm and 70 °F -109.35 °F 70 °F.......................................0.20 BTU/lb (at constant volume)...............0.15 BTU/lb 70 °F temperature.......... ...0.015 centipoise liquid at 0 °F...................... ....0.14 centipoise Solid: -109.25 °F = 246.6 BTU/lb Liquid: +1.7 °F, 300 psig = 119.2 BTU/lb Liquid: +70 °F, 839 psig = 63.9 BTU/lb Dissolved in water, carbon dioxide forms carbonic acid (H2CO3) Becomes chemically active in moisture or high heat Readily dissolves in most liquids Amount is affected by temperature and pressure Under normal conditions water dissolves its own volume of carbon dioxide The greater the pressure, the more CO2 a liquid can hold Once the pressure is released, CO2 escapes in the effervescent characteristic of uncapped soft drinks The colder the liquid, the more CO2 it can hold Carbon dioxide flows downhill, settles in the bottom, and displaces air Ammonia Plants Fermentation ethanol plants Hydrogen Plants within refineries Ethylene oxide Natural gas process plants Extractions from flue gases from the burning of natural gas or fuels In some parts, CO2 is obtained from CO2 wells at extremely high concentrations and pressures Zinc Oxide Beds Dryers Adsorbers Noble Catalyst Beds Carbon Beds Molecular Sieve Beds Water Wash Columns Potassium Permanganate Beds

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Table 7. Hazardous and emergencies for CO2 Carbon dioxide hazards

Handling leaks and emergencies

Heavier than air-accumulates in low or confined areas Asphyxiant; − Even in normal concentrations of oxygen carbon dioxide can paralyze the respiratory system − Concentrations of 10% CO2 or more can cause unconsciousness or death The seriousness of the symptoms of asphyxiation experienced depends on the concentration levels and length of exposure Carbon dioxide should only be used in areas with good ventilation Ventilate areas to prevent the formation of toxic concentrations of carbon dioxide. If carbon dioxide content exceeds 3% you must wear an SCBA to enter that area. Avoid contact of the skin or eyes with cold carbon dioxide. Evacuate the immediate area if the leak is large. If water spray is used to dissipate leak, a dense fog may form as well as carbonic acid. Leak will dissipate itself given time if the ambient temperature is above freezing.

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[2]

[3] [4] [5]

[6] [7] [8] [9]

Abramson, D., Demianyk, C. J., Fields, P. G., Jayas, D. S., Mills, J. T., Muir, W. E., Timlick, B., & White, N. D. G. (2001). Protection of farm-stored grains, oilseeds and pulses from insects, mites and moulds. In N.D. G. White, Protection of farm-stored grains, oilseeds and pulses from insects, mites and moulds (pp:1-56, Pub. No: 1851/E “revised”), Manitoba: Cereal Research Centre, Canadian Agriculture and Agrifood Canada Publ. Adler, C., Corinth, H. G., and Reichmuth, C. (2000). Modified atmospheres. In B. Subramanyam and D. W. Hagstrum, (Eds.). Alternatives to Pesticides in StoredProduct (pp. 105-146). Boston, USA, Kluwer Academic Publishers. Bailey, J. E. (1974). Whole grain storage. In C. M. Christensen, Storage of cereal grains and their products (pp. 333-360), St. Paul, Minnesota: AACC Press. Bauer, M. (2007). Product safety and technical documentation. Martin Bauer Co., Germany. Bousquet, Y. (1990). Beetles: associated with stored products in Canada: An identification guide. Biosystematics Research Centre Research Branch, Agriculture Canada Publication, 1-214. Buckley, E. (2004). A mill’s sanitation toolbox. World Grain, April, 46-49. Carvex, 2007. Verfahrenstechnologies für Lebensmittel und Pharma GmbH, Technical documentation. Carvex Co., Germany. Cotton, R. T., & Wilbur, D. A. (1974). Insects. In C. M. Christensen, Storage of cereal grains and their products (pp. 194-225), St. Paul, Minnesota: AACC Press. DKSH, (2007). High pressure treatment: Rapid and environmentally friendly solution for extending the shelf life of commodities. DKSH Brochure, Holland.

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[10] Fleurat-Lessard, F. (1990). Effect of modified atmospheres on insects and mites infesting stored products. In M. Calderon and R. Barkai-Golan, (Eds.), Food Preservation by Modified Atmospheres (pp. 21-38). Boca Raton, FL, CRC Press Inc. [11] Fleurat-Lessard, F., Fields, P. and Le Torc'h J. (1996). Methyl bromıde alternatıves: a European perspectıve. Workshop on Alternatives to Methyl Bromide, Toronto Canada, May 30-31, 1996, pp 83-90. [12] Gerard, D., Kraus, J., and Quirin, K. W. (1988a). Rückstandsfreie Druckentwesung mit natürlicher Kohlensaure. [Residue-free pressure disinfestation with natural carbon dioxide]. Gordian, 88, 90-94. [13] Gerard, D., Kraus, J., Frohlingsdorf, B., Jerga, C. and Dallüge, A. (1990). Rückstandsfreier Vorratsschutz für Arznei- und Teedrogen. [Residue free stored product protection of medical and tea drugs]. Dtsch. Apoth. Ztg., 130, 2014-2018. [14] Gerard, D., Kraus, J., Quirin, K. W., and Wohlgemuth, R. (1988b). Anwendung von Kohlendioxid (CO2) unter Druck zur Bekampfung vorratsschadlicher Insekten und Milben. [Use of carbon dioxide (CO2) to control stored product pest insects and mites]. Pharm. Ind., 50, 1298-1300. [15] Karabat, S. (2007). Personel communication, Tariş R-D Department, İzmir, Türkiye. [16] Le Torc’h, J. M., and Fleurat-Lessard, F. (1991). The effects of high pressure on insecticide efficiency of modified atmospheres against Sitophilus granarius (L.) and S. oryzae (L.) (Coleoptera: Curculionidae). In F. Fleurat-Lessard and P. Ducom (Eds.), Proc. 5th Int. Work. Conf. Stored-Product Protection (pp. 847-856). Bordeaux, France. [17] Locatelli, D. P., Suss, L. and Frati, M. (1999). The use of high pressure carbon dioxide (20 bar) to control some insect of foodstuffs. In Z. Jin, Q. Liang, Y. Liang, X. Tan and L. Guam (Eds.), Proc. 7th Int. Work. Conf. Stored-Product Protection (pp. 671–675). Beijing, P. R. China, Sichuan Publishing House of Sci. and Tech. [18] Maba-Pex, (2007). Advertising brochures. [19] Methyl Bromide Industry Government Working Group, (1998). Integrated Pest Management in Food Processing:Working Without Methyl Bromide. Sustainable Pest Management Series S98-01. [20] Nakakita, H. and Kawashima, K. (1994). A new method to control stored-product insects using carbon dioxide with high pressure followed by sudden pressure loss. In E. Highley, E. J. Wright, H. J. Banks and B. R. Champ (Eds.), Proc. 6th Int. Work. Conf. Stored-Product Protection (pp. 126-129). Canberra, Australia, CAB International. [21] Navarro, S. (2006). Modified Atmospheres for the Control of Stored-Product Insects and Mites. In J.W. Heaps (Ed.), Insect Management for Food Storage and Processing, (Second Edition, pp. 105-146), St. Paul, MN, AACC International. [22] Pohlen, W., Rau, G., & Finkenzeller, F. (1989). Erste praktische Erfahrungen mit einem Verfahren zur Druckentwesung mit Kohlendioxid. [First practical experiences with a procedure to disinfest with pressurised carbon dioxide]. Pharm. Ind. 51: 917- 918. [23] Prozell, S. and Reichmuth, C. (1991). Response of the granary weevil Sitophilus granarius (L) (Coleoptera; Curculionidae) to a controlled atmosphere under high pressure. In F. Fleurat-Lessard and P. Ducom (Eds.), Proc. 5th Int. Work. Conf. StoredProduct Protection (pp. 911-918). Bordeaux, France. [24] Prozell, S., Reichmuth, C., Ziegleder, G., Schartmann, B., Matissek, R., Kraus, J., Gerard, D., and Rogg, S. (1997). Control of pests and quality aspects in cocoa beans and hazelnuts and diffusion experiments in compressed tobacco with carbon dioxide

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under high pressure. In E. J. Donahaye, S. Navarro and A. Varnava, (Eds.), Proc. Int. Conf. on Controlled Atmosphere and Fumigation in Stored Products (pp. 325-333). Nicosia, Cyprus, Printco Ltd. Rajendran, S. (2001). Alternatives to methyl bromide as fumigants for stored food commodities. Pesticide Outlook, December, 249-253. Rajendran, S. (2003). Grain storage: perspectives and problems. In A. Chakraverty, A.S. Mujumdar, G.S.V. Raghavan and H.S. Ramaswamy, (Eds.), Handbook of Postharvest Technology (Chapter 8, pp. 183-214). N.Y. USA, Marcel Dekker Inc. N.Y. Rau, G. (1985). Die Anwendung von verdichtetem Kohlendioxid zur Qualitiitsverbesserung von Drogen. [Application of pressurized carbon dioxide to improve the quality of drugs.] Ph.D. dissertation, University of the Saarland, Saarbrücken, Germany. 100 pp. Reichmuth, C and R. Wohlgenmuth (1994) Carbon dioxide under high pressure of 15 bar and 20 bar to control the eggs of the Indian meal moth Plodia interpunctella (Hubner) (Lepidoptera: Pyralidae) as the most tolerant stage at 25°C. In E. Highley, E. J. Wright, H. J. Banks and B. R. Champ (Eds.), Proc. 6th Int. Work. Conf. StoredProduct Protection (pp. 163-171). Canberra, Australia, CAB International. Reichmuth, C. (1991). New techniques in fumigation research today. In F. FleuratLessard and P. Ducom (Eds.), Proc. 5th Int. Work. Conf. Stored-Product Protection (pp. 701-725). Bordeaux, France. Reichmuth, C. (1997). There is no resistance of stored-product moths against treatment with carbon dioxide under high pressure. In E. J. Donahaye, S. Navarro and A. Varnava, (Eds.), Proc. Int. Conf. on Controlled Atmosphere and Fumigation in Stored Products (pp. 519-525). Nicosia, Cyprus, Printco Ltd. Shazalli, M. E. H., Imamura, T. and Miyanoshita, A. (2004). Mortality of eggs of the cowpea bruchid, Callosobruchus maculatus (F.) (Coleoptera: Bruchidae) in carbon dioxide under high pressure. Appl. Entomol. Zool. 39, 49–53. Sinha, R.N. and Watters, F.L. (1985). Insect pests of flour mills, grain elevators, and feed mills and their control. Agric. Can. Publ., 1776. 290. Song, W., Yang, H. and Wang, H. (1999). The effect of high pressure carbon dioxide, nitrogen and their mixture on the mortality of two species of stored-product insects. In Z. Jin, Q. Liang, Y. Liang, X. Tan and L. Guam (Eds.), Proc. 7th Int. Work. Conf. Stored-Product Protection (pp. 706–709). Beijing, P. R. China, Sichuan Publishing House of Sci. and Tech. Stahl, E. and Rau, G. (1985). Neues Verfahren zur Entwesung. [A new method for disinfestations]. Anz. Schadlingskd. Pflanzensch. Umweltsch., 58, 133-136. Stahl, E., Rau, G. and Adolphi, H. (1985). Entwesung von Drogen durch KohlendioxidDruckbehandlung (PEX-Verfahren). [Disinfestation of drugs by pressure treatment with carbon dioxide (PEX procedure)]. Pharm. Ind., 47, 528-530. Stahl, E., Rau, G. and Adophi, H. (1985). Entwesung von Drogen durch kohlendioxide Drukbehandlung (PEX-verfahren). Pharmazeutische Industrie, 47, 528–530. Ulrichs, C. (1994). Effects of different speed of build up and decrease of pressure with carbon dioxide on the adults of the tobacco beetle Lasioderma serricorne (Fabricius) (Coleoptera: Anobiidae). . In E. Highley, E. J. Wright, H. J. Banks and B. R. Champ (Eds.), Proc. 6th Int. Work. Conf. Stored-Product Protection (pp. 214-216). Canberra, Australia, CAB International.

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[38] Ulrichs, C. (1995). Zur Empfindlichkeit des Tabakkafers Lasioderma serricorne (Fabricius) (Coleoptera: Anobiidae) gegnüber Behandlung mit Kohlenstoffdioxid unter Hochdruck. [Susceptibility of the tobacco beetle Lasioderma serricorne (Fabricius) (Coleoptera: Anobiidae) towards treatment with carbon dioxide under high pressure.] (2 Fiche.) Diplomarbeit, Fachbereich Biologie, Freie Universitat Berlin, Verlag DHS Hansel-Hohenhausen, Egelsbach, 2, 134. [39] Ulrichs, C., Reichmuth, C. and Rassmann, W. (1997a). Carbon dioxide under high pressure to control the tobacco beetle Lasioderma serricorne. In E. J. Donahaye, S. Navarro and A. Varnava, (Eds.), Proc. Int. Conf. on Controlled Atmosphere and Fumigation in Stored Products (pp. 335-341). Nicosia, Cyprus, Printco Ltd. [40] Ulrichs, C., Reichmuth, C., Tauscher, R. and Westphal, K. (1997b). Rate of gas exchange during treatment of compressed tobacco with nitrogen or carbon dioxide for pest control. In E. J. Donahaye, S. Navarro and A. Varnava, (Eds.), Proc. Int. Conf. on Controlled Atmosphere and Fumigation in Stored Products (pp. 343-347). Nicosia, Cyprus, Printco Ltd.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 397-452 © 2007 Nova Science Publishers, Inc.

Chapter 7

EFFECTS OF PERMEATION ON MASS TRANSFER COEFFICIENT FOR LAMINAR NON-NEWTONIAN FLUID FLOW IN MEMBRANE MODULES DURING CLARIFICATION/CONCENTRATION OF FRUIT JUICE Sirshendu De*, Sunando DasGupta and S. Ranjith Kumar Department of Chemical Engineering; Indian Institute of Technology, Kharagpur; Kharagpur - 721 302; India

ABSTRACT Membrane based clarification and concentration of fruit juice has become a popular unit operation in modern fruit juice processing industries. The well known membrane modules used for this purpose are tubular and spiral wound modules. Therefore, design of these modules is of utmost industrial importance. The key parameter for design of membrane modules is mass transfer coefficient. Most of the fruit juices have nonNewtonian rheology, e.g., power law, ellis fluid, etc. Till today, the mass transfer coefficient for such systems used is approximated from the corresponding relations developed for Newtonian fluids. Hence, a detailed fluid flow modeling with nonNewtonian rheology is urgently warranted. In the present work, this aspect is attempted. The expressions of the mass transfer coefficients are derived from the first principles for laminar, non-Newtonian fluid flow in a porous conduit. The effects of the permeation are incorporated quantitatively in the mass transfer coefficient from a theoretical basis. The analysis is carried out for various non-Newtonian rheologies. Effects of the operating conditions, i.e., Reynolds number, permeate flux, etc. on mass transfer coefficient are also investigated. Two flow geometries are considered. Flow through a tube and that through a rectangular thin channel, which are useful for the design of the tubular and spiral wound cross flow membrane modules. The developed relations of mass transfer coefficients would be of tremendous help to the design engineers.

*

Corresponding author: Sirshendu De; Tel: + 91 – 3222 – 283926; Fax: +91 – 3222 – 2755303; E Mail: [email protected]

398

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

1. INTRODUCTION The mass transfer coefficient is an important parameter for designing membrane separation modules, where the flow occurs through a porous conduit. Therefore, accurate estimation of the mass transfer coefficient is necessary. These coefficients are generally calculated from the Sherwood number correlations, obtained from the heat and mass transfer analogies. The major limitations of these correlations are, (i) they are applicable for nonporous conduits, (ii) the effects of pressure on the mass transfer coefficient are not incorporated, (iii) it is assumed that the mass transfer boundary layer is fully developed and (iv) the property variations with concentration remain unaccounted. Therefore, the standard correlations lead to inaccurate estimation of the mass transfer coefficient. One way to avoid this is to carry out a detailed numerical simulation of the fluid and the mass transfer in the membrane modules [1,2] and the subsequent estimation of the mass transfer coefficient. But, this method is not attractive to the engineers due to the extensive computation effort and complications. The available mass transfer coefficient correlations had been reviewed in detail [3,4]. The shortcomings of these correlations as stated earlier have been extensively discussed in detail in both the reviews. The alternative approaches, like velocity variation technique [3] or osmotic pressure model [4,5], are also examined. But, both of these methods have their own limitations. The effect of the permeation is extremely important in the mass transfer. The effect is two fold; first, it enhances the mass transfer and second, it stabilizes the laminar flow by delaying the onset of laminar to turbulent transition [3]. Membrane based separation processes are used extensively in various process industries, e.g., food processing [6], dairy products [7], clarification and concentration of fruit juice [8,9], polymer concentration, separation and fractionation [10], biomedical applications [10], protein concentration and separation [11], etc. Most of the fruit juices, polymeric solutions, blood, etc. follow non-Newtonian rheology. Power law is the commonest rheology of these non-Newtonian process streams [12]. The Sherwood number relations for the non-Newtonian fluids are scarce in literature. Most of the available relations are empirical or semi-empirical in nature and these are for power law fluids only [13-15]. The generalized expressions of Sherwood number including the effects of membrane permeation for Newtonian laminar flow as well as the turbulent flow regimes are already developed [16,17]. The Sherwood number relations for the entire range of the power law fluids, from pseudoplastic to dilatant, have been developed both for the laminar and turbulent flow regimes [18,19]. The present work is aimed at the developing expressions of the Sherwood numbers for three non-Newtonian fluids, namely, Ellis, Reiner-Philippoff and Eyring fluids in a tube. The analysis is applicable for laminar cross flow in reverse osmosis (RO) as well as in ultrafiltration (UF).

2. THEORY 2.1. Flow Through a Tube The theoretical developments of the tubular flow for Ellis, Eyring and Reiner-Philippoff fluids are presented herein. The flow geometry is shown in figure 1.

Effects of Permeation on Mass Transfer Coefficient for Laminar...

399

Figure 1. Schematic of the flow geometry in the tube.

2.1.1. Ellis Fluid The rheology of the Ellis fluid is [20],

−

(

dv x = ϕ 0 + ϕ 1 τ rx dr

α −1

)τ

rx

(1)

The laminar velocity profile of the Ellis fluid can be obtained by solving the x-component equation of motion and using Eq.(1),

⎧⎪ Fϕ R 2 vx = ⎨ 0 ⎪⎩ 2 for 0 ≤ r ≤ R,

⎛ ⎛ r ⎞2 ⎞ F α ⎛ ⎛ r ⎞ α +1 ⎞⎫⎪ α +1 ⎜ ⎜1 − ⎜ ⎟ ⎟ + ϕ R 1 − ⎜ ⎟ ⎟⎬ ⎜ ⎝ R ⎠ ⎟ α +1 1 ⎜ ⎝ R ⎠ ⎟⎪ ⎠ ⎝ ⎠⎭ ⎝

(2)

dP u where, F = (- dx ). F, can be expressed as a function of average velocity ( 0 ) over the cross-section of the channel as,

400

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar R

∫ v rdr x

u0 =

0

R

∫ rdr 0

⎧ ⎛ R2 u 0 = ⎨ Fϕ 0 ⎜⎜ ⎝ 4 ⎩

or,

⎫ ⎞ Fα ⎟⎟ + ϕ1 R α +1 ⎬ ⎠ α +3 ⎭

(

)

(3)

The steady state solute mass balance in the concentration boundary layer is obtained as,

vx

∂c ∂c D ∂ ∂c (r ) + vr = ∂x ∂r r ∂r ∂r

(4)

by neglecting the diffusive flux compared to the convective flux in the axial direction. Considering a thin concentration boundary layer adjacent to the wall, the curvature effects may be neglected and the problem may be treated as though the wall were flat [20]. If the distance from the wall is denoted as y=R-r, the fluid may be regarded as being confined between a flat mass-transfer surface extending from y=0 to y=∞. Therefore, the solute mass balance equation (Eq. (4)) can be expressed as,

vx

∂c ∂c ∂ 2c + vy =D 2 ∂x ∂y ∂y

(5)

The x-component velocity profile is expressed by Eq. (2). By substituting r=R-y in Eq. (2)

y2 2 and neglecting R and the higher order terms in the binomial expansion of the Eq. (2) (since the thickness of the concentration boundary layer is small), the simplified velocity profile becomes,

v x = {Fϕ 0 R 2 + F α ϕ1 R α +1 } y 0 ≤ y ≤ R , R for

(6)

v

Since the x-component velocity x is large enough compared to the permeate flux, the ycomponent velocity can be approximated as [21], assuming no solute adsorption on the membrane surface,

v y = −v w The initial and boundary conditions of Eq. (5) are,

(7)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

401

c = c0 at x = 0 ,

(8)

c = c0 at y = ∞,

(9)

At the membrane surface, the net solute flux towards the membrane is zero at the steady state. Therefore, the boundary condition at the membrane surface is,

at y = 0,

v w (c − c p ) + D

∂c =0 ∂y

(10)

Rr = 1 − Eq. (10) can be expressed in terms of real retention of the membrane ( which is constant for a membrane solute system [22],

at y = 0,

v w cRr + D

∂c =0 ∂y

cm cp

),

(11)

Inserting the velocity profiles, Eqs. (6) and (7) in Eq. (5) and after nondimensionalization, the following equation is obtained,

A1 y *

* ∂c * ∂ 2c* * ∂c − Pe x = w ∂x * ∂y * ∂y *2

( )

(12)

where, 3 α α ⎛ d ⎞ vw d x y c* = c , * ⎜ { } A F R F R ϕ ϕ = + 1 0 1 x = , y = , Pe w = ⎜ LD ⎟⎟ c0 ⎝ ⎠ and L d D , d = 2R .

*

The rheological parameter A1 can be expressed in terms of Reynolds and Schmidt numbers as,

⎛d A1 = Fϕ 0 R + F α ϕ1 R α ⎜⎜ ⎝ u0

{

}

⎞⎛ u 0 d 2 ⎟⎟⎜⎜ ⎠⎝ LD

⎞ ⎟ ⎟ ⎠ or,

d2 d α +1 ⎫⎛ d⎞ 1 ⎧ α + F ϕ1 α ⎬⎜ Re Sc ⎟ A1 = ⎨ Fϕ 0 u0 ⎩ L⎠ 2 2 ⎭⎝

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Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

d⎞ ⎛ A1 = A11 ⎜ Re Sc ⎟ L⎠ ⎝ or

(13)

where, the non-dimensional rheological parameter becomes,

A11 =

Fϕ 0

Re = and

d2 d α +1 + F α ϕ1 α 2 2 u0

(14)

ρu 0 d μ eff Sc = μ eff ρD ,

The effective viscosity is generally expressed for the non-Newtonian fluid as [12],

μ eff =

Fd 2 32u 0

(15)

The derivation of Eq. (15) is presented in the appendix. The non-dimensional boundary conditions for Eq. (5) become, at x = 0 , c = 1

(16)

* at y = ∞, c = 1

(17)

*

*

*

and at y = 0, *

Pe w c * R r +

∂c * =0 ∂y *

(18)

Eq. (12) is a parabolic partial differential equation with one of the boundary at infinity. Hence, it admits a similarity solution. The similarity parameter in this case is derived in the appendix and is expressed as,

η = A1

1

3

y* 1 x* 3

(19)

In terms of the similarity parameter, Eq (12) becomes an ordinary differential equation,

⎞ dc * d 2c* ⎛η 2 ⎜ B + + 1⎟ ⎟ dη = 0 dη 2 ⎜⎝ 3 ⎠

(20)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

403

where, B1 is a constant and is given as,

B1 =

Pe w A1

1

x*

1

3

3

or

Pe w

B1 = A11

1

x*

d 1 3 (Re Sc ) 3 L

1

3

(21)

The transformed boundary conditions become, * at η = ∞ , c = 1

(22)

dc * + B1 Rr c * = 0 η = 0 , d η at

(23)

The solution of Eq. (20) with the boundary conditions, Eqs. (22) and (23) is,

⎛ η3 ⎞ c * (η ) = K 1 ∫ exp⎜⎜ − − B1η ⎟⎟dη + K 2 ⎝ 9 ⎠

(24)

The integration constants, K 1 and K 2 are obtained using Eqs. (22) and (23) as,

K1 = −

B1 Rr 1 − B1 Rr I 1

K2 = and

(25)

1 1 − B1 Rr I 1

(26)

∞

⎛ η3 ⎞ − B1η ⎟⎟dη I 1 = ∫ exp⎜⎜ − ⎝ 9 ⎠ 0 where,

(27)

The constant B1, can be expressed in terms of length averaged dimensionless permeate flux

(Pe ) from Eq. (21), w

1

Pe w =

∫ Pe w dx = 1 .5 A11 *

0

1

3

d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝

1

3

B1

404

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

or,

B1 = 0.67

Pew 1 d 1 ( A11 3 )(Re Sc ) 3 L

(28)

Estimation of the Mass Transfer Coefficient The definition of mass transfer coefficient is,

⎛ ∂c ⎞ k (c m − c0 ) = − D⎜⎜ ⎟⎟ ⎝ ∂y ⎠ y =0

(29)

In terms of the similarity parameter, Eq. (29) is expressed as

(

)

Sh c m − 1 = −

cm

*

*

A11

1

3

d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝ * 13 x

1

3

⎛ dc * ⎞ ⎜⎜ ⎟⎟ ⎝ dη ⎠ η = 0

(30)

⎛ dc * ⎞ ⎟⎟ are expressed in terms of the integration constants K1 and K 2 as, and ⎜⎜ d η ⎝ ⎠η = 0 A11

Sh = −

1

3

d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝ * 13 x

1

3

K1 K2 −1

(31)

Substituting K 1 and K 2 from Eqs. (25) and (26), the Sherwood number profile along the membrane length becomes,

( )

Sh x = *

A11

1

d 1 (Re Sc ) 3 L x 3 I1 3

*1

(32)

The definite integral I1, can be expressed in terms of non-dimensional, length averaged flux (

Pew

) using Eqs. (27) and (28),

Effects of Permeation on Mass Transfer Coefficient for Laminar...

⎞ ⎛ ⎟ ⎜ η3 Pe w I 1 = ∫ exp⎜ − η ⎟dη or, − 0.67 1 d 1 ⎜ 9 0 A11 3 (Re Sc ) 3 ⎟⎟ ⎜ L ⎠ ⎝ ∞ 3 ⎛ η ⎞ − 0.67λeff 1η ⎟⎟dη I 1 = ∫ exp⎜⎜ − ⎝ 9 ⎠ 0

405

∞

where,

Pe w

λeff 1 = A11

1

3

(33)

(34)

d 1 (Re Sc ) 3 L

The length averaged Sherwood number becomes, (from Eq. (32)), 1

1

3 1 1.5 ( A11 ) (Re Sc d ) 3 ShL = ∫ Sh( x )dx = L I1 0 *

*

(35)

Based on the extent of permeation, results of various cases are discussed below.

Case 1: No Permeation In this case,

Pew

∞

∫

⎛ η3 ⎞ ⎟⎟dη = 1.8575 ⎝ 9 ⎠

=0 and hence, I 1 = exp⎜⎜ − 0

Therefore, the expression for average Sherwood number is, 1 d 1 ShL = 0.81( A11 ) 3 (Re Sc ) 3 L

(36)

Case 2: With Permeation (RO/UF System) To trap the effects of suction as well as the non- Newtonian behavior, I1 is evaluated numerically for various values of λeff 1 (in the range of λeff 1 , between nearly 0 and 16, typically encountered in RO/UF), and the inverse of the integral is plotted against λeff 1 , in figure 2. The results are fitted in polynomial function of more than 0.99, as

λeff 1 with a correlation coefficient

406

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

[

1 = 0.54 1 + 0.74λ eff 1 + 0.12λ 2eff 1 − 8.4 × 10 −3 λ3eff 1 I1

]

(37)

The expression of the average Sherwood number from Eq. (35) is then,

d⎞ ⎛ Sh L = 0.81 × ( A11 ) ⎜ Re Sc ⎟ L⎠ ⎝ 1

1

3

3

[1 + 0.74λ

eff 1

+ 0.12λ 2eff 1 − 8.4 × 10 −3 λ3eff 1

]

(38)

Case 3: ϕ1 = 0 , Newtonian Fluid In this case, gradient ( −

F=

ϕ0

becomes inverse of the viscosity (μ) of the solution. Hence, the pressure

dP = F ) from Eq. (3) becomes, dx

4u 0 μ , R2

(39)

The rheological parameter A11 is simplified from Eq. (14),

A11 =

Fd 2 2μu 0

(40)

Using Eq.(39), the value of A11 is obtained as 8. Therefore, from Eq.(38), the expression of the length averaged Sherwood number becomes, 1

[

d⎞ 3 ⎛ Sh L = 1.62⎜ Re Sc ⎟ 1 + 0.74λ eff 1 + 0.12λ 2 eff 1 − 8.4 × 10 −3 λ3 eff 1 L⎠ ⎝ where from Eq. (34), λeff 1 is,

λeff 1 = 0.5

Pe w d 1 (Re Sc ) 3 L

]

(41)

(42)

Hence, Eq. (41) can be written as, ⎡ ⎤ 1 2 3 ⎥ d⎞ 3⎢ Pe w Pe w Pe w ⎛ −3 + 0.03 − 1.05 × 10 Sh L = 1.62⎜ Re Sc ⎟ ⎢1 + 0.37 ⎥ 1 2 d d d L⎠ ⎢ ⎝ (Re Sc ) 3 (Re Sc ) 3 (Re Sc ) ⎥ L L L ⎦⎥ ⎣⎢

(43)

407

Effects of Permeation on Mass Transfer Coefficient for Laminar...

It may be noticed that this result coincides with the reported one for the mass transfer coefficient with permeation for Newtonian fluid [16] in a porous tube. For, no permeation i.e.,

Pe w

d⎞ ⎛ Sh L = 1.62⎜ Re Sc ⎟ L⎠ ⎝

1

=0 and Eq. (43) becomes,

3

(44)

which is identical with the Leveque solution [3].

Case 4:

ϕ0 = 0 , Power Law Fluid

In this case, the pressure gradient is obtained from Eq. (3) as, 1

⎧ 2α +1 u 0 (α + 3) ⎫ α F =⎨ ⎬ α +1 ⎩ ϕ1 d ⎭

(45)

From Eq. (14), rheological parameter A11 becomes,

F α ϕ1 d α +1 A11 = u 0 2α

(46)

Using Eqs. (45) and (46), A11 becomes

A11 = 2(α + 3)

(47)

Therefore, the expression of average Sherwood number is (from Eq. 38), 1

[

1 ⎛ d⎞ Sh L = 1.02(α + 3) 3 ⎜ Re Sc ⎟ 1 + 0.74λ eff 1 + 0.12λ 2 eff 1 − 8.4 × 10 −3 λ3 eff 1 L⎠ ⎝

where,

3

]

(48)

λeff 1 is (from Eq. 34),

λeff 1 =

0.794 Pe w

d 1 (α + 3) (Re Sc ) 3 L 1

3

(49)

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Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

It may be noted here that in the definition of Re and Sc, the viscosity is μeff. The expression of μeff is shown in the appendix.

2.1.2. Reiner-Philippoff Fluid The rheological equation for Reiner-Philippoff fluid is [20],

⎛ ⎜ dv x ⎜ 1 − =⎜ μ0 − μ∞ dr ⎜ μ∞ + 2 ⎜ 1 + (τ rx τ s ) ⎝

⎞ ⎟ ⎟ ⎟τ rx ⎟ ⎟ ⎠

(50)

An analysis of the equation of motion in x-direction yields the following equation,

1 d (r τ rx ) = − dP = F r dr dx

(51)

The solution of the above equation using Eq. (50) yields the laminar velocity profile as, vx =

2 F ⎧⎪ R 2 ⎛⎜ ⎛ r ⎞ ⎞⎟ − 1 − C ln (2 μ 0 − μ ∞ ) + μ 0 a 2 r 2 + C ln (2μ 0 − μ ∞ ) + μ 0 a 2 R 2 ⎜ ⎟ ⎨ μ 0 ⎪⎩ 2 ⎜⎝ ⎝ R ⎠ ⎟⎠

(

where, a = − F

)

(

⎫

)⎪⎬

(52)

⎪⎭

τs

⎛μ −μ ⎞

∞ ⎟ and C = ⎜⎜ 0 2 ⎟ 2 μ a 0 ⎝ ⎠

The term F can be related with tube as follows,

u0 =

u 0 using the definition of average velocity (Eq. 3) in the

⎛ 2μ − μ 2 F ⎪⎧ R 4 CR 2 + − C ⎜⎜ 0 2 ∞ 2 ⎨ 2 μ 0 R ⎪⎩ 8 ⎝ 2μ 0 a

⎫ ⎞ ⎛ μ0a 2 2 ⎞⎪ ⎟ ln⎜1 + ⎟ ⎟ ⎜ (2μ − μ ) R ⎟⎬⎪ 0 ∞ ⎠ ⎝ ⎠⎭

Inside the very thin concentration boundary layer, r=R-y and y << R , hence,

(53)

y2 and R2

higher order terms are neglected, to get the approximate velocity profile as,

vx =

F

μ0

Ry

(54)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

409

The solute mass balance equation and its boundary conditions within the concentration boundary layer remain in the same form as Eqs. (12), Eqs.(16-18). Only, A1 is replaced by A2.

A2 = A22 Re Sc

where, A22 =

d L

(55)

Fd 2 2μ 0 u 0

(56)

In the definition of Re and Sc in Eq. (55), μeff is expressed by Eq. (15), where F can be calculated from Eq. (53) for a known u0 , rheological parameters and tube radius. The concentration profile is obtained by solving Eq. (12) and Eqs. (16-18) using the similarity method with the similarity parameter,

ξ = A2

1

3

y* x

*1

(57)

3

Using the similar calculation steps as discussed in detail for the Ellis fluid, profile of Sherwood number along the membrane length becomes,

Sh( x ) = *

( A22 ) I2x

1

*1

3

3

d 1 (Re Sc ) 3 L

(58)

∞

⎛ ξ3 ⎞ where, I 2 = ∫ exp⎜⎜ − − 0.67λeff 2ξ ⎟⎟dξ ⎝ 9 ⎠ 0

and

λeff 2 =

PeW

(60)

d 1 ( A22 ) (Re Sc ) 3 L 1

(59)

3

The length averaged Sherwood number becomes,

( A ) 3 ⎛ Re Sc d ⎞ 1.5 22 1

ShL =

I2

⎜ ⎝

⎟ L⎠

1

3

Based on the extent of permeation, following simplifications are discussed.

(61)

410

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

Case 1 No permeation:

PeW

ShL = 0.81( A22 )

1

3

=0 and I2=1.8575. Hence, the average Sherwood number is,

d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝

1

3

(62)

Case 2: With Permeation In this case, I2 is evaluated by numerical integration for various values of is plotted with

λeff 2 and 1 / I 2

λeff 2 in figure 2. The expression of the length averaged Sherwood number is

given by Eq. (38), replacing A11 by A22 and

λeff 1 by λeff 2 .

Case 3: μ 0 = μ ∞ = μ , Newtonian fluid In this case, as for Ellis fluid, A22 = 8 and the length averaged Sherwood number becomes similar to Eq. (41), replacing λ eff 1 by λeff 2 , while λeff 2 is expressed by Eq. (42). Therefore, the final expression for the length averaged Sherwood number is identical with Eq. (43).

2.1.3 Eyring Fluid The rheological equation for the Eying fluid is [20],

1 dv x ⎞ ⎟ ⎝ B dr ⎠ ⎛

τ rx = ASinh −1 ⎜ −

(63)

An analysis of x-component equation of motion results in Eq. (51). Using Eqs. (51) and (63), the expression of the x-component velocity profile becomes,

vx =

B {Cosh(KR ) − Cosh( Kr )} K

where, K =

(64)

F A

The relation between the cross sectional average velocity u0 and F can be obtained as before (from Eq. 3),

Effects of Permeation on Mass Transfer Coefficient for Laminar...

u0 =

⎫ 2B ⎧ R 1 R2 ( ) ( ) Sinh KR ( 1 Cosh KR ) Cosh( KR )⎬ + − − 2 ⎨ 2 2 KR ⎩ K K ⎭

Inside the concentration boundary layer, r=R-y and y << R , hence

411

(65)

y2 and higher order R2

terms are neglected, to get the velocity profile as,

v x = [BSinh( KR )]y

(66)

The solute mass balance equation and its boundary conditions within the concentration boundary layer remain in the same form as Eq. (12) and Eqs. (16-18), with A1 is replaced by A3 ,

d L

A3 = A33 Re Sc

(67)

where,

A33 =

Bd Kd Sinh( ) u0 2

(68)

The definition of Re and Sc, in Eq. (68), μeff is expressed by the Eq. (15), where F can be calculated from Eq. (65) for a known value of u0 , rheological parameters and channel height. The concentration profile is obtained by solving Eq. (12) and Eqs. (16-18) using the similarity method with similarity parameter,

φ = A3

1

3

y* x

*1

(69)

3

Using the similar calculation steps as discussed in detail for the Ellis fluid, the Sherwood number profile along the membrane length becomes,

( A33 ) 3 ⎛ 1

Sh( x ) = *

I3x ∞

∫

*1

3

1

3

⎛ φ3 ⎞ − 0.67λeff 3φ ⎟⎟dφ ⎝ 9 ⎠

where, I 3 = exp⎜⎜ − 0

d⎞ ⎜ Re Sc ⎟ L⎠ ⎝

(70)

(71)

412

and

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

λeff 3 =

PeW

(72)

d 1 ( A33 ) (Re Sc ) 3 L 1

3

The length averaged Sherwood number becomes,

( A33 ) 3 ⎛ 1

ShL = 1.5

d⎞ ⎜ Re Sc ⎟ L⎠ ⎝

I3

1

3

(73)

The simplifications with various extents of permeation are as follows,

Case 1: No Permeation, PeW =0 The length averaged Sherwood number becomes

ShL = 0.81( A33 )

1

d⎞ ⎛ 3 Re Sc ⎟ ⎜ L⎠ ⎝

1

3

(74)

Case 2: With Permeation As earlier, I3 is evaluated numerically for various values of

λeff 3 and 1 / I 3 versus λeff 3

is plotted in figure 2. The Sherwood number relationship is given by Eq. (38), replacing A11 and A33 and λeff 1 by λeff 3 .

Effects of Permeation on Mass Transfer Coefficient for Laminar...

413

10

1/I1,2,3

8

6

4

2

0 0

2

4

6

8

10

12

14

16

λeff1, eff2, eff3

Figure 2. Variation of the definite integrals I1,2,3 with

λeff 1, eff 2, eff 3

for tubular geometry.

2.2. Flow through a Rectangular Channel The theoretical developments of the rectangular thin channel for Ellis, Eyring and ReinerPhilippoff fluids are presented herein. The flow geometry is shown in figure 3.

Figure 3. Schematic of the flow geometry in the rectangular channel.

414

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

2.2.1. Ellis Fluid The rheology of the Ellis fluid is [20],

−

(

dv x = ϕ 0 + ϕ1 τ dy

α −1 yx

)τ

(75)

yx

The laminar velocity profile of the Ellis fluid can be obtained by solving the x-component equation of motion using Eq.(75), α +1 ⎡ ⎧ ⎛ y ⎞ Fα y ⎞ ⎫⎪⎤ ⎛ α +1 ⎪ for 0 ≤ y ≤ h, v x = ⎢ Fϕ 0 hy⎜1 − ϕ 1 h ⎨1 − ⎜1 − ⎟ ⎬⎥ ⎟+ ⎪⎩ ⎝ h ⎠ ⎪⎭⎦⎥ ⎝ 2h ⎠ α + 1 ⎣⎢

⎡

⎛ ⎝

for h ≤ y ≤ 2h, v x = ⎢ Fϕ 0 hy ⎜1 −

⎢⎣

where, F = (-

⎧ ⎛ y ⎞α +1 ⎫⎪⎤ y ⎞ Fα α +1 ⎪ h + ϕ ⎟ ⎨1 − ⎜ − 1⎟ ⎬⎥ 1 2h ⎠ α + 1 ⎪⎩ ⎝ h ⎠ ⎪⎭⎥⎦

(76)

(77)

dP u ). F, can be expressed as a function of average velocity ( 0 ) over the dx

cross-section of the channel as, 2h

∫ v dy x

u0 =

0 2h

∫ dy 0

or,

⎧ ⎛ h2 u 0 = ⎨ Fϕ 0 ⎜⎜ ⎝ 3 ⎩

⎫ ⎞ Fα ⎟⎟ + ϕ1 (h α +1 )⎬ ⎠ α +2 ⎭

(78)

The solute mass balance in the concentration boundary layer is obtained as,

∂ 2c ∂c ∂c =D 2 + vy vx ∂y ∂x ∂y

(79)

The x-component velocity profile is expressed by Eq. (76). Since the thickness of the

y2 concentration boundary layer is small, 2 and the higher order terms in the binomial h expansion of the Eq. (76) are neglected and the simplified velocity profile becomes,

Effects of Permeation on Mass Transfer Coefficient for Laminar...

