Horticultural Reviews (Volume 20)

HORTICULTURAL REVIEWS Volume 20

Horticultural Reviews is sponsored by: American Society for Horticultural Science

Editorial Board, Volullle 20 David C. Ferree Michael S. Reid Hazel Y. Wetzstein

HORTICULTURAL REVIEWS Volume 20

edited by

Jules Janick Purdue University

John Wiley & Sons, Inc. NEW YORK / CHICHESTER / WEINHEIM / BRISBANE / SINGAPORE / TORONTO

This text is printed on acid-free paper. Copyright © 1997 by John Wiley

&

Sons, Inc.

All rights reserved. Published simultaneously in Canada. Reproduction or translation of any part of this work beyond that permitted by Section 107 or 108 of the 1976 United States Copyright Act without the permission of the copyright owner is unlawful. Requests for permission or further information should be addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012.

This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If legal advice or other expert assistance is required, the services of a competent professional person should be sought. Library of Congress Catalog Card Number: 79-642829 ISBN 0-471-18906-5 ISSN 0163-7851 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

Contents List of Contributors Dedication 1. Technologies for Nondestructive Quality Evaluation of Fruits and Vegetables Judith A. Abbott, Renfu Lu, Bruce 1. Upchurch, and Richard Stroshine I. II. III. IV. V. VI. VII.

Introduction Density Mechanical Properties Electromagnetic Properties Electrochemical Properties Statistical Methods Overview and Conclusions Literature Cited

2. Texture of Fresh Fruit F. Roger Harker, Robert J. Redgwell, Ian C. Hallett, and Shona H. Murray I. II. III. IV. V. VI. VII. VIII. IX. X.

Introduction What Is Fruit Texture? Cellular Basis of Texture Food-Mouth Interactions Consumer Awareness and Attitudes Why Measure Texture? Methods for Measuring Texture Factors That Influence Texture Texture Disorders Concluding Remarks Literature Cited

vii ix 1

2 6 9 35 91 94 95 99

121

122 123 127 146 157 159 161 187 197 201 202 v

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3. The Use of Magnetic Resonance Imaging in

Plant Science

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Miklos Faust, Paul C. Wang, and John Moos I.

II. III. 1\1. \1.

Introduction Theory of MR Imaging MRI J\rtifacts Conclusions Literature Cited

4. Postharvest Technology and Utilization of Almonds

226 227 236 255 257 259 267

Mario Schirra I.

II. III. 1\1. \1.

Introduction Kernel J\nalysis Postharvest Operations Utilization Future Prospects Literature Cited

Subject Index Cumulative Subject Index Cumulative Contributor Index

268 273 277 282 290 292 313 315 337

Contributors Judith A. Abbott, Horticultural Crops Quality Laboratory, U.S. Department of Agriculture, Agricultural Research Service, Beltsville MD 20705-2350

Gordon Carter, Department of Restorative Dentistry, University of Otago, P.O. Box 56, Dunedin New Zealand Miklos Faust, Fruit Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, Beltsville, MD 20705 Martin C. Goffinet, Cornell University, Department of Horticultural Sciences, New York State Agricultural Experiment Station, Geneva NY 14456

Ian C. Hallett, The Horticulture and Food Research Institute of New Zealand, Mt. Albert Research Centre, Private Bag 92 169, Auckland New Zealand F. Roger Harker, The Horticulture and Food Research Institute of New Zealand, Mt. Albert Research Centre, Private Bag 92 169, Auckland New Zealand Renfu Lu, Instrumentation and Sensing Laboratory, U.S. Department of Agriculture, Agricultural Research Service, Beltsville MD 20705-2350 John Maas, Fruit Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, Beltsville, MD 20705 Shona H. Murray, The Horticulture and Food Research Institute of New Zealand, Mt. Albert Research Centre, Private Bag 92 169, Auckland New Zealand Robert J. Redgwell, The Horticulture and Food Research Institute of New Zealand, Mt. Albert Research Centre, Private Bag 92 169, Auckland New Zealand Mario Schirra, CNR-IFMCFSAM, Localita Palloni, Oristano Italy 09170 Richard L. Stroshine, Department of Agricultural & Biological Engineering' Purdue University, West Lafayette IN 47907-1146 Bruce L. Upchurch, Appalachian Fruit Research Laboratory, Production and Storage Research, USDA-ARS, Kearneysville WV 25430-9802 Paul C. Wang, Department of Radiology, Howard University Hospital, Washington D.C. 20060

vii

Charlotte S. Pratt

Dedication: Charlotte S. Pratt Charlotte S. Pratt, an extraordinary and dedicated woman, served 30 years as the de facto fruit-plant anatomist and cytologist in the Department ofPomology and Viticulture (now Department of Horticultural Sciences) at Cornell University's New York State Agricultural Experiment Station in Geneva, New York. Throughout her career, Charlotte, through self-directed and interdisciplinary research, sought solutions to practical problems of fruit-plant growth and development from the perspective of careful anatomical and morphological study. Although she retired from active duty as senior research associate in 1981, she has continued to write critical reviews on fruit-plant anatomy, to author or co-author articles, and to serve as a volunteer in the Experiment Station's library. Charlotte Serena Pratt was born in 1920 in Winsted, Connecticut. She attended Radcliffe College for two years before transferring, against parental wishes, to Cornell University, from which she received her B.A. in biological sciences in 1941. At Cornell her mind was opened to world-class research and teaching programs in the plant sciences and to an exciting mix of a diverse student body and noted professorial staff. She went on to earn a M.A. in botany at Smith College in 1944, while serving there as a teaching fellow. From 1944 to 1951, she worked at Harvard University in the program of Ralph Wetmore, a leader in the emerging field of plant morphogenesis, especially in the area of plant growth regulator effects. Charlotte arrived in Geneva, New York, in 1951 (and resides there today) to work as a research assistant in John Einset's program in fruit breeding and genetics. Over the years, she was promoted to research associate and senior research associate. Of major significance to horticulture, the position offered the possibility to develop both independent and original research as well as to collaborate with Cornell faculty at Geneva and Ithaca. Charlotte more than fulfilled that potential and has received worldwide recognition of her work, while also producing over 60 publications in the most respected botanical and horticultural journals. Her first duties at Geneva, chromosome counts in the grape, apple, and small-fruits breeding programs, followed on the work of Einset and Barbara Imhofe Lamb. One of the major achievements of these studies was ix

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the analysis of nonreduction during apple gametogenesis and its usefulness in the breeding of high-quality progenies. The selection of triploid seedlings soon after seed germination resulted in such varieties as 'Spijon' and 'Jonagold' apple, now one of the most important cultivars in Europe. Charlotte's experience and methodologies soon expanded to the effects of shortwave irradiation of plant cells and meristems. Many of her efforts were directed toward understanding the effects of irradiation on ploidy level in several temperate fruit crops and irradiation's usefulness in development of sports and genetic chimeras. This included extensive studies of the histogenesis of organs and tissues from the various cell layers within the shoot apex of irradiated plants, followed by studies ofradiation injury to the shoot apex and other tissues. This work led to many publications, including an overview of somatic selection and chimeras in the book Methods in Fruit Breeding (1983). Charlotte has been committed to relating her laboratory studies in histology and cytology to the solution of practical questions concerning the growth and productivity of crop plants. She published on many aspects of grapevine anatomy and development, especially those concerning nodal architecture, bud structure with respect to yield, and the relation of anatomy to physiology and cultural practices. At retirement she was still actively engaged in studies of vine cold injury, sulfur and ozone injury to leaves, grapevine powdery mildew injury and resistance breeding, reproductive systems, chromosome studies in several fruit crops, genic sports, and a variety of other structural investigations. Charlotte also contributed to collaborative research on the effects of natural and applied growth regulators in fruit development, fruit-set relations, the seedless genetic trait in grapes, ovule abortion, and the relation of seeds to fruit set and growth. Notable studies with Einset and also with Nelson Shaulis were published on grape fruit set and seedlessness. Other studies with Loyd Powell, Louis Edgerton, and others resulted in an understanding of the interrelationships among flower development, pollination, fertilization, ovule and embryo development, and activity of endogenous and applied growth regulators. In addition to these accomplishments, Charlotte has been an effective unofficial advisor to many graduate students and visiting scientists, training them in histological methods and providing discourse on questions of structure and plant development. Throughout her active career, she contributed lectures and labs to Cornell's viticulture course and also served to test botanical stains and staining protocols for the Biological Stain Commission. Charlotte has always been a bibliophile with a penchant for accumulating and indexing the world literature on the anatomy and develop-

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ment of horticultural crop plants. With this talent, she has published four remarkable and useful reviews of the literature of grape and apple anatomy and morphology. Her 1974 review of the vegetative anatomy of cultivated grapes was chosen by the American Society for Enology and Viticulture as the most outstanding scientific paper published in that society's journal. Her two meticulous reviews of the morphology and anatomy of the apple flower and fruit (1988) and tree (1990), published in Horticultural Reviews after her retirement, are examples of her dedication to her discipline and to the scientific community. Charlotte is keenly aware that readers of the anatomical literature come from diverse backgrounds and interests. She wisely has added a "glossary of terms" to many of her publications and teaching materials to facilitate understanding, to foster common usage, and to allow communication among the scientific and lay communities. She put her indexing talents to use as an associate editor of the American Society for Horticultural Science (ASHS) by helping to develop the annual and cumulative subject-author indexes for the Journal and HortScience and served as indexer of ASHS publications until retirement. In postretirement, she served as indexer of 14 volumes of the Pisum Newsletter. For her work in ASHS and for her scholarly contributions to the field of anatomy and development of horticultural crop plants, she was elected a Fellow of the ASHS in 1981, at that time only the second woman to receive that honor. More remarkable is that Charlotte received this recognition without the prop of a professorial title or doctoral degree. Charlotte loves to travel and has taken many opportunities to do so, both before and after retirement. She is a naturalist at heart and enjoys the wonder of plant communities and bird life. She continues to study and to write in retirement, and recently co-authored a chapter with .Bruce Reisch on grape breeding for the monograph, Fruit Breeding, Vol. II, Vine and Small Fruits (1996). Charlotte has been a role model for women in scientific research and, after many decades of service, she continues to serve horticulture in postretirement. Although quiet and unassuming, Charlotte is warm and friendly with a wry sense of humor. She is extremely generous of herself and her expertise. Her life has been dedicated to horticultural science and her family and we proudly dedicate this volume of Horticultural Reviews to her indomitable spirit. Martin C. Goffinet Cornell University, Department of Horticultural Sciences New York State Agricultural Experiment Station Geneva, New York 14456

1 Technologies for Nondestructive Quality Evaluation of Fruits and Vegetables Judith A. Abbott* Horticultural Crops Quality Laboratory U.S. Department of Agriculture Agricultural Research Service Beltsville, Maryland 20705-2350 Renfu Lu Instrumentation and Sensing Laboratory U.S. Department of Agriculture Agricultural Research Service Beltsville, Maryland 20705-2350 Bruce 1. Upchurch ** Production and Storage Research Appalachian Fruit Research Laboratory U.S. Department of Agriculture Agricultural Research Service Kearneysville, West Virginia 25430-9802 Richard 1. Stroshine Department of Agricultural and Biological Engineering Purdue University West Lafayette, Indiana 47907-1146 *The authors acknowledge the critical review and comments of D. R. Massie, Instrumentation Engineer, USDA; G. G. Dull, Univ. of Georgia; and K. H. Norris, USDA, retired. We also acknowledge the contributions of reviewers T. A. Campbell, J. Janick, R. Rohrbach, M. Saltveit, and J. E. Simon. Reference to a specific brand or firm name is for information only and does not constitute endorsement by the USDA over others of a similar nature not mentioned. **Present address: Union Camp Corp., Forest Resources Group, Savannah, GA 31402.