{

for 0 ≤ y ≤ h, v x = Fϕ 0 h + F 2

α

ϕ 1 hα +1 }

y h

415

(80)

v

Since the x-component velocity x is large enough compared to the permeate flux, the ycomponent velocity can be approximated as expressed in Eq.(7). The boundary conditions of Eq.(79) are given in Eqs.(8-11). Inserting the velocity profiles and after nondimensionalization, the following equation is obtained,

A10 y *

* ∂ 2c* ∂c * * ∂c = Pe x − w ∂y * ∂y *2 ∂x *

( )

(81)

where,

x* =

v d x y c , c * = , Pew = w e and , y* = L de c0 D

{

α

A = Fϕ 0 h + F ϕ 1 h 0 1

α

}

⎛ de3 ⎞ ⎜ ⎟ ⎜ LD ⎟ ⎝ ⎠

The equivalent diameter for a thin channel is defined as

d e = 4h [23].The rheological

0

parameter A1 can be expressed in terms of Reynolds and Schmidt numbers as,

{

α

A = Fϕ 0 h + F ϕ 1 h 0 1

α

}

⎛ de ⎜⎜ ⎝ u0

2 ⎞⎛ u 0 d e ⎜ ⎟⎟ ⎜ ⎠⎝ LD

⎞ ⎟ ⎟ ⎠

or, α +1 2 ⎧⎪ de d e ⎫⎪⎛ d ⎞ α ϕ ϕ F F + ⎜ Re Sc e ⎟ ⎨ 0 1 α ⎬ L⎠ 4 4 ⎪⎭⎝ ⎪⎩ de ⎞ 0 0 ⎛ or A1 = A11 ⎜ Re Sc ⎟ L⎠ ⎝

A10 =

1 u0

(82)

where, the non-dimensional rheological parameter becomes, 2

α +1

d d Fϕ 0 e + F α ϕ 1 e α 4 4 A110 = u0

(83)

416

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar and Re =

μ eff ρu 0 d e , Sc = ρD μ eff

The effective viscosity is generally expressed for the non-Newtonian fluid as [12]

μ eff

2

Fd e = 32u 0

(84)

The derivation of Eq. (84) is presented in the appendix. The non-dimensional boundary conditions for Eq. (81) are given by Eqs.(16-18). Eq. (81) is a parabolic partial differential equation with one of the boundary at infinity. Hence, it admits a similarity solution. The similarity parameter in this case is derived in the appendix and is expressed as,

η 0 = A10

1

y* 1 x* 3

3

(85)

In terms of the similarity parameter, Eq (81) becomes an ordinary differential equation,

d 2c* dη 0

2

⎛η0 2 ⎞ * 0 ⎟ dc ⎜ + + B1 =0 ⎜ 3 ⎟ dη ⎝ ⎠ 0

(86)

0

where, B1 is a constant and is given as,

B 10 =

Pe w 0 1

A

1

x*

1

3

3

or

Pe w

B 10 = 0 11

A

1

d 1 3 (Re Sc e ) 3 L

x*

1

3

(87)

The transformed boundary conditions become, at

η 0 = ∞ , c* = 1

dc * η = 0 , 0 at + B10 R r c * = 0 dη 0

(88)

(89)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

417

The solution of Eq. (86) with the boundary conditions, Eqs. (88) and (89) is,

⎛ η 3 ⎞ c * (η 0 ) = K 10 ∫ exp⎜ − 0 − B10η 0 ⎟dη 0 + K 20 ⎜ 9 ⎟ ⎝ ⎠ 0

(90)

0

The integration constants, K 1 and K 2 are obtained using Eqs. (88) and (89) as,

K 10 = −

B10 R r

(91)

1 − B10 R r I 10

and K 2 = 0

1 1 − B10 R r I 10

(92)

⎛ η03 ⎞ − B10η 0 ⎟dη 0 where, I = ∫ exp⎜ − ⎜ 9 ⎟ 0 ⎝ ⎠ ∞

0 1

(93)

0

The constant B1 , can be expressed in terms of length averaged dimensionless permeate flux

(Pe ) from Eq. (87), w

1

Pe w =

∫ Pe

1 0 3 11

dx = 1 .5 A *

w

0

d ⎞ ⎛ ⎜ Re Sc e ⎟ L ⎠ ⎝

1

3

B 10

or,

Pe w

B10 = 0.67 0 11

(A

1

3

(94)

d 1 )(Re Sc e ) 3 L

Estimation of Mass Transfer Coefficient From the definition of mass transfer coefficient, in terms of similarity parameter, Eq. (29) is expressed as

(

)

Sh c m − 1 = − *

0 11

A

1

3

d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝ * 13 x

1

3

⎛ dc * ⎜⎜ ⎝ dη 0

⎞ ⎟⎟ ⎠η 0 = 0

(95)

418

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

cm

*

⎞ 0 0 ⎟⎟ are expressed in terms of the integration constants K 1 and K 2 as, ⎠η 0 = 0

⎛ dc * ⎝ dη 0

and ⎜⎜

0 11

A

1

3

Sh = −

d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝ * 13 x 0

1

3

K 10 K 20 − 1

(96)

0

Substituting K 1 and K 2 from Eqs. (91) and (92), the Sherwood number profile along the membrane length becomes,

( )

Sh x = *

A110

1

3

1

x * 3 I 10

(Re Sc

d e 13 ) L

(97)

0

The definite integral I 1 , can be expressed in terms of non-dimensional, length averaged flux (

Pew

) using Eqs. (93) and (94),

⎛ ⎞ 3 ⎜ ⎟ η Pe w η 0 ⎟dη 0 or, I 10 = ∫ exp⎜ − 0 − 0.67 1 d 1 ⎜ 9 ⎟ 0 A110 3 (Re Sc e ) 3 ⎜ ⎟ L ⎝ ⎠ 3 ∞ ⎞ ⎛ η I 10 = ∫ exp⎜ − 0 − 0.67λ 0eff 1η 0 ⎟dη 0 ⎟ ⎜ 9 0 ⎠ ⎝ ∞

where,

Pe w

λ0eff 1 = 0 11

A

1

3

(98)

(99)

d 1 (Re Sc e ) 3 L

The length averaged Sherwood number becomes, (from Eq. (97)), 1

( )

1.5 Sh L = ∫ Sh( x )dx = 0 A110 I1 0 *

*

1

3

(Re Sc

d e 13 ) L

Based on the extent of permeation, results of various cases are discussed below.

(100)

419

Effects of Permeation on Mass Transfer Coefficient for Laminar...

Case 1: No Permeation Pew

In this case,

∞

⎛ η03 ⎞ ⎟dη = 1.8575 ⎜ 9 ⎟ 0 ⎠ ⎝

=0 and hence, I 1 = exp⎜ − 0

∫ 0

Therefore, the expression for average Sherwood number is,

Sh L = 0.81( A110 ) 3 (Re Sc 1

d e 13 ) L

(101)

Case 2: With Permeation (RO/UF System) 0

To trap the effects of suction as well as the non- Newtonian behavior, I 1 is evaluated

λ 0eff 1 (between nearly 0 and 16, typically encountered in

numerically for various values of

RO/UF), and the inverse of the integral is plotted against λ eff 1 , in figure 4. The results are 0

fitted in polynomial function of

λ 0eff 1 with a correlation coefficient more than 0.99, as

[

1 = 0.54 1 + 0.743λ0eff 1 + 0.105(λ0eff 1 ) 2 − 9.66 × 10 −3 (λ0eff 1 ) 3 0 I1

]

(102)

The expression of the average Sherwood number from Eq. (100) is then, d ⎞ ⎛ Sh L = 0.81 × ( A ) ⎜ Re Sc e ⎟ L⎠ ⎝ 0 11

Case 3:

1

3

1

3

[1 + 0.743λ

0 eff 1

+ 0.105(λ0eff 1 ) 2 − 9.66 × 10 −3 (λ0eff 1 ) 3

]

(103)

ϕ1 = 0, Newtonian Fluid

In this case, gradient ( −

F=

ϕ0

becomes inverse of the viscosity of the solution. Hence, the pressure

dP = F ) from Eq. (78) becomes, dx

3u 0 μ , h2

(104)

0

The rheological parameter A11 is simplified from Eq. (83),

420

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar 2

A110 =

Fd e 4μu 0

(105) 0

Using Eq.(104), the value of A11 is obtained as 12. Therefore, from Eq.(103), the expression of the length averaged Sherwood number becomes, 1

[

d ⎞ ⎛ ShL = 1.85⎜ Re Sc e ⎟ 1 + 0.743λ0eff 1 + 0.105(λ0eff 1 ) 2 − 9.66 × 10 −3 (λ0eff 1 ) 3 L⎠ ⎝ 3

λ0eff 1 = 0.44

where from Eq. (99), λ eff 1 is, 0

]

Pe w d 1 (Re Sc e ) 3 L

(106)

(107)

Hence, Eq. (106) can be written as, ⎡ ⎤ 1 2 3 ⎥ de ⎞ 3 ⎢ Pe w Pe w Pe w ⎛ −4 + 0.02 − 8.05 × 10 ShL = 1.85⎜ Re Sc ⎟ ⎢1 + 0.32 1 2 d ⎥ d d L⎠ ⎢ ⎝ (Re Sc e ) 3 (Re Sc e ) 3 (Re Sc e ) ⎥ L L L ⎦⎥ ⎣⎢

(108)

It may be noticed that this result coincides with the reported one for the mass transfer coefficient with permeation for Newtonian fluid [16]. For, no permeation i.e.,

Pe w

d ⎞ ⎛ ShL = 1.85⎜ Re Sc e ⎟ L⎠ ⎝

1

=0 and Eq. (108) becomes,

3

(109)

which is identical with the Leveque solution [3].

Case 4:

ϕ0 = 0 , Power Law Fluid

In this case, the pressure gradient is obtained from Eq. (78) as, 1

⎧⎪ 4α +1 u 0 (α + 2) ⎫⎪ α F =⎨ ⎬ ⎪⎩ ϕ1 d e α +1 ⎪⎭

(110)

0

From Eq. (83), rheological parameter A11 becomes,

421

Effects of Permeation on Mass Transfer Coefficient for Laminar...

F α ϕ1 d e

A110 =

α +1

(111)

u 0 4α 0

Using Eqs. (110) and (111), A11 becomes

A110 = 4(α + 2)

(112)

Therefore, the expression of average Sherwood number is (from Eq. 103), 1

[

d ⎞ ⎛ Sh L = 1.28(α + 2) ⎜ Re Sc e ⎟ 1 + 0.73λ 0eff 1 + 0.105(λ 0eff 1 ) 2 − 9.66 × 10 −3 (λ0eff 1 ) 3 L⎠ ⎝ 1

where,

3

3

]

(113)

λ 0eff 1 is (from Eq. 99),

λ0eff 1 =

0.63Pe w

(114)

d 1 (α + 2) (Re Sc e ) 3 L 1

3

It may be noted here that in the definition of Re and Sc, the viscosity is μeff. The expression of μeff is shown in the appendix.

2.2.2. Reiner-Philippoff Fluid The rheological equation for Reiner-Philippoff fluid is [20],

⎛ ⎜ dv x ⎜ 1 − =⎜ μ0 − μ∞ dy ⎜ μ0 + 2 ⎜ 1 + (τ yx τ s ) ⎝

⎞ ⎟ ⎟ ⎟τ yx ⎟ ⎟ ⎠

(115)

An analysis of the equation of motion in x-direction yields the following equation,

dτ yx dy

=−

dp =F dx

(116)

The solution of the above equation using Eq. (115) yields the laminar velocity profile as, vx =

(

)

(

)

F ⎧ ⎛ y ⎞ 2 2 2 2 ⎫ ⎨hy ⎜1 − 2h ⎟ − C ln (2 μ 0 − μ ∞ ) + μ 0 a (h − y ) + C ln (2 μ 0 − μ ∞ ) + μ 0 a h ⎬ ⎠ μ0 ⎩ ⎝ ⎭

(117)

422

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

where, a = − F

τs

⎛μ −μ ⎞

∞ ⎟ and C = ⎜⎜ 0 2 ⎟ 2 μ a 0 ⎠ ⎝

The term F can be related with the channel as follows,

u0 =

u 0 using the definition of average velocity (Eq. 78) in

3 F ⎧h3 2 2 −1 ⎛ h ⎞ ⎫ ⎟⎟⎬ ⎨ − Ch ln ((2 μ 0 − μ ∞ ) + μ 0 a h ) + 2Ch − 2C 2 tan ⎜⎜ μ0h ⎩ 3 ⎝ C ⎠⎭

(118)

y2 and higher order terms are Inside the Concentration Boundary layer, where h2 neglected, the velocity profile becomes,

vx =

F

μ0

hy

(119)

The solute mass balance equation and its boundary conditions within the concentration 0

0

boundary layer remain in the same form as Eqs. (81), (16-18). Only, A1 is replaced by A2 . 0 A20 = A22 Re Sc

de L

(120)

2

where, A22 = 0

Fd e 4μ 0 u 0

(121)

In the definition of Re and Sc in Eq. (120), μeff is expressed by Eq. (84), where F can be calculated from Eq. (118) for a known u0 , rheological parameters and channel half height. The concentration profile is obtained by solving Eq. (81) and Eqs. (16-18) using the similarity method with the similarity parameter, 1 0 3 2

ξ0 = A

y* x

*1

(122)

3

Using the similar calculation steps as discussed in detail for the Ellis fluid, profile of Sherwood number along the membrane length becomes,

Effects of Permeation on Mass Transfer Coefficient for Laminar...

Sh( x ) = *

0 ( A22 ) 0 2

I x

1

*1

3

(Re Sc 3

d e 13 ) L

423

(123)

⎞ ⎛ ξ03 where, I = ∫ exp⎜ − − 0.67λ 0eff 2 ξ 0 ⎟dξ 0 ⎟ ⎜ 9 0 ⎠ ⎝ ∞

0 2

and

λ0eff 2 =

(124)

PeW

(125)

d 1 ( A ) (Re Sc e ) 3 L 1

0 22

3

The length averaged Sherwood number becomes,

Sh L

(A ) = 1.5 0 22 0 2

1

3

I

d ⎞ ⎛ ⎜ Re Sc e ⎟ L ⎠ ⎝

1

3

(126)

Based on the extent of permeation, following simplifications are discussed.

Case 1: No permeation PeW

0

=0 and I 2 =1.8575. Hence, the average Sherwood number is,

( )

Sh L = 0.81 A

0 22

1

3

d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

3

(127)

Case 2: With Permeation 0

In this case, I 2 is evaluated by numerical integration for various values of

λeff 2 and

1 / I 20 is plotted with λ 0eff 2 in figure 4. The expression of the length averaged Sherwood 0

0

number is given by Eq. (103), replacing A11 by A22 and

λ 0eff 1 by λ 0eff 2 .

424

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

Case 3: μ 0 = μ ∞ = μ , Newtonian Fluid 0

In this case, as for Ellis fluid, A22 = 12 and the length averaged Sherwood number becomes similar to Eq. (106), while

λ 0eff 2 is expressed by Eq. (107). Therefore, the final

expression for the length averaged Sherwood number is identical with Eq. (108).

2.2.3. Eyring Fluid The rheological equation for the Eying fluid is [20],

⎛

1 dv ⎞

x ⎟⎟ τ yx = ASinh −1 ⎜⎜ − ⎝ B dy ⎠

(128)

An analysis of x-component equation of motion results in Eq. (116). Using Eqs. (116) and (128), the expression of the x-component velocity profile becomes,

vx =

B {Cosh(K (h − y )) − Cosh(Kh)} K

(129)

⎛ dP ⎞ ⎜− ⎟ F ⎝ dx ⎠ = where, K = A A The relation between the cross sectional average velocity u0 and F can be obtained as before (from Eq.78),

u0 =

B ⎧2 ⎫ ⎨ Sinh(Kh ) − 2hCosh(Kh )⎬ 2 Kh ⎩ K ⎭

Inside the Concentration Boundary layer, where

(130)

y2 and higher order terms are h2

neglected, the velocity profile becomes,

v x = B Sinh( Kh) y

(131)

The solute mass balance equation and its boundary conditions within the concentration 0

boundary layer remain in the same form as Eq. (81) and Eqs. (16-18), with A1 is replaced by

A30 ,

Effects of Permeation on Mass Transfer Coefficient for Laminar...

de L

A30 = A330 Re Sc

425

(132)

where,

A330 =

Bd e Kd Sinh( e ) u0 4

(133)

The definition of Re and Sc, in Eq. (133), μeff is expressed by the Eq. (84), where F can be calculated from Eq. (130) for a known value of u0 , rheological parameters and channel height. The concentration profile is obtained by solving Eq. (81) and Eqs. (16-18) using the similarity method with similarity parameter, 1 0 3 3

φ0 = A

y* x

*1

(134)

3

Using the similar calculation steps as discussed in detail for the Ellis fluid, the Sherwood number profile along the membrane length becomes,

Sh( x

*

(A ) )= 0 33

I 30 x

1

*1

3

3

d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

3

(135)

⎞ ⎛ φ 3 0 where, I = ∫ exp⎜ − 0 − 0.67λ eff 3φ 0 ⎟dφ 0 ⎟ ⎜ 9 0 ⎠ ⎝ ∞

0 3

and λ eff 3 =

PeW

0

d 1 ( A ) (Re Sc e ) 3 L 1

0 33

(136)

(137)

3

The length averaged Sherwood number becomes,

Sh L

(A ) = 1.5 0 33 0 3

I

1

3

d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

3

The simplifications with various extents of permeation are as follows,

(138)

426

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

Case 1: No Permeation, PeW =0 The length averaged Sherwood number becomes

ShL = 0.81(A

0 33

)

1

d ⎞ 3⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

3

(139)

Case 2: With Permeation As earlier,

I 30 is evaluated numerically for various values of λ 0 and 1 / I 30 versus eff 3

λ 0eff 3 is plotted in figure 4. The Sherwood number relationship is given by Eq. (103), 0

replacing A11 by

A330 and λ 0 by λ 0 . eff 1 eff 3

10

1/I1,2,3

8

6

4

2

0 0

2

4

6

8

10

12

14

λeff1, eff2, eff3 Figure 4. Variation of the definite integrals

I 10, 2,3

with

λ 0 eff 1, eff 2, eff 3 for rectangular channel.

16

Effects of Permeation on Mass Transfer Coefficient for Laminar...

427

3. RESULTS AND DISCUSSION 3.1. Tubular Geometry 3.1.1. Ellis Fluid Aqueous solution of carboxymethyl cellulose (CMC) follows Ellis fluid behavior. Various rheological parameters of the CMC solutions are given below [20],

ϕ 0 = 0.2891 cm2 s-1 dyne-1, ϕ = 0.028 cm2α s-1 dyne-α, α =1.707 for 0.6% CMC and 1 ϕ 0 = 0.421 cm2 s-1 dyne-1, ϕ = 0.2724 cm2α s-1 dyne-α, α =1.185 for 1.5% CMC 1 solution. In order to calculate the Sherwood number, first F (i.e., −

dP ) is fitted with the dx

average velocity (u0), using the following steps, i) a value of u0 is assumed. ii) using the rheological parameters and assumed values of u0 and channel geometry R (0.001 m), F is obtained from Eq. (3) using Newton-Raphson iteration method. iii) μ eff is calculated using the F value from step (ii) by Eq. (15). iv) Reynolds number is calculated from its definition

ρu 0 d and is checked whether it μ eff

is lying within the laminar flow regime ( Re ≤ 2200 ). Using the above method, F is evaluated for various value of u0 and is fitted as a function of u0. The tubular channel radius (R) is considered to be 0.001 m in this study. The relationship of F with u0 is presented below,

F = − 1 . 1 × 10 5 u 0 + 1 . 8 × 10 5 u 0 + 9 . 8 × 10 3 , for 0.6% CMC solution 2

(140)

F = 2 . 8 × 10 5 u 0 + 5 . 5 × 10 3 , for 1.5% CMC solution

(141)

where, F is in Pa/m and u0 is in m/s. Using Eq. (14), A11 is evaluated at u0 =0.3 m/s and its values are, A11 = 9.66 , for 0.6% CMC solution = 8.23 , for 1.5% CMC solution 142) The profiles of Sherwood numbers along the channel length are obtained from Eq. (32),

428

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar 1

( )

d⎞ 3 2.13 ⎛ Sh x = Sc Re ⎜ ⎟ , for 0.6% CMC solution *1 L⎠ I1 x 3 ⎝ *

1

2.02 ⎛ d⎞ 3 = *1 ⎜ ReSc ⎟ , for 1.5% CMC solution L⎠ I1 x 3 ⎝ The values of the permeation parameter, λeff 1 is obtained from Eq. (34),

λeff 1 =

=

0.469 Pe w d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝

0.495PeW

d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝

1

1

(143)

for 0.6% CMC solution 3

for 1.5% CMC solution

(144)

3

The length averaged Sherwood numbers are obtained from Eq. (38) as, ⎤ ⎡ ⎥ ⎢ 2 3 PeW PeW d⎞ 3 PeW ⎛ −4 ⎥ 0 . 026 8 . 67 10 ShL = 1.73⎜ Re Sc ⎟ ⎢1 + 0.35 + − × 1 2 ⎥ d 3 3 L⎠ ⎢ ⎛ ⎞ ⎝ d⎞ d⎞ ⎛ ⎛ Re Sc ⎜ ⎟ ⎥ ⎢ Re Re Sc Sc ⎜ ⎟ ⎜ ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦ for 0.6% CMC solution 1

⎤ ⎡ ⎥ ⎢ 2 3 3 PeW PeW PeW −3 ⎥ = 1.64⎛⎜ Re Sc d ⎞⎟ ⎢1 + 0.366 + 0.029 − 1.02 × 10 1 2 ⎥ d 3 3 L⎠ ⎢ ⎛ ⎞ ⎝ d⎞ d⎞ ⎛ ⎛ ⎜ Re Sc ⎟ ⎥ ⎢ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦ for 1.5% CMC solution (145) 1

The variation of the Sherwood number along the channel length for values of

Re Sc

d PeW = 103 and 105 for =200 is shown in figure 5. It is observed from the figure L

that, the Sherwood number decreases along the tube length. The decline is sharp in the upstream of the channel and gradual thereafter. This is due to the fact that the build up of the concentration boundary layer over the membrane surface becomes sluggish at the downstream of the channel because of the forced convection imposed by the cross flow. This reduces the concentration difference between the membrane surface and the bulk of the solution. Therefore, the Sherwood number and consequently, the mass transfer coefficient

429

Effects of Permeation on Mass Transfer Coefficient for Laminar...

decreases along the channel length. It may be observed from figure 5 that the Sherwood number is more at higher Re Sc

d Pe at the same level of the permeate flux ( w ). This L

indicates that at higher Reynolds number, forced convection is more due to higher cross flow velocity, which enhances the mass transfer coefficient. It may also be observed that the Sherwood number profiles for both concentrations of CMC solution almost coincide at

Re Sc

d d =103 and they differ marginally at Re Sc =105. L L 340 320 300 280

Re Sc d/L=10

*

Sh(x )

260

5

240

0.6% CMC

220 200

1.5% CMC

180 160

Re Sc d/L=10

140 120 0.0

0.2

3

0.4

0.6

x

0.8

1.0

*

Figure 5. Variation of Sherwood number along the tube length for the Ellis fluid at

Pe w = 200

.

The variation of the length averaged Sherwood number with the average wall Peclet number is presented in figure 6, for Re Sc

d =103 and 105. It is evident from the figure that L

the Sherwood number increases with the wall Peclet number. This implies the mass transfer coefficient increases with the extent of permeation. As permeation increases, the convective solute flux through the membrane increases and at the steady state, to maintain the solute balance, the backward diffusion from the surface to the bulk also increases. This leads to an enhancement of the mass transfer coefficient. It may also be observed that at lower Re Sc Sherwood number for both concentrations of CMC becomes identical.

d , L

430

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

400

Re Sc d/L=10

5

1.5% CMC

__ ShL

Re Sc d/L=10

3

0.6% CMC 200

0 0

100

200

300

400

__ Pew Figure 6. Variation of length averaged Sherwood number with the average dimensionless permeate flux for the Ellis fluid for flow through a tube.

The effects of permeation on Sherwood number can be quantified from Eq. (145). The ratio of Sherwood number with permeation to that without permeation ( below,

PeW

⎡ ⎤ ⎢ ⎥ 2 3 PeW PeW PeW −4 ⎥, Q = ⎢1 + 0.35 0 . 026 8 . 67 10 + − × 1 2 ⎢ ⎥ d 3 3 ⎛ ⎞ d⎞ d⎞ ⎛ ⎛ ⎜ Re Sc ⎟ ⎥ ⎢ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦

=0) is presented

for 0.6% CMC

solution, ⎡ ⎤ ⎢ ⎥ 2 3 PeW PeW PeW −3 ⎥, 0 . 029 1 . 02 10 + − × = ⎢1 + 0.366 1 2 ⎢ ⎥ d 3 3 ⎛ ⎞ d⎞ d⎞ ⎛ ⎛ ⎜ Re Sc ⎟ ⎥ ⎢ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦

solution

for

1.5%

CMC

(146)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

431

The variation of Q with the average wall Peclet number is shown in figure 7. It is observed from the figure that the permeation results in about 2 times increase in the Sherwood number for Re Sc

PeW

d d PeW =105 and =400, whereas for Re Sc =103, at L L

=400, permeation causes about six fold increase in the Sherwood number. 6 5

Re Sc d/L=10 5

1.5% CMC 3

0.6% CMC

Re Sc d/L=10

Q

4

3

2

1

0

100

200

300

400

500

Length Averaged Pe NO Figure 7. Variation of Q with the average dimensionless permeate flux for Ellis fluid for flow through a tube.

3.1.2. Reiner-Philippoff Fluid A typical set of rheological parameters for Reiner-Philippoff fluid is given below [20],

μ 0 = 0.0215 Kg/m.s, μ = 0.00105 Kg/m.s and τ s = 0.0073 N/m2. ∞ As discussed in section 3.1 F versus u0 relationship becomes,

F = 8.6 × 10 4 u 0 + 0.014

(147)

where, F is in Pa/m and u0 is in m/s.

u

For a typical velocity, 0 =1.18 m/s, rheological parameters and tube geometry, A22 is evaluated from Eq. (56) as 8. From Eq. (55) A2 becomes,

d⎞ ⎛ A2 = 8⎜ Re Sc ⎟ L⎠ ⎝

(148)

432

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar The Sherwood number profile along the tube length is obtained from Eq. (58) as,

d⎞ ⎛ 2⎜ Re Sc ⎟ L Sh x* = ⎝ *1 ⎠ x 3 I2

1 3

( )

(149)

The value of suction parameter

λ eff 2 = 0.5

λeff 2 in this case is obtained from Eq. (60),

PeW d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝

1

(150) 3

The length averaged Sherwood number is obtained from Eq. (38) as, ⎡ ⎤ ⎢ ⎥ 2 3 PeW PeW PeW ⎛ Re Scd ⎞ 3 ⎢ ⎥ −3 + − × 0 . 03 1 . 05 10 ShL = 1.62⎜ ⎟ ⎢1 + 0.37 1 2 ⎥ d 3 3 ⎛ ⎞ ⎝ L ⎠ d⎞ d⎞ ⎛ ⎛ ⎜ Re Sc ⎟ ⎥ ⎢ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦ 1

(151)

The ratio (Q) of the average Sherwood number with permeation to that without permeation (

PeW

=0) is expressed as,

⎡ ⎤ ⎢ ⎥ 3 2 PeW PeW PeW ⎢ ⎥ −3 Q = ⎢1 + 0.37 − 1.05 ×10 + 0.03 1 2 d ⎞⎥ ⎛ d⎞ 3 d⎞ 3 ⎛ ⎛ ⎢ ⎜ Re Sc ⎟ ⎥ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦

(152)

The variation of the Sherwood number along the channel length for Re Sc

d = 103 and L

Pe

W =200, is shown in figure 8. The results show the usual 105 at the wall Peclet number, trend. The variation of the length averaged Sherwood number with the average wall Peclet

number is shown in figure 9. The ratio Q with the extent of suction ( figure 10. The figures show the expected trend.

PeW

) is presented in

Effects of Permeation on Mass Transfer Coefficient for Laminar...

433

340 320 300 280 260

5

Re Sc d/L=10 *

Sh(x )

240 220 200 180 3

160

Re Sc d/L=10

140 120 0.0

0.2

0.4

0.6

x

0.8

1.0

*

Figure 8. Variation of Sherwood number along the tube length for the Reiner-Philippoff fluid at

Pe w = 200

.

400

Re Sc d/L=10

5

300 3

__ ShL

Re Sc d/L=10 200

100

0

0

100

200

300

400

__ Pew Figure 9. Variation of length averaged Sherwood number with the average dimensionless permeate flux for the Reiner-Philippoff fluid for flow through a tube.

434

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

6

5 5

3

Re Sc d/L=10

Re Sc d/L=10

Q

4

3

2

1 0

100

200

300

400

500

__ Pew Figure 10. Variation of Q with the average dimensionless permeate flux for Reiner-Philippoff fluid for flow through a tube.

3.1.3. Eyring Fluid The typical values of the rheological parameters for Eyring fluid are [23], A= 600 N/m2, B= 200 S-1 The expression of F with u0 is obtained as before,

F = 2.0 × 10 7 u 0 − 2.2 × 10 7 u 0 + 9.7 × 10 6 u 0 + 1.5 × 10 5 3

2

(153)

u 0 =0.3 ms-1 and R=0.001 m, the value of A is found out 33 A = 9.875 . From Eq. (67), from the Eq. (68) as 33 For an average velocity of

d⎞ ⎛ A3 = 9.875⎜ Re Sc ⎟ L⎠ ⎝ The Sherwood number profile along the channel length is obtained from Eq. (70) as,

(154)

435

Effects of Permeation on Mass Transfer Coefficient for Laminar...

( )

2.15 ⎛ d⎞ Sh x = *1 ⎜ Re Sc ⎟ L⎠ x 3 I3 ⎝ *

1 3

(155)

The value of the permeation parameter,

λ eff 3 = 0.466

λ eff 3 is obtained from Eq. (72) as,

Pe W d⎞ ⎛ ⎜ Re Sc ⎟ L⎠ ⎝

1

(156) 3

The length averaged Sherwood number is obtained from Eq. (38) as, ⎤ ⎡ ⎥ ⎢ 2 3 PeW PeW PeW ⎛ Re Scd ⎞ ⎢ −3 ⎥ 1 0 . 345 0 . 026 1 . 06 10 ShL = 1.74⎜ − × + + ⎟ 1 2 ⎥ d 3 3 ⎞ ⎛ ⎝ L ⎠ ⎢ d⎞ d⎞ ⎛ ⎛ ⎜ Re Sc ⎟ ⎥ ⎢ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠ ⎥⎦ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ 1

3

(157)

Q, the ratio of average Sherwood number with suction to that without suction is given as ⎡ ⎤ ⎥ ⎢ 2 3 PeW PeW PeW ⎥ −4 0 . 023 9 . 78 10 + − × Q = ⎢⎢1 + 0.346 1 2 d ⎛ ⎞⎥ d⎞ 3 d⎞ 3 ⎛ ⎛ ⎢ ⎜ Re Sc ⎟ ⎥ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎝ ⎢⎣ L⎠ L⎠ ⎝ ⎝ ⎦

(158)

The variation of the Sherwood number along the channel length for Re Sc

d = 103 and L

Pe

W =200, is shown in figure 11. The results show the usual 105 at the wall Peclet number, trend. The variation of the length averaged Sherwood number with the average wall Peclet

number is shown in figure 12. The ratio Q with the extent of suction ( figure 13. The figures show the expected result.

PeW

) is presented in

436

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar 350

300

Re Sc d/L=10

5

*

Sh(x )

250

200

150 3

Re Sc d/L=10 100 0.0

0.2

0.4

0.6 x

0.8

1.0

*

Figure 11. Variation of Sherwood number along the tube length for the Eyring fluid at

Pe w = 200

.

400 5

Re Sc d/L=10 300

3

__ ShL

Re Sc d/L=10 200

100

0

0

100

200

300

400

__ Pew

Figure 12. Variation of length averaged Sherwood number with the average dimensionless permeate flux for the Eyring fluid for flow through a tube.

Effects of Permeation on Mass Transfer Coefficient for Laminar...

437

6

3

Re Sc d/L=10

5

Q

4

5

Re Sc d/L=10

3

2

1

0

100

200

300

400

500

__ Pew Figure 13. Variation of Q with the average dimensionless permeate flux for Eyring fluid for flow through a tube.

3.2. Rectangular Geometry 3.2.1. Ellis Fluid Using the method described in section 3.1.1 for CMC solution F is evaluated for various value of u0 and is fitted as a function of u0. The rectangular channel half height (h) is considered to be 0.001 m in this study. The relationship of F with u0 is presented below,

F = 9 . 58 × 10 4 u 0 + 1 . 5 × 10 4

, for 0.6% CMC solution

(159)

F = 2 . 09 × 10 5 u 0 + 6 . 7 × 10 3 , for 1.5% CMC solution

(160)

where, F is in Pa/m and u0 is in m/s. 0

Using Eq. (83), A11 is evaluated at u0 =0.3 m/s and its values are,

A110 = 12.33 , for 1.5% CMC solution = 13.7 , for 0.6% CMC solution

(161)

438

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar The profiles of Sherwood numbers along the channel length are obtained from Eq. (97), 1

de ⎞ 3 2.39 ⎛ Sh x = Sc Re ⎜ ⎟ , for 0.6% CMC solution 1 0 * 3 ⎝ L ⎠ I1 x

( ) *

1

2.31 ⎛ d ⎞3 = 0 *1 ⎜ Re Sc e ⎟ , for 1.5% CMC solution L⎠ I1 x 3 ⎝ The values of the permeation parameter,

λ0eff 1 =

=

0.418 Pe w d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

0.433PeW d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

1

(162)

λ 0eff 1 is obtained from Eq. (99),

for 0.6% CMC solution 3

for 1.5% CMC solution

(163)

3

The length averaged Sherwood numbers are obtained from Eq. (103) as, ⎡ ⎢ 2 3 PeW PeW PeW ⎛ Re Scd e ⎞ 3 ⎢ −4 Sh L = 1.93⎜ + 0.018 − 7.06 × 10 ⎟ ⎢1 + 0.31 1 2 d L ⎛ ⎝ ⎠ ⎢ de ⎞ 3 de ⎞ 3 ⎛ ⎛ ⎜ Re Sc e Sc Sc Re Re ⎜ ⎟ ⎜ ⎟ L ⎢ ⎝ L ⎠ L ⎠ ⎝ ⎝ ⎣ 1

⎤ ⎥ ⎥ ⎥ ⎞⎥ ⎟ ⎠ ⎥⎦

for 0.6% CMC solution =

⎡ ⎢ 2 3 PeW PeW PeW ⎛ Re Scd e ⎞ 3 ⎢ −4 + 0.02 1.87⎜ − 7.84 × 10 ⎟ 1 + 0.316 1 2 d L ⎠ ⎢ 3 3 ⎛ ⎝ d d ⎛ ⎞ ⎛ ⎞ ⎢ e e ⎜ Re Sc e Re Re Sc Sc ⎜ ⎟ ⎜ ⎟ L ⎢ ⎝ L⎠ L⎠ ⎝ ⎝ ⎣ 1

for 1.5% CMC solution

⎤ ⎥ ⎥ ⎞ ⎥⎥ ⎟ ⎠ ⎥⎦

(164)

The variation of the Sherwood number along the channel length for values of

Re Sc

de PeW = 103 and 105 for =200 is shown in figure 14. It is observed from the L

figure that, the Sherwood number decreases along the channel length. The decline is sharp in

439

Effects of Permeation on Mass Transfer Coefficient for Laminar...

the upstream of the channel and gradual thereafter. This is due to the fact that the build up of the concentration boundary layer over the membrane surface becomes sluggish at the downstream of the channel because of the forced convection imposed by the cross flow. This reduces the concentration difference between the membrane surface and the bulk of the solution. Therefore, the Sherwood number and consequently, the mass transfer coefficient decreases along the channel length. It may be observed from figure 14 that the Sherwood number is more at higher Re Sc

de Pe at the same level of the permeate flux ( w ). This L

indicates that at higher Reynolds number, forced convection is more due to higher cross flow velocity, which enhances the mass transfer coefficient. It may also be observed that the Sherwood number profiles for both concentrations of CMC solution almost coincide at

Re Sc

de d =103 and they differ marginally at Re Sc e =105. L L 360 340 320 300 280

5

Re Sc de/L=10

*

Sh(x )

260 240

0.6% CMC

220

1.5% CMC

200 180 160 140 120 0.0

3

Re Sc de/L=10 0.2

0.4

0.6

0.8

1.0

*

x

Figure 14. Variation of Sherwood number along the channel length for the Ellis fluid at

Pe w = 200

.

The variation of the length averaged Sherwood number with the average wall Peclet number is presented in figure 15, for Re Sc

de =103 and 105. It is evident from the figure that L

the Sherwood number increases with wall Peclet number. This implies the mass transfer coefficient increases with the extent of permeation. As permeation increases, the convective solute flux through the membrane increases and at the steady state, to maintain the solute balance, the backward diffusion from the surface to the bulk also increases. This leads to an

440

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

enhancement of the mass transfer coefficient. It may also be observed that at lower

Re Sc

de , Sherwood number for both concentrations of CMC becomes identical. L

400 5

Re Sc de/L=10

1.5% CMC __ ShL

3

0

Re Sc de/L=10

0.6% CMC

200

0

100

200

300

400

__ Pew Figure 15. Variation of length averaged Sherwood number with the average dimensionless permeate flux for the Ellis fluid for flow through the rectangular channel.

The effects of permeation on Sherwood number can be quantified from Eq. (164). The ratio of Sherwood number with permeation and without permeation ( below,

PeW

⎡ ⎢ 2 3 PeW PeW PeW ⎢ −4 + 0.018 − 7.06 × 10 Q = ⎢1 + 0.31 1 2 d ⎛ de ⎞ 3 de ⎞ 3 ⎛ ⎛ ⎢ ⎜ Re Sc e ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎢ ⎝ L ⎠ L ⎠ ⎝ ⎝ ⎣

=0) is presented

⎤ ⎥ ⎥ , for 0.6% CMC ⎞ ⎥⎥ ⎟ ⎠ ⎥⎦

solution, ⎡ ⎤ ⎢ ⎥ 2 3 PeW PeW PeW −4 ⎢ ⎥, + 0.02 − 7.84 × 10 = 1 + 0.316 1 2 ⎢ ⎥ d 3 3 ⎛ ⎞ d ⎞ d ⎞ ⎛ ⎛ ⎜ Re Sc e ⎟ ⎥ ⎢ ⎜ Re Sc e ⎟ ⎜ Re Sc e ⎟ L ⎠⎥ ⎝ L⎠ L⎠ ⎝ ⎝ ⎣⎢ ⎦

for

1.5%

CMC

solution (165)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

441

The variation of Q with the average wall Peclet number is shown in figure 16. It is observed from the figure that the permeation results in about 5 times increase in the Sherwood number for Re Sc

PeW

de d PeW =105 and =400, whereas for Re Sc e =103, at L L

=400, permeation causes about three fold increase in the Sherwood number.

5 1.5% CMC

4 3

Re Sc de/L=10

0.6% CMC

Q

3

5

Re Sc de/L=10 2

1

0

0

100

200

300

400

__ Pew Figure 16. Variation of Q with the average dimensionless permeate flux for Ellis fluid for flow through the rectangular channel.