Horticultural Reviews, Volume 20, Edited by Jules Janick ISBN 0-471-18906-5 © 1997 John Wiley & Sons, Inc. 1

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I. Introduction A. Fruit and Vegetable Quality B. Objectives II. Density A. Flotation B. Fluidized-Bed Technology C. Machine Vision III. Mechanical Properties A. Quasi-Static or Dynamic Loading B. Impact C. Low-Frequency Vibrations D. Sonic Vibrations E. Ultrasonic Sensing IV. Electromagnetic Properties A. Optical Properties B. Fluorescence and Delayed Light Emission C. X-Ray and Gamma Ray D. Magnetic Resonance and Magnetic Resonance Imaging E. Dielectric and Electrical Properties V. Electrochemical Properties VI. Statistical Methods VII. Overview and Conclusions Literature Cited

I. INTRODUCTION The term quality connotes a degree of excellence of a product or its suitability for a particular use. Quality is not a single, well-defined attribute but is a human construct comprising many properties or characteristics. Quality of produce may be assessed in terms of sensory properties, nutritive values, chemical constituents, mechanical properties, or functional properties. A definition of quality useful for postharvest quality evaluation was given by Kramer and Twigg (1970): "the composite of those characteristics that differentiate individual units of a product, and have significance in determining the degree of acceptability of that unit by the buyer." The choices of what to measure, how to measure it, and what values are acceptable are determined by the person or institution requiring the measurement, with consideration of the intended use of the product and of the measurement, available technology, economics, and often tradition. A. Fruit and Vegetable Quality Individual quality characteristics can and should be measured separately (Kramer and Twigg 1970; Ballinger et al. 1978). Any work per-

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formed on a food item that differentiates it from the mass of the product adds something to its quality. Therefore, operations such as sorting by size, color, or defects add to quality and therefore to value. Some authors question whether one can perform any postharvest operation to improve (maximize) the quality of an individual unit of a fruit or vegetable. That may be true from a physiological viewpoint. Nevertheless, appropriate sorting clearly enhances the quality of a lot of a commodity from an economic viewpoint. The quality of a fruit or vegetable is ultimately judged by the person who consumes it (Koster 1990). It is not our intent to review sensory evaluation here, but it is relevant to note that people use all of their senses to evaluate quality: sight, smell, taste, touch, and even hearing. The methods people use during initial evaluation of the quality of a fruit or vegetable are essentially nondestructive; methods used during preparation and consumption are destructive. The ultimate consumer integrates all of those sensory inputs-appearance, hand-feel, aroma, flavor, mouth-feel, and chewing sounds-into a final judgment of the quality of that fruit or vegetable. Before produce reaches the ultimate consumer, it passes through a commercial marketing chain involving harvest, storage, handling, and marketing. Judgments about the quality of the produce are made at several points along the way. To a large extent, packinghouse graders and professional inspectors judge quality using the same nondestructive sensing methods that consumers use (USDA Agricultural Marketing Service; Abbott et al. 1992). Such personnel are trained to be more consistent than untrained consumers and to weight specific quality attributes to fulfill the purpose of the inspection. Many of the quality sensors discussed in this review were developed to imitate human sensing methods, for example, reflectance measurements for color and puncture tests for firmness. Other sensors detect signals outside the limits of the human senses, such as magnetic resonance, near-infrared wavelengths, or X-rays. These latter sensors detect characteristics or constituents that are related to the physiochemical state of the product, which in turn is related to quality. In the commercial marketing system, quality decisions are made at several stages for different purposes. The methods used to make those decisions might be destructive tests made on representative samples or, preferably, nondestructive tests on the actual fruit or vegetable to be sold. Because a different purpose is involved at each decision point, it is likely that different quality attributes are critical or that different levels of the same attributes are considered acceptable at each point. The final consumer might not even notice some of the attributes considered critical at earlier decision points. Such criteria relate to functional proper-

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ties, such as resistance to injury or expected shelf life, or to marketing concerns, such as uniformity within a lot. The most common point for automation of quality evaluation is at final packing for shipment. In this review, we are concerned with sensors that are, or could potentially be, used to assess quality at packout, rather than sensors for harvest or storage decisions or for the retail trade (although the same technologies may be applicable to those, too). Commercially, destructive assessments often are made on very small samples taken from bulk lots to get a statistical indication of the overall quality of the lot. However, fruits and vegetables are notoriously variable, and the quality of individual pieces may differ greatly from the average. Additionally, sampling does not identify the undesirable or the outstanding individual pieces; only 100% sampling-sorting the entire lot-can accomplish that. Disposition and pricing are usually based on the average quality of the lot actually sold. To maximize quality of a lot, packers need to examine and make a quality judgment on every individual piece of produce within the lot and to sort out those individual pieces that are substandard. There is usually economic incentive to further classify the satisfactory produce into several grades. Size and color are mandated in grades and standards (USDA Agricultural Marketing Service), and sorting for these attributes now is largely automated. However, sorting for most other quality attributes and defects is accomplished by visual examination and manual removal. Some internal defects and some attributes related to consumer quality (such as flavor and texture) simply cannot be detected on the sorting line at this time. Human inspection is limited by the human senses; by the sensitivity, speed, endurance, and availability of inspectors; and by labor cost. Automation is needed to improve sorting accuracy, uniformity, and efficiency. Practical, commercial sorting operations require high-speed, nondestructive sensors to measure several properties or quality attributes on each piece of fruit or vegetable, a means to combine those measurements into a decision on its quality classification, and a mechanism to physically place the piece into its proper category. Some large produce-packing operations have facilities to segregate as many as 32 categories, presently based on size and color, although they do not generally use so many. Often, empirical methods developed to measure some particular quality attribute actually measure ripeness. Many physiological processes involved in ripening and senescence occur more or less simultaneously and in parallel. To the extent that the characteristic of interest and the one actually measured (for example, firmness and chlorophyll content, respectively) are directly related to ripeness, the empirical measure-

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ment may be satisfactory. However, while it might appear that measuring either one of these would suffice to estimate quality, a mutation (nonripening tomato) or abnormal growing conditions in one season (apple) may decouple the physiological processes and there will be essentially no relationship between the two characteristics. For this reason, care must be taken to ensure that sensor testing and calibration are done over as wide a range of conditions as possible and that what is really being detected is understood. In all testing, including nondestructive, it is assumed that there is a consistent and monotonic relationship between the sensor response and the amount of the constituent or attribute. This monotonic relationship is not necessarily the case for "quality." Consumers often prefer intermediate levels of a particular attribute to either very low or high levels. For example, a tomato with a very low acid content-no perceivable acidity-tastes flat, whereas one with an unusually high acid content tastes sour or sharp; neither is desirable. Apples that are too firm or too soft-tough or mealy-are unacceptable; intermediate levels provide a crisp, "just right" texture. Research continues to establish the relationships among fundamental properties, functional properties, and perceived quality. Care must be taken to ensure that the attributes measured are the appropriate ones or at least that they are very closely associated with the desired characteristic, that their relationships to quality are properly established, and that appropriate cutoff values are chosen (Lipton 1980; VangdaI1985). B. Objectives We present here a review of sensors for nondestructive assessment of quality-related attributes of fresh fruits and vegetables. We discuss sensor technologies and some techniques that are in use, under development, or being investigated for measurement of quality-related properties. We generally organized the discussion by the properties sensed and then by sensor technologies within those broad topics. Organization based strictly on the sensory or functional characteristics sensed, the physicochemical properties detected, or the methods of measurement led to redundancies. The present organization seemed to be a reasonable compromise between horticultural and engineering viewpoints. We emphasize sensors with potential for automated sorting on the packing line, but many of the sensing methods could also have applications in production, harvesting, handling, and marketing.. We have tried to be thorough, to describe the underlying principles, to cite the seminal studies, and to present the current state of research. It is not our intention to

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list manufacturers of sensors or automated equipment; our list could not be complete and, at any rate, would be out of date even before publication. We provide an extensive bibliography from which more detailed information can be obtained. Ultimately, however, the user must decide which quality attributes are important, what sensor technologies to use, and what values are acceptable. II. DENSITY Density (or specific gravity) is an important physical characteristic of horticultural products. Density of fruits and vegetables generally changes with maturation and postharvest storage. This change in density is a result of changes in cell structure (shape, size, and void space), soluble solids, moisture or water content, and the effects of damage, disease, and the like. Therefore, density may be used as an index for nondestructively assessing maturity and quality. Also, certain defects are characterized by alterations in density, such as pithiness in celery, hollow heart in potato, puffiness in tomato and cucumber, and watercore in apple. Zaltzman et al. (1987) reviewed a number of studies that related density to maturity and quality attributes of agricultural products. The current techniques may be classified into three categories: flotation, fluidized-bed technology, and machine vision-based density measurement. A. Flotation

Flotation is a simple, commonly used method to separate agricultural products from foreign materials or to sort the same product into various classes. When the product is placed in a tank of fluid having a specific gravity intermediate between the highest and lowest densities of the individuals, those with higher density will sink while those of lower density will float; thus, two density classes can be obtained. Multipleclass sorting can be achieved by using a series of tanks, each containing a fluid with a different density. Kattan et al. (1968) used this concept to sort tomatoes into five maturity classes. Multiple-class sorting may also be achieved by using a flowing channel system in which the fluid flows continuously at a specific velocity and has a density such that all of the product will float to the surface. An unsorted mixture of product introduced in the bottom of the channel will float downstream as well as toward the surface. The lower the density of the individual piece, the faster it will rise to the surface, that is, at a point nearer the release point. Thus, the product may be separated into as many density classes as

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desired by collecting the individual pieces from the stream surface at different distances from the release point. A mass flow density sorter based on this principle was reported by Gutterman (1976) for sorting fruits and vegetables. The effectiveness of this type of sorting is directly influenced by the physical characteristics of the product, such as shape, size, and surface characteristics. For an elongated product such as green bean, this sorting method may not be effective because shape and orientation significantly affect hydrodynamic properties. The flotation method was used by a number of investigators to sort sweet potato (Bryant 1942), potato (Kunkel et al. 1952), pea (Nielse et al. 1947), snap bean (Kattan and Sharp 1970), and small-sized fruits such as grape (Coleman et al. 1983), sour cherry (Kattan et al. 1969), and blueberry (Wolfe et al. 1975; Pazlaff 1980). This method has also been used for segregation of defective from sound products. Porritt et al. (1963) separated watercored and watercore-free apples using solutions of ethanol and water. Perry and Perkins (1968) developed a system to separate freeze-damaged citrus fruits based on flotation in an oil and water emulsion (hydrodynamic separation). Major difficulties with flotation segregation of fruits and vegetables are choice of a separation medium and control of the medium density. Zhao et al. (1993) proposed the concept of using air-water mixtures as separation media for quality sorting of agricultural products such that the specific gravity of the flotation system can be adjusted by controlling the flow of bubbles into the tank of water. Based on this concept, Macquarrie (1993) effectively separated apples with moderate to severe watercore from those with mild or no watercore in a horizontally flowing flume with air bubbles in a gas-liquid mixture. The flotation method, though simple in implementation, generally uses liquid solutions such as brine, oil, or alcohol as the separation media. These fluids have inherent disadvantages, such as safety, environmental hazard, and possible detrimental effect on product quality. Further, the liquids tend to get contaminated during the sorting process, which changes the specific gravity, requiring periodic correction. Alternatives to the flotation method have been proposed, including fluidizedbed technology and real-time machine vision for density measurement. B. Fluidized-Bed Technology Fluidized-bed technology is widespread in the chemical, metallurgical, and mining industries, but only limited research has been done on its application to quality sorting of horticultural commodities. During the fluidized-bed process, air is forced through a bed of granular particles

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(e.g., sand) to produce enough buoyant force to counterbalance the particle weight and maintain the particles in a suspended state. As a result, the particles behave like a flowing fluid with certain physical properties. The specific gravity of the fluidized bed varies with the particle size and density and depends on the volume of air injected. Separation of product is achieved by properly adjusting the density of the fluidized bed such that it will lie between the densities of the products to be separated. The lower-density products will float to the top of the fluidized bed and the higher-density ones will sink to the bottom. Zaltzman et al. (1983,1985) developed a laboratory fluidized-bed device for separating potato tubers or flower bulbs from clods and stones in a continuous separation process. A 99.9% separation efficiency was reported. Zaltzman et al. (1987) further suggested that the concept of fluidized-bed technology presents some potential for density sorting of horticultural products. Chinnan et al. (1988) developed a computer simulation model to evaluate the potential of fluidized-bed technology for separating pecans during the cleaning operation. C. Machine Vision A combination of machine vision and automatic weighing systems has been used to determine the density of selected fruit. The volume of the fruit is estimated based on the dimensions measured from the camera's images. This method has been used for on-line density sorting of orange and tangerine (Miller et al. 1988), grapefruit (Miller and Verba 1987), and apple (Throop et al. 1989). An 82% correct classification of defective citrus fruit (e.g., freeze-damaged) was reported. Currently, a number of Florida citrus packinghouses are using the density-sorting technique with a throughput of 5 to 6 fruit/silane (Upchurch et al. 1994a). Throop et al. (1989) used this technique to estimate apple volume from top and side views. Estimated volume was highly correlated with actual volume (r = 0.98); however, only 87% of the apples were correctly classified by the weight density method. The major limitation for accurate classification of fruit densities lies in estimation of the fruit volume based on the dimensions of fruit images. Significant errors in volume estimation can be incurred if the product has irregular shapes and a large range of sizes. Miller and Verba (1987) reported a low correlation (r = 0.37) between machine vision-determined and actually measured density for 'Marsh' grapefruit. Although density sorting has some potential for quality segregation of fruits and vegetables, it is a relatively crude method compared to other techniques (e.g., mechanical measurements). While the density of the

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whole product changes in some commodities (apple), in others (celery; Saltveit and Mangrich 1996) changes in only small portions may seriously affect quality. In addition to the problems previously mentioned, there are a number of factors that may limit the use of density sorting for horticultural products. The change in density with maturity or other quality parameters for some fruits or vegetables, such as avocado (Lewis 1978), is often so small as to make it difficult to use these technologies to effectively sort the product. The variations in the fruit or vegetable density caused by some nonquality factors may be more significant than those due to the change in quality attributes. Furthermore, density may not be a proper index for indicating the maturity or quality of some fruits and vegetables. III. l\1ECHANICAL PROPERTIES

Fruits and vegetables exhibit viscoelastic behavior under mechanical loading, which not only depends on the amount of load applied but also on the rate of loading. This time-dependent mechanical property is directly related to the tissue structure, primarily including the cells, intercellular bonds, and extracellular volume (Pitt 1982). However, for practical purposes, horticultural products are often assumed to be elastic, that is, their mechanical behavior is assumed to be independent of the loading rate. Mechanical measurements have been a primary tool for evaluating textural attributes, particularly firmness, of fresh fruits and vegetables and of other foods (Kramer and Szczesniak 1973; DeMan et al. 1976; Bourne 1979; Sherman 1979). Most nondestructive mechanical methods measure elastic properties, such as modulus of elasticity, and relate them to quality attributes. Modulus of elasticity (also called Young's modulus)-a measure of the capacity of a material for taking elastic deformation-is the ratio of stress (force per unit area) to strain (relative deformation with respect to the original size or shape) and is commonly measured from the force-deformation curve for a specimen with constant cross-sectional area. A typical force-deformation curve for a cylindrical apple specimen under constant strain-rate compression is shown in Fig. 1.1. Similar curves may also be obtained for other horticultural products and for penetration of intact products by cylindrical probes. This curve can be divided into three regions. In the first region (section O-A), the force increases almost linearly with deformation (neglecting the nonlinearity of the initial part which is likely due to specimen irregularity and the imperfect contact between the specimen and loading device). When the force is released, the specimen will recover to the original shape. Hence,

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c Force

ok:':.----------------.... Deformation Fig. 1.1. The force-deformation curve for a cylindrical specimen of apple flesh under compression. (A-point of inflection, B-bioyield point, and C-rupture point).

in the first region the specimen undergoes elastic deformations and no damage occurs in the tissues. In the second region (section A-B), the slope of the curve gradually decreases as the deformation increases. A noticeable change occurs in the force-deformation curve where further increases in deformation do not result in an increase in force (point B). This point is generally referred to as the bioyield point (Mohsenin 1986) and is not usually seen in engineering materials such as metals. In the second region, the tissues start to rupture and the bioyield point is an indication of a failure in tissue microstructure. Beyond the bioyield point (the third region), the force may either continue to increase or may decrease as deformation increases. Rupture occurs at point C when the macrostructure of the specimen fails. In some force-deformation curves, the bioyield may not be distinguishable from rupture (Bourne 1965). Force-deformation curves that differ from the one shown in Fig. 1.1 are also reported for apple and other commodities. Bourne (1976a, 1980) examined a number of force-deformation curves for horticultural products and discussed their implications in texture measurement. Mohsenin (1986) provided a comprehensive review, from an engineering perspective, of the basic mechanical properties of agricultural products and their relationship to quality evaluation. Firmness of horticultural products can be measured at different force or deformation levels in all three regions of Fig. 1.1, depending on the purpose of the measurement and how the quality attributes are defined. A truly nondestructive method should limit the force or deformation

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level to the first region so that no tissue damage will be incurred during measurement. The slope of the force-deformation curve, reflecting the elasticity of the material, is often used by engineers as an index of product firmness. On the other hand, horticulturists and food technologists tend to consider the force or deformation at the bioyield point or at rupture to be a better indicator of firmness. In the texture profile analysis (Friedman et al. 1963; Breene 1975), the force-deformation characteristics in the third region of Fig. 1.1 are often more important than those in the first two regions because they simulate the destruction that occurs in bruising or eating (Szczesniak 1963; Bourne 1968). This review is confined mainly to those mechanical methods that normally do not result in deformations beyond the bioyield point, with the exception of the current industry standard puncture test and a few related testers which are destructive. We include some discussion of destructive mechanical measurements because they are so widely used in the fruit and vegetable industry and because they serve as the reference tests to which nondestructive firmness measurements are compared. It is important to understand the fundamental properties measured by both destructive reference tests and nondestructive methods, the differences between them, and the factors that can affect the tests. A large number of mechanical measurement methods have been developed for nondestructive evaluation of horticultural products; however, none is presently in common commercial use. Based on the rate (or frequency) of loading, these methods may be classified into the following categories: quasi-static or dynamic loading, impact, low-frequency vibrations, sonic vibrations, and ultrasonics. A. Quasi-Static or Dynamic Loading Quasi-static loading is the "classical" method for measuring fruit and vegetable firmness and is usually destructive. In the past several decades, a large diversity of mechanical instruments has been reported for use with horticultural products. Haller (1941) provided a comprehensive review of early research on firmness measurements beginning in 1917. Bourne (1976b, 1979) reviewed some techniques for evaluating texture of horticultural products. Despite the large variations in design, these mechanical instruments either measure force or deformation or both. The types of loading by these instruments include puncture, shearing, twisting, extrusion, crushing, compression, tension, and bending. Many of these types of loading are unsuitable for nondestructive evaluation of fruits and vegetables; however, they are frequently the reference methods against which nondestructive mechanical measurements are com-

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J. ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

pared and judged. Many instruments that use quasi-static loading are developed for simple, hand-held measurements. They are often slow or inefficient and not suitable for rapid on-line quality evaluation. In the following, we will discuss techniques according to whether the variable measured is force, deformation, or dynamic force-deformation. 1. Force. In this type of instrument, the variable measured is the force that is required to reach a certain amount of indentation or penetration into the fruit or vegetable. To achieve the measurement, the instrument system must have mechanisms to measure force and to control the indentation or penetration of the samples. Morris (1925) used a marble partially embedded in paraffin resting on a scale and measured force required to penetrate an apple. A more elaborate instrument was developed by Lewis et al. (1919) in which a cylindrical plunger was used with a lever, and the depth of penetration was controlled by electrical contact. These principles were later used by Magness and Taylor (1925) to devise a portable tester for apple, pear, and peach. The Magness-Taylor Fruit Firmness Tester (formerly called the Magness-Taylor Fruit Pressure Tester, the USDA Pressure Tester, or the Ballauf Pressure Tester) is a penetrometer that measures the maximum force required to penetrate a rounded plunger of 7.9-mm (5/16 in.) or 11.1-mm (7/16 in.) diameter into the fruit to a depth of 7.9 mm (5/16 in.). Two probes are used for different commodities and values obtained with one size cannot be readily compared to those obtained with the other size (Bourne 1965). The popular Effe-gi fruit firmness tester is a smaller variation on the Magness-Taylor tester (Abbott et al. 1976; Voisey 1977), and the Lake City Electronic Pressure Tester is yet another variation. The term Magness- Taylor firmness is used generically for the measurements made with the several variants of that penetrometer. Magness-Taylor (MT) measurements are moderately well correlated with human perception of firmness, and hence this technique has received widespread acceptance in the fruit industry for a number of horticultural commodities, such as apple, pear, peach, and cucumber. However, several studies have shown that caution is warranted in the use ofMT for determining quality. Blanpied and Blak (1977) concluded that, on average, overripe apples had lower MT values than ones that were not overripe, but the variability was so great that firmness could not be used to separate out all the overripe apples of a given cultivar. More than half of the apples that were not overripe were within the firmness range of the overripe apples. Liu and King (1978) found that consumers generally scored 'McIntosh' apples with higher MT values as being firmer, but the correlation was only 0.40. Abbott et al. (1992) com-

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13

pared the average firmness score of six professional apple inspectors to MT value over a wide range of 'Delicious' apples and obtained a correlation of 0.83; however, correlations for individual inspectors were lower. Bourne (1979) stated that, while Magness-Taylor is almost the only method used by horticulturists to measure texture of fruit, there needs to be a wider understanding of the multidimensional nature oftexture and the fact that "firmness" is only one of the group of properties that constitute texture (see also Corey 1970). That is still largely true. There have been many other studies published in the horticultural and food science literature that compare MT firmness to sensory evaluations by both consumers and trained judges for numerous fruits and vegetables, with varying levels of correlation. It is not within the scope of this paper to review those. It should be noted that prediction of sensory texture or eating quality is only one of several valid reasons to measure firmness. A number of studies reported that the measurements obtained with the MT tester tend to be influenced by both the operator and the rate of loading (Nicholas 1960; Claypool et al. 1966; Voisey 1977; Harker et al. 1996; Lehman-Salada 1996). In an effort to provide more objective measurement results, several researchers developed more elaborate loading and recording systems so the measurement could be better controlled and would not be influenced significantly by the operator. Pflug et al. (1960) devised a firmness tester that had a mechanically driven plunger and a force-deformation recording system. Fridley (1969) patented a fruit firmness tester (the D.C. Firmness Tester) that used a mechanical force gauge mounted on a movable platform. When a lever was depressed, the platform moved downward, forcing the plunger into the fruit. Claypool et al. (1966) reported that the D.C. Firmness Tester provided more objective measurements than the MT tester. Mohsenin and Gohlich (1962) developed an instrument that could be used to measure a number of mechanical properties of fruits and vegetables, and this machine was used in conjunction with a strip chart recorder to record force-displacement curves from punch tests on apple. Bourne (1965) studied the performance of the MT tester on apple by mounting only the probe in a universal testing machine which recorded the forcedeformation curve for each test. Breene et al. (1974) compared firmness measurements of cucumber made with the traditional manual MT tester to measurements made with the entire MT or only its probe mounted in a universal testing machine. They discussed the fundamental behavior of the MT tester: Maximum force did not differ significantly among the three methods, but distance to maximum force and area under the forcedeformation curve were significantly less with the probe only compared to the whole MT device in the universal testing machine because the