3.2.2. Reiner-Philippoff Fluid For a typical set of rheological parameters for Reiner-Philippoff fluid, as discussed in section 3.1.2, F versus u0 relationship becomes,

F = 6.45 × 10 4 u 0 + 0.03

(166)

where, F is in Pa/m and u0 is in m/s. For a typical velocity,

u 0 =1.18 m/s, rheological parameters and channel height, A 0 is 22

evaluated from Eq. (121) as 12.52. From Eq. (120),

A20 becomes,

442

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

d ⎞ ⎛ A20 = 12.52⎜ Re Sc e ⎟ L⎠ ⎝

(167)

The Sherwood number profile along the channel length is obtained from Eq. (123) as,

d ⎞ ⎛ 2.32⎜ Re Sc e ⎟ L⎠ ⎝ Sh x * = * 13 0 x I2

1 3

( )

(168)

The value of suction parameter

λ 0eff 2 = 0.43

λ 0eff 2 in this case is obtained from Eq. (125),

Pe W d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

(169) 3

The length averaged Sherwood number is obtained from Eq. (103) as,

⎛ Re Scd e ⎞ Sh L = 1.88⎜ ⎟ L ⎝ ⎠

1

3

⎡ ⎢ 2 3 PeW PeW PeW ⎢ −4 + + 1 0 . 32 0 . 02 7 . 68 10 − × ⎢ 1 2 d ⎛ de ⎞ 3 de ⎞ 3 ⎛ ⎛ ⎢ ⎜ Re Sc e Sc Sc Re Re ⎜ ⎟ ⎜ ⎟ L ⎢ ⎝ L ⎠ L ⎠ ⎝ ⎝ ⎣

⎤ ⎥ ⎥ ⎞ ⎥⎥ ⎟ ⎠ ⎥⎦

(170)

The ratio (Q) of the average Sherwood number with suction and without suction (

PeW

=0) is expressed as,

⎡ ⎤ ⎢ ⎥ 3 2 PeW PeW PeW ⎢ ⎥ −4 Q = ⎢1 + 0.32 + 0.02 − 7.68 × 10 2 1 ⎥ d 3 3 ⎞ ⎛ e de ⎞ de ⎞ ⎛ ⎛ ⎢ ⎥ Sc Re ⎜ ⎟ ⎜ Re Sc ⎟ ⎜ Re Sc ⎟ L ⎠⎥ ⎢⎣ ⎝ L L ⎝ ⎠ ⎝ ⎠ ⎦

(171)

The variation of the Sherwood number along the channel length for Re Sc

de = 103 and L

Pe

W =200, is shown in figure 17. The results show the usual 105 at the wall Peclet number, trend. The variation of the length averaged Sherwood number with the average wall Peclet

number is shown in figure 18. The ratio Q with the extent of suction ( figure 19. The figures show the expected trend.

PeW

) is presented in

Effects of Permeation on Mass Transfer Coefficient for Laminar...

443

340 320 300 280

240

5

*

Sh(x )

260 Re Sc de/L=10

220 200 180 160

3

Re Sc de/L=10

140 120 0.0

0.2

0.4

0.6

0.8

1.0

*

x

Figure 17. Variation of Sherwood number along the channel length for the Reiner-Philippoff fluid at

Pe w = 200

.

400

5

Re Sc de/L=10 300

3

__ ShL

Re Sc de/L=10 200

100

0

0

100

200

300

400

__ Pew Figure 18. Variation of length averaged Sherwood number with the average dimensionless permeate flux for the Reiner-Philippoff fluid for flow through the rectangular channel.

444

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

Figure 19. Variation of Q with the average dimensionless permeate flux for Reiner-Philippoff fluid for flow through the rectangular channel.

3.2.3. Eyring Fluid For typical values of the rheological parameters for Eyring fluid as discussed in section 3.1.3, the expression of F with u0 is obtained as before,

F = 3 × 10 5 + 5.44 × 10 6 u 0 − 4.65 × 10 6 u 0

For an average velocity of the Eq. (133) as

2

(172)

u 0 =0.3 ms-1 and h=1 mm, the value of A330 is found out from

A330 = 16.54 . From Eq. (132),

d ⎞ ⎛ A30 = 16.5353⎜ Re Sc e ⎟ L⎠ ⎝

(173)

The Sherwood number profile along the channel length is obtained from Eq. (135) as,

445

Effects of Permeation on Mass Transfer Coefficient for Laminar...

( )

d ⎞ 2.55 ⎛ Sh x = * 1 0 ⎜ Re Sc e ⎟ L⎠ x 3 I3 ⎝ *

1 3

(174)

The value of the permeation parameter,

λ0eff 3 = 0.39

λ 0eff 3 is obtained from Eq. (137) as,

Pe W d ⎞ ⎛ ⎜ Re Sc e ⎟ L⎠ ⎝

1

(175) 3

The length averaged Sherwood number is obtained from Eq. (103) as,

⎛ Re Scd e ⎞ Sh L = 2.06⎜ ⎟ L ⎝ ⎠

1

3

⎡ ⎢ 2 3 PeW PeW PeW ⎢ −4 1 0 . 28 0 . 016 5 . 73 10 + + − × ⎢ 1 2 d ⎛ de ⎞ 3 de ⎞ 3 ⎛ ⎛ ⎢ ⎜ Re Sc e Re Re Sc Sc ⎜ ⎟ ⎜ ⎟ L ⎢ ⎝ L ⎠ L ⎠ ⎝ ⎝ ⎣

⎤ ⎥ ⎥ ⎥ ⎞⎥ ⎟ ⎠ ⎥⎦

(176)

Q, the ratio of average Sherwood number with suction to that without suction is given as ⎡ ⎢ 2 3 PeW PeW PeW ⎢ −4 Q = ⎢1 + 0.28 0 . 016 5 . 73 10 + − × 1 2 d ⎛ de ⎞ 3 de ⎞ 3 ⎛ ⎛ ⎢ ⎜ Re Sc e Sc Sc Re Re ⎜ ⎟ ⎜ ⎟ L ⎢ ⎝ L ⎠ L ⎠ ⎝ ⎝ ⎣

⎤ ⎥ ⎥ ⎥ ⎞⎥ ⎟ ⎠ ⎥⎦

The variation of the Sherwood number along the channel length for Re Sc

(177)

de = 103 and L

Pe

W =200, is shown in figure 20. The results show the usual 105 at the wall Peclet number, trend. The variation of the length averaged Sherwood number with the average wall Peclet

number is shown in figure 21. The ratio Q with the extent of suction ( figure 22. The figures show the expected trend.

PeW

) is presented in

446

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

350

250

*

Sh(x )

300

5

Re Sc de/L=10 200

150

100 0.0

3

Re Sc de/L=10

0.2

0.4

0.6

0.8

1.0

*

x

Figure 20. Variation of Sherwood number along the channel length for the Eyring fluid at

Pe w = 200

.

400

Re Sc de/L=10

5

300

__ ShL

Re Sc de/L=10

3

200

100

0 0

100

200

300

400

__ Pew Figure 21. Variation of length averaged Sherwood number with the average dimensionless permeate flux for the Eyring fluid for flow through the rectangular channel.

Effects of Permeation on Mass Transfer Coefficient for Laminar...

447

5

4 3

Re Sc de/L=10 3

5

Q

Re Sc de/L=10

2

1 0

100

200

300

400

__ Pew Figure 22. Variation of Q with the average dimensionless permeate flux for Eyring fluid for flow through the rectangular channel.

4. CONCLUSION The Sherwood number relations incorporating the effects of permeation are derived from the first principles for a laminar flow in a tubular module and rectangular channel for three non-Newtonian fluids. Effects of permeation on the mass transfer coefficient have been quantified. Because of permeation, the Sherwood number can increase several times compared to that without permeation depending upon the permeate flux. The derived analytical expressions are useful to the engineers for design of the tubular and spiral wound membrane modules for flow of the non-Newtonian fluids covered in this study.

NOMENCLATURE A A1,A2,A3

A10 , A20 , A30

rhelogical parameter in Eyring fluid dimensionless parameters in Eqs.(12), (55) and (67)

A11,22,33

dimensionless parameters in Eqs.(82), (120) and (132) dimensionless rheological parameters in Eqs.(14), (56) and (68)

A110 , 22,33

dimensionless rheological parameters in Eqs.(83), (121) and (132)

B B1

rhelogical parameter in Eyring fluid constant in Eq.(21)

448

Sirshendu De, Sunando DasGupta and S. Ranjith Kumar

B10

constant in Eq.(87)

c c0 cp c* cm D d F f I1,2,3

solute concentration, kg/m3 feed concentration, kg/m3 permeate concentration, kg/m3 dimensionless concentration membrane surface concentration, kg/m3 solute diffusivity, m2/s tube diameter, m pressure gradient across the channel length (-dP/dx), Pa/m friction factor definite integrals in Eqs. (27), (59) and (71)

I 10, 2,3

definite integrals in Eqs. (93), (124) and (136)

K K1,2

parameter in Eq.(64) integration constants in Eq.(24)

K 10, 2

integration constants in Eq.(96)

k L P Pew

mass transfer coefficient, m/s channel length, m pressure, Pa dimensionless permeate flux (wall Peclet number)

Pe w R Re Rr r Sh

length averaged non-dimensional permeate flux tube Radius, m Reynolds number real retention of the membrane radial direction, m Sherwood number

ShL

length averaged Sherwood number

Sc u0 vx vy vw x x* y y*

Schmidt number average velocity across the cross section, m/s x-component velocity, m/s y-component velocity, m/s permeate flux, m3/m2s axial dimension, m dimensionless axial dimension dimension normal to the flow direction, m dimensionless normal dimension

Greek letters:

α δ δ* ϕ0 , ϕ

Ellis fluid rheological parameter concentration boundary layer thickness, m dimensionless concentration boundary layer thickness 1

Ellis fluid rheological parameters

Effects of Permeation on Mass Transfer Coefficient for Laminar...

τ yx μ0 , μ∞ μ eff

449

Shear stress, Pa rheological parameters in Eq.(50), Pa.s effective viscosity, Pa.s

η , ξ , φ ,η 0 , ξ 0 , φ 0 similarity parameters ρ density, kg/m3 τs rheological parameters in Eq.(50), Pa

REFERENCES [1]

Bouchard, C. R.; Carreau, P. J.; Matsuuara, T.; Sourirajan, S. (1994). Modeling of ultrafiltration: prediction of concentration polarization effects. J. Membrane Sci., 97, 215-229. [2] Kleinstreuer, C ; Paller, M. S. (1983). Laminar dilute suspension flows in plate and frame ultrafiltration units. AIChE J., 29, 533-539. [3] van den Berg, G. B; Racz, I. G.; Smolders, C. A. (1989). Mass transfer coefficients in cross flow ultrafiltration, J. Membrane Sci., 47, 25-50. [4] Gekas, V; Hallstrom, B. (1987). Mass transfer in the membrane concentration polarization layer under turbulent cross flow. I. Critical literature review and adaptation of existing Sherwood correlations to membrane operations. J. Membrane Sci., 80, 153169. [5] De, S.; Bhattacharjee, S.; Bhattacharya, P. K. (1995). Development of correlations for mass transfer coefficient in ultrafiltration systems. Develop. Chemical Engg. Mineral Proc., 3, 187-206. [6] Girard, B.; Fukumoto, L. R. (2000). Membrane processing of fruit juices and beverages: a review. Crit. Rev. Food Sci. Nutrition, 40 (2), 91-157. [7] Cheryan, M. Ultrafiltration Handbook, Technomic Publishing Co. Ltd.: Lancaster, PA, 1986, pp. 349-369. [8] Pepper, D.; Orchard, A.C. J.; Merry, A. J. (1985). Concentration of tomato juice and other fruit juices by reverse osmosis. Desalination, 53, 157-166. [9] Thomas, R. L.; Gaddis, J. L.; Westtfall, P. H.; Titus, T. C.; Ellis, N. D. (1987) Optimization of apple juice production by single pass metallic membrane ultrafiltration. J. Food Sci., 52, 1263-1266. [10] Aptel, P., and Clifton, M. (1983). Ultrafiltration. In P. M. Bungay, H. K. Lonsdale and M. N. de Pinho (Eds.), Synthetic Membranes: Science, Engineering and Applications (pp. 249-305). Dordrecht : D. Reidel Publishing Co. [11] Porter, M. C. (2005). Ultrafiltration. In Handbook of Industrial Membrane Technology (pp. 136-256). New Delhi: Crest Publishing House. [12] Chhabra, R. P.; Richardson, J. F.; Non-Newtonian flow in the Process Industries: Fundamentals and Engineering Applications; Butterworth Heinemann: Oxford, 1999, pp. 77.

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[13] Charcosset, C; Choplin, C. (1996). Ultrafiltration of non-Newtonian fluids. J. Membrane Sci., 115, 147-160. [14] Howell, J. A; Field, R.; Wu, D. (1996). Ultrafiltration of high viscosity solutions: theoretical developments and experimental findings. Chem. Engg. Sci., 51, 1405-1415. [15] Pritchard, M.; Howell, J. A.; Field, R.; Wu, D. (1995). The ultrafiltration of viscous fluids. J. Membrane Sci., 102, 223-235. [16] De, S.; Bhattacharya, P. K. (1997). Prediction of mass transfer coefficient with suction in the applications of reverse osmosis and ultrafiltration. J. Membrane Sci., 128, 119131. [17] Minnikanti, V. S.; DasGupta, S.; De, S. (1999). Prediction of mass transfer coefficient with suction for turbulent flow in cross flow ultrafiltration. J. Membrane Sci., 137, 227239. [18] Ranjan, R.; DasGupta, S.; De, S. (2004). Mass transfer coefficient with suction for laminar non-Newtonian flow in application to membrane separations. J. Food Engg., 64, 53-61. [19] Ranjan, R; DasGupta, S.; De, S. (2004). Mass transfer coefficient with suction for turbulent non-Newtonian flow in application to membrane separations. J. Food Engg., 65, 533-541. [20] Bird, R. B.; Stewart, W. E.; Lightfoot, E. N. Transport Phenomena; Wiley: N.Y.,1960; pp 13-14. [21] De, S.; Bhattacharjee, S.; Sharma, A.; Bhattacharya, P. K. (1997). Generalized integral and similarity solutions of the concentration profiles for osmotic pressure controlled ultrafiltration. J. Membrane Sci., 130, 99-121. [22] Opong, W. S.; Zydney, A. L. (1991). Diffusive and convective protein transport through asymmetric membranes. AIChE J., 37, 1497-1510. [23] Yurusoy, M.; (2003). A study of pressure distribution of a slider bearing lubricated with Powell-Eyring fliuid, Turkish J. Eng. Env. Sci., 27, 299-304. [24] White, F. M. Viscous Fluid Flow; McGraw Hill Inc.: Singapore, 1991; pp. 123.

APPENDIX A.1. Effective Viscosity ( μ eff ) From the definition of friction factor, f [12],

f =

τw 1 2 ρu 0 2

(A1)

From the definition of the wall shear stress [12],

d ⎛ dP ⎞ d ⎟ =F 4 ⎝ dx ⎠ 4

τ w = ⎜−

From Eqs. (A1) and (A2), the expression of friction factor becomes,

(A2)

Effects of Permeation on Mass Transfer Coefficient for Laminar...

f =

Fd 2 2 ρu 02

451

(A3)

16 Re

For laminar flow, f =

Hence, from Eqs.(A3) and (A4),

(A4)

μ eff =

Fd 2 32u 0

(A5)

A.2. Similarity Parameter Evaluating Eq.(12) at the edge of the concentration boundary layer,

Δc * Δc * A1δ ≈ *2 x* δ *

(A6)

1

⎛ x* ⎞ 3 * ⎟⎟ From the above equation, δ = ⎜⎜ ⎝ A1 ⎠ Similarity parameter is defined as, η =

y*

δ*

= A11 / 3

y* x *1 / 3

(A7)

A.3. Effective Viscosity for Power Law Fluid Flow Through a Tube In this case,

F =2

α +1 α

ϕ 0 = 0 . From Eqs.(46) and (47), the explicit expression of F becomes, 1

(α + 3)α u 0α 1

1

ϕ1α d

α +1 α

(A8)

From Eqs. (A5) and (A8), the expression of effective viscosity is obtained in this case,

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Sirshendu De, Sunando DasGupta and S. Ranjith Kumar 1−α

1

1 ⎛ u ⎞ α ⎛ 2(α + 3) ⎞ α ⎟ μ eff = ⎜ 0 ⎟ ⎜⎜ 16 ⎝ d ⎠ ⎝ ϕ1 ⎟⎠ Flow through a Rectangular Thin Channel In this case,

F =4

α +1 α

(A9)

ϕ 0 = 0 . From Eqs.(111) and (112), the explicit expression of F becomes, 1

(α + 2)α u 0α 1

(A10)

α +1 α

1

ϕ1α d e

From Eqs. (A5) and (A10), the expression of effective viscosity is obtained in this case,

μ eff

1⎛u = ⎜⎜ 0 8 ⎝ de

⎞ ⎟⎟ ⎠

1−α

α

1

⎛ 4(α + 2) ⎞ α ⎟⎟ ⎜⎜ ⎝ ϕ1 ⎠

(A11)

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 453-466 © 2007 Nova Science Publishers, Inc.

Chapter 8

EFFECT OF SMOOTH ROLL GRINDING CONDITIONS ON REDUCTION OF SIZINGS IN THE WHEAT FLOUR MILLING PROCESS Aleksandar Fistes and Gavrilo Tanovic Department of Carbohydrate Food Engineering, Faculty of Technology, University of Novi Sad, Bulevar Cara Lazara 1, 21 000 Novi Sad, Serbia

ABSTRACT A laboratory roll stand Variostuhl, equipped with smooth rolls (250 mm diameter, 100 mm length), was used to examine, under simulated commercial conditions, the effect of roll speed and roll differential on the reduction of sizings and coarse middlings from the primary break passages of the wheat flour milling process. The samples were obtained from the industrial mill, intercepting the sizings and coarse middlings from the 1st, 2nd and 3rd break stage that normally would have gone to the purification system, as well as intercepting the purified sizings (cleaned middlings) that normally would have gone to the reduction system of the wheat flour milling process. As roll velocity increases flour release was increased, milling energy consumption rose while flour quality (as determined by ash content) was not affected. By increasing roll velocity it is possible to increase feed rate to the rolls and, therefore, the disposable roll surface is used more efficiently. Flour release rose when differential was increased from 1.1 up to 1.25 but decreased when differential increased from 1.25 up to 5.0. Increasing roll differential led to an increase in milling energy consumption. These effects can be explained by the relative contribution of compressive and shearing forces acting on the particles passing through the grinding zone of the smooth rolls. Considering the results obtained in this study (flour release, flour quality and milling energy consumption) a differential of 1.25, relative to a fast roll speed of 5 m/s could be designated as optimal.

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INTRODUCTION The modern wheat flour milling process involves breaking open the grain, scraping off as much endosperm from the bran and germ, and then gradually reducing the chunks of endosperm into flour [1]. Repeated breakage (by roller milling) and separation (by sifting and purifying) in the gradual reduction process allows effective removal of bran and germ from endosperm and highly efficient recovery of flour, relatively free from bran contamination [2,3]. This is possible due to the differences in structural and mechanical characteristics between the anatomic parts of the wheat kernel. The three parts of the wheat kernel, bran, germ and endosperm, differ in relative toughness and friability, giving different breakage patterns on roller milling. These differences are exaggerated by adding water to the wheat prior to milling, in a process known as conditioning or tempering. The need and advantages of proper conditioning are well established [4,5]. During comminution operations, both material properties and milling methods affect particle breakage [6]. The factors affecting particle size reduction can be classified into those arising from the pshysicochemical properties of the material and those related to the design and operation of the milling equipment [7]. The material is stressed by the action of the processing equipment, and the stress is absorbed internally by the material as strain energy. When the local strain energy exceeds some critical level, which is a function of the material properties, fracture occurs along lines of weakness [8]. In wheat flour milling, the predominant comminution tool is the roller mill in which the feed material is passed between two counter-rotating rolls of, usually 250 mm diameter and with either a corrugated or smooth finish [9]. The rolls are separated by a small gap and rotate at different speeds. Particle size reduction is achieved by passing cleaned and conditioned wheat through a series of break (fluted) and reduction (smooth) rolls. For fracture to occur in a wheat endosperm particle during roller milling, the stress in the region that fractures must exceed internal forces. Wheat endosperm exhibits viscoelasticity when fracturing, a condition intermediate between complete brittleness and gross plastic yielding [10]. In a roller mill, particles are subjected to shear and compressive forces. Previous work of Austin et al. [11, 12] and Hague [8] studied the situation of a particle being drawn into the nip of smooth rolls counter rotating at the same speed. However, for most grain grinding operations, the two rolls run at different speeds. If two rolls were to rotate at different speeds, shear stress would be induced in the particles [13]. Figure 1 shows the idealized situation for one particle (assumed to be perfectly spherical in shape) being drawn into the grinding zone of the rolls (in reality a collection of particles are passing through the grinding zone). The grinding zone is from the point where a particle is initially engaged between two rolls to the point at which the gap is at minimum. Particles passing through the grinding zone of the roller mill are subjected to shear forces from contact between points on the particles and the roll surfaces, and compressive forces on the particles as a whole. Shear force from the side of the fast roll (FF) tends to draw particle into the roll nip while shear force from the side of the slow roll (FS) tends to eject it because of the different speeds of the rolls. Particle is also subjected to the compressive forces (FF1 and FS1) reaching the maximum at the line connecting the centers of the cross section of the rolls [14]. Once the particle is drawn into the roll nip, the strain increases as the particle goes toward the roll nip, and the particle is crushed. The

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings…

455

particles are held on the slow roll [15] while the rotation of the fast roll causes both compressive and shear deformation. The reduction in size resulting from particle fracture occurs after a given amount of deformation. This deformation will be ductile or brittle depending on the applied stresses and the particle components upon which the stresses act [16]. The roll parameters: the gap between the rolls, uniformity and the feed rate of stocks to rolls, the roll velocities (speeds), the roll differential (the ratio of speeds of the fast and slow rolls), and the type and condition of roll surface, influence the magnitude of the stress and the relative contributions of compressive and shearing forces. The magnitude and the nature of the forces acting on particles will determine the degree of particle size reduction, energy required for grinding and bran contamination of the flour [17].

FS

FF1

FS1

α

fast roll

slow roll FF

Figure 1. Idealized grinding zone and forces acting on an individual particle.

Fast and slow roll velocities, vf and vs respectively, roll differential (vf / vs), as well as difference between the fast and slow roll velocity (vf – vs), have strong influence on the treatment the particles receive in the grinding zone. Shear strain arises from the action of roll differential. A larger differential gives a larger shear strain, compressive strain remains constant [13] but the ratio between compressive and shearing forces is altered. With the increase of roll velocity and roll differential the degree of particle size reduction also increases. However, to study the effectiveness of the wheat flour milling process, along with the quantity rates (break release, flour yield, particle size distribution of the output), qualitative analyses (such as ash or protein content in the flour or size fraction of the output) are of great importance [18] considering that, along with size reduction, efficient separation of the bran and germ from the endosperm of the wheat kernel has to be achieved.

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The main goal of the wheat flour milling industry is to produce a selection of flours of defined quality followed by low investment and energy costs. The trend in recent years has been to shorter mill flows necessitating increased feed rate to rolls. The faster the roll, the larger the capacity [8]. As roll velocity increases the particles are drawn through the grinding zone more quickly which also contribute to increased degree of particle size reduction. The ribbon theory for flour production, presented by Perry and Chilton [19], predicts that grinding action is proportional to the ratio of roll velocity to feed rate. Increased feed rate reduces the amount of grinding any given particle receives. When roll velocity is increased the feed ribbon spreads out, reducing the load in the grinding zone. The increased grinding action on particles resulting from reduced ribbon width causes greater flour release as roll velocity increases. The results of Scanlon et al. [16,17] support the ribbon theory. In their investigation of smooth roll grinding conditions on farina milling, they found that by increasing roll velocity or decreasing feed rate flour release was increased, milling energy consumption rose and flour quality (as determined by ash content and color) was improved. They also reported that increasing roll differential led to an increase in flour starch damage, flour water absorption and milling energy consumption and a deterioration in flour quality (ash and color). The effect of increasing the differential up to 1.5 was to increase flour yield. Similar results were reported by Evers et al. [20] as differential was increased to 1.25. Further increasing the magnitude of the shear forces imparted by the differential had little effect on flour release, in fact the amount of flour decreased. The purpose of this work was to examine, under simulated commercial conditions, the effect of roll speed and roll differential on the reduction of sizings and coarse middlings from the primary break passages of the wheat flour milling process. The changes were observed in the degree of particle size reduction, flour yield, flour ash and milling energy requirements and discusses in terms of the forces acting on the particles.

MATERIALS AND METHODS Sample Preparation The samples were obtained from the commercial mill intercepting the sizings and coarse middlings from the 1st, 2nd and 3rd break stage that normally would have gone to the purification system, as well as intercepting the purified sizings (clean middlings) that normally would have gone to the reduction system of the wheat flour milling process. Sizings and coarse middlings are the coarsest endosperm separates of the primary breaks representing a mixture of pure endosperm, endosperm with various degrees of attached bran, and bran particles. The purpose of the purifier is to separate the middlings into these fractions. The clean separations from the purifier are sent to the head end of the reduction system [9]. Sizings and coarse middlings from the first and second break passages, particle size range 560-1000 μm, which according to the flow sheet would have gone to the same section of the purifier, were intercepted (sample A in the following text). Stream leaving the purifier, that normally would have gone to the front passages of the reduction system, consisted of particles

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings…

457

passing through 950 and 1000 μm bolting cloths (22 GG1 and 20 GG respectively), obtained by purification of the sample A, was intercepted and designated as sample B. Sizings and coarse middlings from the fine side of the second break passage, particle size range 560-850 μm, and from the third break passage, particle size range 530-850 μm, which according to the flow sheet would have gone to same section of the purification system, were intercepted (sample C in the following text). Stream leaving the purifier, obtained by purification of the sample C and consisted of particles passing through 600 and 670 μm bolting cloths (32 GG and 30 GG respectively), was intercepted and designated as sample D. In each of the investigated samples, moisture and ash contents have been determined according to ICC standard methods No. 110/1 [21] and 104/1 [22] respectively.

Rolls Samples were milled on the laboratory roll stand Variostuhl; model C Ex 2 (Miag, Braunschweig, Germany). It is a single pass, fully variable test mill, which uses full scale diameter rolls (250 mm – diameter and 100 mm – length) to simulate commercial flour milling conditions. Rolls are interchangeable to allow fluted rolls to be studied in different dispositions, as well as the use of smooth rolls. It also provides wide range of roll speeds (up to 18 m/s) and roll differentials (up to 1:85). Increasing the speed of the fast roll the slow roll speed also increases according to differential which remains constant. Changing the speed of the slow roll it is possible to change roll differential because fast roll speed remains constant. Feed rate and roll gap are also adjustable.

Milling Conditions The samples were separated into 1 kg batches and milled on a Miag Vario mill using smooth rolls. Roll gap was set at 0.10 mm, for the grinding runs with sample A and B, and at 0.08 mm, for the grinding runs with sample C and D. Roll gaps were set using the indicator on the mill and checked with a feeler gauge. All samples were feed at the rate of 0.4 kg/m/s. The effect of roll speed on milling results was investigated using a constant roll differential of 1.25 relative to fast roll speeds of 3, 4, 5 and 6 m/s. The effect of roll differential on milling results was investigated setting the constant speed of the fast roll at 5 m/s relative to roll differentials of 1.1, 1.25, 1.50, 1.75, 2.0, 3.0 and 5.0. Tables 2 and 3 summarize experimental range of variables tested.

Analysis Sieve analysis of obtained stocks was performed on the Bühler laboratory sifter (gyratory in horizontal plane), model MLU-300 (Uzwil, Switzerland). Different stacks of sieves that have been used (tables 2 and 3), were chosen according to results of the preliminary sieve 1

GG – grit gauze

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Aleksandar Fistes and Gavrilo Tanovic

analysis of stocks following the milling of the investigated samples at different roll speeds and roll differentials. All samples were sieved for 3 min and stock held on each sieve and the bottom collecting pan was weighed. Three samples were milled and sifted at the same conditions. The weight distribution among the streams was highly reproducible. Moisture and ash contents in flour and other size fraction of the milling output have been determined according to ICC standard methods No. 110/1 [21] and 104/1 [22] respectively. The milling energy consumption during all grinding runs was determined from the wattmeter fitted as an integral part of the Variostuhl laboratory roll stand. Power readings that have been recorded (P, kW) correspond to operation with the material flow. The milling energy consumption, E kJ/kg, was calculated by Eq. (1):

E=

Pt

(1)

m

Here m (kg) is the mass of flour obtained and t (s) is the time of the grinding run determined by the chronometer.

RESULTS AND DISCUSION Sample Characteristics Ash content in the samples A and C is considerably higher compared to the ash content of the samples B and D (table 1). Ash is concentrated in the bran and the ash content increases from the inner to the outer part of the wheat kernel [23], with over the half the total in the pericarp, testa and aleurone [24]. Higher ash content indicates that the samples A and C are relatively enriched in bran and germ compared to samples B and D. However, it was to be expected considering that samples B and D were obtained by purifying the samples A and C respectively. As it was mentioned earlier in a chapter, the purpose of the purifier is to separate sizings and coarse middlings into three fractions: pure endosperm, endosperm with attached bran, and bran particles. Samples A and C represent a mixture of these fractions while in samples B and D pure endosperm particles, relatively free from bran and germ, dominate. This is important because of the differences of the mechanical properties. Endosperm and bran do not break in the same way under the same stresses. Bran, being tough and fibrous, is more prone to the ductile fracture imparted by shear forces than to brittle fracture. In contrast, under dominating compressive forces, the bran particles remain relatively intact while brittle endosperm is crushed into many small pieces. Arnold and Roberts [25], studying the stress distribution in wheat grains, concluded that under compression the bran does not carry any of the applied load, serving only to contain the endosperm material. Moisture contents of the samples are within usual range (table 1). At these values, differences in the structural and mechanical characteristics between the particles that originate from different anatomic parts of the wheat kernel still exist, and therefore efficient separation of the remaining bran from the endosperm, along with the particle size reduction, was possible.

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings…

459

Table 1. Particle size range, moisture and ash content of the investigated samples Sample A B C D

Size range [μm] 560 – 1000 670 – 1000 530 – 850 530 – 670

Moisture content (%) 14.6 14.4 14.6 14.3

Ash content (%)dm 1.04 0.54 1.55 0.54

dm – dry matter basis.

Table 2. Summary of experimental range of variables tested (the effect of roll speed)

Sample

Roll surface

A

Roll gap [mm]

Feed rate [kg/m/s]

Differential

Fast roll speed [m/s]

0.4

1.25

3; 4; 5; 6

0.10

B

0.10 Smooth

C

0.08

D

0.08

Sieve openings* [μm] 570, 400, 250, 150 450, 350, 250, 150 570, 400, 250, 100 450, 350, 250, 100

*along with the bottom collecting pan. Table 3. Summary of experimental range of variables tested (the effect of roll differential)

Sample

Roll surface

A

Roll gap [mm]

Feed rate [kg/m/s]

Fast roll speed [m/s]

Differential

0.4

5

1.10; 1.25; 1.50; 1.75; 2.0; 3.0; 5.0

0.10

B

0.10 Smooth

C

0.08

D

0.08

Sieve openings* [μm] 570, 400, 250, 100 450, 350, 250, 100 570, 400, 250, 100 400, 350, 250, 100

*along with the bottom collecting pan.

The Effect of Roll Speed Changes in the particle size distribution of the stocks, brought about by the increase of the roll speed, followed the same trends for all investigated samples (figure 2). By increasing fast roll speed, yield of two largest size fractions of the milling output (>570 μm and 400-570 μm - samples A and C; >450 μm and 350-450 μm - samples B and D) tends to decrease while the quantity of the medium sized stocks and both flour fractions (fine: <100 μm and coarse: 100-250 μm - samples C and D and fine: <150 μm and coarse: 150–250 μm – samples A and B) increased. Flour production (<250 μm) was directly related to roll speed (figure 3a).

460

Aleksandar Fistes and Gavrilo Tanovic

Similar results were reported by Scanlon et al. [17] as well as Schumacher [26] and support the ribbon theory of Perry and Chilton [19] mentioned earlier in a chapter. The increased grinding action on particles resulting from reduced ribbon width causes greater flour release as roll velocity increases. As roll velocity increases the particles are drawn through the grinding zone more quickly, enhancing their brittleness, which would also contribute to increased flour production [17]. Sample A 35

Yield of the milling output size fractions [%]

Yield of the milling output size fractions [%]

45

Size fractions [ μm ] >570 400-570 250-400 150-250 <150

40 35 30 25 20 15 10

3

5

30

25

20

15

10

6

3

45 40 Size fractions [μm ] >570 400-570 250-400 100-250 <100

Sample C

25 20 15 10

4

5

6

Fast roll speed [m /s]

45

Yield of the milling output size fractions [%]

Yield of the milling output size fractions [%]

4

Fast roll speed [m /s]

50

30

Sample B

5

5

35

Size fractions [μm ] >450 350-450 250-350 150-250 <150

40 Size fractions [μm ] >450 350-450 250-350 100-250 <100

35 30

Sample D

25 20 15 10 5

5 3

4

5

Fast roll speed [m /s]

6

3

4

5

6

Fast roll speed [m /s]

Figure 2. Effect of fast roll speed on the weight percentage of various streams.

Under the present grinding conditions (smooth rolls, constant roll differential of 1.25 and constant roll gap), a larger roll speeds would induce a larger shear strain, compressive strain remains constant but the ratio between compressive and shearing forces is altered. As it was mentioned earlier in chapter, the nature of deformation depends not only on the applied stresses but as well on the particle components upon which the stresses act. Compressive stresses, compared to shear, are more effective in causing the disintegration of the brittle endosperm material. This explains why the flour release and degree of particle size reduction in general are not even more noticeable under present grinding conditions. Flour release, obtained by milling the samples B and D (following the purification) is higher compared to samples A and C (before the purification). Samples B and D, mainly consisted of pure endosperm particles, are relatively free from bran while samples A and C represent a mixture of pure endosperm, endosperm with various degrees of attached bran, and bran particles. Bran takes on some portion of the forces in the grinding zone which would be otherwise directed to the reduction of the endosperm explaining the lower flour yield and

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings…

461

milling efficiency in general. However, different stacks of sieves that have been used for sieve analysis make the comparison of the degree of particle size reduction between investigated samples more difficult. Milling energy consumption exhibited similar relationship to flour release as roll speed was altered. By increasing roll speed milling energy consumption slightly rose (figure 3b). The goal of the wheat flour milling industry is to produce a selection of flours of defined quality at lowest possible price. Therefore, the consumption of energy in the process, since large part of it is energy required for grinding, should be observed as energy consumption relative to the mass of produced flour. Eq. (1) defines milling energy consumption relative to the mass of flour obtained. Therefore, increase of the flour release contributes to the decrease of milling energy consumption calculated on the basis of eq. (1). Since flour release was directly related to roll speed there was only slight increase of the milling energy consumption following the increase of roll speed. However, by increasing roll speeds power requirement increases and therefore the rolls should be run at lowest possible speed to handle the capacity. This means that rolls with small loads should be run at lower speeds than rolls with heavy loads [9]. On the other hand, increasing roll speed can be used to form a similar feed flow pattern when an increase in feed rate is desired [27]. The faster the roll, the larger the capacity [8]. These are very important considerations in the modern wheat flour milling process because trend in recent years has been to shorter mill flows necessitating increased feed rate to rolls to retain or even increase the capacity. With slow rolls the demands for increased capacity could not be met. It was mentioned earlier in a chapter that bran takes on some portion of the forces in the grinding zone contributing to the lower degree of particle size reduction that is reduced milling efficiency. This is the reason why milling energy consumption, at the same fast roll speed, is constantly higher for the samples A and C (before purification) compared to samples B and D (following the purification) respectively (figure 3b). By increasing roll speed flour quality, as determined by ash content, was not affected (table 4). It shows that increasing roll speed wasn’t followed by increased grinding of the bran which would otherwise increase flour ash, considering that ash is concentrated in the bran and the ash content increases from the inner to the outer part of the wheat kernel. Ash content of the flour obtained by milling samples A and C is constantly higher compared to the ash content of the flour obtained by milling samples B and D respectively. However, it was to be expected considering that samples B and D were obtained by purifying the samples A and C respectively, and that initial ash content of the samples A and C is higher compared to samples B and D. Table 4. Ash content of flour (<250 μm) obtained by milling the investigated samples at different fast roll speed

Fast roll speed [m/s] 3 4 5 6

Ash content of flour (%)dm Sample A B C 0.46 0.42 0.52 0.47 0.38 0.51 0.45 0.41 0.50 0.45 0.39 0.50

D 0.44 0.45 0.41 0.40

Milling energy consumption [kJ/kg]

Flour release (%)

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Aleksandar Fistes and Gavrilo Tanovic

36 34 32 30 28 26 24 22 20 18

Sample: A B C D

a

3

4

5

6

5

6

Fast roll speed [m/s] 160

b

150 140 130 120 110 100 90 3

4

Fast roll speed [m/s]

Figure 3. Effect of fast roll speed on a – the release of flour and b – milling energy consumed.

The Effect of Roll Differential The particle size distribution of the stock followed the same trends for all investigated samples (figure 4). A differential of 1.25 appears to be a turning point. Increasing roll differential from 1.1 up to 1.25 led to a decrease of the yield of two largest size fractions of the milling output while the quantity of medium sized stocks and both flour fractions (fine: <100 μm and coarse: 100-250 μm) increased. Quite opposite to that, further increase of the roll differential, from 1.25 up to 5.0, led to an increase in the yield of the largest size fractions of the milling output while the quantity of medium sized stocks and both flour fractions decreased. This decrease in the degree of particle size reduction, imparted by increase of the roll differential, is caused by two primary reasons. Firstly, the most effective way of grinding bran is by cutting actions of the corrugated rolls. Since smooth rolls have been used in the experiment there was no cutting action of the rolls. Secondly, with roll differential closer to 1 the compressive forces dominate in the grinding zone. As the roll differential increased greater shear stresses were imposed altering the relative contributions of compressive and shearing forces. All investigated samples, especially samples B and D, are composed primarily of endosperm. Compressive stresses, compared to shear, are more effective in causing the disintegration of the brittle endosperm material. These are the reasons why the milling efficiency is higher in the region of small roll differentials.

463

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings… Size fractions [μm] >570 400-570 250-400 100-250 <100

50

65

Yield of the milling output size fractions [%]

Yield of the milling output size fractions [%]

55

Sample A

45 40 35 30 25 20 15 10 5

60

Size fractions [μm] >450 350-450 250-350 100-250 <100

Sample B

55 50 45 40 35 30 25 20 15 10 5 0

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1.0

1.5

2.0

Roll differential

Sample C

35 30

Size fractions [μm ] >570 400-570 250-400 100-250 <100

15

3.5

4.0

4.5

5.0

10 5

Sample D

45

Yield of the milling output size fractions [%]

Yield of the milling output size fractions [%]

40

20

3.0

Roll differential

45

25

2.5

40 35 30

Size fractions [μm] >400 350-400 250-350 100-250 <100

25 20 15 10 5

0 1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1.0

1.5

2.0

2.5

Roll differential

3.0

3.5

4.0

4.5

5.0

Roll diferential

Figure 4. Effect of roll differential on the weight percentage of various streams.

For all investigated samples flour release, as measured by the 250 μm sieve, reached its maximum at roll differential of 1.25 (figure 5a). Further increasing the magnitude of the shear forces imparted by the differential resulted in decrease of the flour yield. It points out that flour particles are more likely formed by compressive fracture than shear. Similar results were seen by Evers et al [20] as well as Scanlon et al [16, 17]. Flour release, obtained by milling samples A and C is lower compared to those obtained by milling samples B and D. Again it could be attributed to the fact that bran takes on some of the forces in the grinding zone which would be otherwise directed to the reduction of endosperm. Milling energy consumption exhibited a near linear response to roll differential (figure 5b). Increasing roll differential from 1.1 up to 1.25 caused very small decrease of milling energy consumption while further increase of roll differential led to a significant increase in milling energy consumption. In fact, decrease in the degree of particle size reduction is followed by increase in milling energy consumption. The reasons for the decrease in the degree of particle size reduction have been explained earlier in a chapter. The relationship between roll differential and milling energy consumption observed in the current study concurs with the results of Scanlon et al [16, 17], Wanzenried [28] and Zwingelberg et al [29].

Flour release (%)

464

Aleksandar Fistes and Gavrilo Tanovic 34 32 30 28 26 24 22 20 18 16 14 12 10

Milling energy consumption [kJ/kg]

1.0 800

Sample: A B C D

a

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

4.0

4.5

5.0

Roll differential

b

700 600 500 400 300 200 100 0

1.0

1.5

2.0

2.5

3.0

3.5

Figure 5. Effect of roll differential on a – the release of flour and b – milling energy consumed.