14

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ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

spring in the device absorbs much of the potential energy until the yield force is reached. Peleg (1974) found a large effect of loading rate in testing papayas at speeds between 20 and 100 cm/min, obtaining higher firmness values at higher speeds. Other studies in other commodities have shown varying loading-rate effects (Pflug et al. 1960; Bourne 1965; Breene et al. 1974). Studman and Yuwana (1992) described a twist testing device for measuring fruit firmness. The device consists of a blade on a spindle which is pushed into the fruit and rotated about the axis of the spindle. Fruit firmness is estimated by measuring the maximum moment (or rotational angle) required to crush the flesh. They reported that the measurement obtained with this device correlated better with soluble solids content in kiwifruit than did Effe-gi penetrometer values. However, one should note that soluble solids content is not a mechanical property so the correlation is likely due to the relationship of each attribute to ripeness. A major problem with MT-type testers is that they are destructive and therefore cannot be used for nondestructive quality evaluation. Efforts were made to develop nondestructive techniques for measuring fruit and vegetable firmness. Verner (1931) developed a firmness tester for use with stone fruits, where the fruit was squeezed between two flat surfaces or disks for a given distance. Ross (1949) reported a firmness tester which used a pneumatic system to press a rounded-end piston (4.0 mm or 5/32 in. diameter) into the sample. The penetration was limited to 0.79 mm (1/32 in.) so that no apparent bruise or injury would be caused. Schomer and Olsen (1962) reported a mechanical thumb attachment for the MT tester that would reduce the depth of penetration of the tip from 7.9 mm to 1.4 mm. Firmness readings with the mechanical thumb were comparable to those obtained with the MT tester but were less reliable, and the thumb could cause small bruises on the apples (Mattus 1965). An examination of force-deformation curves for the penetration of the MT tip into an apple (Bourne 1965) readily explains the lack of agreement. The traditional MT measures maximum force to penetrate to 7.9 mm; but the maximum may occur at a very shallow penetration or at the maximum depth. Only for apples where the MT maximum occurs near the surface will the mechanical thumb and similar measurement devices give high correlations with the traditional MT. A fruit firmness tester based on the measurement of applied force for a preset shallow penetration depth was reported by Fekete (1993). The penetration was generally limited to 0.3 mm (which did not cause bruising) and could be adjusted. The tester comprises a hand-held penetrometer, an interface, and a data logger. The ratio of stress (force per unit area) to deformation, defined as the coefficient of elasticity, was cal-

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15

culated to measure fruit firmness. Fekete (1994) measured the coefficient of elasticity for five apple cultivars, three maturity classes of tomato, and two maturity classes of apricot. Based on the analysis of the frequency distributions, Fekete concluded that this coefficient was appropriate for firmness measurement. The force-type firmness instruments are mainly used with firm fruits such as apple, pear, peach, and cucumber. Their application to softer commodities such as tomato, cherry, and berries has been less successful because the instruments are often not sensitive enough to measure the firmness of these soft products, and the rate of loading can significantly affect the firmness reading (Hamson 1952). Instruments that measure deformation under a constant load are popular for measuring firmness of soft horticultural commodities. 2. Deformation. In this type of instrument, deformation or displacement of the product is measured under a constant load for a specific loading period. A number of deformation-type instruments have been developed and are mainly used to measure firmness of soft commodities such as tomato, kiwifruit, cherry, and various berry fruits. Hamson (1952) devised an instrument for measuring tomato firmness in which a constant weight was applied through an 11.1-mm diameter flat-head plunger and the amount of compression was read. This device could objectively measure firmness; however, significant differences in readings within a fruit were observed due to heterogeneity of the fruit structure. To minimize the effect of loading position on firmness measurement, Kattan (1957) designed a tomato firmness tester based on a multipoint-compression principle. The test fruit was encircled by a chain through which a constant load was applied to the fruit, and the amount of compression was recorded as a measure of firmness. The Asco Firmness Meter, based on the same principle, was introduced commercially in 1959 (Garrett et al. 1960). Other simple, portable testers based on a similar principle were also reported by Shafshak and Winsor (1964) and Diener et al. (1971) for measuring tomato firmness. Perry (1977) developed a nondestructive tester for measuring peach firmness in which the fruit was compressed between two flat plates to which a constant air pressure was applied. Deformation was measured with a dial gauge. He reported that the firmness measurement obtained with this tester correlated reasonably well with that from the MT tester. A nondestructive firmness measurement method for soft fruits (peach) was reported by Bellon et al. (1993) in which a ball was pressed into the fruit with a constant force and the deformation was measured. Takao and Ohmori (1994a,b) and Yakushiji et al. (1995) reported the development

16

J. ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

of two nondestructive fruit firmness testers; one is a benchtop-type and the other is a hand-held meter. Fruit firmness is scored between 0 and 100, based on the deformation of the fruit under a specific load. The deformation is restricted to the elastic range so that no bruising occurs. The benchtop firmness tester ("HIT Counter"), equipped with a computer control and data acquisition system, is suitable for firmness measurement of fruits such as kiwifruit, mango, and persimmon; its loading rate and applied force can be adjusted as required. The hand-held firmness meter ("Handy HIT") was developed mainly for measuring kiwifruit firmness. The meter is equipped with a spring to provide constant load, and the firmness score is read from the dial gauge, reflecting measurement of the fruit deformation. Considerable research has been done on firmness measurement of cherry and berries, and most reported methods are based on the measurement of deformations under constant load. Parker et al. (1966) investigated several techniques for measuring firmness of tart cherry and concluded that only the puncture-load method provided satisfactory results. The puncture-load tester measured the displacement of a rod as it was pushed into the cherry flesh under constant load for 120 s. The slow throughput of the device limited its practicality. Diener et al. (1969) developed a deformation-load instrument, based on the principle of Parker et al. (1966), to automate the puncture-load instrument. Recently, Timm et al. (1993) reported on the development of a portable instrument for measuring firmness of cherry and various berries. The test fruit is compressed between two flat plates, one attached to a load cell and the other to a stepping motor. The instrument is equipped with a portable computer data acquisition system. The force-deformation curve is recorded by the computer and firmness is measured as the displacement of the plunger for a specified load level. Many other methods and instruments were developed to measure firmness of small-sized fruits: Rohrbach (1981), Wolfe et al. (1982), Slaughter and Rohrbach (1984), and Rohrbach and Mainland (1993) for blueberry; Bouyoucos and Marshall (1950), Kenworthy and Silsby (1974), Bernstein and Lustig (1981), and Lustig and Bernstein (1987) for cherry; and Ourecky and Bourne (1968) for strawberry. Prussia et al. (1994) recently patented a nondestructive, noncontact firmness detector. This technique is different from conventional forcedeformation methods in that the system uses a short puff of pressurized air to deflect the product surface less than 1 mm while a laser displacement sensor measures the amount of deflection. This is similar to devices used by ophthalmologists to detect glaucoma. Under a fixed air pressure, firmer products have less deflection than softer ones. The

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pressure can be adjusted for different firmness ranges. Fan et al. (1994) and Hung and Prussia (1995) evaluated the laser-puff firmness detector for a number of horticultural products, including apple, cantaloupe, kiwifruit, nectarine, orange, pear, peach, plum, and strawberry, as well as some other food products. Their results for peach showed that the laser-puff readings were well correlated with those obtained with the MT tester (r = 0.89). Hung and Prussia (1995), however, reported that the detector had higher false alarm rates (rejection of good items) than the MT tester and that further work is needed to improve its precision and accuracy. This technique appears to have some potential for real-time inspection and sorting of horticultural products. 3. Dynamic Force-Deformation. Most quasi-static methods discussed thus far measure local mechanical properties, and hence the results are likely to be influenced by the location in which measurements are taken. The dynamic force-deformation methods discussed herein, on the other hand, are less influenced by the local mechanical properties, so the measurements are more representative of the entire fruit. The test is performed by applying dynamic forces, usually in sinusoidal form, to the fruit over a range of frequencies and recording the corresponding displacement, acceleration, or velocity. Fruit firmness is determined by analyzing the frequency spectrum of the ratio of force to deformation (or velocity or acceleration). Rohrbach and Glass (1980) determined firmness of blueberry by measuring the mechanical impedance using a dynamic device. The mechanical impedance, the ratio of force to velocity in the frequency domain, was measured with a swept sinusoidal vibration in a range of frequencies. An impedance model was developed to fit the experimental data. Rohrbach and Glass found that the spring constant in the model correlated reasonably well with the firmness measurement from quasi-static compression tests. Abbott and Massie (1993) measured the firmness of apple with a dynamic force-deformation device. A force transducer and an accelerometer were mounted on the head of an electrodynamic vibrator. The apple was positioned between a flat rigid surface and the vibrator, and a swept sinusoidal vibration (40 to 440 Hz) was applied. The dynamic measurement had a higher correlation with the slope of the MT forcedeformation curve (r = 0.61) than with the maximum force (r 0.41), as would be expected since both the force-deformation slope and the dynamic measurement relate to the modulus of elasticity. Abbott and Massie (1995) tested kiwifruit with this device and reported correlations between dynamic measurements and MT firmness as high as 0.91.

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ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

A number of factors should be considered in applying quasi-static or dynamic loading principles to measuring fruit and vegetable firmness. First, plunger tip geometry, that is, size and shape, can significantly affect firmness measurement and therefore must be carefully considered in design. The common geometries for plungers include rounded, cone, flat surface, and flat plate. The rounded tip is most commonly used in mechanical force-deformation devices for large fruits, while the flatplate type is common for small-sized fruits. Jackman et al. (1990) measured tomato firmness with a flat-plate probe and found that it could not effectively measure the differences in firmness of fruit with slight chilling injury, whereas a rounded punch probe could distinguish those fruit from noninjured fruit. Second, the rate of loading can affect firmness measurement; the rate effect becomes more pronounced for soft fruits that are highly viscous. Due to their low loading rate, quasi-static force-deformation techniques are not very adaptable for on-line sorting of horticultural products. 4. On-line Applications. Despite significant research on mechanical

measurement of fruit and vegetable firmness, most methods and techniques reported are not suitable for on-line sorting. Only limited research has been conducted on the development of on-line firmness sorting systems and few commercial applications are reported. Mehlschau et al. (1981) reported on the development and testing of an automated system for sorting out advanced maturity pears prior to cold storage and for firmness sorting before canning. A rotating wheel applied a fixed load to the major diameter of each pear and the resultant deformation at the load point was measured. They reported that a 19-mm-diameter wheel with a 3.2-mm edge diameter was suitable to adequately classify pears by firmness and to keep bruising within acceptable levels. This technology was not adopted by the pear canning industry because it lacked accuracy when sorting firm fruits. Mizrach et al. (1992) developed two methods for measuring firmness of tomato and orange by slightly deforming the peel of the fruit using a spring loaded pin. The first method ("go-no-go") was based on indentation when a preadjusted force was reached, while the second method (" continuous") was based on continuous measurement of fruit deformation under a variable load. A prototype on-line sorting machine was built, for which either method could be used. Tests on the machine equipped with the go-no-go device indicated that the system could sort oranges into two firmness groups with relatively high accuracy. The continuous system could effectively separate fully red from green tomatoes but not from those turning red.

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Takao and Ohmori (1994b) reported on the development of a prototype on-line system ("Line HIT") for sorting kiwifruit based on deformation under a constant load. Kiwifruit were sorted into three categories, that is, hard, fairly soft, and soft, at a speed of 2 to 3 fruit/so The firmness values obtained with the Line HIT correlated well (r = 0.88) with those from punch tests. A prototype on-line system, developed by Schmilovitch et al. (1995) to sort dates at a rate of one fruit/s, compressed the fruit between two parallel plates. Firmness was measured as the ratio of the force difference between initial (5 N) and final loading (8 N) to the displacement of the moving plate from initial to final loading. Dates were sorted into four firmness groups. Machine sorting results compared reasonably well with hand sorting results for freshly harvested dates and those stored for five weeks. B. Impact Impact occurs when two objects collide during a very short time interval. Relatively large forces or pressures are exerted over the contact area as a result of the rapid change in the momentum or energy of the impacting objects. The impact response of an object is directly related to its mechanical properties, mass, and contact surface geometry. During impact, the object may undergo different phases of deformation, including initial elastic deformation, plastic or permanent deformation, and final elastic recovery. Mohsenin (1986) gave a detailed discussion of impact theory for agricultural products and reviewed some related studies. Numerous studies have been conducted on the impact responses of horticultural products, both theoretical and experimental (Hamann 1970; Fluck and Ahmed 1973; Finney and Massie 1975; Mohsenin et al. 1978; Franke and Rohrbach 1981; Bartsch and Askariaman 1982; De Baerdemaeker et al. 1982; Rohrbach et al. 1982; Lee and Rohrbach 1983; Nahir et al. 1986; Delwiche 1987; Delwiche et al. 1987a; Lichtensteiger et al. 1988; Luan and Rohrbach 1989; Zapp et al. 1989; Zhang and Brusewitz 1991; Hyde et al. 1992; Zhang et al. 1994; Bajema et al. 1996). A number of impact parameters, derived from impact force response (IFR) curves, have been proposed to measure horticultural product firmness, some of which include peak force, the ratio of the peak force to the square of the time from initial contact to peak force (C2), coefficient of restitution, contact time, and IFR frequency spectrum. The coefficient of restitution is the ratio of the velocities of the product just before and after impact and reflects the energy absorbed in the product .during impact. Fig. 1.2 shows typical IFR curves for three tomatoes of about the

J.