Increased roll differential caused increased flour ash (table 5). The deterioration of flour quality as differential increases can be attributed to bran powdering since increased differential induce more tearing of the bran. Bran being tough and fibrous is more prone to the ductile fracture imparted by shear forces than to brittle fracture. As a result, flour ash increases because of greater bran contamination of flour. It also explains why the ash content of flour obtained by milling samples A and C is constantly higher, under the same roll differential, compared to the ash content of flour obtained by milling samples B and D respectively. However, it was to be expected considering that samples B and D were obtained by purifying the samples A and C respectively, and that initial bran and therefore ash content of the samples A and C is higher compared to samples B and D. Table 5. Ash content of flour (<250 μm) obtained by milling the investigated samples at different roll differentials

Roll differential 1.10 1.25 1.50 1.75 2.00 3.00 5.00

Ash content of flour (%)dm Sample A B 0.43 0.35 0.43 0.36 0.45 0.40 0.46 0.39 0.48 0.43 0.51 0.44 0.55 0.47

C 0.50 0.50 0.54 0.54 0.59 0.63 0.71

D 0.40 0.42 0.43 0.43 0.45 0.48 0.50

Effect of Smooth Roll Grinding Conditions on Reduction of Sizings…

465

CONCLUSION The effects of smooth roll grinding conditions on reduction of sizings can be explained on the basis of the forces imparted on the particles in the grinding zone. With constant feed rate to rolls, by increasing roll speed the load in the grinding zone is reduced. Particles are subjected to greater grinding action, causing more fracture, which results in increased flour release while flour quality as determined by the ash content is not affected. By increasing roll speed milling energy consumption rose. However, increasing roll speed can be used to form a similar feed flow pattern when an increase in feed rate is desired. This way the disposable roll surface is used more efficiently. Setting the roll differential at 1.25 flour release reaches maximum while the milling energy consumption is at minimum. As differential is increased greater shear forces are imparted to the particles causing greater bran contamination of flour and, therefore, deterioration of flour quality caused by increased flour ash. Considering results obtained in this study (flour release, flour quality and milling energy consumption) a differential of 1.25, relative to a fast roll speed of 5 m/s could be designated as optimal for smooth roll grinding of sizings.

REFERENCES [1]

[2]

[3]

[4] [5]

[6]

[7]

[8] [9]

Bass, E.J. (1988). Wheat flour milling. In Y. Pomeranz (Ed.), Wheat: Chemistry and Technology. (Vol.2, 3rd edition, pp. 1-68). St.Paul, Minnesota: American Association of Cereal Chemists. Sudgen, T.D. and Osborne, B.G. (2001). Wheat flour milling. In D.A.V. Dendy, and B.J.Dobraszczyk (Eds.), Cereals and Cereal Products: Chemistry and Technology. (pp. 140-181). New York, Aspen Publishers. Owens, W.G. (2001). Wheat, corn and coarse grains milling. In W.G. Owens (Ed.), Cereals Processing Technology. (pp. 27-52).Cambridge, UK: Woodhead Publishing Ltd. Lockwood, J.F. (1960). Flour Milling. Stockport, Cheshire, England: Henry Simon Ltd. Shellenberger, J.A. (1980). Advances in milling technology. In Y. Pomeranz (Ed.), Advances in Cereal Science and Technology. (Vol. 3, pp. 227-270). St.Paul, Minnesota: American Association of Cereal Chemists. Scanlon, M.G. and Lamb, J. (1995). Fracture mechanism and particle shape formation during size reduction of a model food material. Journal of Material Science, 30, 25772583. Campbell, G.M., Bunn, P.J., C.Webb, C., and Hook, S.C.W. (2001). On predicting roller milling performance Part II: The breakage function. Powder Technology, 115, 243-255. Haque, E. (1991). Application of Size Reduction Theory to Roller Mill Design and Operation, Cereal Foods World, 36, 368-374. Posner, E.S., and Hibbs, A.N. (2005). Wheat Flour Milling. St. Paul, Minnesota: American Association of Cereal Chemists.

466 [10]

[11] [12]

[13]

[14] [15] [16]

[17] [18]

[19] [20]

[21] [22] [23]

[24] [25] [26] [27] [28] [29]

Aleksandar Fistes and Gavrilo Tanovic MacRitchie, F. (1980). Physiochemical aspects of some problems in wheat research. In Y. Pomeranz (Ed.), Advances in Cereal Science and Technology (Vol. 3, pp. 271326). St.Paul, Minesota: American Association of Cereal Chemists. Austin, L.G., van Orden, D.R., and Perez, J.W. (1980). A preliminary analysis of smooth roll crushers. International Journal of Mineral Processing, 6, 321-336. Austin, L.G., van Orden, D.R., McWilliams, B., and Perez, J.W. (1980). Breakage parameters of some materials in smooth roll crushers. Powder Technology, 28, 245251. Fang, C., and Campbell, G.M. (2002). Stress-Strain Analysis and Visual Observation of Wheat Kernel Breakage During Roller Milling Using Fluted Rolls. Cereal Chemistry, 79, 511-517. Мерко, И.Т.: Совершенствование технологических процессоб сортовога помола пшеницы, Колос, Москва, 1979. Scott, J.H. (1951). Flour Milling and Processes (2nd ed.). London, Chapman and Hall. Scanlon, M.G., Dexter J.E., and Biliaderis, C.G. (1988). Particle-Size Related Physical Properties of Flour Produced by Smooth Roll Reduction of Hard Red Spring Wheat Farina, Cereal Chemistry, 65, 486-492. Scanlon, M.G., and Dexter, J.E. (1986). Effect of Smooth Roll Grinding Conditions on Reduction of Hard Red spring Wheat Farina, Cereal Chemistry, 63, 431-435. Farrel, E.P., and Ward, A.B. (1965). Flow rates and analyses for ash and protein of all streams in the Kansas State University pilot flour mill. Association of Operative Millers-Bulletin, March, 2842-2847. Perry, R.H., and Chilton, C.H. (1973). Chemical Engineer`s Handbook (5th ed.). New York, NY: McGraw-Hill. Evers, A.D., Baker, G.J., and Stevens, D.J. (1984). Production and measurement of starch damage in flour. Part 1. Damage due to roller milling of semolina. Starch, 36, 309-312. ICC Standard No. 110/1: Determination of Moisture Content of Cereals and Cereal Products (Practical Method). ICC Standard No. 104/1: Determination of Ash in Cereals and Cereal Products Pomeranz, Y. (1988). Chemical composition of kernel structure. In Y. Pomeranz (Ed.), Wheat Chemistry and Technology (Vol.2, 3rd edition, pp.97-158). St.Paul, Minnesota: American Association of Cereal Chemists. Kent, N.L. (1975). Technology of cereals. Oxford: Pergamon Press. Arnold, P.C., and Roberts, A.W. (1966). Stress distributions in loaded wheat grains. Journal of Agricultural Engineering Research, 11, 38-43. Schumacher, F. (1966). Spiral, cut, pressure among technical aspects of grinding with roller mills. Am. Miller Process., 94(5), 29. Schumacher, F. (1967). Technical aspects of grinding with roller mills. Association of Operative Millers – Bulletin, January, 2956. Wanzenried, H. (1970). Grinding with smooth rolls. Association of Operative Millers – Bulletin, September, 3195. Zwingelberg, H., Meyer, D., and Gerstenkorn, P. (1983). Beeinflussung der Mehlausbeute und Mehlqualität von Weizen durch Glattwalzen unterschiedlicher Beschaffenheit. Getreide Mehl Brot, 37, 112.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 467-482 © 2007 Nova Science Publishers, Inc.

Chapter 9

AN EFFECT OF RELATIVE AIR HUMIDITY ON THE CONTENT OF VOLATILE COMPOUNDS IN ROASTING COCOA BEANS Wieslawa Krysiak,*1 Teresa Majda and Ewa Nebesny1 1

Institute of Chemical Technology of Food; Institute of Food Chemistry Principles Technical Univeristy of Lodz (TUL), 90-924 Lodz, st. Stefanowskiego 4/10

ABSTRACT The Ivory Coast cocoa beans were convectively roasted at 135°C, at the air flow rate of 1.0 m/s and relative air humidity (RH) of 0.4%, 2.0% and 5.0%. Volatile components of raw and roasted beans were analyzed by SPME/GC/GCMS and identified by comparing their retention indices with that of standards included in a database and their mass spectra with standard spectra included in NIST computer library. Almost 100 different volatile compounds were identified in examined samples of roasted cocoa. They ranked among aldehydes, ketones, alcohols, esters, monoterpenes, pyrazines, acids, lactones, furan derivatives, and sulfur-containing compounds. It was found that a rise in the relative air humidity from 0.4% to 2.0 and 5.0% increased the contents of pyrazines, volatile acids, esters, furan derivatives, and sulfur-containing compounds in a headspace of roasted cocoa. In contrast, the contents of alcohols and aldehydes in the headspace were considerably lower when the cocoa beans were roasted at the relative air humidity of 5.0% as compared to that when less humid air was used for convective heating.

Keywords: cocoa beans; convective roasting; roasting of cocoa beans; relative air humidity; volatile components in roasted cocoa beans.

*

Wieslawa Krysiak: e-mail: [email protected], [email protected]

468

Wieslawa Krysiak, Teresa Majda and Ewa Nebesny

INTRODUCTION Roasting is one of principal technological operations in cocoa beans processing. It reduces water, volatile acids and tannins contents, produces a characteristic chocolate aroma, enhances brown coloration of cocoa beans, facilitates separation of shells from nibs and destroys microbial contamination. One of the most important effects derived by roasting is the development of a characteristic and pleasant chocolate aroma [3, 6, 19, 20, 33]. It appears when precursors of aroma generated during fermentation and drying of cocoa beans combine with each other during roasting. Maillard reactions rank among the principal processes occurring during roasting and contributing to generation of the appropriate aroma, changes in texture and stronger brown coloration [5, 6, 9, 19, 21, 29, 30, 33, 37, 38, 39]. Chemistry of these reactions depends on temperature and water activity of raw material [36, 37], concentration and structure of reducing sugars and amino acids, and pH of material [37]. The presence of small amounts of water is thought to favor Maillard reactions because water enables the transport of reacting molecules [44, 46]. However, when water content is too high, pyrazines and other important aromatic substances are not formed. Alkyl derivatives of pyrazine were found to be the most important components of roasted cocoa aroma [6, 7, 15, 24, 42, 48]. Their odor threshold is 4-490ng/g [17]. One of the factors affecting the content of pyrazines in aroma of roasted cocoa is the geographic origin of cocoa. It was found that cocoa beans from Ghana contained 698μg/100 g of these compounds, while the Mexican cocoa of Tabasco variety – only 142 μg/100g [19, 41]. Concentration of pyrazines in roasted cocoa depends also on the genotype of cocoa seeds and parameters of unit processes such as fermentation [1, 2, 6, 10, 18, 30, 40], drying and roasting [6, 7, 15, 21, 33, 41, 42, 45]. The most abundant pyrazines identified in cocoa beans are pyrazine, 2,5dimethylpyrazine, 2,6-dimethylpyrazine, trimethylpyrazine and tetramethylpyrazine, [6, 7, 31, 33, 40]. The contents of pyrazine and tetramethylpyrazine [41] are significantly higher in roasted cocoa than in raw material. The ultimate steps of Maillard reactions are aldol condensation and polymerization of aldehyde amines, which generate aldimines and ketimines, and next the heterocyclic nitrogen compounds [38]. It was found that [17] the unique chocolate aroma of roasted cocoa was derived by reaction of phenylacetic aldehyde and 3-methylbutane. The aldol condensation yields 2phenyl-5-methyl-2-hexanal. The other aromatic compounds generated during roasting are 3methylbutanal [17, 31], phenylacetaldehyde, 2-methyl-3-furan, 2-ethyl-3,5-dimethyl pyrazine and 2,3-diethyl-5-methyl pyrazine [3]. Ziegleder [31] proposed to determine the roast level of cocoa beans on the basis of methylfuran concentration. Cocoa beans are roasted at 130 – 1500C for 15-45 min. The maximum temperature on their surface is 120 – 140oC [32, 34, 44, 45, 47]. Usually whole beans are roasted. In one of several variants of this process, the surface of material is moisturized to make the separation of shells from nibs easier. Moisturizing of the surface of cocoa beans is achieved by spraying with water, steaming or increasing humidity of air in a roaster [4, 13, 16, 44]. Presented studies aimed at estimation of the relationship between the relative air humidity and concentration of aroma components in the headspace of roasted cocoa beans.

An Effect of Relative Air Humidity on the Content…

469

MATERIALS AND METHODS The Ivory Coast cocoa beans (Forastero cultivar, Ivory Coast) were applied for the studies. The cocoa beans were sorted and the medium-size fraction was selected for further experiments to provide uniform processing of all roasted beans.

Roasting of Cocoa Beans Raw cocoa beans were convectively roasted in a forced air flow drying tunnel [26, 27, 28]. The parameters of thermal processing were as follows: • • •

temperature of 1350C; air flow rate of 1.0 ms-1 (it was earlier found to be the optimum flow rate [26]); relative air humidity of 0.4%, 2.0% and 5.0%.

Presented parameters of thermal processing of cocoa beans refer to the air, which was in a direct contact with roasted beans. Process of roasting was conducted without air circulation. Measurements of spent air parameters were also carried out (results not presented). Air temperature was measured by YCY meter (type YC-262 coupled with NiCr-NiAl sensor), and the rate of its flow was determined using the coupled THERM 2285-2B meter equipped with 9915 S120 sensor (produced by AHLBORN). The relative humidity of “dry” air was calculated according to the following equation (presented for the temperature of 110°C):

RH=100×

Y`× P ` 0.622 × ( Psat )110 0 C + Y × ( Psat )110 0 C

where: RH - relative air humidity [%], Y’ – absolute air humidity at 20°C [kg H2O/kg dry air]; 0.0087729 kg H2O/kg dry air; P- pressure of saturated steam [Pa]; 101 x 103 Pa; (Psat)110°C – pressure of steam at 110°C [Pa], 143.2653 x 103 Pa [Manual of Engineer: Sugar Manufacturing, p. 52 (Dobrzycki et al. 1973)]. Air humidity was increased using saturated steam produced in a steam generator. Relative humidity of this air was determined using the coupled THERM 2285-2B meter equipped with FHA636-HR2 sensor (produced by AHLBORN). The precision of measurements of temperature, air flow rate and its humidity was +1 °C, +0.05 ms-1 and +0.5%, respectively. Each time, batches of cocoa beans (200 g) were spread to form a monolayer during roasting. The process was terminated when the water content of material dropped to approximately 2%, since this value is regarded as optimal for further steps of cocoa processing, such as crushing or butter pressing [40].

470

Wieslawa Krysiak, Teresa Majda and Ewa Nebesny

Determination of Chemical Composition of Cocoa Beans’ Headspace Samples of raw and roasted cocoa beans were ground in a laboratory mill WZ-1 (ZBBPP, Poland), and approximately 5g portions were weighed to glass vials (5×10 cm). Concentration of volatiles was determined by solid phase microextraction coupled with gas chromatography and mass spectrometry SPME-GC-GC/MS. Volatiles were adsorbed using a SPME unit (Supelco) consisting of a length (2 cm plug) of fused silica fiber coated with divinylbenzene /carboxen/polydimethylsiloxane DVB/Carboxen/PDMS (thickness of 50/30 μm). Microextraction of volatile aroma components was carried out for 0.5 h. It was preceded by 30 min stabilization of ground cocoa at 600C. Desorption of these compounds occurred at 2500C in a sampler of gas chromatograph. GC analyses were carried out using HRGC 5300 gas chromatograph (CARLO-ERBA INSTRUMENTS, series Mega) equipped with flame ionisation detector (FID) and SSL injector. GC/MS analyses were conducted using GC 8000 gas chromatograph (FISONS INSTRUMENTS) equipped with SSL injector and coupled with MD 800 mass detector. In both cases aroma components were separated on Quadrex capillary column (30 m x 0.32 mm x 0.5 μm) fused with 007-FFAP stationary phase. Nitrogen was the carrier gas (flow rate of 1ml/min) when analyses were conducted using HRGH 5300 gas chromatograph. The temperature was increased (40C/min) from 400C (3 min isothermal period) to 2450C (a final 30 min isothermal period). The temperature of injector and detector was 2500C. The same parameters were maintained when aroma components were separated using GC 8000 gas chromatograph. In this case helium was the carrier gas (flow rate of 0.8ml/min) and ionisation energy was 70 eV. The volatile components of aroma were identified by comparison of both their retention indices with that of standards included in a database and their mass spectra with spectra of compounds included in NIST computer library.

Calculation and Statistics Determinations of the headspace composition were carried out in triplicate. Results are expressed as weight% of total volatiles (mean + SD). Percentage content of each headspace component was calculated as follows: the sum of surface areas below peaks of all volatiles was taken as 100% and then the weight% of each compound was calculated on the basis of surface area below the respective peak. The results were subjected to statistical analysis, which included determination of a mean surface area below each peak (table 1), calculation of standard deviation of the latter and one-way analysis of variation (ANOVA) at the significance level p≤0.05.

RESULTS AND DISCUSSION Many different factors, such as the variety of cocoa, post-harvest treatment, fermentation and roasting conditions determine the aroma of cocoa, which is characteristic of chocolate products. The aroma is affected both by substances contained within the raw cocoa beans and by newly generated components, which are products of pyrolysis of sugars and nitrogen

471

An Effect of Relative Air Humidity on the Content…

compounds. In early phases of roasting this aroma can be acetic, beer-like, bread-like or another. A loss of easily evaporating components of raw cocoa is of importance for the ultimate aroma of roasted cocoa and thereby for the quality of chocolate. Gas chromatography of aroma components of raw and convectively roasted cocoa beans (either in “dry” (RH of 0.4%) or in “humid” air (RH of 2 or 5.0%)), revealed approximately 100 volatile compounds, ranking among aldehydes, ketones, alcohols, esters, monoterpenes, pyrazines, acids, lactones, furyl derivatives and sulfur compounds. All these substances, their retention indices (RI) and percentage contents determined on the basis of surface area under their peaks (the sum of surface area under all the peaks is equivalent to 100%) are displayed in table 1. Table 1. The effect of relative air humidity during roasting on headspace composition of roasted cocoa*. *Roasting was carried out at 1350C and air flow rate of 1.0 m/s. Values of relative air humidity were 0.4, 2.0 and 5.0%. The headspace composition of raw and convectively roasted cocoa beans was determined by SPME/GC/GCMS Peak no

Compound

RI

1. 2. 3. 4. 5. 6. 7.

methanthiol acetaldehyde dimethyl sulfide noidentified isobutyraldehyde propionaldehyde acetic acid methyl acetate tetrahydro-2-methylfuran 2-methylfuran butyraldehyde pivaldehyde(2,2dimethylpropanal) isovaleric aldehyde 2,5-dimethylfuran 2-pentanon 2-methyl-2-pentanal 3-methyl-3-buten-2-one 2-ethyl-5-methyl-furan 2-butanal methyl-3-butenoate dimethyl disulfide hexanal 2-ethylacrolein 2-pentanol 3-penten-2-one β-myrcene hyptanone isoamyl alcohol 2,4-nonadienal

8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20 21. 22. 23. 24. 25. 26. 27. 28.

Roasted at RH of 0.4%; τ=35 min

Raw cocoa beans

697 715 763 809 }829

RT 2.18 2.27 2.52 2.82 }3.05

0.10a 0.20 0.18 0.20a }0.3

841

3.18

Roasted at Roasted at RH RH of of 5.0%; 2.0%; τ=60 min τ=45 min Surface area of GC peak (%) 0.10a 0.14a 0.07a b 0.13 0.28 0.25b 0.35 0.36 0.61 0.22a 0.20a 0.20a 0.73 }2.47 }2.42 1.03 1.15b 1.92 3.07

}887

}3.72

}0.09

}0.11

916 924

4.22 4.38

1.35 1.16b

928 982 995 }102 9 1054 1060 1079 1099 1113 1125 1133 1146 1180 1199 1222 1235

4.48 5.65 5.92 }6.93 7.72 7.92 8.53 9.15 9.67 traces 10.38 10.85 12.08 12.97 13.63 14.13

1.11b }-

1.31 1.17b

0.94 0.16 0.80 1.25

2.45b 0.08 0.46 }0.24b

2.79b 0.02 0.34 }0.20b

2.56b 3.07 }0.12

0.98 }0.03

0.12b 1.14 1.14 0.57 0.24b traces 0.07b 7.33 0.01b 0.54 0.24b 0.13b

0.10b 0.77 1.21 0.28 0.20bbb 0.06b 6.02 0.05b 0.43 0.27bbb 0.16b

0.09b 0.22 0.06 0.15 0.02 0.33 1.16 0.34 0.49 0.15b

0.08 traces 2.54 0.06 0.58 0.42 1.09 3.09 0.22

0.35 2.44

472

Wieslawa Krysiak, Teresa Majda and Ewa Nebesny Table 1. (Continued).

Peak no

Compound

RI

29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45.

Styrene methylpyrazine isobutyl methyl ether noidentified 3-methyl-2-hexanal 2,5-dimethylpyrazine 2,6-dimethylpyrazine ethylpyrazine trans-linalool oxide 2,3-dimethylpyrazine metyl trisulfide 2-ethyl-5-methylpyrazine 2-nonanon 2-ethyl-6-methylpyrazine trimethylpyrazine ester? 2-ethyl-3,5dimethylpyrazine acetic acid 2-ethyl-3,6dimethylpyrazine cis-linalool oxide 3-furalaldehyde tetramethylpyrazine ethylene glycol acetate 6-methyl-6vinylpyrazine 2-furyl-methyl-ketone 2-heptanol pyrrole benzaldehyde linalool 2,3-butandiol 3-methyl-pyrrole isobutyric acid dimethyl sulfooxide 1,3-butandiol 3(2H)-piridazinone pyridinecarboxylic acid derivative 3-butenoic acid benzenacetaldehyde acetophenone 3-furanmethanol isovaleric acid 1-methyl-2pyrrolidinone 2-acetylo-3methylpyrazine ketone ?

1279 1290 1309 1328 }134 6 1351 1357 1365 1370 1382 1400 1406 1412 1426 1450 1466

RT 15.78 16.18 16.90 17.62 }18.2 7 18.47 18.70 19.00 19.17 19.60 20.28 20.48 20.72 21.20 22.05 22.62

}147 4

}22.9 0

}19.82b

19.85b 0.04

13.62 0.50

37.85 traces

1489 }149 8 1505 1515

23.40 }23.7 2 23.98 24.32

0.18b }1.39

śl 0.82 traces 0.14 0.12

}traces

0.04b 0.26

0.14b 1.67 traces 0.03b 0.35

traces

1534 }154 6 1556 1584 1589 1597 1607 1617 1622 1639 1650

24.95 }25.3 8 25.72 26.67 26.83 27.13 27.45 27.77 27.93 28.47 28.82

0.10b }0.22

0.09b }1.20

10.37 traces 0.35b 0.03 0.15 0.34 0.70 0.30b

7.46 0.05 0.36b 0.24 0.45 0.27 0.52 0.46 0.28b

0.06b 1.36 0.08 2.25 0.39 1.86 0.19 1.33 0.06 1.93 0.27 0.13

traces 3.26 0.90 5.07 4.11 3.09 0.07 0.53

1670 }167 7 1684 1690 1710

29.47 }29.6 8 29.92 30.10 30.72

1.30 }7.95

traces 5.61 traces 0.93 7.43 0.05

}1.50

1.99b 4.30b traces

1.58 7.87 traces 1.99b 4.74b traces

1715

30.88

0.07a

0.32b

0.34b

0.04a

1728

31.28

0.20a

0.20a

0.14a

0.14a

46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72.

Roasted at RH of 0.4%; τ=35 min

Roasted at Roasted at RH RH of of 5.0%; 2.0%; τ=60 min τ=45 min Surface area of GC peak (%) 0.51 0.39 0.34 3.30b 3.73ba 3,96a 0.73b 0.62b 0.66b 1.66 1.44 0.18a 0.39 }3.13 }3.65 1.68 1.84b 1.93b 2.00b b b 0.65 0.69 0.62b b b 0.18 0.20 0.46 0.30 0.21 0.48 0.09 0.13 0.03 0.11 0.18b 0.20b 0.59b 0.53b 0.54b b b 0.51 0.60 0.47b 0.01 0.93b 1.01b 0.04 0.11b 0.10b b b 0.51 0.65 0.52b

Raw cocoa beans

0.87 traces 0.37 0.18 0.08 }0.05 traces 0.04 0.07

traces 0.50 14.42 -

473

An Effect of Relative Air Humidity on the Content… Table 1. (Continued). Peak no

Compound

RI

73.

2-methyl-5-transpropenylpyrazine valeric acid 2,3-hexandiol 2-pentanoic acid methylbenzyl alcohol iso-amyl benzoate hexanoic acid methoxyphenol benzyl alcohol phenyl ethyl alcohol methylcinnamaldehyde maltol ketone methyl pyrrol-2yl noidentified furaneol noidentified noidentified 4H-pyran-4-one-2,3dihydro-3,5-dihydroxy6-methyl

74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90.

Roasted at RH of 0.4%; τ=35 min

Raw cocoa beans

1777

RT 32.78

Roasted at Roasted at RH RH of of 5.0%; 2.0%; τ=60 min τ=45 min Surface area of GC peak (%) 0.07a 0.09a 0.10a

0.10a

1801 1825 1839 1844 1855 1868 1897 1911 1948 1972 2003 2014

33.53 34.23 34.62 34.78 35.10 35.48 36.30 36.72 37.72 38.40 38.69 39.55

0.12 0.09b 0.07b 0.25b 0.11b 0.41b 0.16 0.30 2.02b 0.36b 0.88

0.24 0.17 0.08b 0.24b 0.10b 0.36b 0.28 0.23a 2.09b 0.34b 0.02 1.66

0.03 0.10b traces 0.25b 0.12b 0.31b 0.21 0.15 1.39 0.10 traces 0.66

0.99 0.25a 1.41 0.08

2072 2079 2083 2096 2317

41.10 41.28 41.40 41.75 47.33

0.28 0.15b 0.10 0.08 1.43b

0.41 0.23 0.19 0.14 1.53b

0.50 0.19b 0.39 1.20

-

}0.15

RI-retent index, RT-retention time Values in each line bearing the same letters are not significantly different (p>0,050 from one another.

Changes in contents of aroma components are also shown in two representative chromatograms (figures 1 and 2). Numbers of compounds in the table 1 are the same as in figures 1 and 2. Table 2 presents the relationship between the number of individual compounds in each of the groups found in the headspace of cocoa beans roasted at 1350C and air flow rate of 1.0m/s, and the relative air humidity (0.4; 2.0 or 5.0%). Presented results prove that the number of volatile compounds in the headspace of roasted cocoa is higher than in that of raw cocoa irrespective of the relative air humidity. Our finding is consistent with results of other researchers who were involved in studies on cocoa aroma [6, 7, 10, 15, 17, 20, 21, 24, 33, 38, 40, 41, 42, 45]. The dominating headspace components of raw and roasted cocoa were found to be pyrazines, aldehydes and alcohols. The contents of volatile acids, ketones and sulfur compounds were lower. Small amounts of esters and furans were also detected.

Figure 1. Representative GC chromatogram of volatile components of raw cocoa beans.

Figure 2. Representative GC chromatogram of volatile components of convectively roasted cocoa beans (T=1350C, v=1,0m/s, RH=0,4%, τ=35 min).

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Table 2. The number of compounds within each of the groups of volatile compounds identified in raw and convectively roasted cocoa beans and its dependence on the humidity of air used for roasting (temperature and air flow rate were maintained at 1350C and 1.0m/s, respectively)

Group of compounds

Cocoa beans Raw Roasted at RH of 0.4%; τ=35 min

Pyrazines Alcohols Aldehydes Ketones Acids Sulfur compounds Esters Furans

10 13 12 6 6 3 2 1

13 13 13 7 7 3 5 4

Roasted at RH of 2.0%; τ=45 min 14 14 13 7 7 4 4 4

Roasted at RH of 5.0%; τ=60 min 14 14 13 6 5 4 4 4

Pyrazines Pyrazines rank among the most important volatile components of roasted cocoa aroma. They impart the characteristic aroma to roasted products [5, 6, 7, 17, 19, 20, 21, 23, 40, 41 42]. The number of pyrazines rose from 10 (included in the headspace of raw cocoa) to 13 -14 (table 2) during roasting at 1350C regardless of the relative air humidity. Raw cocoa beans contained the following pyrazines: methylpyrazine, 2.3-dimethylpyrazine, 2-ethyl-5methypyrazine, 2-ethyl-6-methypyrazine, trimethylpyrazine, 2-ethyl-3,5-dimethypyrazine, 2ethyl-3,6-dimethypyrazine, tetramethylpyrazine, 6-methyl-6-vinylpyrazine and 2-acetyl-3methylpyrazine. Five of them were hardly detectable and the contents of other five were very low (0.04-0.1%). Also Keeney [23], Jinap et al. [121], Reineccius et al. [40], Sanagi et al. [41] found these pyrazines in raw cocoa beans. Irrespective of the relative air humidity, the process of roasting not only increased the contents of these pyrazines, which were detected in the headspace of raw cocoa beans but also generated the new compounds, mainly dimethylpyrazines, such as 2,5-dimethylpyrazine and 2,6-dimethylpyrazine. 2,5-dimetylopyrazine was found to convey the nutty aroma to thermally processed products [6, 19]. Our results are consistent with findings of Jinap et al. [21], Keeney [23] and others [6, 7, 31, 33, 40]. The relative air humidity had no explicit impact on formation or disappearance of pyrazines. Hence the increased water content in air impaired neither Maillard reactions nor formation of pyrazines and the aroma of cocoa beans was not less rich [44]. The other important factors affecting the quality of roasted cocoa are the ratios of dimethylpyrazines (DMP) to trimethylpyrazines (TMP) and dimethylpyrazines (DMP) to tetramethylpyrazines (TMP). According to Bonvehi et al. [6], Eun-Jung-Lee et al. [14] and Fadini et al. [15] both these ratios should not be lesser than 1 and their low values indicate that synthesis of aroma components was impaired. The ratios (DMP)/(TMP) and (DMP)/(TMP) determined in this work exceeded 1 irrespective of roasting conditions.

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Alcohols The headspace of raw and roasted cocoa beans contained 13 and 14 different alcohols, respectively. Alcohols impart the fruity and plant-like hint [21]. The most abundant alcohols in raw cocoa headspace were isoamyl alcohol, 2,3-butandiol, 1,3 butandiol and pentanol (3.09; 5.07; 3.09 and 1.44%, respectively) [35]. The contents of other alcohols were lower and usually did not exceed 1.0% (0.58-0.09%). Alcohol content was reduced during roasting because of their volatilization with steam and/or thermal degradation. The same phenomenon was observed by Jinap et al. [21]. The content of alcohols in the headspace of roasted cocoa beans was higher when the relative air humidity was 5.0%, most probably because of rapid formation of empty space between the kernel and shell (the so termed “balloon effect”) what hampered the evaporation of volatile compounds from the beans [25, 28, 44]. The new alcohol was formed during roasting of cocoa beans (undetectable in the headspace of raw beans) regardless of the relative air humidity. It was 2-heptanol, which imparts the fruity, herbal, and sharp aroma [21]. The other alcohols important for sensory properties are linalol and phenyl ethyl alcohol. Linalol is responsible for the flower hint of thermally processed products [44]. It was particularly abundant in cocoa beans roasted at the relative air humidity of 5.0%. According to Świechowski [44] this alcohol is derived from pyrazines at high temperature and humidity.

Carbonyl Derivatives Aldehydes Carbonyl compounds were found to be important components of cocoa aroma [19, 21, 43]. As much as 20 different aldehydes and ketones were detected in the aroma of roasted cocoa. Aldehydes are products of Stecker degradation of free amino acids and like pyrazines rank among the principal aroma components formed during roasting. They can be also derived by oxidation of lipids, which occurs at high water content [2, 19]. 11 aldehydes were detected in raw cocoa beans. The most abundant of them were: benzaldehyde and benzenacetaldehyde (3.26 and 1.50%, respectively). The contents of acetic, isobutyric, propionic and butyric aldehydes were lower than 0.5% (0.1-0.35%). These compounds could be the result of selfoxidation and degradation of fatty acids [2, 8]. The number of aldehydes rose from 12 to 13 during roasting, irrespective of the relative air humidity. Their content was the highest in cocoa beans roasted using “dry” air (RH of 0.4%). Application of more humid air (RH of 2.0 or 5.0%) reduced the amounts of aldehydes and their depletion was more pronounced at the highest RH value. It could result from the longer time of processing (60 min) and evaporation of these highly volatile substances with steam. Also according to Jinap et al. [21] the longer time of roasting decreases the contents of these compounds. The most susceptible to distillation with steam were benzaldehyde and benzenacetaldehyde. Their contents increased 3 and 5 fold, respectively, when cocoa beans were roasted with “dry” air but when the relative air humidity was increased to 2.0 and 5.0% their contents were 30-70% lower as compared to that achieved at RH of 0.4%.

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Changes in the contents of ketones during roasting of cocoa beans were like in case of aldehydes.

Esters Esters impart the fruity taste and aroma to plant products. The content of methyl acetate, which was one of two esters detected in the headspace of raw cocoa beans, reached 1.11%. Roasting increased the number of these compounds to 3 (RH of 5.0%) or 4 (RH of 0.4 and 2.0%). Percentage contents of esters rose with humidity of air used for roasting. For instance, the contents of methyl acetate increased by 4, 73 and 177% for RH of 0.4; 2.0 and 5.0%, respectively. This character of changes could result from the longer time of roasting when RH was higher. It favored condensation of methanol and acetic acid. The latter is one of abundant components of raw cocoa beans and GC analysis revealed that its content was decreased during roasting. Also Jinap et al. [21] found that longer roasting at 1400C beneficially affected esters formation. Volatile Acids Acetic acid was the most abundant volatile acid (above 37%) in raw cocoa beans. The contents of isovaleric (14.42%) and isobutyric (4.11%) acids were also appreciable. Percentage contents of other acids, like hexanoic and butenoic (1.0-1.5%) were lower. Depletion of undesirable acidic substances, mainly acetic acid, is one of the goals of roasting of cocoa beans [19, 20, 22, 25, 34, 44, 47]. Concentrations of acetic, isobutyric, isovaleric, hexanoic and pentanoic acids were reduced by roasting, regardless of air humidity. The ultimate content of acids was decreased with a rise in RH with the exception of isovaleric acid. It was believed to result from more advanced evaporation of volatile acids with steam during longer roasting at higher RH. Although the content of isovaleric acid was also reduced (by 50-70%) during roasting, its highest percentage content was detected in the headspace of cocoa beans roasted at RH of 5.0%, like in case of valeric acid. The abundance of isovaleric acid and the presence of valeric acid in the headspace of cocoa beans roasted at RH of 5.0% brought about the relatively high acidity of this material. The similar phenomenon was reported earlier by one of the authors [25]. Furans The raw cocoa beans contained only one volatile furan derivative, i.e. 2,5-dimethylfuran (0.98%) while the roasted material contained 4 of them. These compounds are generated by degradation or caramelisation of sucrose [12]. Because the concentration of the latter in raw cocoa beans is very low (approximately 1.0%), so the degradation of sugars due to Maillard reactions occurring during roasting is also believed to be the source of these compounds [11, 42]. The majority of furans impart the sweet and caramel-like aroma of burnt sugar. The contents of furans in the headspace of roasted cocoa beans increased with the relative air humidity and was the highest for RH of 5.0%. The highest rise was observed for 2,5dimethylfuran (3 folds increment). The same tendency was observed for 2-methylfuran and tetrahydro-2-methylfuran. It was arguably caused by longer time of roasting when RH was greater. Elevated humidity and longer thermal processing beneficially affected Maillard reactions occurring in cocoa beans [21]. Water concentration of 3% was found to favor the

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479

non-enzymatic browning. In the atmosphere of more humid air, particularly at RH of 5.0%, water content of 3.0% in cocoa beans subjected to roasting is maintained for 30 min whereas in the “dry” air (RH of 0.4%) – only for 20 min [27].

Sulfur Compounds Raw cocoa beans contained dimethyl sulfide, dimethyl disulfide and methanthiol (0.18, 2.54% and 0.10%, respectively). Because of very low thresholds these compounds enhance the intensity of aroma of other aromatic substances. The number of these compounds rose to 5 during roasting in the atmosphere of more humid air (RH of 2.0 or 5.0%). The headspace of roasted cocoa beans contained also methyl trisulfide and dimethyl sulfoxide. Their contents were higher for RH of 2.0%. When cocoa beans were convectively heated with “dry” air, only methyl trisulfide was generated during roasting. The application of more humid air resulted in depletion of dimethyl disulfide. Its content was reduced 4.5, 9 and 17 folds for RH of 0.4, 2.0 and 5.0%, respectively. It was distilled with steam during longer roasting, like other very volatile substances. The other reason of their disappearance was degradation to dimethyl sulfide, which caused a rise in concentration of the latter (by 2% for RH of 0.4 and 2.0%, and 3.5 fold for RH of 5.0%). Summary The conclusions drawn from the results of the determinations are as follows: • • • •

GC/MS chromatography revealed almost 100 different volatile compounds in the headspace of cocoa beans convectively roasted at 135°C; these substances include aldehydes, ketones, alcohols, esters, monoterpenes, pyrazines, acids, lactones, furan derivatives, and sulphur-containing compounds; a rise in air humidity from 0.4% to 2 and 5% increased the content of pyrazines, volatile acids, esters, furan derivatives, and sulfur-containing compounds. the content of alcohols and aldehydes was significantly reduced when cocoa beans were roasted at the relative air humidity of 5%.

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[2]

[3] [4]

Abeygunasekera D. D., Mubarak A., M., Jansz. E., R. (1989). The volatiles of cocoa by GC/MS analysis and sensory evaluation with special emphasis on the effect of fermentation time and maturation. Journal of the National Science Council of Sri Lanka, 17 (2), 229-235. Abeygunasekera D. D., Jansz. E., R. (1991). Effect of the maturation process on fermented cocoa bean. I. Free amino acids and volatile carbonyls. Journal of the National Science Council of Sri Lanka, 17 (1), 23-33. Belitz H.-D., Grosch W. (1999). Food Chemistry. Cocoa and Chocolate, 2nd ed., Berlin, Springer-Verlag, 894-903. Benz K.: Debacterization of Cocoa Beans Gains Increasing Acceptance. Available since 2003, http://www.buhlergroup.com/sc/en/publikationen.asp

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Wieslawa Krysiak, Teresa Majda and Ewa Nebesny Bonvehi J.S., Coll F.V. (1997). Evaluation of bitterness and astringency of polyphenolic compounds in cocoa powder. Food Chemistry, 60, 365-370. Bonvehi J.S., Coll F.V. (2002). Factors affecting the formation of alkilpyrazines during roasting treatment in natural and alkalinized cocoa powder. J. Agric. Food Chem., 50, 3743-3750. Bonvehi J.S. (2005). Investigation of aromatic compounds in roasted cocoa powder. European Food research and tenchnology, 221 (1-2), 19-29. Boosfeld J., et. al.. (1993). Characterization of unsaturated aldehydes in green coffee. 15th Scientific Symposium on Coffee, Sinscinati, Available since 2003, http://www.asic-café.org/ Brito E.S., Narain N. (2003). Effect of pH distillate volume on monitoring aroma quality of bittersweet chocolate. Food Quality and Preference, 14, 219-226. Cros E. (1999). Cocoa flavor development. Effects of post-harvest processing. Manufacturing and Confectioner, 79 (2), 70-77. Czerny M., Mayer F., Grosch W. (1999). Sensory study on the character impact odorants of roasted Arabica coffee. J. Agric. Food Chem., 47, 695-699. De Maria C. A. B., et al. (1996). Composition of green coffee water-soluble fractions and identification of volatiles formed during roasting. Food Chemistry, 55 (5), 203207. Dodson C. (1996). Fruitful new opportunities in food processing industry. FoodTech-Europe, 3(2), 52, 54, 56. Eung-Jung-Lee et al. (2000). Changes in flavor components during microwave roasting of cocoa mass. Journal of the Korean Society of Food Science and Nutrition, 29 (3),384-388. Fadini A. L., Garcia N-H-P. (1997). Evolution of pyrazines during the roasting of cacao beans (Theobroma cacao, L.) variety Forastero. Coletanea-do-Instituto-deTecnologia-de Alimentos, 1999, 27(1/2), 69-74. Finken H. (1996). A new thermal cocoa process. Food Marketing and Technology, 4, 25-27. Grabowska J. (2002). Aroma compounds. In: Food Chemistry. Ed. Z.E. Sikorski., Warsaw, PWN, 427-456. Groot H. (2003). Cocoa aroma- a complex puzzle. Susswaren, (7-8), 20-21. Hoskin J.C., Dimick P.S. (1994). Chemistry of flavour development in chocolate. In Industrial chocolate manufacture and use. II-nd ed., Chapman and Hall, , 102-111. Jinap S., Dimick P.S., Hollender R. (1995). Flavour evaluation of chocolate formulated from cocoa beans from different countries. Food Control, 6, 105-110. Jinap S., Wan Rosli W.I., Russly A.R., Nordin L.M. (1998). Effect of roasting time and temperature on volatile component profiles during nib roasting of cocoa beans (Theobroma cacao). J. Sci. Food Agric., 77, 441-448. Jinap S., Zeslinda A. (1995). Influence of organic acids on flavour perception of Malaysian and Ghanian cocoa beans. J. Food Sci. Technol., 32, 27-33. Keeney P.G. (1972). Various interactions in chocolate flavor. JAOCS, 49, 567-572. Koehler P.E., Odell G.V. (1970). Factors affecting the formation of pyrazine compounds in sugar-amine reactions. J. Agr. Food Chem., 18, 895-898.