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ABBOTT, R. LV, B. UPCHURCH, AND R. STROSHINE

80 70 60

z 50
~

0 u..

40 30 20 10 0

2

3

4

5

6

7

8

9

10

Force - mSec Fig. 1.2. Impact force response curves for three tomatoes dropped 10 cm. (Lichtensteiger et al. 1988.)

same mass (Lichtensteiger et al. 1988). The green (firm) tomato had the highest peak force with the shortest contact time, and the red (soft) had the lowest peak force and the longest contact time. The firm fruit exhibited a relatively symmetric IFR curve, while the soft fruit had a more skewed IFR curve. The degree of skewness generally varies with variety, firmness, and drop height. Firm horticultural products (cabbage) exhibit relatively symmetric IFR curves (Bartsch and Askariaman 1982); less firm products (peach) show intermediate skewness (Delwiche 1987); and soft products (ripe tomato) produce a more skewed curve (Fig. 1.2). Table 1.1 lists the impact parameters that have been reported for measuring firmness of horticultural products. This diversity of impact parameters used as measures of firmness reflects not only the complexity of measuring horticultural product quality but also the lack of a consensus definition of firmness. No comprehensive research has been reported as to which parameter(s) is most appropriate for firmness evaluation. It appears that selection of one particular impact parameter over the others is largely dependent on commodity and impact method, as well as the firmness reference used by individual investigators.

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Table 1.1. Impact parameters used by various researchers as indexes of firmness for horticultural products. Impact Parameter Peak force

Product

Reference

apple,pear,peach

Delwiche et al. 1991; Delwiche and Sarig 1991; Sugiyama et al. 1994 Brusewitz et al. 1991; Delwiche 1987; Delwiche et al. 1987a; Zhang et al. 1994 Delwiche 1987; Delwiche et al. 1987a; Delwiche and Sarig 1991; Delwiche et al. 1989; Rohrbach et al. 1982 Brusewitz et al. 1991; McGlone and Schaare 1993; Patel et al. 1993; Zhang et al. 1994 Brusewitz et al. 1991; Zhang et al. 1994 Brusewitz et al. 1991; Zhang et al. 1994 Meredith et al. 1990; Sugiyama et al. 1994 De Baerdemaeker et al. 1982; Delwiche 1987; Delwiche et al. 1987a Nahir et al. 1986 Younce and Davis 1995

peach

C2 b

apple, blueberry, peach, pear

Contact time

Absorbed energy

apricot, blueberry, kiwifruit, peach, raspberry peach

IFR skewness

peach

Coefficient of restitution C IFR frequency spectrum

apple, peach

Stiffness parameter Initial acceleration

apple, peach, pear

tomato cherry

aCl is the ratio of the peak force to the time to the peak force.

bC2 is the ratio of the peak force to the square of the time to the peak force.

cCoefficient of restitution is the ratio of the velocities of the specimen just before and after impact and reflects the energy absorbed in the product during impact.

Different impact techniques have been developed to investigate the relationship of impact responses to firmness measurement for fruits and vegetables. Mohsenin (1986) divided impact techniques into five categories: drop test, falling mass (or impact probe), simple pendulum, compound pendulum, and impact ram. Review of the literature shows that most studies have used the drop-test and falling-mass methods to evaluate fruit and vegetable firmness. 1. Drop Test. In a simple drop test, the product falls as a free body onto

a sensing element, and its impact response is recorded and analyzed with a computer data acquisition system. The stiffness and mass of the sensing element are normally much greater than those of the impacting

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J. ABBOTT, R. LU, B. UPCHURCH, AND R.

STROSHINE

product so that the influence of the sensing element on the IFR of the product is negligible. This method has been most widely used in impact firmness sorting systems because it is easily implemented. Rohrbach (1981) developed a computer-controlled impact device that recorded the IFR of blueberries dropping 40 mm onto a rigid plate sensor. Gfthe 26% of berries with firmness defects (soft berries), 75% were correctly sorted into the softest of four categories using C2 (see Table 1.1) as a criterion. A tomato-grading device reported by Nahir et al. (1986) allowed the fruit to fall 70 mm onto a rigid sensing plate. As a grading criterion, they used a stiffness parameter estimated by integrating the impact force over the impact duration and then dividing by the impact duration squared. Based on the computed stiffness, green and overripe tomatoes were separated from red tomatoes by pneumatic pistons on the exit conveyor. Results compared well with visual grading; however, no texture data were presented. Delwiche et al. (1987a) studied the impact responses of peach on a rigid surface and analyzed a number of impact parameters obtained from the time- and frequency-domain IFR curves. They reported that C2 and force at 295 Hz (IFR frequency spectrum) were most highly correlated with elastic modulus and Effe-gi penetrometer measurements. C2 and force at 295 Hz were poorly correlated with fruit mass and radius; that is, they were relatively independent of fruit size. Delwiche et al. (1989) developed a single-lane firmness sorting system with a rate of 5 fruit/so The system was tested for sorting pear and peach into three firmness categories. Correlations of C2 to penetrometer firmness and elasticity were 0.84 and 0.90 for fresh market peach, and 0.78 and 0.81 for pear. Approximately 74% of the peaches were sorted into the correct firmness range. Meredith et al. (1990) reported that the coefficient of restitution determined from two consecutive bounces of peaches on a rigid transducer correlated well (r = 0.94) with the peak force measured from parallelplate compression of the whole fruit and was not highly dependent on weight or drop height within the range 5 to 15 mm. However, they indicated that this coefficient correlated poorly with the MT firmness measurement. Brusewitz et al. (1991) used a drop-impact tester to investigate the relationship between peach ripeness and various impact parameters. They reported that, for constant drop height, peach ripeness was related to Cl (see Table 1.1), percent absorbed energy, and skewness of the IFR curve. These parameters were not affected by fruit mass. Contact time increased with ripeness but was affected by mass. McGlone and Schaare (1993) and Patel et al. (1993) reported on three impact firmness instruments: "SoftSense," an instrument for fruit firmness measurement in research and for off-line quality control in indus-

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try; "BerryBounce," designed for measuring average firmness of batches of small-sized fruit; and "SoftSort," a prototype sorting system for grading medium-sized fruit, such as kiwifruit. The basis of measurement in all three instruments is the contact time (or dwell time), defined as the peak width at half height of the first IFR curve. Results on kiwifruit with SoftSense showed a high correlation coefficient between dwell time and penetrometer measurement (r = 0.91). Experiments with BerryBounce on artificially softened blueberry samples showed a high linear relationship between average dwell time and percent soft berries. Packinghouse trials on kiwifruit showed that, with the penetrometer measurement used as a reference method, about 17% of firm fruit were misclassified as soft with SoftSort, compared with 11 % with hand sorting. Results on blueberry with BerryBounce indicated the influence of fruit mass on the dwell-time measurement (Patel et a1. 1993), an observation also reported by Brusewitz et a1. (1991) for peach. One potential problem with the drop-test method is that some bruising may be incurred during the fruit impact. Some researchers attempted to use a soft sensing element to measure the IFR so that no bruising would be induced. Davis and Pitts (1985) developed an impact sensor for measuring cherry firmness. Cherries were dropped onto a modified intercom speaker, which generated a voltage signal related to the fruit firmness: Firmer cherries produced a higher and shorter duration voltage signal than did softer cherries. Younce and Davis (1995) did a theoretical analysis of the system and constructed a laboratory prototype. Impact parameters, derived from the time-varying voltage signal generated during cherry impact, included initial slope of the voltage (proportional to velocity), peak voltage, and duration of the first voltage half cycle. All the parameters were found to be related to cherry firmness; however, the initial voltage slope or acceleration was the best firmness indicator that was independent of mass. This sensor performed well as a benchtop laboratory instrument. However, it may not be well suited for on-line cherry sorting because of the presence of stems and variations in firmness over the cherry surface. Further work is needed to ensure constant contact velocity. Thai (1994) tested the performance of an experimental soft foam sensor for measuring the IFR of apple and tomato. The sensor showed some potential for measuring firmness of horticultural products; however, improvements are needed in stability and repeatability of the sensor, and in signal conditioning, data acquisition, and analysis. 2. Impact Probe. This method differs from the drop test in that a rigid impact mass strikes the fruit rather than the fruit impacting a stationary surface. This configuration has some potential advantages over the drop

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LU, B. UPCHURCH, AND R. STROSHINE

test method in reducing handling problems after impact sensing and in decreasing the variability in firmness prediction caused by fruit mass and radius of curvature. Delwiche and Sarig (1991) reported on the development of a probe impact sensor for firmness measurement. The sensor is operated by an air cylinder that releases and returns an impact mass having a 12.7-mm diameter ball probe. The acceleration of the impact mass during impact is sensed by an accelerometer. Preliminary results showed relatively high correlation between penetrometer firmness and peak acceleration for pear (r = 0.92) and peach (r = 0.80), better than their previous system in which the fruit was the impacting object. Low correlation for apple (r = 0.55) was speculated to be due to local variations in firmness over the apple surface. Delwiche et al. (1991) developed a prototype firmness sorter which consists of an impact probe sensor, a fruit handling system, and a signal processing and control system. The results on apple showed a higher correlation (r = 0.88) between peak acceleration and penetrometer measurement, which they attributed to better control of the impact mass and to an increase in impact force. Unfortunately, the increased impact force caused slight bruising of the fruit. A firmness impact sensor, similar to the one of Delwiche and Sarig (1991), was also reported by Sugiyama et al. (1994b). A force transducer is attached to a thin rubber sheet that is in contact with the fruit during impact. The IFR of the fruit struck by the impact mass is sensed by the force transducer and the data are transferred to a computer for further analysis. Sugiyama and coworkers found that either peak force or coefficient of restitution could be used as a measure of fruit firmness. No data were reported on the relationship between the impact parameters and other firmness measurements. In summary, the impact method can provide a rapid, nondestructive means for measuring firmness of horticultural products. A number of impact parameters have been used as firmness indexes. Some of these parameters were tested with several fruits and vegetables, while others were used only for specific products. Further research is definitely needed to identify the one (or perhaps two) most reliable, appropriate parameter(s) for quantifying individual fruit and vegetable firmness. The drop test method is easy to implement; but the IFR is likely to be influenced by a number of factors such as drop height, fruit mass, and contact radius. The impact probe method can reduce the influence of fruit mass and contact radius on the IFR; however, some factors specific to this method need to be considered, including probe design, impact speed control, and positioning of the fruit for impacting. The concept of soft sensing appears to be a promising alternative to the rigid transducer in reducing possible bruising during impact. More research is needed with regard to sensor design and its ability for firmness mea-

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surement. Finally, one inherent problem with any impact method is that it measures local mechanical properties of the test product. For some horticultural products such as apple and peach, there exist large variations in mechanical properties over the fruit surface, which, in turn, may limit the usefulness of the impact method as a means of nondestructive firmness measurement. C. Low-Frequency Vibrations Vibration techniques have been widely used for separation of agricultural products such as grains during combine harvesting and during postharvest handling and processing operations. A number of vibrational devices have also been developed for sorting horticultural products. These techniques are generally based on the principle that the dynamic characteristics of fruit, for example, bounce behavior, are dependent on vibration frequency and are directly related to the mechanical properties of the fruit. Hamann and Carroll (1971) developed a sorting device for muscadine grape using low-frequency vibrational energy. [Unlike most grapes, muscadines frequently detach from the cluster after harvest and are handled as separate berries (Goldy 1992).] The grapes were placed in an inclined separatory trough that was vibrated at various frequencies between 100 and 200 Hz. Firmer fruit bounced from the trough at a lower frequency while ripe, softer fruit bounced out at a higher frequency. The device was effective for sorting muscadines into different ripeness groups. Hamann et al. (1973) used the same device to sort blueberry according to firmness and shelf life. Ripeness and bruising affect shelf life of blueberry. Firmness is not an accurate indicator of ripeness in blueberry. However, bruised berries bounced from the vibrating trough at lower frequencies than unbruised fruit. Therefore, they suggested that vibrational sorting with variable frequency and constant energy showed promise as a means of sorting small fruit by firmness and shelf life. Bower and Rohrbach (1976) tested a blueberry sorter based on the method proposed by Hamann and Carroll (1971). During sorting, the vibrational frequency was held constant while the energy level was varied. The instrument sorted blueberries based on the coefficient of restitution rather than firmness per se. A firm berry, for example, would bounce from the trough when it came in contact with the vibrating plate. Berry orientation tended to affect the sorting results, especially when the calyx contacted the vibrating plate. Results showed that sorting did not correlate well with berry firmness or shelf life, but that most good fruit could be separated from damaged fruit. Wolfe et al. (1980) used both roll and bounce in a prototype blueberry

26

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sorter. They concluded, however, that more accurate separation was needed for many marketing situations. Montejano-Gaitan et al. (1982) evaluated the effects on sorting effectiveness of a number of polymer materials used for a vibrating belt. Simulated small-sized fruit were sorted as they were carried on a flat belt over a vibrating metal surface so that the vibration passed through the belt to the simulated fruit, causing them to bounce over a barrier. Results indicated that sorting performance could be improved by selecting a belt material with a specific modulus of elasticity and damping coefficient. Vibrational sorting of tomato into ripeness groups on the basis of firmness was studied by several investigators, including Bayer (1976), Holmes (1979), and McClure et al. (1979). Holmes (1979) developed a laboratory sorting device to separate green from ripe tomatoes. The device consisted of a single horizontal vibrating cylinder with tomatoes being conveyed in contact with each side. Between 75 and 98% of the green tomatoes were rejected, with 0 to 19% of the red tomatoes also being rejected. A pilot model vibration machine was reported for sorting tomatoes (Anon. 1976). Fruit were conveyed by a belt and passed over a rotating eccentric cylinder. Green fruit bounced off the conveyor when they came into contact with the vibrating cylinder. The red tomatoes remained on the conveyor because of their high damping capacity. The sorter was capable of sorting 10 to 15 tons of tomatoes per hour with 90 to 95% accuracy. D. Sonic Vibrations

Sonic (or acoustic) vibrations, usually defined as encompassing the audible frequencies between about 20 Hz and 15000 Hz, provide a means of measuring fruit and vegetable firmness. When an object is acted on by a periodic force, it vibrates at the same frequency as the applied force. The amplitude of the vibration generally varies with frequency. At a particular frequency, the object will vibrate vigorously and a maximum amplitude is observed; such a condition is referred to as resonance. This resonance is a function of the mechanical properties of the object, that is, elasticity and internal friction or damping, and will also depend on object shape, size, and density. For deformable bodies like fruits and vegetables, there generally exist many resonances within a given range of frequencies. There is one, and sometimes more than one, specific vibrational mode or "mode shape" corresponding to each resonant frequency. By studying the resonant frequency and mode shape, one can gain knowledge about the mechanical properties of the vibrating object (e.g., elasticity) and about the requirements for instrument design (Le., location of sensor and vibration input). For a specific instrument con-

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figuration, only certain resonances will be observed while others may never appear, depending on factors such as method of vibration, instrumentation configuration, and relative location of the sensor. Fig. 1.3 shows a typical frequency spectrum for a 'Delicious' apple between 0 Hz and 2000 Hz (Abbott 1994). Four resonant peaks are observed in this curve; the frequencies at these resonant peaks are related to the modulus of elasticity and therefore to the firmness of the fruit. However, it should be mentioned that the preceding discussion serves to demonstrate the basic principle of sonic vibration. The method directly based on this principle is now rarely used because it requires using periodic signals (usually harmonic or sinusoidal) to sweep over a range of frequencies consecutively and, thus, it takes considerable time to measure resonances, which is impractical for on-line measurement of firmness. Fast resonance measurement can be achieved by applying to the product an impulse (e.g., thumping) which, from the mathematical viewpoint, contains a range of frequencies. The signal obtained from the transient response in the product to the impulse will have all information necessary for resonance measurement. The method, therefore, allows for fast determination of firmness and is now widely used (see further discussion in later sections). The traditional watermelon ripeness test is based on the acoustic principle where one thumps the melon and listens to the pitch of the response. Research on sonic vibration for measuring mechanical properties of horticultural products began in the 1940s, when Clark and

-60

-70

L..-_-----''----'-

o

500

:f . 2

--'-

1000

:f . 4

- - - ' - ' -_ _- - ' - - - l

1500

2000

Frequency (Hz)

Fig. 1.3. 1994.)

The frequency spectrum for a 'Delicious' apple under sonic vibration. (Abbott

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STROSHINE

Mikelson (1942) first reported that textural characteristics of fruits were related to their vibrational properties and that the natural frequency of vibration of the whole fruit changed as it ripened. Virgin (1955) developed an experimental technique to measure resonant frequency of excised potato tissue. Virgin (1955) and Falk et al. (1958) reported that Young's modulus was a function of turgor pressure which was, in turn, related to the resonant frequency of tissue specimens. Drake (1962) developed a device for measuring resonant frequencies for specimens of foodstuffs. Nybom (1962) proposed a method for evaluating firmness of small-sized fruits such as raspberry, strawberry, and cherry, based on their vibrational properties at a given frequency. Abbott et al. (1968a,b; Nametre Co. 1966) conducted studies on the vibrational characteristics of excised apple specimens and whole fruit at successive frequencies between 20 and 4000 Hz. They measured the lowest resonant frequencies of intact apples and found them to be significantly influenced by fruit maturity and firmness. Abbott and coworkers proposed a stiffness coefficient, f2 m , where fis the second resonant frequency and m is mass, to measure firmness of fruits and vegetables. This coefficient was highly correlated with the elastic modulus of the flesh tissue (Abbott et al. 1968a; Finney 1971a). Finney and coworkers (Finney 1967,1970; Finney et al. 1967; Finney and Norris 1968) developed an instrument for measuring resonant frequencies of horticultural commodities in which the test product was placed on an acoustic excitor, and an accelerometer was attached to the opposite side to measure the frequency responses. Finney (1970) and Finney et al. (1978) used this instrument to study the change in resonant frequencies of apples during storage and their relationship with harvest time, MT measurement, and sensory evaluation. Garrett (1970), Cooke (1972), and Cooke and Rand (1973) conducted studies on the theoretical basis of the stiffness coefficient. With different approaches, they obtained the same conclusion, that the expression f2 m 2/3 would be a more appropriate firmness index than f2 m . The revised stiffness coefficient tends to give more accurate predictions when the range of sizes of the objects tested is very large; when size variations are small, the advantage of one coefficient over the other may not be significant. There was considerable research in the 1970s and the early 1980s on using sonic vibration to measure firmness of horticultural products, including apple, banana, peach, tomato, and watermelon (Finney 1971a; Stephenson et al. 1973; Finney et al. 1978; Yong and Bilanski 1979; Yamamoto et al. 1980, 1981; Yamamoto and Haginuma 1982). Cooke (1972) and Cooke and Rand (1973) conducted theoretical work on resonant behavior of fruits and vegetables. Other related techniques were

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also developed, including pulse propagation velocity (Garrett and Furry 1972) and vibration transmissibility (Finney 1971b; Clark and Shackelford 1973). Sonic vibration techniques for measuring firmness ofhorticultural products during that time period were reviewed by Finney (1972) and Finney and Abbott (1978). The rapid developments in computing and sensing capabilities in the 1980s made available the technology for developing a sonic vibration system for rapid firmness measurement of fruits and vegetables. Since the mid-1980s there has been renewed interest in using the sonic vibration method for firmness evaluation (Saltveit et al. 1985; Van Woensel et al. 1987; Affeldt and Abbott 1989; De Baerdemaeker 1989; Peleg and Hinga 1989; Armstrong et al. 1990, 1993; Peleg et al. 1990; Farabee and Stone 1991; Abbott et al. 1992; Chen et al. 1992; Armstrong and Brown 1993; Shmulevich et al. 1993,1994,1995; Abbott 1994; Abbott and Liljedahl1994; Liljedahl and Abbott 1994; Stone et al. 1994). A number of sonic instruments and laboratory prototype sorting machines were developed and tested. Notable advances were made in understanding the dynamic behavior of fruits and vegetables under sonic excitation with the help of computer simulation and experimental modal analysis technologies. Simulation studies (e.g., Chen and De Baerdemaeker 1993a,b,c; Chen et al. 1993a; Rosenfeld et al. 1992, 1994; Wu et al. 1994; Lu and Abbott 1996) have shown that, for nonspherical objects such as apple, peach, pineapple, and watermelon, the stiffness coefficient f2 m 2/3 is linearly related to the elastic modulus. There are basically three classes of vibrational modes in apple: torsional, first-type spheroidal or longitudinal, and second-type longitudinal modes (Chen and De Baerdemaeker 1993c). Lu and Abbott (1996) showed that the second-type longitudinal modes are nonaxisymmetric and that the majority of modes within the frequency range of interest belong to this class. According to the models, apple skin has more effect on torsional modes and the core has more effect on spheroidal modes. Overall fruit shape can have a large effect on resonance measurements, and an effective way of compensating for the shape effect has yet to be developed. Other researchers conducted experimental modal analyses on apple (Kimmel et al 1992; Huarng et al. 1993), pineapple (Chen and De Baerdemaeker 1993b), and muskmelon (Sugiyama et al. 1994a). Resonance measurement can be implemented in a variety of instrumental configurations, depending on how the input signal is generated and how the output signal is sensed or received. Although it is technically possible to excite vibration by a noncontact sonic beam, this method has not been reported since the very early exploratory work (Nametre Co. 1966) and is impractical for on-line applications. Based on

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J. ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

the way the output signal is received, the instruments may be divided into contact and noncontact (microphone) sensing methods. In the following, we will review the current techniques applied to measuring firmness of horticultural products. Other sonic techniques, such as pulse propagation and vibration transmissibility, will not be covered here because not much progress has been made since the reviews by Finney (1972) and Finney and Abbott (1978). 1. Contact Sensing. In this method, a sensor, normally an accelerometer or a piezoelectric sensing film, is in direct contact with the vibrating fruit and detects its dynamic response. The vibration of the fruit is often induced by using a mechanical impulse or a swept or successively varied discrete (sinusoidal) signal over a range of frequencies. This method was first used by Abbott et al. (1968a,b; Nametre Co. 1966) and Finney and coworkers (Finney and Norris 1968; Finney 1970; Finney et al. 1978). A sonic instrument was developed and patented by Affeldt and Abbott (1989, 1992), which is related to that of Finney and coworkers; however, the vibrator is driven by a digital signal analyzer that generates the signal to drive the vibrator. In earlier research (e.g., Affeldt and Abbott 1989; Abbott et al. 1992), a periodic chirp (about 0.25 s duration)-a sinusoidal signal swept over a range of frequencies-was used to drive the vibrator. In a later configuration, only an impulse (less than 0.01 s duration), one cycle of the swept sine signal at frequencies around 1500 Hz, was used as an input to the vibrator. The output signal from the accelerometer is recorded by the analyzer and converted to the frequency-domain by fast Fourier transform. Abbott and coworkers (Abbott et al. 1992; Abbott 1994; Abbott and Liljedahl 1994; Liljedahl and Abbott 1994) used this instrument to study sonic resonances of apple and their relation to firmness measurements by MT, compression, and sensory evaluation. The resonant frequencies decreased during maturation, ripening, and storage (Liljedahl and Abbott 1994). Frequencies of the second and third resonant modes were closely related (r = 0.95) (Abbott 1994). Correlations of stiffness coefficients to MT values and to firmness evaluations by USDA Agricultural Marketing Service inspectors were low for individual apples but high (r = 0.98 and 0.97, respectively) for means of ripeness categories (Abbott 1994). Abbott and Liljedahl (1994) studied the relationship of sonic resonant frequency to compression and MT measurement for three apple cultivars. They found that the resonant frequencies and f2 m 2/3 were most highly correlated with compression slope and least correlated with maximum force. Correlations between the stiffness coefficient and MT slope were 0.84,0.96, and 0.56 for 'Golden Delicious,' 'Delicious,' and 'York Imperial' apples, respectively.