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Krysiak W. (2001). Physico-chemical changes in cocoa bean roasted in changing air conditions. In Proceedings of the XIIth seminar of Properties of Water in Food ed. P.P. Lewicki, Warsaw Agricultural University Press, 107-119. Krysiak W., Iciek J., Motyl-Patelska L. (2003). Infulence of roasting conditions on selected physico-chemical properties of cocoa bean. Inzynieria Chemiczna i Procesowa, 24 (3),509-524. Krysiak W., Motyl-patelska L. (2005). Effects of roasting conditions on the degree of lipid migration from cocoa bean kernel. Inzynieria Chemiczna i Procesowa, 26 (4),817-829. Krysiak W., Motyl-Patelska L. (2006). Effects of air parameters on changes in temperature inside roasted cocoa beans. Acta Agrophysica, 7 (1),113-128. Martins S.I.F.S., Jongen W.M.F., van Boeckel M.A.J.S. (2001). A review of Maillard reaction in food and implications to kinetic modelling. Trends Food Sci. Technol., 11, 364-373. Mascaros A. (1993). Sensory properties of cocoa. Origin, processing and evaluation. Alimentaria (244), 85-89. McFarlane I. (1994). Instrumentation. In Industrial chocolate manufacture and use. II-nd ed., Chapman and Hall, 281-311. Minifie B.W. (1981). Chocolate, cocoa and confectionery: In Science and Technology. Pitman Press, 15, 25-28. Misnawi , Jinap S., Jamilah B., Nazamid S. (I). (2004). Effect of polyphenol concentration on pyrazine formation during cocoa liquor roasting. Food Chem., 85,73- 80. Nebesny E., Rutkowski J. (1998). Effect of roasting and secondary fermentation on cocoa bean enrichment. Pol. J. Food Nutr. Sci., 7/48, 437-444. Oberparleiter S., Ziegleder G. (1997). Amyl alcohols as indicative of raw cocoa bean quality. Zeitschrift-fuer-Lebensmittel-Untersuchung-und-Forschung-A/Food Reserch and Technology, 204 (2), 156-160. Őzdemir M., Devres Y.O. (2000). Analysis of color development during roasting of hazelnuts using response surface methodology. J. Food Eng., 45, 17-24. Őzdemir M., Devres Y.O.(I). (2000). Kinetics of color changes of hazelnuts during roasting. J. Food Eng., 44, 31-38. Pijanowski E., Dłużewski M., Dłużewska A., Jarczyk A. (1997). In Food technology, VI ed., Warsaw, WNT, 88,120,192. Redgwell R.J., Trovato V., Curti D. (2003). Cocoa bean carbohydrates: roastinginduced changes and polymer interactions. Food Chemistry, 80, 511-516. Reineccius G.A., Keeney P.G., Weissberger W. (1972). Factors affecting the concentration of pyrazines in cocoa beans. J. Agr. Food Chem., 20, 202-206. Sanagi M.M., Hung W.P., Yasir S.M. (1997). Supercritical fluid extraction of pyrazines in roasted cocoa beans. Effect of pod storage period. J. Chromatography A, 785, 361-367. Shibamoto T., Akiyama T., Sakaguchi M., Enomoto Y. et. al. (1979). A study of pyrazine formation. J. Agric. Food Chem., 27, 1027-1031. Silwar R. (1988). Quantitative determination of steam-volatile aroma constituents. Café, caco, The, 32 (3), 243-250.

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Wieslawa Krysiak, Teresa Majda and Ewa Nebesny Świechowski Cz. (1996). Chocolate manufacturing. Roasting of cocoa beans – comparison of methods. Bakery and Confectionery Rev., 4, 20-22. Świechowski Cz. (1994). Thermal processing of cocoa beans. Bakery and Confectionery Rev., 1, 35-37. Świtka J. (1994). Water as ingredient of food. In Chemical and functional properties of ingredients of food ed. Z.E. Sikorski. Warsaw, WNT, 63-64. Wyczański S. (1994). Confectionery. Warsaw, WSiP, 249-252,257,264. Zamalloa W., A., Pezoa N., H., Meireles M., A., A. (1995). The extraction of volatile components from cocoa beans with a micro-extractor and using subcritical CO2. Ciencia-e-Tecnologia-de-Alimentos, 15 (1), 70-73.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 483-495 © 2007 Nova Science Publishers, Inc.

Chapter 10

BULGURATION: COMBINED COOKING AND DRYING OPERATION Mustafa Bayram Assoc. Prof. Dr., University of Gaziantep, Faculty of Engineering, Department of Food Engineering, 27310-Gaziantep-TURKEY

ABSTRACT Cooking and drying are two main unit operations used widely in food processing. Consecutive cooking and drying operations supplies perfect properties to gain to food products and called as bulguration. Individually, the former method is used nearly for all food products before consumption. Cooking is a well-known way to destruct microorganisms, insect, insect eggs and larvaes for food safety. Also, it increases the digestive property of food with starch gelatinization, protein gelation and textural softening. However, it is very difficult to store this product without drying due to its high moisture content after cooking. Therefore, food products should be dried. Drying is required to prolong storage time of food products. Bulguration is the gaining of the some functional characteristics on the finished product such as the resistance to mold contamination, insect attacks and radiation, inactivation of enzymes and microorganisms, encapsulation of numerous nutritional components in food products, easy preparation after bulguration due to semi and ready-to-eat form, obtaining long shelf-life having economical products with safety, decreasing undesired components e.g. phytic acid in contrast to increasing desired one e.g. folate/folic acid. As raw materials, cereals, pulses, seeds, vegetables, fruits etc. can be used. Recently, the use of bulguration in the food industry dramatically increases as an optimal method due to above situations. Bulguration is an ancient technique; however, the modern technology re-discovered it. In this chapter, the techniques of bulguration are explained with examples. Also, the results of the recent researches are given.

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INTRODUCTION Cooking and drying operations are well-known processes individually. However, the evaluation of their combination is limited in the literature. Recently, bulguration as a combination of cooking and dying has been studied by the researchers. During bulguration, significant functional properties are gained to finished product. Globally, cereal/legumesbased foods are prepared as a dry finished form using roasting and extrusion operations. However, bulguration technique is different, due to its specific processing steps - i.e. cooking in water and subsequent drying. It should be noted, and also added to the production method specifications for the processing of cereals and legumes, that bulguration is more suitable to cereals and legumes than other methods. For example, extrusion and roasting equipments are more expensive, and they work at high temperatures (~150-180°C). These high temperatures cause high (and costly) energy consumption, high nutrition loss, high quality loss (primarily color) and the formation of undesired and unhealthy byproducts, such as i.e. hydroxylmethyl furfural (HMF, a Maillard-Amidori browning reaction byproduct), acrylamide etc. In turn, cereals and legumes with and without blending with other ingredients could be produced using bulguration technique, instead of extrusion and roasting. Such products would be both healthier and cheaper (Bayram, 2007). Bulgurated product has been regarded as being the nutritional equivalent of whole raw material but more stable and resistant to attack by insects and vermin. The simple processing and low-fuel requirements for food preparation were additional advantages, which supply high resistance to humid and hot environmental conditions Bayram, 2007). Bulguration’s benefits can be summarized as follows (Bayram, 2000, 2005, 2007; Bayram and Öner, 2002, 2005; Bayram et.al., 2004a, b, c, d, e): • • • •

•

increases nutrient absorption due to high mineral and insoluble cellulose fiber content of cereals and legumes, and also prevents constipation and intestinal cancer risk the finished product can be classified as a functional foods this process using grains is suitable for vegetarian diets is a good processing for expectant women and babies, due to folate/folic acid content of cereals and legumes. Folate/folic acid is important in the development of babies’ brains, particularly during the first three months of pregnancy. However, folate/folic acid is continuously broken down by metabolism and must therefore be continually replenished through nutrition. (Consequently, bulguration plays a very important role for pre-natal brain development and the health of expectant mothers). For example, folate/folic acid content of wheat-bulgur (Shepherd, et. al., 1965; Pence et. al., 1964) varies between 41-150 μg/100 g, which depends on wheat species and processing parameters. Its folate/folic acid content is higher than some unbulgurated-food products such as patent flour: 13, white bread: 25, brown bread: 45, wholemeal bread: 40, raw brown rice: 49, plain popcorn: 3, raw pearl barley: 20, orange: 31, grapefruit: 26, rye crispbread: 35 and wheatgerm bread: 46 μg/100 g (McKevith, 2002, 2004; Calhoun et. al. 1960). has the best property to decrease the available phytic acid content (a mineral-binding antinutrient), while simultaneously increasing its bran content (high fiber, high mineral). The decrease in phytic acid content is about 18.9-33.9% due to cooking and

Bulguration: Combined Cooking and Drying Operation

• • • •

• • •

485

drying operations. During cooking and drying, heat causes the degradation of phytic acid (Özkaya et. al., 2004; Lasztity and Lasztity, 1990). resistant to mold contamination due to the cooking and drying operations resistant to insect attacks and formation of larva due to the cooking and drying operations inactivates microorganisms and enzymes nutrients (minerals and vitamins) originally present in the grain and legumes are reabsorbed with water into the kernel during the cooking operation, thereby curbing the loss of nutrients provides a longer shelf life than other food materials is economical due to pre-processing of product before consumption lends itself to easy home preparation, as it is pre-cooked. is used in ready-to-eat or semi-ready-to-eat food products.

According to literature, there are different kinds of bulgurated-food-products such as wheat-bulgur, corn-bulgur, barley-bulgur, soy-bulgur, rye-bulgur, triticale-bulgur, lentilbulgur, chickpea-bulgur, rice-bulgur (called as parboiled rice) etc. (Bayram et.al., 2004a, b; Elgun et.al., 1990; Köksel et.al., 1999; Bayram et. al., 2004c, d, e; Singh and Dodda, 1979). Some of them are also produced commercially. The number of the plants for the bulguration of wheat now stands around 600 worldwide (comprising 500 in Türkiye, 15-20 in the United States and Canada, 4-6 in the EU, 10-15 in Arabic countries). For example, more-than one million metric tones of bulgurated wheat are produced annually in Türkiye. The production capacity is totally 250,000-300,000 tons/year in USA and Canada. The annual per-capita consumption of bulgurated wheat in the major producer country, Türkiye, reflects its economical and nutritional value. Turkish consumption of bulgurated-wheat is 2.5-3.5 and 1.5-2.5 times greater than consumption of pasta (macaroni) and raw-rice, respectively, with an average annual consumption of about 12 kg/person. In the eastern and southern parts of Türkiye, consumption averages 25 kg/person, whereas in Syria, Iraq, Iran, Israel, Lebanon, Arabia i.e. Middle East countries, (consumption averages 30-35 kg/person). Due to increased demands for bulguration, some pasta and flour plants in these areas have integrated their systems for bulguration operation and new factories are planned or are under construction. Recently, new bulguration factories have been constructed in Europe (3, 10-15).

BULGURATED PRODUCTS Bulgurated-Wheat In the literature, there is a lot of knowledge about wheat-bulgur, especially produced from durum-wheat due to its yellow color and hard texture (Bayram, 2000, 2006; Bayram and Öner, 2002, 2005).

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Bulgurated-Barley Traditions and customs control the consumption of barley as a human food. Barley was once used extensively in the diets of rural areas in England, Denmark, Near East and Far East (Kent, 1975; Munch, 1981). Taste and appearance factors along with its poor baking quality have limited the use of barley in human foods and the consumption of barley has declined in the last century. Nonetheless, barley is suitable for a wide range of food applications. It can be processed into a number of palatable, nutritious food products or blended with other foods (Bhatty, 1986; Newman and Newman, 1991). Barley has also been promoted in many parts of the world for its ability to contribute to a healthy life. The high level of soluble fibre in barley is reputed to reduce heart disease (Newman et. al., 1989; McIntosh et. al., 1991; Klopfenstein and Hoseney, 1987), colon cancer (McIntosh et. al., 1993) and post-prandial glucose concentrations (Jenkins et. al., 1978). Reducing the levels of post-prandial glucose is an important tool for controlling glucose levels in diabetic subjects12. The ability of barley to reduce heart disease is thought to result from reduced levels of blood cholesterol. Soluble, (1Æ3; 1Æ4)- b-D-glucan (bglucan) is believed to be the barley component most responsible for this hypocholesterolemic activity (Bhatty, et. al., 1993). Using barley for bulguration is, therefore, be a way of increasing the nutritional value of this food (Köksel et. al. 1999). Three barley cultivars (Hordeum vulgare L.) were processed by Köksel et. al. (1999) for bulguration by pressure cooking or cooking at atmospheric pressure. The effect of processing on levels of thiamine, riboflavin, minerals (Fe, Cu, Zn, Mn, Ca, Mg) as well as the phytic acid and beta-glucan was investigated. Significant decreases (p<0·05) were observed in ash, riboflavin and thiamine contents during bulguration. Neither the cooking methods nor the dehulling process had significant influence on the content of Fe, Cu, Zn or Mg. However, the Mn and Ca content of the finished product were significantly (p<0·05) lower compared with the corresponding raw barleys. For all cultivars total P and phytate P contents were significantly (p<0·05) lower compared with the corresponding raw barleys. In contrast, levels of beta-glucan were significantly higher in processed product vs raw barley. Protein contents of the samples did not change significantly during bulguration. Bulguration using barley appeared to retain most of the nutritional value of raw barley, in particular it showed high levels of soluble dietary fibre.

Bulgurated Triticale Bulguration of triticale was studied by Singh and Dodda (1979). Bulgurated triticale of acceptable quality was prepared from two commercially available cultivars of triticale by boiling 10 min and cooking for 3 min or 10 min at 15 psi and 120°C. Moisture contents of 3241% were needed to achieve a desirable level of gelatinization of starch. They evaluated protein, amino acids, gross energy, crude fat, thiamine, riboflavin, folic acid, vitamin E, calcium, iron, copper, phosphorus, manganese, zinc, potassium, sodium and magnesium.

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Activation energy can be used to explain the degree of the effect of temperature during cooking (Eq. 1) on the rates of the changes of the physical and chemical properties of product. k=A.exp(-Ea/RT)

(1)

where k is rate order (min-1), A is frequency factor (min-1), Ea is activation energy (J), R is gas constant (8314 J/K) and T is absolute temperature (K). Instead of activation energy concept many researchers in the food field have used the Q10 approach for temperature acceralation where the Q10 is the increase in rate for a 10oC temperature increase (Eq. 2), (Labuza, 1984). ln Q10 = [10*Ea]/[R*T*(T+10)]

(2)

In addition to previous quantities, z-value can be used to explain the influence of temperature on product properties during cooking using Eqs. (3 and 4). Also, it can be used to calculate the cook value and control the cooking process (Eq. 5). The z-value is defined as the necessary temperature rise needed for 10-fold increase in the rate. Differential practical cooking processes take place at the varying temperatures. Hence, the effect of each process must be described in a different way. log k= p+d τ

(3)

z=1/d

(4)

t

C=

∫

10(τ-Tr)/z dt

(5)

0

where p and d are constant, z is the temperature range over which the rate changes 10-fold (°C), C is cooking value (min), Q10 is the ratio of the rate at one temperature to that at a temperature 10°C higher, t is time (min), τ is actual process temperature (K) and Tr is reference temperature (100°C). The z-value was evaluated using d-value with Eq. (3). Low values of z, or high values of d indicate that the temperature is very important. The influence of temperature on the rate constant k (Eq. 3) is also considerable, thus the intensity of a rate is determined mainly by temperature. On the other hand high values of z or low values of d indicate that temperature is of secondary importance. For microorganisms, for instance, the z-values are much lower, for heat resistant enzymes and most vitamins z-values are higher [Harada et. al., 1985). The zvalue is also used to evaluate the cook value for thermal processing of food materials in Eq. (5). Ea, Q10 and z-values can be used effectively to predict temperature effect on the physical properties of the product during the cooking operation. Also, these values can be used to determine the optimum cooking parameters for bulguration. During bulguration, the behavior and properties of the raw material kernel should be well analyzed because of whole kernel form of bulguration product. Therefore, some studies are available in the literature. For example, in the study of Bayram et. al. (2004a), the effect of

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Bulgurated-Corn Bulguration of corn was studied by Elgun et. al. (1990) to determine the effects of the maturation stage (milk, yellow, or ripe) and cooking form (on the cob or shelled) of Narman sweet corn on some selected parameters obtained for bulguration. Also, physical and chemical properties of products were examined. During cooking below 95 oC for approximately 60 min, there was an increase in the dry matter content and color intensity of cooking wastewater. As the grain matured, the amount of material diffused into the cooking wastewater decreased. The yellow stage corn provided the hardest and most vitreous endosperm texture to the finished product after drying and showed the highest resistance to grinding, whereas the milky state corn bulgur showed the lowest resistance. On a raw material basis, the yields of the pilav bulgur (greater than 1.5 mm) and total bulgur (greater than 0.5 mm) increased sharply with grain maturation, especially in the ripe stage. In either total or yellow color intensity of the bulgur fractions, the yellow stage corn bulgur showed the highest values with amberlike yellowness and brightness. Increasing particle size from the bulgur flour (less than 0.5 mm) to the pilav bulgur (less than 1.5 mm) increased the color intensity. Ash, fiber, and protein contents of the all bulgurs decreased with maturation, but fat content increased. In general, the finer the corn bulgur fraction is the higher the ash, fiber, protein, and fat contents. In conclusion, the best stages were yellow corn for bulgur color, appearance, and functionality; milky corn for nutritional value; and ripe corn for bulgur yield.

PRINCIPLES OF BULGURATION Bulguration can be discussed in two steps such as cooking and drying. Both are very critical operations during bulguration. There is also a range of important chemical reactions, which help to develop flavour and texture during cooking. The first group contains the enzymatic reactions. There are natural chemical reactions that affect the food. All foods contain enzymes- there are many different enzymes that are present in different foods-in nature these enzymes control biochemical reactions essential for the life of the organism. The second group of reactions is those which affect sugars and carbohydrates when they are heated. Many disaccharide and oligosaccharide sugars will undergo a process known as hydrolysis when heated with some water (Barham, 2001). Cooking time and temperature affect product due to water absorption. The effects of the processing variables and the modeling of the cooking operation are necessary to systematically design the cooker and to understand the effects of cooking on the production. Kinetics deal with the changes in a system, their associated levels, the rate at which the changes occur and the mode and mechanism by which the changes are affected. Understanding the behaviour of product during cooking is of practical importance since it governs the subsequent operations and quality of the final product. Hence, modelling the changes during cooking has attracted considerable attention. Determination of the cooking mechanism and the prediction of the changes are also essential to the control of the cooking operation. (Bayram, Öner, Eren, 2004a).

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the cooking time and temperature on the dimensions and crease of the wheat kernel was studied during bulguration. Wheat was cooked at 87, 92 and 97°C for 140 minutes. The change of the length, two widths, weight, volume and density for the wheat kernel were determined during the cooking operation. In addition, in order to determine the effect of cooking time and temperature, rates, activation energy, Q10 and z-values were determined. Negative percent change values and the rate constant for the length and density of the wheat kernel were obtained at different cooking temperature (P>0.05). A positive rate constant was determined for other properties. The crease side width increased gradually at each cooking temperature between 6.95-68.05%. Cooking time also significantly affected the change of the crease side (P<0.05). The secondary width was affected significantly (P<0.05) by the cooking temperature and time, and its value changed between 9.95-69.00%. The big change for the secondary width was obtained at 97°C. The crease has important guarding property for the length and secondary width and it also guards the kernel for the secondary width against the internal force up to 97°C. The volume of the kernel also increased (27.35-185.20%) during the cooking operation significantly (P>0.05), and has greater percent change value than the weight (19.25-160.70%) due to high amount of swelling of the wheat kernel. Therefore, density changed negatively. Ea, Q10 and z-values were determined for all physical properties of the wheat kernel and found that cooking temperature significantly affected the change of the c, W and ρ values (Bayram et. al., 2004a). Also, the same dimensions were evaluated by Bayram et.al. (2004b). Physicochemical quantities (activation energy, enthalpy, entropy and Gibbs free energy) as kinetic models were used to describe the changes of physical attributes of the wheat kernel during cooking. Rate constant for the length and density of the wheat kernel were obtained as negative in contrast to positive rate constant values of the widths, weight and volume of the wheat kernel. Influence of cooking temperature on the physical properties of the wheat kernel was also detected from kinetic models and it was found that, the rate of the change in the secondary width, weight and density of wheat kernel were more sensitive to temperature due to their greater activation energies as 5.95x107, 5.26x107 and 6.11x107 J, respectively. In contrast, width of the wheat kernel required less amount of energy due to crease side. All of the changes in physical properties illustrated the non-spontaneous property with their activation Gibbs free energy values. A lot of quality parameters (cooking degree, colour, size, shape etc.) are obtained during this critical operation. During bulguration, the cooking can be controlled with the amylose/iodine, centre cutting, DSC and light scattering methods to determine the cooking degree based on starch gelatinization. Excess cooking causes significant deformation on the intact kernel during the cooking operation. During cooking, the two most important criteria’s are the protection of the wholeness of the kernel i.e. no deformation, and getting complete cooking i.e. absence of an opaque white centre (100% gelatinization, the gelatinized endosperm is essentially translucent but the ungelatinized starch appears in the endosperm as central white spots). Deformed kernel cannot be used for subsequent bulguration operation i.e. drying. Therefore, cooking conditions should be well arranged to complete gelatinization without darkening the product or making it so sticky as to interfere with subsequent drying. Determination of the gelatinization degree of starch in wheat and triticale during the cooking operation based on the traditional centre cutting method was also used by Smith et. al. (1964), and Singh and Dodda (1979). Their evaluation scales for cooking of intact wheat

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were given as follow; a product with a white centre count below 5% was considered to be adequately gelatinized and rated as excellent; a count below 10% as good, below 20% as fair, and above 20% as poor (unacceptable). The gelatinization of starch in solid foods occurs with water absorption. Absorbed water migration in solid foods has been widely analysed using the concept that water migration is driven by the water content gradient. This principle was also applied in the case of cooking starchy food (Suzuki, Aki, Kubota, and Hosaka, 1977; Bakshi and Singh, 1980; Watanabe, Fukuoka, Tomiya, and Mihori, 2001). For the automation of the cooking operation for bulguration, the diffusion of high intensity light through the kernel (especially for durum wheat) can also be used to determine the gelatinization degree based on translucent and opaque parts of the intact kernel. Light will pass through the translucent part easily and colour differences can differentiate the gelatinized part in the centre of the kernel (Bayram, 2004).

BULGURATION OPERATION The cooking operation can be made in hot water under high or atmospheric pressures. However, the soaking operation before the cooking operation can be used to control moisture content of finished product. If pressure is obtained directly with live-steam, soaking should be made to reach the moisture to 40-50%. In general, during soaking, temperature is raised to 5080 oC and moisture content is increased to 40-50 %.

Figure 1. Open-to-atmosphere cooker.

Bulguration: Combined Cooking and Drying Operation

Figure 2. Screen-atmospheric cooker.

Figure 3. Direct steam inject continuous pressure cooker.

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Figure 4. Pressure-drum cooker.

Cooking can be made using open-to-atmosphere cooker (Figure 1), screen-atmospheric cooker (Figure 2), steam jacketed screw cooker, direct steam inject continuous pressure cooker (Figure 3), belt in water type cooker, screw-silo combination systems and pressuredrum (Figure 4) cookers. During the cooking operation, first step is gelation of starch and/or protein in product. In the cooking systems, temperature, time, water absorption and gelation of starch/protein should be controlled. In the pressure cooker, it remains enough time (5-20 minutes) under 1-3 atms of pressure. This procedure insures complete cooking without discoloration. After cooking, vacuum or cooling can be applied to evaporate surface water and also to prevent stickiness of products. For drying during bulguration which depends on raw material, fluidized bed dryer, radiofrequency, tower (Figure 5), mixing type dryer can be used. The purpose is the removal of 40-50% of initial moisture of cooked product. Drying must be gradual at comparatively low temperature and preferably with continuous movement or frequent turning/moving product. Drying is made with tower dryer for 6-8 hours by using hot air between 70-140 oC until moisture of wheat decreases under 13 %. Of all food preservation methods, that of drying foods has received the most widespread and enthusiastic publicity in recent years. Actually, drying is one of the oldest methods of food preservation. Techniques have been passed from one generation to another based on what worked and what didn't. Methods used for drying food have become sophisticated over time. The benefit of drying during bulguration is to remove moisture from cooked product to prevent growing molds, yeast and bacteria. When foods are sufficiently dehydrated (dried), microorganisms cannot grow and foods will not spoil. Therefore, its shelf-life increases. By the way, bulguration supplies high resistant semi-ready to eat food product using different raw materials.

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Figure 5. View from tower-type dryer.

Of all food preservation methods, that of drying foods has received the most widespread and enthusiastic publicity in recent years. Actually, drying is one of the oldest methods of food preservation. Techniques have been passed from one generation to another based on what worked and what didn't. Methods used for drying food have become sophisticated over time. The benefit of drying during bulguration is to remove moisture from cooked product to prevent growing molds, yeast and bacteria. When foods are sufficiently dehydrated (dried), microorganisms cannot grow and foods will not spoil. Therefore, its shelf-life increases. By the way, bulguration supplies high resistant semi-ready to eat food product using different raw materials.

REFERENCES [1] [2] [3]

Bakshi, A. S. and Singh, R. P. (1980). Kinetics of water diffusion and starch gelatinization during rice parboiling. Journal of Food Science, 45, 1387-1392. Barham, P. (2001). The science of cooking (pp. 32-33). Berlin, Germany, Springer Verlag. Bayram, M. (2000). Bulgur around the world. Cereal Foods World, 45, 80-82.

494 [4] [5]

[6] [7] [8]

[9] [10]

[11]

[12] [13]

[14]

[15] [16] [17] [18]

[19] [20]

[21] [22]

Mustafa Bayram Bayram, M. (2005). Modelling of cooking of wheat to produce bulgur. Journal of Food Engineering, 71, 379-186. Bayram, M. (2005). Bulgur: The product of ancient world and mystery. In M. Bayram, A. Avcı and O. Arslan, (Eds.), Bulgur (pp:22-23). Gaziantep, Türkiye, University of Gaziantep and Bulgur Industrialists Association Press. Bayram, M. (2006). Determination of the cooking degree for bulgur production using amylose/iodine, centre cutting and light scattering methods. Food Control, 17, 331-335. Bayram, M. (2007). Application of bulgur technology to food aid program. Cereal Foods World, (inpress). Bayram, M. and Öner, M. D. (2002) Bulgur: The new old wheat. World Grain, November, 51-53. Bayram, M. and Öner, M.D. (2005). Stone, disc and hammer milling of bulgur. Journal of Cereal Science, 41, 291-296. Bayram, M., Öner, M.D. and Eren, S. (2004a). Effect of the cooking time and temperature on the dimensions and crease of the wheat kernel during the bulgur production. Journal of Food Engineering, 64, 43-51. Bayram, M., Öner, M.D. and Eren, S. (2004b). Thermodynamics of the dimensional changes in the wheat kernel during cooking for bulgur production. Food Science and Technology International, 10, 243-253. Bayram, M., Kaya, A. and Öner, M. D. (2004c). Changes in properties of soaking water during production of soy-bulgur. Journal of Food Engineering, 61, 221-230. Bayram, M., Öner, M.D. and Kaya, A. (2004d). Influence of soaking on the dimensions and colour of soybean for bulgur production. Journal of Food Engineering, 61, 331339, 2004. Bayram, M., Kaya, A. and Öner, M. D. (2004e). Water absorption, leaching and color changes during the soaking for production of soy-bulgur. Journal of Food Process Engineering, 27, 119-141. Bhatty, R.S. (1933). Nonmalting uses of barley. In A.W. MacGregor and R.S. Bhatty, (Eds.), Barley: Chemistry and Technology (pp. 355-417), St. Paul, MN, AACC. Bhatty, R.S. (1986). The potential of hull-less barley-a review. Cereal Chemistry, 63, 97-103. Calhoun, W. K., Hepburn, F. N. and Bradley, W. B. (1960). The distribution of the vitamins of wheat in commercial mill products. Cereal Chemistry, 37, 755 - 761. Elgun, A., Ertugay, Z. and Certel, M. (1990). Corn Bulgur: Effects of corn maturation stage and cooking form on bulgur-making parameters and physical and chemical properties of bulgur products. Cereal Chemistry, 67, 1-6. Harada, T., Tirtohusodo, H. and Paulus, K. (1985) Influence of temperature and time on cooking kinetics of potatoes. Journal of Food Science, 50, 459-462, 472. Jenkins, D.J.A., Wolever, T.M.S., Leeds, A.R., Gassull, M.A., Haisman, P., Dilawari, J., Goff, D.V., Metz, G.L. and Alberti, K.G.M.M. (1978). Dietary fibres, fibre analogues, and glucose tolerance: importance of viscosity. British Medical Journal, 1, 1392–1394. Kent, N.L. (1975). Technology of cereals with special reference to wheat, (2nd edition, pp 226–240), New York, USA, Pergamon Press. Klopfenstein, C.F. and Hoseney, R.C. (1987). Cholesterol low-conering effect of betaglucan enriched bread. Nutrition Reports International, 36, 1091–1097.

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[23] Köksel, H, Edney, M. J. and Özkaya, B. (1999). Barley bulgur: Effect of processing and cooking on chemical composition. Journal of Cereal Science 29, 185–190. [24] Labuza, T. P. (1984). Application of chemical kinetics to deterioration of foods. Journal of Chem. Education, 61, 348-357. [25] Lasztity, R. and Lasztity, L. (1990). Phytic acid in cereal technology. In Y. Pomeranz (Ed.), Advances in Cereal Science and Technology, (Volume 10, pp. 309-371), St. Paul, MN, AACC. [26] McIntosh, G.H., Jorgensen, L. and Royle, P. (1993). The potential of insoluble dietary fiber from barley to protect from DMH-induced intestinal tumours in rats. Nutritional Cancer, 19, 213–221. [27] McIntosh, G.H., Whyte, J.,McArthur, R. and Nestel, P.J. (1991). Barley and wheat foods. Influence on plasma cholesterol concentrations in hypercholesterolemic men. American Journal of Clinical Nutrition, 53, 1205–1209. [28] McKevith, B. (2002). Folate and folic acid. British Nutrition Foundation Nutritional Bulletin. 1-16. [29] McKevith, B. (2004). Nutritional aspects of cereals. British Nutrition Foundation Nutritional Bulletin, 29, 111-142. [30] Munck, L. (1981). Barley for food, feed and industry. In Y. Pomeranz and L. Munck (Eds.), Cereals: A renewable resource, theory and practice (pp. 427–459), AACC, St. Paul, MN. [31] Newman, R.K. and Newman, C.W. (1991). Barley as a food grain. Cereal Foods World, 36, 800-805. [32] Newman, R.K., Lewis, S.E., Newman, C.W., Boik, R.J. and Ramage, R.T. (1989). Hypochoesterolemic effect of barley foods on healthy men. Nutritional Reports International, 39, 749-760. [33] Özkaya, H., Özkaya, B., Bayrak, H. and Gökpınar, F. Bulgurun fitik asit içeriğine prosesin etkisi. Geleneksel Gıdalar Sempozyumu, Van Yüzünyıl Üniversitesi, VanTürkiye, 23-24.Eylül.2004. [34] Pence, J.W., Ferrel, R.E. and Robertson, J.A. (1964). Effects of processing on Bvitamin and mineral contents of bulgur. Food Technology, May, 171-174. [35] Shepherd, A. D., Ferrel, R.E., Bellard N. and Pence J. W. (1965). Nutrient composition of bulgur and lye peeled bulgur. Cereal Science Today, December, 590-592. [36] Singh, B. and Dodda, M. (1979). Studies on the preparation and nutrient composition of bulgur from triticale. Journal of Food Science, 44, 449–452. [37] Smith, G.S., Barta, E.J. and Lazar, M.E. (1964). Bulgur production by continuous atmospheric pressure process. Food Technology, 18, 89-92. [38] Suzuki, K., Aki, M., Kubota, K., and Hosaka, H. (1977). Studies on the cooking rate equations of rice. Journal of Food Science, 42, 1545-1548. [39] Watanabe, H., Fukuoka, M., Tomiya, A., and Mihori, T. (2001). A new non-Fickian diffusion model for water migration in starchy food during cooking. Journal of Food Engineering, 49, 1-6.

In: Focus on Food Engineering Research and Developments ISBN: 978-1-60021-898-9 Editor: Vivian N. Pletney, pp. 497-515 © 2007 Nova Science Publishers, Inc.

Chapter 11

WATER SORPTION ON FOODSTUFFS ALTERNATIVE MODELS Sylwester Furmaniak1, Artur P. Terzyk*1, Leszek Czepirski2, Ewa Komorowska-Czepirska2, Joanna Szymońska3 and Piotr A. Gauden1 1

N. Copernicus University, Department of Chemistry, Physicochemistry of Carbon Materials Research Group, Gagarin St. 7, 87-100 Toruń, Poland 2 AGH – University of Science and Technology, Faculty of Fuels and Energy, al. Mickiewicza 30, 30-059 Cracow, Poland 3 Agriculture University, Department of Chemistry, al. Mickiewicza 24/28, 30-059 Cracow, Poland

ABSTRACT It is well known that sorption isotherms of foodstuffs are very important for design, modeling and optimization of important processes for example drying, aeration, predicting of stability and quality during packaging and storage of food. Many literature reviews conclude that the BET (and its modifications) and the GAB sorption isotherm equations are the most popular and applicable for description of isotherms of foodstuffs. We showed recently the applicability of the GDW model for description of water sorption on different foodstuffs. Moreover, it was also shown that the GAB model (also widely applied in food science) is the special case of the GDW equation. In this review we present the current state of art and also an attempt of application of different models of water sorption, namely CMMS, DD and modified CDS for description of water sorption data on different starch samples and other foodstuffs.

*

Corresponding author: Artur P. Terzyk; e-mail:[email protected] URL:http://chem.uni.torun.pl/_aterzyk/

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1. INTRODUCTION It is well known that sorption isotherms of foodstuffs are very important for design, modeling and optimization of important processes for example drying, aeration, predicting of stability and quality during packaging and storage of food [1-3]. Many literature reviews conclude that the BET (and its modifications) and the GAB sorption isotherm equations are the most popular and applicable for description of isotherms of foodstuffs [1,4-6]. It was shown recently the applicability of the GDW model for description of water sorption on different foodstuffs (pineapple, macaroni, sardine, pistachio nut paste, chickpea seeds, lentil seeds, potato and on green peppers) [7,8]. Moreover, it was also shown that the GAB model (also widely applied in food science) is the special case of the GDW equation [8]. Obtained results explained the total failure of the BET model in description of multitemperature data and the similarities between the GAB and GDW. Finally the general mechanism of water sorption on foodstuffs was also proposed [8]. This mechanism can be of the GAB or GDW type, depending on the arrangement and features of the primary water sorption sites. If the geometrical constraints for creation of the BET – like type clusters do not occur on surface, and if each from primarily sorbed water molecules convert only into one secondary sorption site, one can say that the mechanism follows the GAB scenario. Contrary, in the case of rough or porous surfaces, where there are the geometric constraints for creation of secondary sites, and/or where one primary site produces more than one secondary site, the mechanism of water sorption is of the GDW type. All models discussed above originate from adsorption science, therefore it is interesting to check the validity of different (i.e. alternative) approaches to describe water sorption data on foodstuffs. In this review we also present the current state of art and an attempt of application of GAB, GDW, CMMS, simplified DD and modified CDS models for description of water sorption data on different starch granules and other foodstuffs.

2. MODELS First studied model is the Generalized D’Arcy and Watt (GDW) one proposed previously for description of adsorption of water on carbonaceous adsorbents [9], and next applied successfully for description of water sorption on many foodstuffs [7,8]. It was applied in the form:

Me =

mKhr 1 − k (1 − w ) hr ⋅ 1 + Khr 1 − khr

(1)

where Me is the moisture equilibrium content, hr – relative humidity, m – the maximum adsorption value on the primary sorption centers. K and k are the kinetic constants related to sorption on primary and secondary centres, and w is the parameter determining what part of water molecules sorbed on primary sorption sites convert into the secondary sorption sites.

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Next studied model is proposed in 2000 (to description of adsorption of alcohols on polymers) by Malakhov and Volkov [10] and widely propagated by Rutherford et al. (to description of water sorption on different adsorbents) [11-13] and others [14,15]. This new equation of cooperative the multimolecular sorption (called the CMMS) assumes that the sorption process follows the scenario of cooperative filling of channels (interrelated nanovoids) of the sorbent, and this process is combined with the growth of associates of sorbed molecules within the sorbent bulk. The final sorption equation, which can be reduced to the Henry’s, Langmuir, Ising and/or BET models, can be written as:

Me =

mK0 hr

(2)

( 1 − K as hr ) ( K0 hr + ω 2 ( 1 − K as hr ) )

where: 2 ⎛ ⎛ 4K0 hr K1 hr K1 hr ⎞ 1⎜ + ⎜1 − + ω = 1− ⎟ 2⎜ 1 − K as hr 1 − K as hr ⎠ 1 − K as hr ⎝ ⎝

⎞ ⎟ ⎟ ⎠

(3)

and m is the maximum sorption on primary sites, K0 is the equilibrium constant for sorption of the central unit on the primary sites, K1 – the equilibrium constant for sorption of the side unit on the primary side, Kas – the equilibrium constant for sorption of the site associate. As the reference model we used the GAB one [1,4-6]. This model, widely accepted as valid and the most popular in the field of food engineering, is applied in the form:

Me =

mCKhr (1 − Khr )(1 − Khr + CKhr )

(4)

where m is the monolayer capacity, C is the kinetic constant related to the sorption in the first layer, K is the kinetic constant related to multilayer sorption. Finally, since the mentioned above concepts are more or less applicable to description of water sorption on foodstuffs (see below), we wish to mention unsuccessful attempt of application of two models. The Corrected Dubinin - Serpinsky (CDS) concept, recently derived (from some empirical observation) for description of water sorption on carbons containing high energetic sites [15-17] as well as its modifications are inapplicable to description of water sorption data on foodstuffs. Similarly the Do - Do model [18], with modifications [19] and simplifications (i.e. the neglecting of the term related to the micropore filling, or the assumption of the existence of one type of sorption centres). The model led to good fit however, some numerical problems occurred with the irreproducible results (irreproducibility of the parameter N (the maximum number of water molecules adsorbed on the secondary surface sites) values). This is caused by the mathematical form of this equation namely, for the smaller than unity values of the constant describing sorption of subsequent water molecules on the secondary sorption sites, the rise in the value of the parameter N does not change the results in the meaningful way.

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3. TEMPERATURE DEPENDENCE OF THE PARAMETERS OF SORPTION ISOTHERMS As it was shown recently [7,8] in the case of the data determined at different temperatures the simultaneous description of the whole branch of adsorption isotherms leads to many different advantages. However, before the application of this procedure the exact definition of the temperature dependence of the parameters is necessary. On the other hand, it is well known from fundamental physical chemistry, that for all kinetic and or equilibrium constants (X) parameters one can write:

⎛q ⎞ X = X 0 exp ⎜ X ⎟ ⎝ RT ⎠

(5)

where X0 is the almost temperature independent entropic term, qX is the enthalpy related to this parameter, T is the temperature, and R is the gas constant. Below we show the forms of the Eq. (5) for the models presented in the previous paragraph. GDW:

⎛ Q ⎞ K = K 0 exp⎜ ⎟ ⎝ RT ⎠

(6)

⎛ q ⎞ k = k 0 exp⎜ ⎟ ⎝ RT ⎠

(7)

CMMS:

⎛ q ⎞ K 0 = K 00 exp ⎜ 0 ⎟ ⎝ RT ⎠

(8)

⎛ q ⎞ K1 = K10 exp ⎜ 1 ⎟ ⎝ RT ⎠

(9)

⎛q ⎞ K as = K as0 exp ⎜ as ⎟ ⎝ RT ⎠

(10)

GAB:

⎛q ⎞ C = C0 exp ⎜ C ⎟ ⎝ RT ⎠

(11)

Water Sorption on Foodstuffs - Alternative Models…

⎛q ⎞ K = K 0 exp ⎜ K ⎟ ⎝ RT ⎠

501

(12)

4. THE EQUATIONS FOR ISOSTERIC ENTHALPY OF SORPTION FROM THE STUDIED MODELS The simultaneous multitemperature fitting of the data of water sorption makes it possible to generate the plot of the theoretical isosteric enthalpy of this process (qst) calculated from the model (and the comparison of this plot with this determined experimentally). For the GDW model the equation defining the isosteric enthalpy of sorption was derived recently [79] and has the form:

q st − L =

⎛ wkhr ⎞ Khr wk ⎟⎟ ⋅ Q + ⋅ ⎜⎜1 + ⋅ ⋅q 1 + Khr (1 − khr )2 ⎝ 1 − khr ⎠ ⎛ wkhr ⎞ Khr K wk ⎟⎟ + ⋅ ⎜1 + ⋅ 2 ⎜ (1 + Khr ) ⎝ 1 − khr ⎠ 1 + Khr (1 − khr )2

K (1 + Khr )2

(13)

For the CMMS model [14,15] this equation can be written as: q st − L =

q0 mK 0 + qas M e K as ( K0 hr + ω 2 ( 1 − K as hr ) ) + M e ( 1 − K as hr ) ( − q0 K 0 + ω B2 ( 1 − K as hr ) + qasω 2 K as ) mK0 + M e K as ( K0 hr + ω 2 ( 1 − K as hr ) ) + M e ( 1 − K as hr ) ( − K0 − ω B1 ( 1 − K as hr ) + ω 2 K as )

(14) where:

⎛ ⎛ K1hr ⎞ ⎜ K1 ⎜ 1 − ⎟ − 2K0 ⎜ 1 − K as hr ⎠ 1 ⎝ B1 = − K + 2 ⎜ 1 2 (1 − K as hr ) ⎜ ⎛ 4K0 hr K1hr ⎞ ⎜⎜ ⎜1− ⎟ + ⎝ 1 − K as hr ⎠ 1 − K as hr ⎝

⎞ ⎟ ⎟ ⎟ ⎟ ⎟⎟ ⎠

⎛ ⎞ ⎛ K1hr ⎞ ⎜ ⎟ + − − + − K1 ⎜ 1 − q q q K h 2K q q q K h ( ) ( ) ( ) ( ) ⎟ 1 as 1 as r 0 0 as 0 as r ⎜ ⎟ 1 − Kas hr ⎠ 1 ⎝ B2 = K q + ( qas − q1 ) Kas hr ) + ⎟ 2 ⎜ 1( 1 2 (1 − Kas hr ) ⎜ ⎟ ⎛ 4K0 hr K1hr ⎞ ⎜1− ⎟ + ⎜⎜ ⎟⎟ ⎝ 1 − Kas hr ⎠ 1 − Kas hr ⎝ ⎠

Finally, for the GAB model [8]:

(15)

(16)

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Sylwester Furmaniak, Artur P. Terzyk, Leszek Czepirski et al.

q

st

(1 − Khr ) −L=

2

qC + qK + ( C − 1) K 2 hr2 qK

(17)

1 + ( C − 1) K 2 hr2

where L is the enthalpy of water condensation.

5. THE DESCRIPTION OF EXPERIMENTAL DATA Theoretical models were fitted to experimental data by applying the minimization procedure using the differential evolution (DE) algorithm proposed by Price and Storn [20,21]. The DE algorithm is a very simple heuristic approach for minimizing non-linear and non-differentiable continuous space functions. Moreover, it was successfully used in different fields [14-17,19,22], and also in the field of food engineering [7,8] for verification of different theoretical models. In the other words, to optimize the objective function with DE the following settings for the input file are taken into account: DE/best/2/bin method is chosen (this time, the new vector to be perturbed is the best performing vector of the current generation); the number of parents (i.e. number of population members), NP is 10 times greater than the number of parameters of the objective function, D; weighting factor, F is equal to 0.8 and crossover probability constant CR=0.5; the value to reach, VTR is equal to 1⋅10-25 (the procedure stops when ofunc < VTR, if either the maximum number of iterations (generations) "itermax" is reached, or the best parameter vector "bestmem" has found a value f(bestmem) <= VTR). The algorithm seems to work well only if [XVmin, XVmax] covers the region where the global minimum is expected. Therefore, we taken into account the very wide ranges of XV. Moreover, the calculations are repeated at least five times. To describe the goodness of the fit of theoretical models to experimental data the value of the determination coefficient can be chosen. This parameter for the description of the single temperature data is defined as:

DCT = 1 − ηT

(18)

where:

ηT =

∑(M

o e ,i

− M et ,i )

i

∑(M i

o e ,i

−M

2

)

(19)

o 2 e

and M eo,i is the observed moisture content for i–th experimental point, M et ,i is the theoretical o

value of the moisture content calculated from theory, and M e is the average observed moisture content.

Water Sorption on Foodstuffs - Alternative Models…

503

For the simultaneous fitting of the data determined at different temperatures the global DC value is calculated from deviations (ηT) determined for the data at individual temperatures:

DC = 1 −

∑η

2 T

T

(20)

N

where N is the number of considered temperatures. Table 1 collects the best - fit parameters for all considered in this study models. Table 1. The best-fit parameters of the considered models

GDW

Best fit parameters Single temperature fit m, K, k, w

Multitemperature fit*) m, K0 (log K0), k0 (log k0), Q, q, w

CMMS

m, K0, K1, Kas

m, K 00 (log K 00 ), K10 (log K10 ), K as0 (log K as0 ), q 0 , q1 , q as

GAB

m, C, K

m, C0 (log C0), K0 (log K0), qC, qK

Model

*)

– in the case of entropic terms, due to the large range of values the logarithms were used in minimization.

6. EXPERIMENTAL DATA AND RESULTS The samples of corn starch (National Starch and Chemical Co., USA), rye starch (Kröner Stärke, GmbH and Co., Ibbenburen, Germany), oat starch (Alko Ltd., Ramajaki, Finland) and sago starch (Wah Chang Int. Group Co., Singapore) were used either in the air-dried state (as native starch) or after fourfold freezing/thawing in liquid nitrogen according to the procedure described by Szymońska and coworkers [23,24]. After such preparation following by equilibration to the room temperature starch was stored in closed vessels for adsorption investigations. Water vapour sorption isotherms were measured volumetrically at t = 25 oC using a device for simultaneous determination of isotherms and kinetic of sorption [25]. Before the experiment samples were outgassed at 70 oC to residual pressure about 10-4 mmHg. The values of the best fit parameters are put together in tables 2-4, and some selected results are shown in figure 1. The second set of experimental data contains the series of isotherms measured for chosen foodstuffs however, at different temperatures. Here we applied the method of simultaneous fitting of all sorption isotherms described previously [8]). In this group we analyzed the data measured on: quinoa grains [26] (at t = 20, 30 and 40 oC), chickpea flour [27] (at t = 10, 20, 30 and 40 oC), fufu [28] (at t = 25, 32 and 45 oC), vetch seeds [29] (at t = 5, 20, 40 and 60 oC) and on potatoes [30] (at t = 30, 45 and 60 oC). Tables 5-7 show the values of the best – fit parameters, and figures 2-6 the results for all considered products. Figure 7 shows the plots of the isosteric sorption enthalpy generated for considered models (Eqs.(13-17)) basing on the best fit parameters collected in tables 5-7.

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Table 2. The values of the best-fit parameters obtained from the fitting of experimental data by the GDW model (Eq. (1))

Starch sample Corn native Corn frozen Oats native Oats frozen Sago native Sago frozen Rye native Rye frozen

m [mmol g-1] 8.072 10.87 4.680 4.652 4.471 6.038 6.260 10.44

K

k

w

DCT

5.107 2.646 11.34 8.214 12.81 8.197 7.353 2.832

0.6588 0.9012 0.5856 0.5367 0.6718 0.6588 0.8585 1.021

0.5436 0.1300 1.270 1.657 1.079 0.6523 0.3699 0.05009

0.9992 0.9997 0.9997 0.9996 0.9996 0.9974 0.9978 0.9937

Table 3. The values of the best-fit parameters obtained from the fitting of experimental data by the CMMS model (Eqs.(2) and(3)) Starch sample Corn native Corn frozen Oats native Oats frozen Sago native Sago frozen Rye native Rye frozen

m [mmol g-1] 5.784 5.200 4.970 5.380 4.458 5.478 4.344 4.426

K0

K1

Kas

DCT

5.782 4.990 12.02 7.454 14.11 13.14 13.06 18.26

8.478 7.422 9.489 5.951 12.09 9.367 13.44 14.09

0.5930 0.6436 0.6077 0.5936 0.6747 0.5695 0.7229 0.7027

0.9995 0.9986 0.9998 0.9995 0.9997 0.9977 0.9969 0.9897

Table 4. The values of the best-fit parameters obtained from the fitting of experimental data by the GAB model (Eq.(4)) Starch sample Corn native Corn frozen Oats native Oats frozen Sago native Sago frozen Rye native Rye frozen

m [mmol g-1] 6.612 5.742 4.687 4.942 4.336 5.097 4.359 4.291

C

K

DCT

12.14 10.18 17.29 11.62 18.95 18.10 18.53 20.33

0.5342 0.6110 0.6286 0.6204 0.6834 0.5922 0.7219 0.7120

0.9991 0.9981 0.9997 0.9995 0.9996 0.9974 0.9969 0.9895

Table 5. The values of the best-fit parameters obtained from the fitting of experimental data by the GDW model (Eqs.(1),(6), and (7))

Foodstuff Quinoa grains Chickpea flour Fufu Vetch seeds Potatoes *)

m [% (db)] 9.476 8.812 4.933 8.248 4.765

k0

K0 -9

2.605 10 3.679 10-6 1.041 10-11 1.424 10-3 7.688 10-4

0.8263 0.6186 0.1936 0.6563 0.1852

Q q [kJ mol-1] 54.64 ~0 37.99 0.8411 72.26 3.194 22.41 0.7592 26.19 3.939

w

DCT*)

DC

0.6180 0.6202 0.9927 0.5842 1.843

0.9822, 0.9908, 0.9845 0.9913, 0.9905, 0.9890, 0.9928 0.9906, 0.9877, 0.9962 0.9892, 0.9825, 0.9905, 0.9880 0.9974, 0.9931, 0.9891

0.9854 0.9908 0.9908 0.9872 0.9924

– the values arranged according to the rise in temperature.

Table 6. The values of the best-fit parameters obtained from the fitting of experimental data by the CMMS model (Eqs.(2),(3), and (8)-(10)) Foodstuff Quinoa grains Chickpea flour Fufu Vetch seeds Potatoes *)

m [% (db)] 18.55 14.95 9.258 13.56 11.23

K 00

K 0as

K10 -13

4.681 10 6.841 10-13 2.299 10-16 1.271 10-6 0.02053

-4

1.317 10 5.731 10-17 1.069 10-14 1.037 10-15 2.010 10-3

– the values arranged according to the rise in temperature.

0.6951 0.7868 0.4504 0.4901 0.2140

q0 q1 [kJ mol-1] 75.15 14.87 78.46 89.78 100.4 82.43 41.71 - 0.5999 14.83 17.12

qas

DCT*)

DC

~0 ~0 0.8895 1.276 3.507

0.9787, 0,9920, 0.9856 0.9896, 0.9866, 0.9926, 0.9963 0.9917, 0.9882, 0.9964 0.9891, 0.9768, 0.9904, 0.9897 0.9973, 0.9933, 0.9890

0.9845 0.9906 0.9914 0.9854 0.9924

Table 7. The values of the best-fit parameters obtained from the fitting of experimental data by the GAB model (Eqs.(4),(11), and (12)) m [% (db)] 8.007 7.343 4.807 6.400 6.431

Foodstuff Quinoa grains Chickpea flour Fufu Vetch seeds Potatoes

K0

5.270 10-12 2.776 10-9 5.440 10-12 1.412 10-4 6.798 10-3

0.3540 0.3594 0.1060 0.3873 0.2114

qC qK [kJ mol-1] 71.34 1.779 57.76 1.965 74.99 4.700 30.26 1.855 18.74 3.718

DCT*)

DC

0.9802, 0.9934, 0.9836 0.9871, 0.9870, 0.9903, 0.9926 0.9896, 0.9865, 0.9947 0.9866, 0.9751, 0.9873, 0.9841 0.9948, 0.9914, 0.9889

0.9847 0.9890 0.9897 0.9826 0.9914

– the values arranged according to the rise in temperature. 14

14

GDW

14

CMMS

12

10

Me [mmol g-1]

Me [mmol g-1]

12

8 6 4

8 6 4

2

2

0

0 0

0.2

0.4

0.6

0.8

1

0.4

GDW

0.8

1

0

0.4

6

CMMS

8 6 4

2

2

0

0 0.6

0.8

1

0.8

1

0.6

0.8

1

GAB

12

10

4

0.6

hr

Me [mmol g-1]

8

hr

0.2

14

12

Me [mmol g-1]

Me [mmol g-1]

0.6

hr

10

0.4

6

2

14

0.2

8

0 0.2

hr

0

10

4

Corn frozen Oats frozen

0

14 12

GAB

12

10

Me [mmol g-1]

*)

C0

10 8 6 4

Sago frozen Rye frozen

2 0

0

0.2

0.4

0.6

0.8

hr

Figure 1. The results of fitting the sorption data on starch, points – experimental data, lines – fitting.

1

0

0.2

0.4

hr

Water Sorption on Foodstuffs - Alternative Models…

507

6.1. Fitting the Single Temperature Data From tables 2 - 4 and from figure 1 it can be seen that all three models describe very well the data on starch. If one considers the arithmetic average values of DCT the models can be arranged in the sequence GDW (0.9983), CMMS (0.9977), and GAB (0.9975). The strongly marked predominance of the GDW approach is visible for the data measured on rye frozen (figure 1) especially, in the range of large hr values. Interesting is the comparison of the values of the parameter m i.e. the concentration of primarily sorption centres. This concentration is similar for all approaches. The parameter w of the GDW model shows that the process of conversion of primarily sorption sites into the secondary ones is for some cases strongly restricted (this is the most pronounced for the case of rye frozen), for some cases one primarily sorption site practically converts into one secondary centre (sago native), and sometimes from one primarily site arise more than one secondary centre (most pronounced for oats frozen).

6.2. Fitting the Multitemperature Data From the results shown in tables 5 - 7 and from figures 2 - 6 it can be concluded that the GAB model leads to the poorest fit. This model poses the smallest number of the best fit parameters, moreover it assumes the simplest mechanism of sorption, and as it was shown previously it is in fact the simplified form of the GDW model [8]. It is surprising that the seven best fit parameter CMMS leads to worse fit than the GDW model (having six best fit parameters). This is caused by differences in the mechanisms assumed in the both models. In the CMMS model there is only one parameter (Kas) responsible for the shape of isotherm at larger hr values, contrary in the GDW there are two such parameters (k and w). In the CMMS model the constants K0 and K1 have the most visible influence for small hr values i.e. in the range where there is small number of experimental points. Scheme 1 shows the comparison of the mechanisms of all three models. One can see the similarities between GAB and GDW model however the latter assumes the possibility of the incomplete conversion of primarily centres into the secondary ones and/or the creation of more than one secondary centre from the primarily site. In the CMMS model the basic differences (comparing to the GAB and GDW) are caused by the behaviour of water molecules sorbed on two adjacent sites. In this model the mechanism of sorption at larger hr values is similar to this assumed in the GAB model. From figure 7 one can see the similarities in the behaviour of the plots of generated isosteric enthalpy values. At low Me values all models generate relatively large enthalpy, usually the largest for the CMMS model and the smallest for the GDW one. At larger Me values all enthalpy plots converge and are similar. The reason of the differences at small Me ‘s is relatively small number of experimental points in this range, leading to the enthalpy values that are in fact approximated by the considered models. Neglecting the quantitative differences in enthalpy one can see that foodstuffs contain two types of sorption sites i.e. high - energetically (primarily sites - responsible for sorption at low hr values) and low energetically ones (secondary centers) - where the enthalpy of sorption is close to the enthalpy of water condensation.

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Sylwester Furmaniak, Artur P. Terzyk, Leszek Czepirski et al.

Scheme 1. The comparison of the mechanisms proposed in the considered models. 25

25 GDW

CMMS 20 Me [% (db)]

Me [% (db)]

20 15 10

15 10

5

5

0

0 0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

hr

0.6

0.8

1

hr 25 GAB

Me [% (db)]

20 20 oC 30 oC 40 oC

15 10 5 0 0

0.2

0.4

0.6

0.8

1

hr

Figure 2. The results of fitting the sorption data on quinoa grains, points – experimental data, lines – fitting.

509

Water Sorption on Foodstuffs - Alternative Models… 30

30 CMMS

20

Me [% (db)]

Me [% (db)]

GDW

10

20

10

0

0 0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

hr

0.6

0.8

1

hr 30

Me [% (db)]

GAB 10 oC 20 oC 30 oC 40 oC

20

10

0 0

0.2

0.4

0.6

0.8

1

hr

Figure 3. The results of fitting the sorption data on chickpea flour, points – experimental data, lines – fitting. 16

16 GDW

CMMS 12 Me [% (db)]

Me [% (db)]

12

8

4

8

4

0

0 0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

hr

0.6

0.8

1

hr 16 GAB

Me [% (db)]

12

25 oC 32 oC 45 oC

8

4

0 0

0.2

0.4

0.6

0.8

1

hr

Figure 4. The results of fitting the sorption data on fufu, points – experimental data, lines – fitting.

510

Sylwester Furmaniak, Artur P. Terzyk, Leszek Czepirski et al. 30

30 CMMS

20

Me [% (db)]

Me [% (db)]

GDW

10

20

10

0

0 0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

hr

0.6

0.8

1

hr 30

Me [% (db)]

GAB 5 oC 20 oC 30 oC 40 oC

20

10

0 0

0.2

0.4

0.6

0.8

1

hr

Figure 5. The results of fitting the sorption data on vetch seeds, points – experimental data, lines – fitting. 40

40 GDW

CMMS 30 Me [% (db)]

Me [% (db)]

30

20

10

20

10

0

0 0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

hr

0.6

0.8

1

hr 40 GAB

Me [% (db)]

30

30 oC 45 oC 60 oC

20

10

0 0

0.2

0.4

0.6

0.8

1

hr

Figure 6. The results of fitting the sorption data on potatoes, points – experimental data, lines – fitting.

511

Water Sorption on Foodstuffs - Alternative Models… 80 Quinoa grains

60 40 20

qst - L [kJ mol-1]

qst - L [kJ mol-1]

80

0

40 20 0

0

4

8

12 Me [% (db)]

16

20

0

120

5

10 15 Me [% (db)]

20

25

50

100

Fufu

qst - L [kJ mol-1]

qst - L [kJ mol-1]

Chickpea flour

60

80 60 40 20 0

Vetch seeds

40 30 20 10 0

0

2

4

6 8 10 Me [% (db)]

12

14

0

5

10 15 Me [% (db)]

20

25

qst - L [kJ mol-1]

30 25

Potatoes

20 15 10 5 0 0

10 20 Me [% (db)]

30

Figure 7. Theoretical enthalpy of sorption plots generated from considered models (Eqs.(13-17), parameters from Tabs. 5 – 7). solid line – GDW, dashed line – CMMS, circles – GAB. The curves were generated for the following temperatures: quinoa grains: 30 oC, chickpea flour: 20 oC, fufu: 32 oC, vetch seeds: 20 oC, potatoes: 45 oC).

7. CONCLUSION Our results confirm, mentioned previously, large applicability of the GDW model to description of water sorption data (determined at single as well as at different temperatures) on foodstuffs. Moreover, the CMMS model shows good applicability in this field. We point out that the both models lead to better results than the GAB approach, which is the reference model in the food science engineering. Although the CMMS as well as the GDW contain more best - fit parameters than the GAB approach, they lead to better and more realistic description of sorption process. In the age of the progress in computer science and the development of new numerical algorithms fitting of many - parameter equations is relatively

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easy. Moreover the GDW points out the importance of the effect of conversion of primarily sorption sites into the secondary ones.

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[15] Furmaniak, S; Gauden, PA; Terzyk, AP; Rychlicki, G. Water adsorption on carbons – critical review of the most popular analytical approaches. Advances in Colloid and Interface Science, (in press). [16] Gauden, PA. Does the Dubinin–Serpinsky theory adequately describe water adsorption on adsorbents with high-energy centers? Journal of Colloid and Interface Science, 2005 282, 249-260. [17] Furmaniak, S; Terzyk, A.P; Gauden, PA; Rychlicki, G. Parameterisation of the corected Dubinin–Serpinsky adsorption isotherm equation. Journal of Colloid and Interface Science, 2005 291, 600-605. [18] Do, DD; Do, HD. A model for water adsorption in activated carbon. Carbon, 2000 38, 767-773. [19] Furmaniak, S; Gauden, PA; Terzyk, AP; Rychlicki, G; Wesołowski, RP; Kowalczyk, P. Heterogeneous Do–Do model of water adsorption on carbons. Journal of Colloid and Interface Science, 2005 290, 1-13. [20] Storn, R; Price, K. Minimizing the real functions of the ICEC’96 contest by differential evolution. In Proceedings of 1996 IEEE International Conference on Evolutionary Computation (ICEC’96), Nagoya University, 1996, 842-844. [21] Storn, R; Price, K. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997 11, 341359. [22] Furmaniak, S; Terzyk, AP; Gauden, PA; Rychlicki, G. Simple model of adsorption in nanotubes. Journal of Colloid and Interface Science, 2006 295, 310-317. [23] Szymońska, J; Krok, F; Tomasik, P. Deep-freezing of potato starch. International Journal of Biological Macromolecules, 2000 27, 307-314. [24] Szymońska, J; Krok, F; Komorowska – Czepirska, E; Rębilas, K. Modification of granular potato starch by multiple deep-freezing and thawing. Carbohydrate Polymers, 2003 52, 1-10. [25] Ciembroniewicz, A; Komorowska – Czepirska, E. An apparatus for measurements isotherms and kinetics of hydrocarbon adsorption on porous adsorbents. Przemysł Chemiczny, 1986 66, 265-268 (in Polish). [26] Tolaba, MP; Peltzer, M; Enriquez, N; Pollio, ML. Grain sorption equilibria of quinoa grains. Journal of Food Engineering, 2004 61, 365-371. [27] Durakowa, AG; Menkov, ND. Moisture sorption characteristics of chickpea flour. Journal of Food Engineering, 2005 68, 535-539. [28] Sanni, LO; Atere, C; Kuye, A. Moisture sorption isotherms of fufu and tapioca at different temperatures. Journal of Food Engineering, 1997 34, 203-212. [29] Menkov, ND. Moisture sorption isotherms of vetch seeds at four temperatures. Journal of Agricultural Engineering Research, 2000 76, 373-380. [30] Kaymak-Ertekin, F; Gedik, A. Sorption and isosteric heat of sorption for grapes, apricots, apples and potatoes. Lebensmittel-Wissenschaft und -Technologie, 2004 37, 429-438.

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NOMENCLATURE B1 , B2 C C0 DC DCT hr K

K0

the constants defined in Eqs.(14 and 15) the kinetic constant related to the sorption in the first layer the pre-exponential entropic factor related to C constant the global determination coefficient value the value of DC calculated for the data measured at the considered temperature relative humidity the kinetic constant related to multilayer sorption (GAB model), the kinetic constants related to the sorption on primary sorption sites (GDW model) the equilibrium constant for sorption on the central unit on the primary side (CMMS model) and/or the pre-exponential entropic factor related to K constant (GDW and GAB model)

K 00

the pre-exponential entropic factor related to K0 constant (CMMS model)

K1

the equilibrium constant for sorption of the side unit on the primary side

K10

the pre-exponential entropic factor related to K1 constant

Kas

the equilibrium constant for sorption of the site associate

K 0as

the pre-exponential entropic factor related to Kas constant

k k0 L Me M oe,i

the kinetic constants related to sorption on secondary sorption sites the pre-exponential entropic factor related to k constant the enthalpy of condensation of water, 43.96 kJ mol-1 equilibrium moisture content, % (dry basis) observed moisture content for i-th experimental point, % (dry basis)

t M e,i o

Me m

N Q q q0 q1 qas qC qK qst qX R T

theoretical value of the moisture content, % (dry basis) the average value of the observed moisture content, % (dry basis) monolayer capacity (GAB model) and/or the concentration of primary active surface sites (GDW model) and/or maximum sorption on primary sites (CMMS model), % (dry basis), mmol g-1 the number of temperatures for which the experimental data were measured the enthalpy values related to the primary sorption sites, kJ mol-1 the enthalpy values related to the secondary sorption sites, kJ mol-1 the value of the enthalpy related to K0 constant, kJ mol-1 the value of the enthalpy related to K1 constant, kJ mol-1 the value of the enthalpy related to Kas constant, kJ mol-1 the value of the enthalpy related to C constant, kJ mol-1 the value of the enthalpy related to K constant (GAB model), kJ mol-1 isosteric enthalpy of sorption, kJ mol-1 the value of the enthalpy related to X constant, kJ mol-1 the universal gas constant, 0.008314 kJ mol-1 K-1 absolute temperature, K

Water Sorption on Foodstuffs - Alternative Models… t w X X0 ηT ω

515

temperature, oC the parameter determining what part of water molecules adsorbed on primary sites convert into the secondary adsorption sites kinetic constants the pre-exponential entropic factor related to X constant the sum (normalized) of the squares of deviations between theoretical and experimental data at considered temperature the parameter defined by Eq.(3)

In: Food Engineering Research Developments Editor: Terrance P. Klening, pp. 517-530

ISBN: 978-1-60021-906-1 © 2007 Nova Science Publishers, Inc.

Chapter 12

A FORECAST ANALYSIS ON FOOD NUTRITION SUPPLY AND DEMAND WORLDWIDE Wenjun Zhang1, Wengang Zhou1, Xiyan Zhang2 Yongkai Xia2 and Wei He3 1

Research Institute of Entomology, School of Life Sciences, Sun Yet-sen (Zhongshan) University, Guangzhou 510275, China 2 Lucheng Banana Institute of Hainan, Hainan 570216, China 3 Guangdong AIB Polytech College, Guangzhou 510507, China

ABSTRACT This paper aimed to make a longer-term forecast analysis on global food nutrition supply and demand. The forecasts of supplies of food calories and proteins for the world and various regions over the period 2010-2030 were given, and food nutrition supply and demand balance in the forecast period was discussed. If the past pattern continues, the global total food calorie supply would grow at the annual rate of 13.43±0.71 kcal/cap/day and reach 3210.4±67.3 kcal/cap/day in 2030. Total food calorie supplies for all of the regions would grow during the forecast period and, in most regions they are forecast to be greater than 3000 kcal/cap/day from 20152020. Total food protein supply for all regions but not Oceania, is forecast to grow during the forecast period. The proportion of animal sourced protein in total food protein supply is in 2030 forecast to increase and reach 35.5%, 61.6%, 56.8%, and 21.7% for Asia, Europe, South America, and Africa. Food calorie supply in the world is expected to exceed the adequate energy intake after around 2015. Strong focus should be worldwide put on the over-intake of food calorie in the near future. Global food protein supply is not expected to be greater than the adequate range during the period 2010-2030. Food protein supply in Africa and Caribbean would be just a little greater than the basic demand in the forecast period. Food protein intake in these regions should be improved in the coming years.

Key Words: food; calories and protein; supply and demand; forecast; world

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1. INTRODUCTION There are 0.85 billion of chronically or acutely malnourished people in the world, mostly in Asia and Africa1. More than 90% of them are chronically malnourished and 10% are acute hunger. Hidden hunger from micronutrient deficiencies additionally affects more than 2 billion people worldwide1. Moreover, the numbers of malnourished people are increasing2. Increased population and rapid urbanization will drive sustained growth in food demand3. The demand for higher valued commodities, such as meats and vegetables is also increasing4. According to a baseline projection, global cereal production was estimated to increase by 56% between 1997 and 2050, and livestock production by 90%. Developing countries will account for 93% of cereal demand growth and 85% of meat-demand growth to 2050 4,5. By 2020, world demand for rice, wheat, and maize is projected to increase by ~40% and livestock production by more than 60%6. By 2050, global population was projected to be 50% larger than at present and global grain demand is projected to double. This doubling will result from a projected 2.4-fold increase in per capita real income and from dietary shifts towards a higher proportion of meat7. Given the pace of global change, human welfare is utterly dependent upon forwardlooking, adaptive, and informed decisions8. Translation of data into a workable set of social indicators will enable policy debate to be conducted in an illuminating manner9. Meanwhile, having these research data available to assess rapidly emerging problems and quickly respond to public outcries for a solution would offer significant benefits to publics10. Since the era of Malthus, forecasts of food security have been an important part of the policy decisionmaking. Over the past decades, some researchers have expressed concern about the ability of food supply to keep up with food demands, whereas others have forecast that modern technologies or expansions of cultivated area would boost production sufficiently to meet rising demands11-14. Food production must be coupled with both poverty reduction and environmental conservation, which makes the balanced food supply-demand even more difficult3,15. As a consequence, the forecast and outlook of food supply and demand based on the latest data is therefore necessary for having an in-depth insight into these questions. On the other hand, regional problems in food distribution can be difficult to counter even when global supplies are adequate. To ask merely whether global food supplies can be sustainably increased to meet future requirements misses much of the question9. Regional forecasts would thus be more informative compared to merely global forecast. A number of agricultural businesses would also benefit from the ability to forecast food supply at regional scale16. This paper tried to make a longer-term forecast analysis on global food nutrition supply and demand, and provide researchers with basic data on food nutrition supply and demand in the future. The forecasts of food nutrition supplies for the world and various regions over the period 2010-2030 were conducted. Balance of food nutrition supply and demand in the forecast period was discussed.

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2. MATERIALS AND METHODS 2.1. Data Source Belonging to a poverty solution-oriented topic and due to the data availability, food nutrition in present study means only calories and protein provided by food products. Annual data on daily per capita supplies of food calories and proteins from animal and vegetable products since 1961 for the world, the developed countries, the developing countries, Africa, Asia, Caribbean, Europe, North & Central America, South America, and Oceania were obtained from the recent records of UN Food and Agriculture Organization17. Detailed definition of developed and developing countries can be found in the publications of FAO before 2006.

2.2. Forecast Model Mechanistic forecasting of agriculture and social development is difficult and imprecise. In this study I fitted the dynamics of food nutrition supplies with the polynomial function: x(t)=c+b1 t+ b2 t2+ b3 t3+…, where t is year. It was found that the dynamics of food nutrition supplies for various regions could be optimally fitted by the first-order polynomial function. We thus used the following univariate general linear model (GLM)18,19 to fit the dynamics of food nutrition supplies: x(t)=c+r t , where t is year, x(t) is food nutrition supply at the year t, and r is the annual rate of food nutrition supply. The model was statistically tested with F-test against the adjusted R2 of the GLM. All forecasts and their confidence intervals were calculated from GLM. These forecasts were made over the period 2010-2030. They were model extrapolations of past trajectories and thus assumed similar technological and social patterns in the forecast period. In addition, the forecast based on mechanistic model with various variables could supplement our forecasts and are thus needed.

3. RESULTS Overall the GLM showed a better fitness on trajectories of food nutrition supplies in most cases (Table 1), so it may be used to represent the longer-term trends of global food nutrition supplies. Detailed forecasts of global food nutrition supplies, over the period 2010-2030, are given in Table 1. In order to analyze variability and similarity across regions and periods, we will compare the changes in the year 2030 relative to the current values. Current supplies were extrapolated from the GLM 20.

Table 1. GLM forecasts of global supplies of food calorie and protein over the period 2010 to 2030. r: the annual rate of food calorie or protein supply, r: half-width of the confidence interval for r (p<0.05), x(t): the forecast, x(t): half-width of the confidence interval for x(t) (p<0.05), Adj R2: adjusted R2. Significance levels for GLM: **: p< 0.01, *: p< 0.05.

(kcal/cap/day/yr)

Total Food Calorie Food Calorie Supply (kcal/cap/day)

(kcal/cap/day/yr)

Animal Calorie Supply Food Calorie (kcal/cap/day)

r r Adj. R2 2010 x(t) 2010 x(t) 2015 x(t) 2015 x(t) 2020 x(t) 2020 x(t) 2025 x(t) 2025 x(t) 2030 x(t) 2030 x(t) r r Adj. R2 2010 x(t) 2010 x(t) 2015 x(t) 2015 x(t) 2020 x(t) 2020 x(t) 2025 x(t) 2025 x(t) 2030 x(t) 2030 x(t)

World

Africa

13.427 0.706 0.972** 2941.8 61.4 3009.0 62.6 3076.1 64.0 3143.2 65.6 3210.4 67.3 2.9378 0.191 0.958** 479.9 16.7 494.6 17.0 509.2 17.4 523.9 17.8 538.6 18.3

8.2067 0.458 0.969** 2492.8 39.8 2533.8 40.6 2574.8 41.5 2615.9 42.6 2656.9 43.7 0.302 0.213 0.146** 182.6 18.5 184.2 18.9 185.7 19.3 187.2 19.8 188.7 20.3

Asia

Caribbean Developed Developing Europe Countries Countries 20.696 7.1121 6.5216 18.824 4.4964 1.144 3.458 1.304 0.918 1.938 0.969** 0.279** 0.706** 0.976** 0.333** 2919.3 2600.0 3372.6 2865.0 3379.7 99.5 300.7 113.3 79.9 168.6 3022.8 2635.5 3405.2 2959.1 3402.2 101.5 306.7 115.6 81.5 171.9 3126.2 2671.1 3437.8 3053.2 3424.7 103.7 313.5 118.2 83.3 175.8 3229.7 2706.7 3470.4 3147.3 3447.2 106.3 321.2 121.1 85.3 180.1 3333.2 2742.2 3503.0 3241.4 3469.6 109.0 329.5 124.2 87.5 184.7 6.614 -1.5065 1.7218 5.4946 1.5254 0.578 1.438 1.014 0.458 1.707 0.927** 0.077* 0.204** 0.933** 0.051 406.1 345.3 922.1 380.6 1000.8 50.2 125.0 88.2 39.9 148.4 439.2 337.7 930.7 408.1 1008.5 51.2 127.5 89.9 40.7 151.4 472.2 330.2 939.3 435.6 1016.1 52.4 130.3 91.9 41.6 154.7 505.3 322.7 947.9 463.1 1023.7 53.6 133.5 94.2 42.6 158.5 538.4 315.1 956.5 490.5 1031.4 55.0 137.0 96.6 43.7 162.6

N & C Oceania South America America 18.417 1.2838 12.486 0.592 1.117 0.927 0.989** 0.095 0.946** 3595.4 2995.6 2941.6 51.5 97.1 80.6 3687.4 3002.0 3004.0 52.5 99.0 82.2 3779.5 3008.4 3066.4 53.7 101.2 84.1 3871.6 3014.8 3128.8 55.0 103.7 86.1 3963.7 3021.2 3191.3 56.4 106.4 88.4 -0.0844 -6.0874 5.101 0.531 0.753 0.676 0 0.863** 0.846** 806.5 811.8 621.9 46.1 65.5 58.8 806.1 781.3 647.4 47.0 66.8 60.0 805.7 750.9 672.9 48.1 68.3 61.3 805.3 720.4 698.4 49.2 69.9 62.8 804.8 690.0 723.9 50.5 71.7 64.5

Table 1. Continued

(kcal/cap/day/yr)

Vegetable Calorie Supply Food Calorie (kcal/cap/day)

(g protein/cap/day/yr)

Total Food Protein Supply

Food Protein (g protein/cap/day)

r r Adj. R2 2010 x(t) 2010 x(t) 2015 x(t) 2015 x(t) 2020 x(t) 2020 x(t) 2025 x(t) 2025 x(t) 2030 x(t) 2030 x(t) r r Adj. R2 2010 x(t) 2010 x(t) 2015 x(t) 2015 x(t) 2020 x(t) 2020 x(t) 2025 x(t) 2025 x(t) 2030 x(t) 2030 x(t)

World

Africa

10.489 0.77 0.947** 2461.9 67.0 2514.4 68.3 2566.8 69.9 2619.3 71.6 2671.7 73.4 0.3294 0.019 0.967** 77.87 1.66 79.52 1.69 81.16 1.73 82.81 1.77 84.46 1.82

7.9047 0.4623 0.966** 2310.1 40.2 2349.6 41.0 2389.2 41.9 2428.7 42.9 2468.2 44.1 0.15494 0.018 0.883** 61.66 1.52 62.44 1.56 63.21 1.59 63.99 1.63 64.76 1.67

Asia

Caribbean Developed Developing Countries Countries 14.082 8.6185 4.7998 13.33 1.3509 2.3165 0.5702 1.1063 0.913** 0.569** 0.873** 0.934** 2513.2 2254.7 2450.5 2484.3 117.4 201.4 49.6 96.2 2583.6 2297.8 2474.5 2551.0 119.8 205.4 50.6 98.1 2654.0 2340.9 2498.5 2617.6 122.4 210.0 51.7 100.3 2724.4 2384.0 2522.5 2684.3 125.4 215.1 53.0 102.8 2794.8 2427.1 2546.5 2750.9 128.7 220.7 54.4 105.4 0.56962 0.13591 0.22742 0.49773 0.038 0.101 0.053 0.035 0.956** 0.133** 0.633** 0.952** 74.69 60.95 103.74 72.29 3.29 8.74 4.66 3.04 77.54 61.63 104.88 74.77 3.36 8.92 4.76 3.10 80.39 62.30 106.01 77.26 3.44 9.12 4.86 3.17 83.24 62.98 107.15 79.75 3.52 9.34 4.98 3.24 86.09 63.66 108.29 82.24 3.61 9.58 5.11 3.33

Europe 2.971 0.7579 0.595** 2378.9 65.9 2393.7 67.2 2408.6 68.7 2423.4 70.4 2438.3 72.2 0.26185 0.072 0.556** 104.12 6.28 105.43 6.40 106.74 6.55 108.05 6.71 109.36 6.88

N & C Oceania South America America 18.502 7.3712 7.3848 0.5495 0.7018 0.8052 0.991** 0.915** 0.891** 2788.8 2183.8 2319.7 47.8 61.0 70.0 2881.3 2220.7 2356.6 48.8 62.2 71.4 2973.8 2257.5 2393.5 49.9 63.6 73.0 3066.4 2294.4 2430.5 51.1 65.1 74.8 3158.9 2331.2 2467.4 52.4 66.8 76.7 0.36651 -0.07128 0.34416 0.033 0.063 0.056 0.926** 0.092* 0.783** 102.84 95.42 76.92 2.80 5.46 4.89 104.68 95.07 78.64 2.85 5.57 4.98 106.51 94.71 80.36 2.92 5.70 5.10 108.34 94.35 82.08 2.99 5.84 5.22 110.17 94.00 83.80 3.07 5.99 5.36

Table 1. Continued

(g protein/cap/day/yr)

Animal Protein Supply

Food Protein (g protein/cap/day)

(g protein/cap/day/yr)

Vegetable Protein Supply Food Protein (g protein/cap/day)

r r Adj. R2 2010 x(t) 2010 x(t) 2015 x(t) 2015 x(t) 2020 x(t) 2020 x(t) 2025 x(t) 2025 x(t) 2030 x(t) 2030 x(t) r r Adj. R2 2010 x(t) 2010 x(t) 2015 x(t) 2015 x(t) 2020 x(t) 2020 x(t) 2025 x(t) 2025 x(t) 2030 x(t) 2030 x(t)