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Van Woensel et al. (1987) used the same technique to study the relation between mechanical properties measured by compression and resonance tests on whole fruit and flesh specimens. The elastic modulus measured from specimens and the stiffness coefficient, f2 m 2/3, from sonic tests correlated highly with apple fruit acceptability from sensory evaluations. Peleg (1989) developed and patented a sonic instrument that uses two accelerometers to monitor the input acceleration of the vibrator and the output of the fruit on the cheek opposite the vibrator. Four firmness indexes were derived using the input (AJ and output (A o) accelerations (amplitudes) (Peleg and Binga 1989). These are absolute transmissibility T a = AalA j , relative transmissibility T r = (Ao-AJIAi> mean power Pill' and energy dissipated by the fruit per cycle Ee- Peleg and Binga claimed that these indexes can classify fruit into different firmness categories better than devices that only measure A o • Peleg and coworkers (Peleg and Binga 1989; Peleg et al. 1990) used this instrument to evaluate orange, tomato, and avocado and reported satisfactory firmness measurement. Pitts et al. (1991) used the Peleg instrument to evaluate apple firmness and concluded that it measured apple firmness as well as the MT tester (differentiated among various treatment lots) but that one cannot use one method to predict the other due to large variations in firmness data and the different mechanical properties measured by the two methods. A portable sonic instrument for determining ripeness of watermelon in the field was developed by Farabee and Stone (1991). A handheld sensing unit functions as both an excitor for delivering a mechanical impulse and a sensor for detecting the vibrations of the fruit. The dynamic response of the fruit is measured by a piezoelectric sensor at the location where the mechanical impulse is applied. They used this instrument to measure ripeness of two watermelon cultivars and compared the results with destructive measurements of sugar content and flesh firmness. Fair correlation to ripeness (r 0.77) was found in both cultivars. Stone et al. (1994) further improved the instrument and used it in the field to measure maturity and hollow heart of watermelon. They reported that the correlations between sonic and destructive firmness measurements were cultivar dependent, varying from 0.19 to 0.43, and that the instrument could be used to measure watermelon ripeness and to detect hollow heart. A sonic instrument was developed by Shmulevich et al. (1993, 1994, 1995) that uses a lightweight and flexible piezoelectric film sensor to detect the resonant frequencies of the fruit. The instrument consists of an instrumented fruit-bed equipped with a mechanical impulse device, signal amplifiers, and a personal computer. A force transducer was placed beneath the fruit-bed to measure the mass of the fruit, and a

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piezoelectric film sensor was located in the fruit-bed to pick up the dynamic responses of the fruit to the mechanical impulse. Shmulevich et al. (1994) measured the first resonant frequency and the damping ratio for three cultivars of mango. Sonic firmness measurements correlated well with those from destructive methods but were influenced by fruit orientation. 2. Noncontact Sensing. Noncontact sensing uses a microphone to detect the sounds generated by striking the fruit with a mechanical impulse or thump. In this method, first reported by Yamamoto et al. (1980), resonant frequencies of intact fruit were obtained by recording the sound generated by striking the fruit with a wooden ball pendulum. The first resonant frequency of both apple and watermelon was correlated with fruit firmness and sensory measurement. Armstrong et al. (1990) used this technique to measure resonant frequencies of apple and compared them with MT measurements. The sensing element in the instrument was a simple microphone, and a solenoid-activated hammer was used to tap the apple. They reported that the modulus of elasticity determined from resonant frequencies correlated well with that measured by conventional compression and that resonant frequencies were correlated poorly with MT measurements (r < 0.52). Based on this technique, Armstrong et al. (1993) developed a laboratory tester that used the acoustic response from the apple, when lightly tapped, to predict firmness in terms of elastic modulus. About 94 % of apples had consistent elastic firmness results when two readings per apple were compared, but the correlation between MT and resonance measurements was low. A number of researchers (e.g., Saltveit et al. 1985; Chen et al. 1992; Chen and De Baerdemaeker 1993a,b) also used this technique to measure firmness of apple, tomato, and pineapple. Kawano et al. (1994) reported a commercial sorting machine for detecting internal voids in watermelon. The sorting machine applies a mechanical impulse to the fruit with a small hammer and the sounds generated are recorded by three microphones evenly arranged around the equator. The sound signals from the three microphones are compared using a computer program, and if significant differences in the signal shape and phase exist, the fruit is considered to have an "internal void." It should be noted that Japanese watermelon are of a small, round type; the size and geometry of other cultivars may complicate the measurement. Additionally, sorting speeds may not be suitable for the scale of watermelon production in the United States. Compared with contact sensing, the noncontact sensing method is easy to implement because the sensor, a microphone, does not have to

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be in contact with the test object. One potential problem for on-line use of this technique is the control or isolation of the ambient noise and mechanical vibrations occurring in a packinghouse. The preceding review shows that the sonic vibration method is truly nondestructive and suitable for rapid, efficient evaluation of horticultural product firmness. Sonic measurement generally represents the mechanical properties of whole fruit rather than local tissues. Despite considerable research in the past, sonic vibration has not been used for on-line sorting of horticultural products (except for detecting internal voids in watermelon). Results reported on sonic firmness measurements appear to vary significantly, depending on commodity and especially on the firmness distribution within the sample. Fruit shape is a factor influencing the accuracy of sonic firmness measurements and, perhaps, a stiffness index that eliminates or better compensates for the shape and mass needs to be developed. Researchers are frustrated by the fact that the mechanical properties measured by the sonic vibration method (and other nondestructive mechanical methods as well) are different from those measured by the conventional destructive method. The fruit and vegetable industries have accepted the conventional destructive method as a standard method for assessing firmness or maturity. Perhaps some change in the concept of firmness measurement is needed so that, with further research efforts, the sonic vibration technology will eventually be accepted by the fruit and vegetable industries for quality evaluation. E. Ultrasonic Sensing

Ultrasonic sensing is based on the measurement of the response of the product to sound waves above the audible frequency range (above 20,000 Hz). Ultrasound waves, once propagated in the material, generate the phenomena of transmission, reflection, refraction, diffraction, interference, scattering, and dispersion as they interact with the material. Transmission and reflection are the two most important phenomena that can be used for nondestructive quality evaluation. As a sound wave propagates through a medium, its amplitude will be attenuated due to scattering and absorption of the wave energy. Attenuation of the sound wave is dependent upon the properties of the material through which it travels. The attenuation coefficient, commonly denoted as a, is a measure of the rate of decrease in the intensity (or amplitude) of an ultrasonic wave with distance as it propagates through the medium. Reflection will occur at each interface or boundary; and the amount of wave energy reflected is determined by the difference in mechanical properties, for example, elas-

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tic modulus, of the two materials at the interface. Wave propagation velocity, attenuation, and reflection are the important ultrasonic parameters used to evaluate the tissue properties of horticultural commodities. The acoustic properties of horticultural commodities can be measured by pulse-echo and through-transmission methods. Pulse-echo systems are widely used for ultrasonic measurement of attenuation properties of materials. Attenuation is measured using successive back-surface echoes (reflections) arising from a single pulse. When the material is very attenuative and the multiple back-surface echoes from the specimen cannot be obtained, the through-transmission method can be used. Two transducers are needed; one is used to generate the pulse, while the other is used to receive the transmitted wave. Ultrasonic techniques are widely used in the metal industry and in medicine (Krautkramer and Krautkramer 1990). They have also been used successfully on live animals to evaluate fat, total fat, lean, and other components (Povey 1989). However, progress on applying ultrasonic technology to evaluation of horticultural product quality has been slow. This is because horticultural commodities are usually porous, contain significant air spaces, and are nonhomogeneous. This makes it difficult to transmit ultrasonic waves through whole fruits and vegetables. Several studies have used ultrasonic techniques to measure the quality of horticultural products with excised tissue specimens and whole fruit. Sarkar and Wolfe (1983) investigated its potential for quality evaluation of fresh and processed foods. They measured the attenuation coefficients of potato, cantaloupe, and apple tissues in the 500 to 1000 kHz frequency range. The coefficients were extremely high because of the porous nature of the tissues and the high frequencies used. Mizrach et al. (1989) used an ultrasonic instrument at frequencies between 50 and 500 kHz to measure acoustic properties (i.e., wave propagation velocity, attenuation coefficient, and reflection loss) of tissue specimens of a number of fruits and vegetables, including avocado, potato, cucumber, carrot, pumpkin, melon, and apple. They reported that it was possible to determine the basic acoustic properties of the test fruits and vegetables. In a later study, Mizrach et al. (1991) found that the wave propagation velocity increased as measurements were taken at successive depths beginning with the outer melon tissues (next to the rind) and proceeding to the inner tissues (close to the seed cavity). However, velocity did not change significantly with the tissue elastic modulus. The attenuation of ultrasound strongly depended on the location from which the specimen was excised, which may indicate some potential for using this property for evaluation of internal fruit quality. Galili et al. (1993) used the same instrument with two movable 50-kHz ultrasonic probes to study the acoustic properties of intact avocado and melon. The atten-

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uation of the sound wave in the whole melons correlated well with their color and estimated ripeness. Attenuation in the avocados appeared to be related to their firmness, suggesting that it could be used for firmness, maturity, and shelf life determination. Galili and coworkers concluded that a more powerful ultrasonic source is needed in order to penetrate the melon rind for internal properties determination. Self et al. (1994) measured wave propagation velocity in cylindrical specimens of avocado flesh using a 37-kHz ultrasonic instrument. Ultrasonic velocity in the avocado flesh decreased from about 350 m/s to 200 m/s over a 12-day ripening period. This velocity was positively correlated with the water content of the flesh (r = 0.84). Sarkar and Wolfe (1983) found that the skin texture of oranges could be evaluated by reflectance measurements; and the cracks in tomatoes, by the backscatter technique. Upchurch et al. (1985, 1987) attempted to use I-MHz ultrasound to detect bruises on apple. They found that, because of the cuticle and the high percentage of void space in apple flesh, most of the incident energy was reflected. Similar problems with high attenuation were also reported by Povey (1989) who tested a number of vegetables and fruits. These results suggest that use of high-power excitation amplitude and lower frequency may overcome some of the problems encountered in evaluation of fruits and vegetables. Watts and Russell (1985) did a preliminary study on use of ultrasonics for detection of potato tubers with hollow heart. The major problem they encountered was the high attenuation characteristics of the tissues at high frequencies, which excluded the use of a pulse-echo technique requiring only one transducer. At low frequencies, the resolution decreased and a large transducer was required. Watts and Russell suggested that the optimum frequency range for obtaining a good indication of hollow heart must be determined. Cheng and Haugh (1994) successfully detected hollow heart in 'Atlantic' potatoes using a through-transmission system at 25 kHz. The system setup included a higher-power burst pulser, a broadband receiver, and dry-coupling transducers. Potatoes with hollow heart transmitted much less ultrasonic energy than solid potatoes; they could be differentiated using the power density spectra. Cheng and Haugh calculated the spectral moment, the area under the power density spectra, for all test potatoes and found that all potatoes with hollow heart could be identified using this parameter.

IV. ELECTROMAGNETIC PROPERTIES Electomagnetic wavelengths encompass, from longest to shortest, radio wave, microwave, light, X-ray, and gamma ray (Table 1.2). Optical prop-

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36 Table 1.2.

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Regions of the electromagnetic spectrum.