World

Africa

0.207 0.010 0.977** 29.75 0.87 30.78 0.88 31.81 0.90 32.85 0.92 33.88 0.95 0.123 0.017 0.823** 48.12 1.54 48.74 1.57 49.35 1.60 49.97 1.64 50.58 1.69

0.037 0.014 0.401** 13.30 1.21 13.49 1.24 13.67 1.27 13.86 1.30 14.05 1.33 0.118 0.020 0.766** 48.36 1.76 48.95 1.79 49.54 1.83 50.13 1.88 50.72 1.92

Asia

Caribbean Developed Developing Europe Countries Countries 0.363 0.012 0.283 0.31 0.303 0.035 0.064 0.065 0.031 0.091 0.914** 0 0.65** 0.909** 0.513** 23.28 22.95 61.47 22.18 61.29 3.01 5.57 5.58 2.66 7.90 25.10 23.01 62.88 23.73 62.81 3.07 5.68 5.69 2.72 8.06 26.91 23.07 64.30 25.29 64.32 3.14 5.81 5.82 2.78 8.24 28.73 23.13 65.71 26.84 65.83 3.21 5.95 5.96 2.84 8.44 30.54 23.19 67.12 28.39 67.35 3.30 6.10 6.11 2.92 8.66 0.207 0.124 -0.055 0.187 -0.041 0.028 0.053 0.021 0.023 0.027 0.837** 0.336** 0.384** 0.866** 0.168** 51.41 37.99 42.27 50.10 42.83 2.46 4.63 1.86 1.99 2.33 52.45 38.61 42.00 51.04 42.63 2.51 4.72 1.90 2.03 2.38 53.48 39.23 41.72 51.98 42.42 2.57 4.82 1.94 2.08 2.43 54.51 39.86 41.44 52.91 42.22 2.63 4.94 1.98 2.13 2.49 55.54 40.48 41.17 53.85 42.01 2.70 5.07 2.04 2.18 2.56

N & C Oceania South America America 0.122 -0.081 0.349 0.025 0.064 0.041 0.707** 0.115 0.873** 57.57 60.71 40.63 2.13 5.61 3.62 58.18 60.30 42.38 2.17 5.73 3.69 58.79 59.90 44.13 2.22 5.85 3.77 59.41 59.49 45.88 2.27 6.00 3.86 60.02 59.08 47.63 2.33 6.15 3.96 0.244 0.010 -0.006 0.021 0.029 0.027 0.927** 0 0 45.28 34.72 36.28 1.85 2.57 2.34 46.50 34.77 36.25 1.89 2.62 2.39 47.72 34.82 36.22 1.93 2.68 2.44 48.94 34.86 36.20 1.98 2.74 2.50 50.16 34.91 36.17 2.03 2.81 2.57

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3.1. Food Calorie Supply Total Food Calorie If the past pattern continues, the global total food calorie supply would grow at the annual rate of 13.43±0.71 kcal/cap/day and reach 3210.4±67.3 kcal/cap/day in 2030, representing an increase of 8.3-12.9% based on the current level (Table 1). The annual increase of total food calorie in the developing countries (18.82±0.92 kcal/cap/day) is expected to be about 3-fold of that in the developed countries (6.52±1.30 kcal/cap/day). By 2030 food calorie supply in the developing countries (3241.4 kcal/cap/day) would close to developed countries’ food calorie supply (3503.0 kcal/cap/day), an increase of 15.4% against 4.5% in the developed countries. Total food calorie supplies for all of the regions are forecast to grow during the period 2010-2030 and, in most regions they are estimated to exceed 3000 kcal/cap/day after the years 2015-2020, with the exception of Africa and Caribbean. Food calorie supplies in Africa and Caribbean would continue to be lower and are forecast to reach 2656.9 kcal/cap/day and 2742.2 kcal/cap/day by the year 2030. If the past pattern continues, North & Central America would remain to be the greatest in food calorie supply and it is estimated to close to 4000 kcal/cap/day. The following is the regional outlook of total food calorie supply in 2030 (p<0.05): increase: Asia (12.8-20.5%), North & Central America (10.4-13.6%), South America (6.812.9%), Africa (5.9-9.4%); probably increase: Caribbean (-6.4-19.1%), Europe (-2.4-8.6%), Oceania (-2.6-4.5%). Animal and Vegetable Sourced Calories If the past pattern continues in the forecast period, global vegetable sourced calorie supply would grow at the annual rate of 10.49±0.77 kcal/cap/day, equivalent to 3.5-fold of animal sourced calorie supply. Global proportion of animal sourced calorie in total food calorie supply is forecast to slightly increase to 16.8% in 2030. For both animal and vegetable sourced calorie supplies, their annual rates in the developing countries, i.e., 5.49 and 13.33 kcal/cap/day/yr separately, are expected to be about 3-fold of that in the developed countries. By 2030 the animal and vegetable sourced calorie supplies are forecast to separately increase 34.7±12.0% and 12.5±4.3% in the developing countries and, in the developed countries the two values would be 4.3±10.5% and 4.5±2.2%. Vegetable sourced calorie supply in the developing countries would be slightly greater than that in the developed countries by 2030. On the other hand, the animal sourced calorie in total food calorie supply of developed countries is estimated to remain a stable level of 27.3% in the forecast period, but in the developing countries this proportion would grow and reach 15.1% by 2030. The proportion for developed countries would be approximately 2-fold of that for developing countries. Regional animal sourced calorie supply in 2030 is expected to have various outcomes (p<0.05): increase: Asia (25.2-53.6%), South America (8.7-29.9%); probably increase: Africa (-7.3-15.0%), Europe (-12.8-19.9%); probably decrease: Caribbean (-49.1-29.2%), North & Central America (-6.5-6.0%); decrease: Oceania (-25.5- -8.2%). The vegetable sourced calorie supply for all of the regions is projected to increase in the forecast period, with the highest growth in North & Central America (15.6%), and a lowest growth in Europe (2.9%) as compared to the current supplies. By 2030 the vegetable sourced

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calorie supply in all of the regions would increase to about 2500 kcal/cap/day. At the same time the vegetable sourced calorie supply in North & Central America is estimated to be greater than 3000 kcal/cap/day. Of these regions the proportions of animal sourced calorie for Asia and South America only are expected to increase during the period 2010-2030. In 2030 this proportion for Europe (29.7%), Oceania (22.8%), South America (22.7%), and North & Central America (20.3%) should be greater than Asia (16.2%), Caribbean (11.5%), and in particular Africa (7.1%).

3.2. Food Protein Supply Total Food Protein Total food protein supply in the world is forecast to grow at the annual rate of 0.33±0.02 g/cap/day and reach 84.5±1.8 g/cap/day by 2030, increasing 9.9±2.4% relative to the current level (Table 1). In the developed countries the total food protein supply would annually grow 0.49±0.04 g/cap/day, about 2-fold of that in the developed countries. Total food protein supply in the developing countries is forecast to increase to 82.2±3.3 g/cap/day in the year 2030 and in the developed countries it would reach 108.3±5.1 g/cap /day, equivalent to the relative growths of 16.2±4.7% and 5.1±4.9% separately. Total food protein supply in all regions, but not Oceania, is forecast to grow in the coming years. Asia (0.57±0.04 g/cap/day/yr) is expected to have the highest annual growth and Africa (0.15 ±0.02 g/cap/day/yr) and Caribbean (0.14±0.10 g/cap/day/yr) would have the lower annual growth. The total food protein supply in Oceania is estimated to slightly decline in the forecast period. By 2030, the total food protein supply in Europe and North & Central America would reach 110 g/cap/day, and in Asia, Oceania, and South America it would exceed 85 g/cap/day. Total food protein supplies in Caribbean and Africa are expected to only reach 65 g/cap/day by 2030. The regional outlook of total food protein supply in 2030 is as follows (p<0.05): increase: Asia (13.0-22.9%), South America (3.4-17.5%), North & Central America (5.3-11.3%), Africa (3.1-8.6%); probably increase: Europe (-0.8-12.5%), Caribbean (-10.7-20.9%); probably decrease: Oceania (-7.9-4.5%). Animal and Vegetable Sourced Protein Both animal and vegetable sourced protein around the world are forecast to grow, with the annual rates of 0.21±0.01 g/cap/day and 0.12±0.02 g/cap/day respectively. The proportion of animal sourced protein in total food protein supply for the world is expected to grow and increase to 40.1% in 2030. Compared to animal protein supply, the vegetable protein (50.6 vs. 33.9 g/cap/day) would continue to be the major food protein source for human food in 2030. The developing countries would follow the similar trend with the world in the growth pattern of animal and vegetable sourced protein. The proportion of animal sourced protein in total food protein is estimated to increased to 34.5% in 2030. In the developed countries the animal sourced food protein supply would grow and reach 67.1±6.1 g/cap/day by 2030, more than 2-fold of that for developing countries, but the vegetable protein supply is expected to

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slightly decline in the forecast period (-0.06±0.02 g/cap/day/yr). Animal sourced protein proportion for developed countries would grow and increase to 62.0% in 2030. Excepting Oceania (-0.08±0.06 g/cap/day/yr), all regions’ animal protein supplies are expected to grow in the future. Asia and South America would have the higher annual rate (0.35 g/cap/day/yr). With the lower annual rates of 0.04 and 0.01 g/cap/day, Animal protein supplies for Africa and Caribbean are expected to slowly grow during the coming years. Overall the regional outlook of animal protein supply in 2030 is expected to be (p<0.05): increase: Asia (22.8-52.5%), South America (10.3-30.3%), North & Central America (0.88.9%); probably increase: Europe (-2.8-25.9%), Africa (-3.6-16.6%), Caribbean (-25.527.8%); probably decrease: Oceania (-13.2-7.0%). Most regions’ vegetable protein supplies are projected to increase in the coming years, with the exception of Europe (-0.04±0.03 g/cap/day/yr) and South America (-0.01±0.03 g/cap/day/yr). Among these regions Asia (0.21±0.03 g/cap/day/yr) and North &Central America (0.24±0.02 g/cap/day/yr) would have higher annual growths. A regional outlook of vegetable protein supply in 2030 is outlined to be (p<0.05): increase: North & Central America (8.0-17.2%), Asia (4.0-14.5%), Africa (1.6-9.6%); probably increase: Caribbean (-5.9-21.1%), Oceania (-7.5-8.8%); probably decrease: Europe (-8.1-3.8%), South America (-7.4-6.7%). The proportion of animal sourced protein in total food protein is expected to increase and reach 35.5%, 61.6%, 56.8%, and 21.7% for Asia, Europe, South America, and Africa in the year 2030. In North & Central America, Oceania, and Caribbean this proportion would decline to 54.5%, 62.9%, and 36.4% in 2030.

4. CONCLUSIONS AND DISCUSSION 4.1. Conclusions Global total food calorie supply is forecast to grow at the annual rate of 13.43±0.71 kcal/cap/day and would increase to 3210.4±67.3 kcal/cap/day in 2030. Vegetable sourced calorie supply in the world would grow at the annual rate of 10.49±0.77 kcal/cap/day, which is 3.5-fold of that for animal sourced calorie. Global proportion of animal sourced calorie in total food calorie supply is estimated to slightly increase to 16.8% in 2030. Total food protein supply in the world is forecast to grow at the annual rate of 0.33±0.02 g/cap/day and would reach 84.5±1.8 g/cap/day by 2030. Both animal and vegetable sourced protein around the world are forecast to grow, with the annual rates of 0.21±0.01 g/cap/day and 0.12±0.02 g/cap/day respectively. Global proportion of animal sourced protein in total food protein supply would grow and increase to 40.1% in 2030. Compared to animal protein supply (33.9 g/cap/day), the vegetable protein (50.6 g/cap/day) is expected to be the major food protein source in human food by 2030. Total food calorie supplies for all of the regions would grow during the period 20102030. In most regions they are forecast to exceed 3000 kcal/cap/day after 2015-2020, with the exception of Africa and Caribbean. Total food calorie supplies in Africa and Caribbean are projected to reach 2656.9 kcal/cap/day and 2742.2 kcal/cap/day by the year 2030. If the past pattern continues, North & Central America would remain to be the greatest in food calorie

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supply and reach about 4000 kcal/cap/day. By 2030, the vegetable sourced calorie supply in all of the regions would increase to around 2500 kcal/cap/day and it is estimated to exceed 3000 kcal/cap/day in North & Central America. Total food protein supply in all regions excepting Oceania is forecast to grow in the coming years. Asia (0.57±0.04 g/cap/day/yr) is expected to have a greatest annual growth and Africa (0.15 ±0.02 g/cap/day/yr) and Caribbean (0.14±0.10 g/cap/day/yr) would show the lower annual growth. Total food protein supply in Europe and North & Central America would reach 110 g/cap/day in 2030, and it would exceed 85 g/cap/day in Asia, Oceania, and South America. Total food protein supplies in Caribbean and Africa are expected to reach 65 g/cap/day by 2030. Excepting Oceania (-0.08±0.06 g/cap/day/yr), all regions’ animal protein supplies are expected to grow in the coming years. The proportion of animal sourced protein in total food protein supply for Asia, Europe, South America, and Africa is forecast to increase and reach 35.5%, 61.6%, 56.8%, and 21.7% respectively in the year 2030. In North & Central America, Oceania, and Caribbean, this proportion would decline to 54.5%, 62.9%, and 36.4% in 2030.

4.2. Balance of Food Nutrition Supply and Demand Deficient and imbalanced intake of food energy and protein will result in malnutrition of the human beings. Malnourishment weakens people’s immunity and strength, making them succumb more quickly to disease. For example, nearly 57% of malaria deaths are attributable to malnutrition1. People living on a food with sufficient energy but deficient in food protein may suffer from retarded growth. In severe case, various diseases like Kwashiorkor, or even death is resulted. Overweight of body mass have been becoming the most important health problem in developed countries such as United States. Malnutrition will have a great impact on national economy, which results in annual losses of 6 to 10% in Gross Domestic Product (GDP) due to losses in labor productivity1. Adequate nutrition intakes must be defined to determine balanced food supply and demand. Adequate intakes of food energy and protein will change with the gender, ages, body mass, culture, social and economic conditions, etc. Some indices for adequate nutrition intakes have been developed in different countries. For example, the RDAs (Recommended Daily Amounts) for United States, and the PRI (Population Reference Intake) for Europe Union, and the DRIs (Dietary Reference Intakes) for China21. It is almost unlikely to formulate a unique and worldwide used criterion for balanced nutrition intakes. Summarized from various researches21-24, the adequate intake of food calorie and protein may separately fall in the range of 2000-3000 kcal/cap/day and 60-100 g/cap/day. According to the adequate intakes above, global food calorie supply would exceed the adequate energy intake after around 2015. Developing countries are expected to go beyond this range during the period 2015-2020. Food calorie supplies in the developed countries, Europe, and North & Central America have been greater than the upper threshold. Asia, South America, and Oceania would touch this ceiling in 2010-2015. Food calorie supply in Africa and Caribbean is in an adequate range and would not get beyond the range before 2030. In contrast to an early report by the FAO, which estimated that 56% of the human race lived in nations with average per capita food supplies of 2200 food calories per day or less25, our forecasts suggest that in the coming years the focus should be put on the over-intake of

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food calorie for most regions (Fig. 1A). It seems that the tasks to reduce the proportion of hungry people in half from 1990 to 2015, suggested by the Hunger Task Force in 2002 1, may likely be achieved by 2015.

(A)

(B) Figure 1. Expected surplus (>0) or deficit (<0) of food calorie supply (A) and food protein supply (B) by 2030, against the adequate intakes (Food calorie: 2000-3000 kcal/cap/day; food protein: 60-100 g/cap/day). “━”: surplus (upper) or deficit (lower). “▬― ▬”: 95% confidence interval of “━”. “◆”: the expected average.

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World’s food protein supply is estimated to be less than the upper threshold during the period 2010-2030. Food protein supply would be adequate in the developing countries before 2030. However in the developed countries, Europe, and North & Central America, the food protein supply have already exceeded the upper threshold. Asia, South America, and Oceania are estimated to run in the adequate range of food protein supply. Food protein supply in Africa and Caribbean is just a little greater than the baseline in the forecast period. As a consequence, food protein supply in these regions should be improved in the coming years (Fig. 1B). Although malnutrition and hunger are currently more related to poverty and inequitable food access than to inadequate food production, particularly in Africa26. Researchers in Africa emphasize, however, that the continent's social and economic problems remain a major obstacle to development25. Continuous adoption of high-yielding varieties that bred mainly by traditional breeding methods, has contributed to steady growth of crops yield and production since the 1960’s 27. In addition to conventional breeding, recent developments in non-conventional breeding, such as marker-assisted selection and cell and tissue culture techniques, could be employed for crops in developing countries, even if these countries stop short of transgenic breeding14. By any way, major increases in crops production and yield must be achieved to meet the future demand for food worldwide, particularly in Africa and Asia9. Demand for fishery products is also rising and it might double by 2040 28. However more than 60% of the world's major fisheries will not be able to recover from over-fishing without restorative actions. Declining fishery production in natural ecosystems has been partially offset by increased production through aquaculture and plantations8,29. The food production related development issues such as the loss of lands and biodiversity, the depletion of water resources, and the environmental and human health consequences of fertilizer and pesticide application are of growing concern9,15,20,26,30,31. In particular, the expansion of agricultural lands deeps the climate changes and the loss of biodiversity. Fortunately the studies are being conducted to find solutions, e.g., genetic highyielding crops breeding15, transgenic fish technologies8, etc. In a strict sense, food nutrition includes not only calories and protein, but also various vitamins, mineral substance, trace elements, et al. All elements of food nutrition should be considered in future studies. Fruits (bananas, apples, oranges, et al.) are expected to become one of the major sources of food nutrients in the future.

ACKNOWLEDGMENTS This research was supported by 948 program of China (2006-G32), “National Basic Research Program of China” (973 Program)(No. 2006CB102005), and the National Natural Science Foundation of China (30170184).

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Wenjun Zhang, Wengang Zhou, Xiyan Zhang, Yongkai Xia, and Wei He Stokstad E (2002) Science 297(5588):1797 Naylor RL, Goldburg RJ, Primavera JH, Kautsky N, Beveridge MCM, Clay J, Folke C, Lubchenco J, Mooney H, Troell M (2000) Nature 405:1017-1024 Swaminathan MS (2000) Science 287(5452): 425 (2000) Zhang WJ, Qi YH, Zhang ZG. (2006) Environmental Monitoring and Assessment 119(1-3): 609-620

INDEX

A absorption spectra, 254 access, 528 accuracy, 2, 3, 6, 8, 124, 209, 252, 254 acetic acid, 217, 471, 472, 478 acetophenone, 472 acid, viii, xii, 165, 166, 167, 170, 176, 177, 178, 181, 185, 188, 191, 192, 197, 209, 210, 215, 217, 218, 222, 226, 285, 290, 291, 315, 316, 317, 321, 328, 330, 335, 350, 392, 393, 472, 473, 478, 483, 484, 486, 495 acidity, 167, 177, 195, 209, 383, 478 activated carbon, 513 activation, 44, 49, 127, 133, 152, 488, 489 activation energy, 44, 49, 127, 133, 152, 488, 489 activity level, 33 actual output, 5 adaptation, 449 additives, viii, 165, 167, 179, 196, 208, 221, 222, 229, 286, 308 adhesion, 25 adsorption, 35, 54, 76, 400, 498, 499, 500, 503, 512, 513, 515 adsorption isotherms, 35, 500 adults, x, 229, 361, 365, 366, 368, 371, 376, 380, 396 aesthetics, 178 Africa, xiii, 517, 519, 520, 521, 522, 523, 524, 525, 526, 528 agar, 333 age, 371, 378, 379, 511 ageing, 308 agent, 286, 333, 344 agriculture, 375, 519 air quality, 227, 234 alcohol, 471, 473, 477, 512

alcohols, xii, 178, 284, 328, 467, 471, 473, 477, 479, 481, 499 aldehydes, xii, 178, 328, 467, 471, 473, 477, 478, 479, 480 algorithm, 1, 3, 5, 10, 203, 502 allergic reaction, 285 aloe, 222, 223, 224, 225, 226 alternative, vii, ix, 13, 130, 250, 268, 283, 285, 288, 308, 309, 312, 343, 375, 398, 498 alters, 253 aluminium, 173, 174, 176 aluminum, 244 ambient air, 24, 150, 246, 371 amines, 468 amino acid, 468, 477, 479, 486 amino acids, 468, 477, 479, 486 amplitude, 251 anaerobic bacteria, 174, 193, 197, 209, 222, 226 animals, vii, 362, 374 annual rate, xiii, 517, 519, 520, 523, 524, 525 ANOVA, 312, 313, 470 antagonism, 167, 185, 196, 197, 198 anthocyanin, 326, 349, 355, 359 antibiotic, 230 antibiotic resistance, 230 antioxidant, 283, 308 appendix, 402, 408, 416, 421 arginine, 216 argon, 373 arithmetic, 507 aromatic compounds, 468, 480 Arrhenius equation, 133 ascorbic acid, 143, 285, 291, 311, 315, 316, 317, 330, 343 ASEAN, 298 ash, xi, 41, 155, 372, 389, 453, 455, 456, 457, 458, 459, 461, 464, 465, 466, 486, 487

532

Index

Asia, xiii, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 528 aspiration, 382 assessment, viii, 165, 167, 177, 216 assets, 287, 297 assignment, 132 assumptions, 66, 72, 81, 112, 123 atmospheric pressure, 14, 25, 27, 63, 311, 342, 344, 373, 375, 388, 392, 486, 490, 495 atomic emission spectrometry, 315 atoms, 70, 71, 75, 375 attacks, xii, 483, 485 attention, 245, 309, 343, 487 Australia, 301, 394, 395, 396 authority, 380 automation, 298, 490 availability, x, 279, 339, 340, 373 averaging, 8, 9, 37, 79, 118, 119, 121 awareness, 289

B Bacillus subtilis, 168 bacteria, viii, 165, 166, 167, 168, 170, 172, 175, 176, 177, 180, 181, 182, 184, 185, 186, 187, 188, 189, 190, 191, 192, 195, 196, 197, 198, 203, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 229, 231, 232, 234, 492, 493 bacteriocins, 178 bacteriostatic, 179 barley, 365, 484, 485, 486, 494, 495, 512 barriers, 283, 284, 285 beams, 264 beef, 43 beer, 471 beetles, 362, 364, 365, 366, 368, 369, 376, 380 behavior, 35, 70, 78, 81, 89, 123, 124, 125, 126, 135, 240, 283, 299, 405, 419, 427, 488 Beijing, 298, 394, 395, 529 beverages, 449 bias, 209 Bim, 152 binding, 484 bioactive compounds, ix, 308, 309, 310 biodiversity, 528 biological control, 371, 374 biomedical applications, 398 biopolymer, 286 blocks, 350, 392 blood, 398, 486 body fluid, 383 Boltzmann constant, 71, 155

bonding, 254, 346, 350, 353 bonds, 349 brain, 484 brain development, 484 breakdown, 286 breathing, 177, 207 breeding, 221, 255, 528 buildings, 368 bulguration, xii, 483, 484, 485, 486, 487, 488, 489, 490, 492, 493 burning, 392 buyer, 280 by-products, ix, 308, 309, 310, 325

C cabbage, 241, 255, 288, 389 calcium, 244, 265, 285, 291, 315, 334, 335, 343, 350, 357, 359, 486 calibration, 3, 253, 254 calorie, xiii, 517, 520, 523, 524, 525, 526, 527 calyx, 243, 256, 264, 282 Canada, 158, 163, 393, 394, 485 cancer, 308, 484 CAP, 232 capillary, vii, 13, 20, 27, 28, 49, 54, 58, 62, 63, 64, 66, 68, 69, 76, 107, 109, 112, 120, 122, 123, 130, 152, 155, 311, 470 carbohydrate, 41, 155 carbohydrates, 254, 265, 284, 359, 481, 487 carbon, xi, 167, 168, 175, 176, 195, 196, 209, 233, 283, 287, 291, 361, 363, 371, 373, 375, 376, 377, 378, 379, 383, 385, 388, 390, 391, 392, 393, 394, 395, 396, 512 carbon dioxide, xi, 167, 168, 175, 176, 195, 196, 209, 233, 283, 287, 291, 361, 363, 371, 373, 375, 376, 377, 378, 379, 383, 385, 388, 390, 391, 392, 393, 394, 395, 396 carboxymethyl cellulose, 285, 290, 291, 427 carcinogenicity, x, 361 cardiovascular disease, 308 Caribbean, xiii, 517, 519, 520, 521, 522, 523, 524, 525, 526, 528 carotene, 253 carotenoids, 349 carrier, 283, 470 casein, 285, 297 catalysis, 321 catalysts, 33 categorization, 14 cell, 155, 282, 343, 346, 349, 350, 353, 354, 359, 363, 378, 383, 528 cellulose, 291, 484

Index cellulose derivatives, 291 cesium, 372 channels, 499 chemical composition, 37, 318, 495 chemical engineering, vii chemical kinetics, 236, 495 chemical modeling, 231 chemical properties, 343, 487, 488, 494 chemical reactions, 341, 487 chicken, 217, 233 children, 229, 289 China, 240, 283, 298, 394, 395, 517, 528 chlorophyll, 253 cholesterol, 486, 495 chromatograms, 473 chromatography, 337, 470, 471, 479 circulation, 17, 469 classes, 252 classification, 10, 11, 244, 254 clean air, 226 cleaning, 371, 375 climate change, 528 closure, 79 clusters, 264, 498 CMC, 291, 427, 428, 429, 430, 437, 438, 439, 440 CO2, xi, 284, 287, 288, 361, 373, 375, 376, 377, 378, 379, 380, 381, 383, 385, 386, 387, 388, 390, 391, 392, 393, 394, 482 coatings, 283, 284, 285, 286, 289, 290, 291, 297, 299, 301, 302, 303, 304 cobalt, 372 cocoa, xii, 9, 395, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482 coconut, viii, 239, 241, 248, 250, 255, 265, 266, 267, 268, 271, 273, 274, 275, 296, 300, 301, 302, 304 cocoon, 363 cocoons, x, 361 coefficient of variation, 244 coffee, 480 cohesion, 25, 247 collaboration, 376, 379 collagen, 285, 321 collisions, 63, 64, 68 colon, 486 colon cancer, 486 combined effect, 107, 224 commodities, x, xi, 237, 289, 361, 374, 381, 390, 393, 395, 518 commodity, 288, 289, 302, 376, 380, 392 communication, 394 competition, 179, 180 competitive markets, x, 361 complexity, vii, 13, 78, 123, 135

533

compliance, 25 complications, 398 components, ix, xii, 2, 3, 17, 29, 38, 41, 43, 44, 46, 55, 56, 57, 76, 115, 130, 229, 254, 269, 284, 285, 308, 309, 310, 315, 316, 318, 323, 328, 341, 349, 382, 455, 460, 467, 468, 470, 471, 473, 474, 475, 476, 477, 478, 480, 482, 483 composition, ix, 40, 42, 45, 134, 167, 175, 176, 178, 180, 195, 196, 210, 228, 229, 240, 302, 308, 312, 320, 337, 342, 343, 351, 352, 356, 359, 371, 373, 466, 470, 471, 495 compounds, ix, xii, 33, 307, 308, 315, 318, 328, 330, 332, 352, 467, 468, 470, 471, 473, 476, 477, 478, 479, 480 computation, 79, 398 concentration, viii, ix, xi, 13, 29, 30, 48, 55, 62, 63, 64, 70, 72, 74, 75, 76, 78, 79, 80, 87, 92, 111, 113, 114, 122, 129, 130, 131, 132, 135, 150, 152, 154, 155, 160, 167, 176, 196, 205, 209, 216, 218, 219, 222, 253, 284, 307, 310, 311, 312, 316, 318, 321, 323, 326, 328, 332, 333, 336, 345, 346, 349, 375, 376, 393, 397, 398, 400, 408, 409, 411, 414, 422, 424, 425, 428, 439, 448, 449, 450, 451, 468, 478, 479, 481, 507, 514 condensation, 73, 97, 468, 478, 502, 507, 514 conditioning, 17, 263, 385, 454 conduction, 14, 15, 16, 30, 44, 55, 60, 61, 74, 78, 101, 109, 112 conductivity, 20, 37, 42, 43, 44, 45, 48, 51, 54, 76, 78, 95, 96, 98, 102, 109, 111, 115, 120, 123, 153, 154, 341 conductor, 42 confidence, 519, 520, 527 confidence interval, 519, 520, 527 configuration, 251, 255, 260, 264, 351 Congress, 230, 236, 298 conjunctivitis, 362 conservation, 80, 88, 89, 98, 113, 114, 115, 116, 120, 121, 518 constant rate, 59, 60, 61, 150 constipation, 484 constraints, x, 78, 361, 498 construction, 265, 267, 268, 279, 485 consumers, 246, 252, 283, 285, 289, 340, 346, 362 consumption, ix, xi, xii, 240, 242, 263, 266, 267, 268, 269, 307, 308, 310, 322, 340, 453, 456, 458, 461, 463, 465, 483, 485, 486 contaminant, 263 contaminants, x, 361 contamination, ix, xii, 171, 174, 175, 177, 178, 179, 180, 186, 195, 197, 207, 208, 214, 219, 223, 226, 227, 230, 234, 307, 312, 322, 332, 371, 374, 454, 455, 464, 465, 468, 483, 485

534

Index

continuity, 91, 92, 93 control, vii, x, 37, 80, 120, 123, 189, 190, 205, 222, 223, 225, 226, 227, 231, 245, 246, 264, 265, 266, 268, 269, 273, 284, 286, 287, 289, 339, 342, 344, 358, 361, 362, 371, 372, 373, 375, 376, 377, 378, 394, 395, 396, 486, 487, 488, 490 convergence, 132 conversion, 3, 5, 341, 507, 512 cooking, xii, 232, 233, 267, 310, 341, 483, 484, 485, 486, 487, 488, 489, 490, 492, 493, 494, 495 cooling, 24, 124, 287, 492 copper, 486 corn, viii, 125, 160, 240, 256, 269, 277, 278, 296, 299, 304, 365, 465, 485, 487, 494, 503, 512 correlation, 41, 42, 43, 49, 75, 125, 153, 169, 189, 192, 193, 194, 195, 196, 198, 208, 209, 211, 212, 223, 224, 243, 252, 313, 318, 319, 326, 405, 419 correlation coefficient, 125, 153, 196, 198, 209, 252, 405, 419 correlations, 38, 40, 42, 43, 45, 48, 49, 50, 133, 193, 208, 250, 253, 349, 398, 449 corrosion, 374 cortex, 138, 151 costs, 264, 390, 456 coupling, 100, 109, 126 covering, 363, 372 critical value, 315 criticism, x, 361, 374 crops, 288, 528 cross-validation, 254 CRP, 142, 150 crude oil, 375 crystalline, 75, 352 crystallization, 351, 512 crystals, 352 cultivation, 374 culture, 197, 526, 528 curing, 343 customers, 168 cycles, ix, 187, 193, 215, 220, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 325, 332, 333, 336, 364 Cyprus, 395, 396

D data analysis, 208 data availability, 519 data set, 5 database, xii, 467, 470 death, 214, 372, 393, 526 deaths, 526 decay, 14, 242, 357

decomposition, 178 defects, 1, 2, 185, 241, 242, 255, 256, 271, 362 deficit, 527 definition, 58, 404, 408, 409, 411, 417, 421, 422, 425, 427, 450, 500, 519 deformability, 353 deformation, x, 89, 90, 91, 92, 93, 135, 243, 245, 246, 248, 249, 250, 251, 252, 256, 328, 339, 342, 353, 357, 455, 460, 489 degradation, 286, 326, 349, 477, 478, 479, 485 dehydration, ix, x, 166, 307, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 322, 323, 326, 329, 332, 333, 334, 335, 336, 337, 339, 340, 342, 343, 344, 345, 347, 355, 356, 357, 358, 359, 360, 372, 383 demand, x, xii, xiii, 269, 286, 287, 312, 361, 517, 518, 526, 528 Denmark, 486 density, 28, 37, 38, 39, 40, 45, 48, 50, 51, 52, 54, 63, 97, 107, 109, 120, 122, 127, 128, 129, 152, 153, 155, 250, 251, 252, 254, 279, 280, 281, 282, 283, 288, 290, 341, 346, 388, 390, 449, 489 Department of Agriculture, 232, 240 dependent variable, 5, 109 deposits, 365 derivatives, xii, 91, 467, 468, 471, 479 desire, 380 desorption, 35, 74, 153 destruction, 362, 374, 378 detection, 1, 233, 251, 263 developed countries, viii, 239, 270, 519, 523, 524, 526, 528 developing countries, viii, 239, 240, 288, 519, 523, 524, 528 developmental delay, 371 deviation, 125, 154, 169, 345 dew, 24, 47, 155 diet, 229, 308, 310, 336, 340, 346 dietary fiber, 495 differential scanning, 40, 352 differential scanning calorimeter, 40 differential scanning calorimetry, 352 diffraction, 252 diffuse reflectance, 253, 303 diffusion, vii, 13, 22, 30, 49, 50, 54, 55, 56, 62, 63, 64, 65, 67, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 85, 87, 88, 89, 93, 96, 97, 98, 101, 103, 107, 108, 109, 112, 115, 116, 123, 124, 126, 130, 131, 132, 133, 134, 135, 136, 150, 154, 155, 160, 169, 236, 311, 326, 337, 341, 343, 344, 395, 429, 439, 490, 495 diffusion mechanisms, 311 diffusion process, 63, 132

Index diffusivities, 45, 46, 97, 102 diffusivity, 20, 21, 22, 37, 45, 46, 49, 54, 71, 75, 98, 107, 110, 111, 120, 122, 123, 124, 130, 131, 132, 133, 134, 154, 160, 358, 359, 448 direct measure, 50, 245 discriminant analysis, 254 disorder, 254, 304 dispersion, 15 displacement, 37, 87 distillation, 477 distilled water, 318, 354 distribution, 54, 76, 81, 118, 210, 240, 245, 279, 296, 298, 299, 340, 347, 349, 450, 455, 458, 459, 462, 494, 518 divergence, 91 diversification, 193 diversity, 14 dosing, 321, 333 drugs, 390, 394, 395 dry ice, 287, 375, 392 dry matter, 150, 154, 252, 345, 459, 487 drying, vii, viii, x, xii, 13, 14, 15, 16, 17, 18, 21, 22, 31, 34, 37, 46, 48, 50, 53, 59, 60, 61, 77, 78, 79, 81, 82, 89, 92, 94, 98, 102, 109, 111, 118, 120, 123, 124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 157, 160, 161, 205, 271, 286, 309, 334, 339, 340, 341, 342, 343, 344, 345, 346, 347, 349, 350, 351, 352, 353, 355, 356, 357, 358, 359, 360, 371, 390, 468, 469, 483, 484, 485, 487, 489, 492, 493, 497, 498 drying medium, 60 DSC, 352, 489 duration, 377, 390 dusts, 372 dynamic viscosity, 28, 63, 66, 68, 69, 76, 96, 97, 154

E E.coli, 171, 194, 236 earth, 371 East Asia, 265 eating, ix, 270, 271, 283, 307, 308, 310 economic problem, 528 education, viii, 239 Education, 495 egg, viii, 239, 241, 243, 246, 255, 256, 280, 296, 300, 363, 364, 365, 368, 374, 376, 378, 379, 383 elaboration, 309, 312, 322 elasticity, 90, 243, 244, 246, 248, 249, 250, 251, 300 elasticity modulus, 246, 250 electric field, 17 electrical conductivity, 318, 320, 330 electricity, 98, 391

535

electrodes, 17 electromagnetic, 16, 17, 114, 255, 341, 346 electromagnetic waves, 341 electrons, 372 ELISA, 363 embryo, 364 employment, viii, 239 emulsions, 285, 290, 291, 297 encapsulation, xii, 483 endosperm, 125, 365, 454, 455, 456, 458, 460, 462, 463, 487, 489 endothermic, 133 energy, vii, ix, xi, xiii, 13, 17, 26, 28, 40, 44, 47, 49, 55, 74, 76, 78, 98, 99, 100, 102, 110, 114, 115, 116, 120, 121, 133, 152, 153, 154, 157, 253, 254, 283, 307, 309, 341, 343, 372, 385, 453, 454, 455, 456, 458, 461, 462, 463, 464, 465, 470, 484, 486, 488, 489, 513, 517, 526 energy consumption, xi, 343, 453, 456, 458, 461, 463, 465, 484 England, 465, 486 entropy, 28, 54, 57, 99, 100, 153, 489 environment, x, 14, 167, 180, 186, 195, 209, 215, 216, 217, 218, 219, 220, 222, 226, 240, 284, 286, 310, 361, 371, 373 environmental conditions, 167, 186, 214, 216, 484 environmental effects, 362, 373 environmental factors, 203 enzymes, xii, 14, 33, 196, 341, 483, 485, 487, 488 epidermis, 353 equilibrium, 24, 27, 31, 32, 33, 34, 35, 36, 37, 40, 54, 55, 57, 59, 60, 81, 82, 99, 100, 119, 124, 135, 155, 311, 322, 345, 352, 498, 499, 500, 512, 514 equipment, 14, 48, 248, 289, 342, 454 Escherichia coli, 168, 169, 170, 175, 192, 193, 194, 195, 196, 197, 218, 219, 230, 232, 234 ester, 328, 472 esters, xii, 290, 328, 350, 467, 471, 473, 478, 479 estimating, 249 ethanol, 75, 328, 392 ethyl alcohol, 473, 477 ethylene, x, 284, 287, 291, 361, 472 ethylene glycol, 472 EU, 240, 485 Europe, xiii, 166, 480, 485, 517, 519, 520, 521, 522, 523, 524, 525, 526, 528 European Regional Development Fund, 356 evaporation, 24, 48, 57, 60, 61, 62, 71, 73, 78, 94, 95, 96, 97, 110, 112, 119, 121, 122, 154, 155, 156, 341, 344, 477, 478 evolution, x, 308, 316, 325, 326, 329, 330, 352, 502, 513 excitation, 302

536

Index

experimental condition, 207, 237 experimental design, 325 exporter, 269 exports, 240 exposure, 15, 16, 182, 244, 373, 378, 379, 383, 393 extraction, 142, 151, 254, 328, 350, 482 extrapolation, 378 extrusion, 484 eyes, 309, 393

F fabric, 389, 390 failure, 253, 498 family, 328 Far East, 486 farmers, 244 farms, 366, 368 fat, 41, 134, 155, 166, 177, 210, 211, 212, 213, 338, 341, 486, 487 fatty acids, 284, 290, 477 FDA, 176, 231 feces, 362 fermentation, 178, 183, 191, 218, 328, 468, 470, 479, 481 fiber content, 484 fiber optics, 253 film, 26, 60, 61, 173, 174, 279, 280, 283, 284, 285, 286, 289, 297, 304 film formation, 304 films, 176, 219, 283, 284, 285, 286, 289, 291, 297, 299, 301, 302, 303, 304 filtration, 102, 104, 154, 158, 312 financial support, 333, 356 Finland, 503 fish, 34, 176, 341, 528 fisheries, 528 fishing, 528 fitness, 519 flame, 470 flammability, 374 flavonoids, 316, 330, 335 flavor, 245, 283, 284, 289, 480 flexibility, 249, 286 flora, viii, 165, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 185, 188, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 203, 207, 208, 209, 210, 211, 213, 216, 218, 219, 221, 222, 225, 229, 236 flotation, 363 fluctuations, 207, 254 flue gas, 17, 392

fluid, xi, 20, 23, 28, 48, 52, 53, 54, 65, 105, 124, 234, 378, 397, 398, 399, 400, 402, 407, 408, 409, 410, 411, 414, 416, 420, 421, 422, 424, 425, 427, 429, 430, 431, 433, 434, 436, 437, 439, 440, 441, 443, 444, 446, 447, 448, 481 fluid extract, 481 fluidized bed, 15, 492 foils, 173 folate, xii, 483, 484 folic acid, xii, 483, 484, 486, 495 food industry, xi, xii, 14, 19, 356, 361, 483 food poisoning, 232 food processing industry, 480 food production, vii, 221, 528 food products, x, xii, 175, 176, 217, 226, 297, 337, 361, 362, 366, 367, 369, 370, 375, 382, 483, 484, 485, 486, 519 food safety, vii, x, xii, 210, 216, 361, 362, 483 forecasting, 519 fractures, 454 France, 158, 376, 380, 394, 395 free energy, 28, 29, 152, 489 freedom, x, 57, 361, 373 freezing, 287, 288, 337, 340, 343, 358, 393, 503, 513 friction, viii, 239, 247, 257, 258, 268, 296, 300, 448, 450 fructose, 209 fruits, xii, 42, 43, 157, 233, 241, 247, 251, 252, 254, 255, 258, 271, 281, 282, 283, 284, 285, 286, 287, 289, 290, 291, 297, 298, 299, 300, 302, 303, 305, 308, 309, 310, 311, 312, 313, 315, 316, 318, 325, 327, 329, 332, 333, 334, 335, 336, 338, 340, 341, 342, 343, 344, 346, 349, 350, 351, 358, 359, 381, 389, 483 fuel, 484 fumigants, x, 361, 395 fumigation, x, xi, 361, 376, 377, 379, 380, 386, 391, 395 funding, 296 fungi, 170, 171, 175, 195, 196, 198, 222, 223, 224, 226, 227, 228, 302 fungus, 363 furan, xii, 467, 468, 471, 478, 479

G gas diffusion, 70, 71, 107, 130, 134 gas phase, 24, 52, 53, 63, 73, 96, 113, 114, 115, 116, 117, 119, 120, 130 gases, 28, 30, 37, 53, 59, 64, 71, 74, 77, 158, 178, 283, 284, 310, 373 GDP, 526

Index gel, ix, 127, 287, 308, 309, 310, 311, 312, 315, 318, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335 gel formation, 321 gelation, xii, 483, 492 gender, 526 generation, ix, 80, 81, 176, 179, 308, 312, 323, 324, 332, 333, 353, 468, 492, 493, 502 genotype, 468 geometrical parameters, 65 Georgia, 159 Germany, 380, 381, 382, 393, 395, 457, 493, 503 germination, 371 glass, 351, 352, 356, 359, 368, 470 glass transition, 351, 352, 356, 359 glass transition temperature, 351, 352, 359 glucose, 217, 265, 358, 486, 494 glucose tolerance, 494 glucoside, 316, 326, 327, 349 glycerol, 285, 291 glycol, 291 glycoside, 321 goals, 478 government, x, 361 grading, viii, 239, 241, 242, 298 grain dust, 365 grains, 34, 368, 378, 382, 393, 458, 465, 466, 484, 503, 505, 506, 508, 511, 513 granules, 389 graph, 249 gravitational force, 66, 68, 114, 264 gravity, 62, 63, 66, 68, 77, 97, 155, 257, 263, 264, 268, 392 groups, viii, 20, 21, 78, 127, 165, 178, 193, 198, 208, 224, 244, 254, 349, 350, 362, 378, 473, 476 growth, viii, 33, 165, 166, 167, 168, 169, 170, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 185, 187, 190, 191, 192, 195, 197, 198, 208, 209, 210, 211, 213, 214, 215, 216, 218, 219, 220, 221, 222, 224, 225, 226, 229, 230, 231, 232, 233, 234, 235, 236, 237, 253, 286, 287, 289, 300, 362, 371, 372, 499, 518, 523, 524, 526, 528 growth rate, 362 Guangzhou, 517

H happiness, viii, 239 harvesting, 241, 245, 246, 286, 362 hazards, x, 361, 393 HDPE, 279 health, 309, 335, 336, 362, 374, 484, 526, 528 heart disease, 486

537

heat, 14, 15, 16, 17, 22, 23, 30, 32, 37, 40, 41, 42, 44, 45, 46, 47, 48, 55, 60, 61, 62, 63, 74, 76, 77, 78, 98, 99, 100, 101, 102, 105, 106, 109, 111, 112, 113, 114, 115, 124, 126, 152, 153, 154, 157, 287, 310, 341, 349, 356, 364, 371, 376, 382, 385, 387, 392, 398, 485, 488, 512, 513 heat capacity, 32, 98 heat conductivity, 74 heat transfer, 16, 22, 23, 48, 60, 61, 62, 78, 100, 101, 105, 109, 111, 112, 113, 152, 153, 154, 341 heating, x, xii, 14, 16, 17, 33, 60, 153, 217, 315, 339, 341, 350, 371, 467 height, 150, 243, 266, 280, 281, 319, 390, 411, 422, 425, 437, 441 helium, 38, 470 hemisphere, 128 heterogeneity, 79 high density polyethylene, 279 high fat, 217 higher quality, 310 hip, 148, 151, 252 homogeneity, 2, 248 hormone, 284 House, 300, 394, 395, 449 hue, 325, 326, 327, 347, 349 human welfare, 518 humidity, xii, 23, 31, 33, 34, 36, 37, 47, 60, 78, 123, 126, 127, 150, 154, 155, 209, 285, 286, 287, 288, 299, 302, 322, 342, 371, 467, 468, 469, 471, 473, 476, 477, 478, 479, 498, 514 Hungary, 162 hybrid, 18 hydrogen, x, 205, 254, 361 hydrolysis, 321, 331, 487 hydrostatic stress, 28 hydroxide, 137 hygiene, 289

I identification, 255, 328, 364, 393, 480 illumination, 3, 254 image analysis, 10, 11 images, 3, 11, 255 imaging, 1, 10 imbalances, 241 immersion, 342 immunity, 526 impact energy, 250, 280, 283 implementation, vii, 168, 170, 171, 221 impregnation, x, 333, 334, 339, 342, 347, 352, 353, 355, 357, 358 incidence, ix, 240, 242, 253

538

Index

inclusion, 108, 129 income, viii, 239, 240, 268, 270 independent variable, 5 India, 397 indication, 6 indicators, 176, 178 indices, xii, 26, 244, 251, 467, 470, 471, 526 indirect measure, 245 Indonesia, 251, 299, 302 industrial application, 2, 309 industrial production, 1, 10, 197 industry, xi, 362, 371, 456, 461, 495 inequality, 100 infection, 179, 180, 181, 185, 191, 213 infestations, 363, 364, 371 infinite, 131 infrared spectroscopy, 254 inhibition, 175, 182, 192, 197, 209, 218, 222, 224, 226, 231 inhibitor, 209 initiation, 231 inoculation, 167, 218, 232, 234 inoculum, 180, 181, 182, 183, 185, 186, 187, 210, 218, 219 input, 5, 502 insecticide, 394 insects, 362, 363, 364, 366, 368, 371, 372, 373, 374, 375, 376, 377, 378, 383, 384, 393, 394, 395, 484 insight, 518 instability, 168 instruments, 2, 8, 9, 10 insulation, 28 integration, 81, 403, 404, 410, 417, 418, 423, 448 integrity, 283, 284, 350 intensity, 48, 170, 186, 209, 216, 479, 487, 488, 490 interaction, 65, 75, 133, 179, 186, 193, 197, 199, 213, 245, 251, 262, 286, 310, 341, 346, 350 interactions, viii, 28, 75, 165, 174, 179, 180, 181, 185, 186, 187, 192, 193, 194, 195, 196, 197, 198, 199, 203, 204, 205, 206, 208, 209, 224, 229, 341, 480, 481 interface, 25, 26, 57, 72, 117, 230, 344 interference, 359 interpretation, 198, 254, 299 interval, 520 intestine, 226 inventions, ix, 240, 296 inversion, 50 investment, 241, 385, 388, 390, 391, 456 iodine, 489, 494 ionic conduction, 341 ions, 54, 167, 205, 321, 341 IR, 254

Iran, 485 Iraq, 485 iron, 486 irradiation, 371 isotherms, xii, 36, 157, 160, 497, 498, 503, 512, 513 isotonic solution, 344 Israel, 485 iteration, 427 Ivory Coast, xii, 467, 469

J Japan, 240, 244, 301, 304 Jordan, 195, 232

K ketones, xii, 467, 471, 473, 477, 478, 479 killing, 371 kinetic constants, 498, 514, 515 kinetic model, 481, 489 kinetic parameters, 209 kinetics, ix, 160, 161, 172, 173, 217, 230, 231, 307, 311, 313, 337, 341, 342, 344, 345, 356, 357, 358, 359, 360, 494, 512, 513 Korea, 230, 298

L LAB (Lactic Acid Bacteria), viii, 165 labor, ix, 240, 265, 267, 374, 526 labor productivity, 526 labour, 271 lactic acid, viii, 165, 166, 167, 168, 169, 170, 172, 173, 176, 178, 179, 180, 181, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 197, 198, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218, 219, 221, 222, 224, 225, 226, 227, 229 lactones, xii, 467, 471, 479 lactose, 285, 512 lamella, 350, 351 laminar, xi, 68, 279, 280, 397, 398, 399, 408, 414, 421, 427, 447, 450, 451 language, 214 larva, 363, 364, 374, 375, 383, 485 larvae,, x, 361, 376 Latin America, 166 laws, 113, 114 leaching, 494 leaks, 393 learning, 5 Lebanon, 485

Index Lepidoptera, 395 life cycle, 362 light beam, 252 light scattering, 254, 489, 494 limitation, 309 linear model, 42, 193, 219, 519 linkage, 269 lipids, 283, 284, 285, 297, 477 liquid phase, x, 47, 50, 53, 59, 72, 86, 87, 88, 91, 93, 97, 100, 113, 115, 117, 119, 339, 342, 343, 344, 345, 349, 351, 352, 354, 355 liquids, 25, 28, 287, 310, 342, 373, 383, 392 listening, 251 Listeria monocytogenes, 167, 170, 176, 177, 178, 196, 230, 231, 232, 234, 237 livestock, 518 location, 287 low density polyethylene, 279 low temperatures, 286, 383 luminosity, 325, 347, 355 lying, 427

M machinery, vii, viii, 239, 240, 255, 264, 270, 271, 296, 362, 371 magnesium, 315, 334, 374, 486 Maillard reaction, 33, 468, 476, 478, 481 malaria, 526 malathion, x, 361, 371, 373 malnutrition, 526, 528 management, ix, x, 279, 281, 286, 288, 307, 309, 312, 334, 339 manganese, 486 manipulation, 343 mantle, 149 manufacturer, 321, 391 manufacturing, 236, 310, 336, 357, 375, 482 mapping, 85 market, x, 2, 166, 168, 174, 219, 240, 246, 270, 271, 281, 283, 310, 339, 340, 361 marketing, 255, 271, 285, 288, 289 markets, 240, 242, 255, 256, 268, 270, 279, 374 mass spectrometry, 470 mass transfer process, 63, 77 mastitis, 217 material surface, 10, 60, 61, 89 matrix, 51, 91, 202, 284, 325, 326, 327, 328, 330, 346, 353, 354 maturation, 479, 487, 494 maturation process, 479 maximum sorption, 32, 499, 514 meals, 341

539

meanings, 22 measurement, 1, 2, 3, 6, 8, 9, 10, 42, 132, 243, 245, 247, 248, 249, 250, 251, 252, 261, 297, 304, 305, 466 measures, vii, 2, 5, 233, 374, 385 meat, 42, 129, 141, 151, 170, 176, 191, 231, 341, 518 mechanical properties, viii, 239, 243, 245, 248, 251, 256, 285, 286, 296, 300, 302, 303, 304, 325, 327, 342, 343, 346, 349, 458 media, 17, 48, 52, 79, 109, 134, 157, 160, 232 membranes, 310, 450 men, 495 metabolism, 177, 178, 179, 180, 191, 207, 210, 218, 221, 231, 284, 375, 484 metabolites, 196, 198 metallurgy, 375 metals, 16 methanol, 316, 478 methylcellulose, 285, 302, 303 microcrystalline cellulose, 286, 291 microorganism, 167 microscope, 363, 368 microstructure, 79, 359 microwave heating, 341 microwaves, x, 339, 341, 349, 355 Middle East, 485 middle lamella, 346, 349, 350, 354 migration, 77, 90, 326, 481, 490, 495 milk, 43, 166, 179, 180, 181, 182, 183, 188, 191, 197, 217, 219, 230, 231, 232, 234, 237, 267, 285, 340, 487 minerals, ix, 265, 307, 308, 315, 316, 317, 330, 343, 346, 485, 486 Ministry of Education, 356 mites, 362, 363, 371, 393, 394 mixing, 70, 281, 310, 321, 333, 371, 492 mobility, 33, 75, 341, 344, 352 modeling, vii, xi, xii, 3, 13, 21, 46, 78, 127, 230, 234, 251, 359, 397, 487, 497, 498 models, vii, viii, xii, 3, 5, 13, 34, 35, 44, 45, 50, 52, 54, 59, 77, 78, 98, 123, 124, 125, 126, 127, 128, 129, 135, 165, 169, 178, 179, 193, 195, 196, 198, 202, 203, 213, 214, 219, 225, 230, 236, 237, 253, 264, 344, 489, 497, 498, 499, 500, 502, 503, 507, 508, 511, 512 modules, xi, 397, 398, 447 modulus, 66, 90, 152, 243, 244, 246, 248, 249, 250, 257, 300 moisture capacity, 122 moisture content, xii, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 45, 46, 48, 50, 59, 60, 61, 62, 74, 79, 81, 82, 87, 95, 97, 98, 101, 103, 107, 111, 112,

540

Index

123, 124, 126, 127, 128, 129, 130, 132, 133, 134, 135, 150, 153, 230, 345, 365, 368, 390, 483, 490, 502, 512, 514 molar volume, 57, 58, 59, 70, 75, 155 mold, xii, 286, 483, 485 mole, 23, 29, 76 molecular mass, 25, 64, 70, 71, 75, 95, 153 molecular mobility, 352 molecular weight, 67, 286, 351, 352 molecules, 16, 17, 24, 25, 63, 64, 65, 68, 71, 75, 76, 133, 254, 286, 341, 344, 468, 498, 499, 507, 515 momentum, 5, 22, 63, 66, 68, 89, 112, 114, 120 monolayer, 35, 155, 469, 499, 514 monoterpenes, xii, 467, 471, 479 morning, 241, 255 mortality, 371, 372, 376, 378, 379, 388, 390, 395 mortality rate, 390 mothers, 484 motion, 70, 76, 226, 399, 408, 410, 414, 421, 424 movement, 16, 17, 55, 63, 75, 78, 248, 250, 287, 492 multidimensional, 334 multiple regression, 193, 318 multiple regression analysis, 193, 318 multiplication, 85, 108, 109 multivariate calibration, 254 MWD, 342, 344, 345, 348, 349, 352, 353, 355

N NaCl, 138, 160, 209, 291, 343 nanotubes, 513 narcotic, 378 naringin, 316, 330 National Research Council, 298, 304 natural food, 340 natural gas, 392 Navier-Stokes equation, 66, 68 near infrared spectroscopy, 301, 304 negative relation, 252 neglect, 123 Netherlands, 160, 161 network, vii, 5, 6, 11, 13, 73, 78, 79, 179, 284 neural network, 11, 252, 298, 304 neural networks, 11, 252, 298 neurons, 5 NIR, 254, 363 NIR spectra, 254 nitrates, 209 nitrogen, 171, 175, 176, 196, 373, 395, 396, 468, 470, 503 nitrogen compounds, 468, 471 NMR, 363 nodes, 5, 20

noise, 254, 388 nonequilibrium, 98, 99 North America, 166 nutraceutical, 286 nutrients, 229, 245, 308, 310, 312, 315, 332, 341, 485, 528 nutrition, xii, 179, 270, 309, 484, 517, 518, 519, 526, 528 nutritional deficiencies, 270

O observations, 53, 94, 125, 171, 191, 195, 198, 210, 217 Oceania, xiii, 517, 519, 520, 521, 522, 523, 524, 525, 526, 528 ODS, 325 OECD, 280 oil, 17, 142, 151, 283, 290, 291 olive oil, 137 one dimension, 93, 111 open markets, 242 operator, 245, 266, 268, 269 optical properties, 298, 346 optimization, xii, 5, 333, 497, 498, 513 organic food, xi, 362 organic solvent, 75 organic solvents, 75 organism, 168, 170, 171, 179, 180, 188, 192, 193, 194, 195, 196, 198, 207, 208, 209, 216, 222, 223, 315, 322, 363, 487 organization, 343 orientation, 243, 246, 247, 248, 250, 258, 286 oscillation, 252 osmosis, 312, 334, 336, 398, 449, 450 osmotic pressure, 309, 398, 450 output, 5, 455, 458, 459, 462 oxalate, 350, 351, 355 oxidation, 326, 477 oxygen, 176, 195, 209, 217, 283, 285, 299, 304, 312, 373, 375, 393 ozone, x, 361, 373

P packaging, vii, viii, ix, xii, 165, 167, 170, 171, 173, 174, 175, 176, 177, 178, 179, 180, 185, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 203, 205, 206, 207, 208, 209, 210, 213, 214, 215, 217, 219, 220, 223, 224, 226, 227, 228, 229, 231, 233, 234, 237, 240, 241, 242, 243, 245, 255, 279, 280, 281,

Index 282, 283, 286, 287, 288, 290, 292, 296, 298, 303, 305, 346, 374, 375, 376, 385, 389, 391, 497, 498 PAN, 233 parameter, xi, 52, 54, 67, 68, 69, 81, 124, 130, 135, 152, 153, 203, 243, 264, 345, 397, 398, 401, 402, 404, 406, 407, 409, 411, 415, 416, 417, 419, 420, 422, 425, 428, 432, 435, 438, 442, 445, 447, 448, 451, 498, 499, 500, 502, 507, 511, 515 parasites, 363 parents, 502 partial differential equations, 106, 132 partial least squares regression, 253 particles, xi, 15, 362, 364, 453, 454, 455, 456, 458, 460, 463, 465 partition, 267, 282 passive, 158 pasta, 341, 485 pasteurization, 234 patents, 309 pathogens, 196, 362, 374 pathways, 53 payback period, 269 PCA, 254 PCR, 253 Peclet number, 77, 153, 429, 431, 432, 435, 439, 441, 442, 445, 448 pedicel, 265 perception, 289, 480 permeability, 20, 51, 52, 53, 67, 68, 69, 77, 78, 96, 97, 120, 122, 153, 283, 286, 299, 302, 304 permeation, xi, 130, 337, 397, 398, 405, 407, 409, 410, 412, 418, 420, 423, 425, 428, 429, 430, 431, 432, 435, 438, 439, 440, 441, 445, 447 pesticide, x, 361, 374, 528 pests, x, 361, 364, 365, 366, 368, 371, 372, 373, 374, 378, 379, 395 PET, 323 pH, 177, 191, 195, 209, 213, 215, 216, 217, 219, 220, 230, 231, 232, 315, 316, 321, 330, 375, 468, 480 phenol, 331 phosphorus, 265, 315, 334, 486 physical and mechanical properties, 245, 246 physical chemistry, 500 physical properties, viii, 25, 46, 50, 51, 118, 126, 133, 239, 240, 241, 243, 255, 296, 318, 346, 488, 489 physico-chemical properties, 185, 481 physics, 112 physiology, 178, 210, 343 pigments, 346, 349 pitch, 251 planning, 288

541

plants, vii, viii, 172, 191, 226, 229, 234, 239, 288, 392, 485 plasma, 495 plasticizer, 286 plastics, 175, 247, 279, 280, 375 pneumonia, 362 poison, 374 Poisson ratio, 90 Poland, 157, 165, 166, 176, 470, 497 polarization, 16, 449 pollination, 263 pollution, x, 361, 374 polydimethylsiloxane, 470 polymer, 283, 398, 481 polymerization, 468 polymers, 349, 350, 499 polypropylene, 279, 337 polystyrene, 173, 279, 280, 282, 295, 321 poor, 243, 254, 285, 367, 486, 490 population, viii, 165, 170, 172, 173, 181, 182, 183, 184, 185, 186, 187, 189, 190, 191, 192, 193, 194, 195, 197, 198, 203, 209, 210, 211, 213, 214, 215, 216, 217, 218, 220, 222, 224, 225, 226, 229, 502, 518 population growth, 173, 216, 217 population size, 170, 191 porosity, 32, 39, 43, 50, 51, 52, 54, 65, 71, 78, 98, 104, 126, 129, 135, 155 porous materials, 20, 21, 54, 62, 64, 65, 73, 78 porous media, 20, 79, 112, 158 porous space, 108 potassium, 137, 209, 291, 315, 486 potato, 8, 9, 10, 43, 127, 128, 129, 249, 285, 297, 358, 498, 513 potential energy, 55 poultry, 231, 341 poverty, 518, 519, 528 poverty reduction, 518 power, x, xi, 17, 50, 63, 134, 150, 206, 250, 252, 264, 265, 267, 269, 322, 339, 342, 344, 397, 398, 461 prediction, 42, 43, 44, 45, 46, 49, 75, 124, 127, 169, 180, 203, 209, 214, 216, 237, 253, 254, 304, 449, 487 prediction models, 203 pregnancy, 484 pressure, xi, 17, 21, 23, 24, 26, 27, 28, 29, 30, 32, 33, 36, 47, 48, 51, 53, 54, 58, 59, 60, 62, 63, 64, 66, 68, 69, 70, 71, 72, 73, 95, 97, 98, 99, 101, 102, 104, 106, 107, 109, 110, 111, 114, 116, 120, 122, 123, 134, 150, 152, 153, 287, 311, 341, 342, 343, 344, 355, 361, 371, 375, 376, 377, 378, 379, 380, 381, 383, 384, 385, 386, 387, 388, 390, 391, 392,

542

Index

393, 394, 395, 396, 398, 406, 407, 419, 420, 448, 450, 466, 469, 486, 490, 491, 492, 503 prevention, 308, 337 PRI, 526 prices, 391 principal component analysis, 254 principal component regression, 253 prior knowledge, 5 probability, 63, 64, 154, 178, 219, 220, 234, 258, 502 probability distribution, 234 probe, 244, 245, 246, 250 process control, 1, 2 processing variables, 487 producers, 233, 373 production, vii, viii, 99, 165, 166, 168, 169, 172, 173, 174, 175, 180, 181, 182, 185, 186, 191, 195, 196, 216, 217, 218, 220, 221, 222, 226, 227, 231, 232, 234, 237, 240, 241, 246, 251, 263, 265, 269, 270, 271, 286, 287, 334, 335, 340, 341, 363, 373, 449, 456, 459, 484, 485, 487, 494, 495, 518, 528 production technology, 166, 227, 240 profitability, viii, 239 program, viii, 165, 195, 203, 207, 214, 215, 216, 221, 228, 229, 263, 494, 528 propagation, 5 prophylactic, 374 protective factors, 333 proteins, xii, 167, 196, 284, 285, 297, 299, 517, 519 proteolysis, 178 proteolytic enzyme, 321 prototype, 264, 265 Pseudomonas aeruginosa, 218 pulse, 311, 344 pumps, 341 pupa, 363, 364, 375, 383 pure water, 29, 33, 59 purification, xi, 453, 456, 457, 460, 461 PVC, 280 pyrazines, xii, 467, 468, 471, 473, 476, 477, 479, 480, 481 pyrolysis, 470

Q quality assurance, ix, 240 quality control, 2 quality improvement, 309 quality loss, x, 283, 361, 362, 484

R race, 526

radiation, xii, 14, 16, 60, 78, 114, 150, 221, 254, 346, 372, 483 Radiation, 63, 302 radio, 17 radiography, 363 radius, 27, 51, 64, 65, 66, 67, 68, 69, 75, 76, 82, 127, 153, 250, 319, 409, 427 random walk, 76 range, xiii, 2, 5, 32, 42, 43, 50, 53, 75, 77, 123, 125, 126, 131, 185, 251, 253, 254, 258, 333, 350, 352, 365, 368, 371, 383, 398, 405, 456, 457, 458, 459, 486, 487, 488, 503, 507, 517, 526, 528 raw materials, xii, 340, 381, 483, 492, 493 reactants, 33 reaction rate, 33 reactivity, 133 reading, 248 real income, 518 reality, 246, 248, 454 reasoning, 77 receptors, 2 reciprocal interactions, 185, 203 recognition, 240 recovery, 375, 381, 390, 454 recycling, 385, 386, 390 redistribution, 101 reducing sugars, 468 reduction, viii, x, xi, 108, 167, 170, 178, 183, 185, 186, 187, 192, 195, 197, 198, 213, 215, 216, 218, 221, 222, 239, 243, 253, 254, 281, 285, 339, 342, 355, 374, 376, 453, 454, 455, 456, 458, 460, 461, 462, 463, 465 reflectance spectra, 254, 347 reflection, 252, 253 refractive index, 347 regional problem, 518 regression, 70, 132, 191, 194, 208, 220, 243, 250, 252, 253 regression analysis, 70, 132, 220, 252 regression equation, 243, 250 regulations, 226 regulators, 385 rehydration, x, 339, 341, 349, 354, 355, 356, 359 relationship, 5, 31, 35, 123, 126, 176, 199, 220, 246, 250, 269, 276, 277, 412, 426, 427, 431, 437, 441, 461, 463, 468, 473 relationships, 5, 198, 203, 211, 219, 220, 224, 269, 319, 358 relaxation, 91, 152, 341, 357 relaxation model, 91, 152 relaxation times, 341 rent, 390 repackaging, 280

Index repair, 210 repetitions, 2 replacement, 251, 267 replication, 250 representative samples, 251 reproduction, 371 reproductive organs, 364 reputation, 374 residues, x, 338, 361, 373, 374 resins, 284, 312 resistance, vii, x, xii, 13, 51, 55, 61, 73, 78, 107, 108, 124, 155, 215, 216, 250, 279, 289, 290, 302, 327, 339, 342, 344, 346, 353, 354, 355, 361, 395, 483, 484, 487 resolution, 245, 254 resources, viii, 239 respiration, 284, 286, 287, 288, 291, 383 respiratory, 287, 393 retail, 242, 279, 280, 281, 286, 303 retention, x, xii, 34, 94, 241, 339, 346, 354, 355, 401, 448, 467, 470, 471, 473 revenue, viii, 239 Reynolds number, xi, 77, 153, 397, 427, 429, 439, 448 rheology, xi, 397, 398, 399, 414 riboflavin, 486 rice, 125, 148, 151, 365, 378, 389, 484, 485, 493, 495, 518 rigidity, 248, 353 risk, x, 219, 220, 232, 264, 361, 374, 385, 484 risk assessment, 232 rodents, 362 rods, 175, 177, 185, 186, 195, 196, 197, 215, 218, 219 rolling, 247, 258, 300 room temperature, 63, 176, 195, 242, 244, 503 roughness, 48, 60 Royal Society, 229 rubber, 264, 265 rubbery state, 351, 353 rural areas, 486 Russia, 166

S safety, viii, xii, 165, 174, 175, 179, 192, 198, 203, 210, 214, 221, 227, 228, 229, 234, 287, 289, 305, 374, 388, 393, 483 sales, 240 salt, 134, 169, 230, 236, 341, 343, 512 salts, 309, 330, 343 sample, 8, 9, 10, 23, 34, 37, 53, 89, 166, 169, 170, 189, 205, 222, 234, 244, 246, 248, 249, 250, 251,

543

254, 257, 259, 266, 282, 309, 311, 312, 326, 330, 342, 343, 344, 346, 347, 349, 350, 352, 353, 355, 456, 457, 504 sampling, 234, 241, 242 satisfaction, 284, 286 saturation, 3, 23, 27, 28, 31, 39, 52, 53, 94, 95, 98, 104, 130, 153, 287, 325 sawdust, 267 scattered light, 253 scattering, 253, 347 Schmidt number, 153, 401, 415, 448 scientific understanding, 240 scores, 251 SEA, 216, 219, 222 seafood, 231 search, 241, 255 searching, 199 security, 309, 518 sedimentation, 234 seed, 147, 151, 245, 364 seedlings, 374 segregation, 263 selecting, 17 self-improvement, viii, 239 semi-permeable membrane, 343 senescence, 283 sensation, 1, 2 sense organs, 363 sensing, 250, 298, 301 sensitivity, 185, 195, 350 sensors, 1 separation, 57, 263, 270, 280, 283, 352, 363, 398, 454, 455, 458, 468 Serbia, 453 series, vii, 13, 44, 45, 62, 123, 124, 228, 251, 252, 454, 470, 503 severity, 288 shape, 28, 48, 54, 78, 85, 86, 118, 152, 154, 225, 248, 249, 253, 255, 264, 265, 266, 268, 279, 297, 346, 454, 465, 489, 507 shear, 28, 154, 155, 265, 319, 327, 450, 454, 455, 456, 458, 460, 462, 463, 464, 465 shear deformation, 455 shear rates, 28 sheep, 216 Sherwood number, 153, 398, 404, 405, 406, 407, 409, 410, 411, 412, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448 shortage, ix, 240, 241, 265, 270, 271 sign, 100 signals, 253

544

Index

significance level, 243, 246, 247, 470 silica, 470 silk, 269, 270, 276 similarity, 402, 404, 409, 411, 416, 417, 422, 425, 449, 450, 519 simulation, 126, 195, 213, 229, 281, 398 Singapore, 450, 503 single cap, 64 sites, 76, 242, 498, 499, 507, 512, 514, 515 skin, 143, 151, 246, 282, 363, 372, 393 smoothing, 79 smoothness, 343 social development, 519 social indicator, 518 sodium, 137, 141, 150, 230, 265, 285, 290, 315, 486 sodium hydroxide, 141 solar collectors, 14 solid matrix, 51, 342 solid phase, 20, 86, 87, 93, 113, 115, 117, 470 solubility, 33, 352, 353, 355, 378 solvents, 75 sorption, xii, 35, 36, 94, 109, 120, 130, 160, 356, 497, 498, 499, 501, 503, 506, 507, 508, 509, 510, 511, 512, 513, 514 sorption isotherms, xii, 35, 36, 109, 356, 497, 498, 503, 512, 513 sorption kinetics, 130 sorption process, 160, 499, 511 Southeast Asia, 301 soybean, 494 Spain, 298, 307, 334, 335, 336, 337, 339 species, 28, 29, 70, 71, 103, 113, 115, 116, 166, 167, 168, 170, 171, 175, 179, 180, 183, 193, 198, 219, 221, 226, 228, 288, 363, 364, 371, 374, 378, 383, 395, 484 specific gravity, 242, 245, 363 specific heat, 37, 41, 45, 47, 48, 74, 95, 96, 102, 114, 152 spectrophotometry, 315 spectroscopy, 254, 300 spectrum, 250, 254 speed, xi, 15, 64, 244, 250, 255, 264, 265, 298, 341, 396, 453, 454, 456, 457, 459, 460, 461, 462, 465 sports, 340 SPSS, 529 Sri Lanka, 479 stability, ix, x, xii, 176, 177, 178, 198, 221, 248, 302, 308, 313, 315, 320, 322, 327, 332, 333, 336, 337, 339, 343, 359, 497, 498 stabilization, 470 stages, 60, 124, 181, 227, 242, 244, 245, 253, 322, 333, 363, 374, 376, 377, 378, 379, 487

standard deviation, 125, 152, 244, 312, 316, 326, 328, 330, 345, 352, 355, 470 standardization, 246 standards, vii, xii, 13, 198, 246, 467, 470 staphylococci, viii, 165, 167, 168, 170, 172, 175, 180, 181, 185, 186, 187, 188, 193, 194, 195, 196, 197, 198, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 233 starch, xii, 34, 127, 156, 228, 285, 456, 466, 483, 486, 489, 490, 492, 493, 497, 498, 503, 506, 507, 512, 513 starch granules, 498 steel, 244, 247, 258, 263, 264, 266, 267, 268, 375 sterile, 217 sterilisation, 174 stock, 385, 386, 390, 458, 462 strain, 89, 90, 154, 217, 218, 242, 243, 346, 454, 455, 460 stratification, 376 streams, 398, 458, 460, 463, 466 strength, 53, 243, 245, 252, 279, 304, 321, 526 streptococci, 173, 181, 182, 183, 185, 197, 210 stress, 28, 89, 114, 155, 232, 246, 248, 280, 449, 450, 454, 458 stroke, 268 structural changes, 334, 343, 355, 357, 358 structural modifications, 349 substitution, 104, 121, 347 sucrose, 290, 311, 315, 318, 319, 321, 322, 323, 332, 337, 344, 352, 478 suffering, 270 sugar, 43, 150, 156, 244, 311, 315, 318, 319, 320, 328, 333, 341, 343, 345, 349, 350, 351, 353, 355, 478, 480 sulfur, xii, 467, 471, 473, 479 sulphur, 479 Sun, 14, 147, 161, 359, 517 supervision, 288 suppliers, 390 supply, xii, xiii, 217, 269, 308, 310, 484, 517, 518, 519, 520, 523, 524, 525, 526, 527, 528 surface area, 54, 55, 128, 470, 471 surface chemistry, 512 surface diffusion, 76, 77 surface layer, 341 surface structure, 349 surface tension, 25, 26, 27, 57, 76, 155 surplus, 527 survival, 167, 169, 196, 203, 214, 216, 219, 232, 234, 376 survival rate, 167, 169, 196, 203, 214, 216 susceptibility, 223, 250, 282

Index sustainability, 371 Sweden, 157 swelling, 349, 489 Switzerland, 457 symbols, 313, 348 symmetry, 156 symptoms, 176, 322, 393 synthesis, viii, 165, 216, 217, 218, 219, 220, 221, 222, 228, 231, 269, 284, 476 Syria, 485 systems, viii, xi, 3, 28, 48, 91, 165, 171, 172, 176, 193, 194, 195, 202, 205, 207, 263, 282, 295, 357, 359, 371, 374, 381, 382, 385, 390, 397, 449, 485, 492

T tanks, 380, 386, 390 tannins, 468 technology, viii, ix, x, xi, xii, 18, 157, 169, 174, 176, 177, 203, 236, 239, 240, 241, 271, 289, 296, 298, 343, 356, 358, 361, 362, 465, 481, 483, 494, 495 teeth, 267, 270 teflon, 263 TEG, 228, 229 temperature dependence, 133, 500 temperature gradient, 61, 78, 97, 98, 130 tension, 25, 59, 383 terpenes, 308 test data, 5 textiles, 375 Thailand, 239, 240, 241, 242, 269, 271, 279, 289, 298, 300, 301, 302, 303, 304, 305 theory, 45, 64, 70, 75, 98, 99, 133, 198, 246, 456, 460, 495, 502, 512, 513 thermal degradation, 321, 477 thermal energy, 37 thermal expansion, 90, 154 thermal treatment, 315 thermodynamic equilibrium, ix, 57, 99, 308, 310, 322, 323, 325 thermodynamic stability, 323 thermodynamics, 90, 98, 100 thorax, 363, 365 threat, 374 threats, x, 361 threshold, 282, 283, 341, 468, 526, 528 thresholds, 255, 479 time periods, 207 tissue, 132, 252, 310, 343, 346, 347, 349, 355, 528 tobacco, 395, 396 topology, 6, 78 toxic gases, 373

545

toxic products, 373 toxic substances, x, 361, 374 toxicity, 373 toxin, 218, 219 trace elements, 528 trade, x, 207, 340, 361 training, 5, 6, 7, 289 transformation, 3, 92, 153, 220, 252 transformations, 37, 91, 178, 252 transition, 64, 65, 79, 351, 398 transitions, 64 transmission, 252, 265, 267 transparency, ix, 308, 309, 347 transpiration, 280, 286, 288 transport, vii, viii, 13, 14, 17, 20, 22, 28, 37, 52, 54, 55, 62, 63, 64, 66, 68, 73, 76, 78, 87, 97, 98, 99, 100, 107, 108, 113, 118, 119, 120, 122, 123, 126, 130, 134, 135, 153, 158, 240, 242, 280, 287, 299, 300, 310, 341, 343, 345, 356, 358, 374, 380, 450, 468 transport processes, 99 transportation, 241, 279, 280, 287 trees, 251 trend, viii, ix, 207, 239, 240, 241, 255, 269, 271, 288, 330, 432, 435, 442, 445, 456, 461, 524 tumours, 495 turbulence, 48 turgor, 346 Turkey, 1

U UK, 160, 244, 336, 359, 465 ultrasound, 252 UN, 519 uniform, 2, 11, 16, 31, 44, 55, 56, 61, 126, 247, 266, 279, 287, 341, 469 unit cost, 391 United Nations, 299 United States, 240, 334, 335, 337, 338, 485, 526 universal gas constant, 25, 30, 127, 133, 150, 153, 514 unmasking, 253 urbanization, 518 uric acid, 363 USDA, 38, 41, 44, 46, 157

V vacuum, x, 16, 174, 176, 177, 184, 185, 188, 189, 190, 191, 192, 195, 196, 197, 203, 205, 206, 207, 208, 209, 210, 214, 219, 220, 226, 311, 334, 337,

546

Index

339, 342, 343, 344, 347, 350, 352, 353, 355, 357, 358, 359, 381, 492 Valencia, 307, 334, 335, 336, 337, 339, 357 validity, 77, 498 valine, 216 values, 2, 3, 4, 5, 6, 8, 9, 25, 31, 34, 37, 41, 42, 46, 53, 61, 75, 76, 125, 127, 131, 132, 133, 134, 169, 170, 177, 195, 196, 198, 199, 200, 203, 206, 208, 209, 215, 218, 222, 229, 244, 258, 312, 313, 317, 318, 319, 326, 328, 330, 345, 346, 347, 348, 349, 352, 353, 355, 379, 405, 410, 412, 419, 423, 426, 427, 428, 434, 437, 438, 444, 458, 476, 487, 488, 489, 499, 503, 504, 505, 506, 507, 514, 519, 523 vapor, 14, 16, 21, 23, 24, 25, 26, 33, 36, 47, 50, 57, 58, 59, 62, 63, 71, 72, 73, 74, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 107, 108, 109, 110, 111, 112, 113, 115, 116, 119, 154, 156, 286, 287, 299 variability, 173, 182, 183, 185, 189, 193, 194, 195, 198, 206, 211, 312, 519 variable, 48, 178, 214, 222, 247, 457 variables, 5, 46, 81, 99, 120, 189, 199, 203, 242, 243, 247, 313, 318, 319, 335, 457, 459, 519 variance, 223, 244, 312 variation, 50, 89, 90, 121, 123, 193, 247, 250, 251, 254, 256, 257, 281, 398, 428, 429, 431, 432, 435, 438, 439, 441, 442, 445, 470 vector, 42, 70, 75, 91, 114, 117, 120, 153, 502 vegetable oil, 290, 291 vegetables, viii, xii, 34, 42, 43, 157, 216, 233, 239, 240, 241, 243, 246, 251, 252, 255, 257, 279, 283, 284, 285, 286, 287, 288, 290, 297, 299, 302, 333, 334, 335, 336, 338, 341, 344, 346, 357, 389, 483, 518 vehicles, 242 velocity, xi, 48, 55, 56, 65, 66, 68, 69, 72, 87, 89, 104, 113, 114, 115, 116, 117, 123, 150, 153, 154, 252, 342, 398, 399, 400, 401, 408, 410, 411, 414, 415, 421, 422, 424, 427, 429, 431, 434, 439, 441, 444, 448, 453, 455, 456, 460 ventilation, 279, 280, 287, 288, 393 versatility, x, 339 vessels, 385, 503 vibration, 250, 251, 252, 260, 265, 281, 282, 300, 301, 303 viscosity, 52, 66, 68, 75, 76, 122, 154, 299, 318, 319, 320, 321, 354, 402, 406, 408, 416, 419, 421, 449, 450, 451, 452, 494 vision, 1, 3, 6, 8, 9, 10, 255, 280 visual perception, 346 vitamin C, 310 vitamin E, 486 vitamins, ix, 265, 307, 308, 309, 343, 346, 485, 488, 494, 528

volatile substances, 477, 479 volatilization, 477 vulnerability, 350

W waste water, 288 wastewater, 312, 320, 487 water absorption, 456, 487, 490, 492 water diffusion, 344, 493 water evaporation, 344 water resources, 528 water sorption, xii, 157, 497, 498, 499, 501, 511, 512 water vapor, 14, 15, 23, 24, 29, 47, 50, 61, 71, 73, 97, 100, 107, 108, 121, 284, 285, 286, 287, 291, 302 wave propagation, 252 wavelengths, 2, 253 wavelet, 10, 252 weakness, 454 wear, 393 Weibull distribution, 126 weight loss, 254, 280, 291 weight ratio, 354 wells, 392 wheat, xi, 127, 285, 299, 304, 365, 453, 454, 455, 456, 458, 461, 466, 484, 485, 489, 490, 492, 494, 495, 518 wholesale, 256, 279, 280, 281, 290, 292, 298, 303, 305 wind, 368 winter, 371 withdrawal, x, 361 women, 484 wood, 20, 284, 291, 372 workers, 265, 269

Y yeast, viii, 34, 165, 166, 169, 170, 171, 172, 181, 183, 185, 187, 188, 192, 193, 195, 196, 197, 198, 203, 208, 214, 222, 223, 224, 225, 226, 227, 228, 229, 322, 492, 493 yield, 53, 246, 455, 456, 459, 460, 462, 463, 487, 528

Z zinc, 334, 486