Spectral Region

Wavelength Range a (Frequency Range)

Radio wave Microwave Infrared Near-Infrared Visible (Blue to Far Red) Ultraviolet X-Ray Gamma Ray

3 mm to 30,000 m (100 GHz-l0 kHz) 3 mm to 300 mm (100 GHz-l GHz) 0.75)lm to 1000)lm 0.75)lm to 2.5)lm (750 nm to 2500 nm) 400 nm to 770 nm 4 nm to 400 nm 0.002 nm to 100 nm 0.00005 nm to 0.002 nm

aAlthough wavelength is listed for all regions, frequency (Hz) is used for radio wave and microwave. Wavelength region definitions are not mutually exclusive, some regions overlap or are subregions of others (e.g., microwave within radio wave region).

erties indicate the response of matter to visible light wavelengths (variously 400 to 700 nm or 380 to 770 nm), and usage is often extended to include ultraviolet and infrared wavelengths. A. Optical Properties Appearance is one of the major factors the consumer uses to evaluate the quality of fruits and vegetables. The human eye detects visible light reflected from an object while the brain processes and makes a judgment on the basis of the incoming information. Light reflected from the product carries some important information used by the consumer to judge quality; however, human vision is limited to a small region of the spectrum. Some quality features respond to wavelengths in regions outside the visible spectrum, such as the near-infrared. In the following discussion, the term light may generally be interpreted as encompassing the ultraviolet, visible, and near-infrared spectral regions except where clearly limited to the range of human vision. Measurement of the optical properties of fruits and vegetables has been one of the most successful nondestructive techniques for assessing quality. Optical properties are based on reflectance, transmittance, absorbance, or scatter of polychromatic or monochromatic radiation in the ultraviolet (UV), visible, and near-infrared (NIR) regions of the electromagnetic spectrum (Table 1.2). A quality index for the product can be based on the correlation between the spectral response and a specific quality feature of the product, usually a pigment or chemical constituent. Absorbing wavelengths of many constituents are known; but those data were obtained with compounds in relatively pure solutions, not in the

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complex matrix in which they exist in fruits and vegetables. The interactions of the many compounds within plant cells alter the characteristic absorbance wavelength of some constituents significantly (Norris 1983) and cause many overlapping absorbances. The complex physical structure of tissues creates an optically dense product that is difficult to penetrate and alters the pathlength traveled by the light so that the amount of tissue interrogated is not known with certainty. These facts make it difficult to quantify constituents in the way usually possible in chemical spectrophotometry. When a product is exposed to light, part of the light is reflected, some will be transmitted, and some will be absorbed (Fig. 1.4). The interaction of the light with the product can be described using (1) Fresnel's equation for reflections, (2) Snell's law of refraction, (3) Beer-Lambert's law describing absorption, and (4) the law of conservation of energy (Birth 1976; Birth and Hecht 1987). About 4% of the incident light is reflected at the outer surface, and this reflection is commonly called glare or specular reflection. Specular reflectance is considered to be independent of absorption (Birth 1976); that is, specular reflectance contains all incident wavelengths in essentially the original proportions, unchanged by the reflecting surface. Consumers consider glossiness desirable for some products and undesirable for others (Szczesniak Source

Fig. 1.4. Incident light on a fruit or vegetable results in reflections from the surface (specular reflectance) and from the interior ofthe product up to about 5 mm below the surface (body reflectance or interactance). The remainder of the energy is either transmitted through the fruit (body transmittance) or absorbed, resulting in chemical reactions or heat.

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J. ABBOTT, R. LU, B.

UPCHURCH, AND R. STROSHINE

1983); little research on the measurement of gloss has been reported recently. Ward and Nussinovitch (1996) applied a gloss meter specially designed for curved surfaces to study gloss of apple and tomato. Specular reflectance is not of further interest in this review except as it interferes with other measurements. The remaining 96 % of incident energy is transmitted through the surface into the cellular structure of the product and travels through the tissues. A large portion of the energy entering the product will exit close to the point of incidence. This energy is diffused or scattered by the small interfaces within the tissue, and part of the energy is absorbed by the constituents of the tissue within a few millimeters below the surface. The light energy exiting the product close to the incident beam is called diffuse reflectance (K. H. Norris, pers. comm.) or body reflectance (Birth 1976) and is the basis for measuring color of natural products (Birth 1976). (While a highly irregular surface may diffuse specular reflectance by scattering it in all directions, that is not the meaning generally given to diffuse reflectance.) Diffuse reflectance is altered by differential absorbance of portions (wavelengths) of the incident energy within the reflecting body and therefore contains useful chemometric information. Further use of the term reflectance in this review refers only to diffuse reflectance unless specular reflectance is specified. The energy that travels through the product and exits a distance from the point of incidence is referred to as transmittance. Direct transmittance measures energy traveling directly through a material (such as clear liquid or glass), while diffuse transmittance measures the energy that travels through a scattering material (such as a fruit or vegetable). Diffuse transmittance may be measured directly opposite the point of incidence, as is direct transmittance, although the path of travel is not actually direct. Transmittance may also be measured at some smaller angle from the incident beam because of scattering. Further use of the term transmittance herein refers to diffuse transmittance. There is some disagreement in terminology among researchers in this field (Birth and Hecht 1987; K. H. Norris, pers. comm.), and diffuse reflectance and diffuse transmittance are not always clearly distinguished. A term sometimes used for diffuse reflectance is interactance (K. H. Norris, pers. comm.; Conway et al. 1984; Kawano et al. 1992; Slaughter et al. 1996) because the exiting energy that is detected is the residual after the incident energy has interacted with the structure and composition of the tissue, that is, has been reflected, transmitted, absorbed, and scattered. Configurations for measuring the optical properties of fruits and vegetables are shown in Fig. 1.5. For each configuration, the light energy

1. TECHNOLOGIES FOR NONDESTRUCTNE QUALITY EVALUATION

Source

Source

Source

Sample

Sample

39

Sample

Transmittance

Interactance

Body reflectance

(a)

(b)

(c)

Concentric fiber ring emits monochromatic light

Central fiber bundle returns internally reflected Iight to the detector

Monochromatic light source

Light detector

Fig. 1.5. Optical properties of fruits and vegetables can be measured using (a) transmittance, (b) interactance, or (c) body reflectance and specular reflectance. Lower: An interactance probe must be specifically designed to provide a baffle or distance to eliminate specular reflectance from impinging on the sensor. (Lower diagram courtesy of D. C. Slaughter, University of California, Davis.)

40

J. ABBOTT, R. LU, B. UPCHURCH, AND R.

STROSHINE

(incident beam, either monochromatic or polychromatic) is directed toward the product. Transmittance is measured by placing a detector on the opposite side of the product from the incident light (Fig. 1.5a) and is normally used in traditional spectroscopy but is less common for fruits and vegetables. Transmission measurements appear very simple, but they are extremely difficult to implement due to the high light scatter and absorption within the tissues of horticultural products. The intensity of the light at the detector is very low, and the measurement requires a sensitive detector. Also, a light seal to shield the detector from extraneous light-from the incident beam or other sources-is often difficult to achieve because of the irregular shapes of fruits and vegetables. Successful transmission measurements are difficult to accomplish in very dense or intensely pigmented commodities; therefore, the configuration is modified by placing the detector at an angle from the direction of illumination (Fig. 1.5b), making the optical path from the incident light to the detector short compared to the path through the whole product. The angle between detector and incident beam is critical. If the angle between the irradiating beam and detector is small, the amount of edible flesh interrogated is limited. Signal strength approaches the noise level as wider angles (greater distances) are employed (Dull et al. 1989a). Chen and Nattuvetty (1980) recommended preliminary tests to determine the proper distance between the detector and incident light to achieve proper penetration depth while maintaining an acceptable intensity of the transmitted energy. When the detector is located in close proximity to the point of incidence, reflectance is measured (Fig. 1.5c). Information from a reflectance measurement is usually limited to the contents of the tissue within one or two millimeters of the surface, depending on optical density of the tissue. Specular reflectance may confound diffuse or body reflectance measurement unless precautions are taken to exclude it. Birth et al. (1984) used a simple probe geometry with multiple fiber optic bundles surrounding a central opening through which the product was illuminated. A probe geometry (Fig. 1.5, lower) designed by Norris for interactance measurements employs an outer ring of optical fibers to deliver the incident beam to the surface of the product and a central core of optical fibers to collect the exiting light, with a moderately broad (2 to 5 mm) metal barrier between the two sets of fibers to eliminate surface reflection (Conway et al. 1984; Kawano et al. 1992; Slaughter et al. 1996). Such a probe is intended to be in contact with the product. Illuminating around the perimeter of the probe and collecting the exiting light at the center tends to minimize confounding by environmental light. Selection of an optical property and a quality feature of the product

1.

TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

41

are critical when establishing an optical quality index. Color is the basis for sorting many products into commercial grades, but often concentration of pigments or other specific constituents might provide a better quality index. Color relates more directly to consumer perception of appearance, while pigment concentration may be more directly related to ripeness. To measure the quality of the product, the optical index must be closely correlated with the quality attribute of the product. The selected index should not be affected by physical parameters such as shape and size of the product. When an optimum optical index is selected, changes in system response, light intensity, or detector sensitivity have minimal effect on the relationship between the index and quality feature. The general procedure for selecting an optical index is to study the spectral response of the product at various wavelengths (Fig. 1.6) and select a wavelength or set of wavelengths that correlate with the quality

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42

J. ABBOTT, R. LU, B. UPCHURCH, AND R.

STROSHINE

feature. A review of a mathematical basis and specialized multiple regression methods for selecting specific wavelengths is given by Hruschka (1987). Methods that use data reduction of the full spectrum to predict constituents or quality are reviewed by Martens and Naes (1987); these include Fourier transform, principal components, and partialleast squares regression methods. These two reviews focus on analysis of near-infrared spectra but are equally applicable to other regions of the spectrum. Additional methods using the full spectrum are being developed; they include artificial neural networks (e.g., Bochereau et al. 1992) and wavelet analysis (Resnikoff 1992; Strang 1994). The simplest optical index is the magnitude of the reflectance (R) or transmittance (T) at a single wavelength (e.g., R 670 or T 670 ); however, this index is affected by changes in geometry of the product and instrumentation. A difference measurement (e.g., R 670 - R S20 ) indicates the general slope of the spectral curve in the region between the two wavelengths. This measurement reduces the effects of instrumentation and geometric variabilities. The ratio between two wavelengths (e.g., R6701Rs20) is an optical index independent of instrument sensitivity. Frequently, one of the wavelengths in either the difference or ratio is selected in a spectral region where there is minimal absorbance by pigments or other constituents; this wavelength serves as a reference to null instrument or geometrical effects. The closer this wavelength is to the wavelength absorbed by the constituent of interest, the better it serves this function. Another optical index that has been used is a combination of the difference and ratio measurements. The difference, ratio, and combination optical indexes attempt to characterize the spectral curve. Generally, reflectance data are expressed as percent reflectance, while transmittance and interactance are expressed as optical density, OD = log (JoII), where [0 is incident energy and [ is intensity of the energy exiting the product. An optical density scale permits a comparison of a wider range of intensities, and a linear relationship exists between OD and the concentration of the absorbing compound. The diffuse reflectance in the 400 to 700 nm region creates color that is frequently used to evaluate the quality of fruits and vegetables. Color measurement is the evaluation of radiant energy in terms that correlate with visual perception (Judd and Wyszecki 1963; Little 1976). Color measurement was reviewed by Clydesdale (1978), Francis (1980), and Delwiche et al. (1994), among others. Color of an object can be described by several different color coordinate systems. Some of the most popular systems are RGB (red, green, and blue) which is used in color video monitors, Hunter Lab, crn (Commission International del'Eclairage) L*a*b*, CIE XYZ, CIE L*u*v*, CIE Yxy, and CIE LCH (Hunter and

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

43

Harold 1987). Although the Lab and the L*a*b* systems are more commonly used, research has shown that the CIE LCH system is closely related to the human perception of color. Thai and Shewfelt (1991a) therefore recommended the LCH system for describing the color of fresh market fruit and vegetables. The lightness or brightness of the object is quantified by 1. The C is for chroma and refers to the purity or vividness of the color. Color is defined by the hue angle H such as 0° (red), 90° (yellow), 180° (green), and 270° (blue). Formulas are available for conversion among the various color systems (Judd and Wyszecki 1963; Wyszecki and Stiles 1967; Clydesdale and Podlesny 1968) and are often built into color-measuring instruments. 1. Body Reflectance. The majority of research in this area has concentrated on the measurement of changes in the chlorophyll content of the product, although other pigments have also been considered. As a fruit or vegetable matures, the chlorophyll degrades, causing a change in color. Development or exposure of carotenoids causes yellow, orange, or red color, while anthocyanins produce red to purple. Ripening is often characterized by a change in color from green to yellow or red. Certain defects are also characterized by abnormal surface color or finish.

Maturity. Generally, color is a critical factor when sorting fruits and vegetables by USDA grade standards. Gorini and Testoni (1990) reviewed the importance of color to quality judgment of vegetables in Europe. Rood (1957) concluded that flesh firmness and skin ground color were the best quality indexes for fresh peach. Aulenbach and Worthington (1973) found that G (green) tristimulus values correlated highly with visual color and best indicated ripeness of tomato and peach. Chromaticity values X and Y were linearly related to picking dates for apple, peach, and pear (Bittner and Norris 1976). As fruit matures, the chromaticity value X increases while Y decreases. The a* coordinate was used to separate peaches into four maturity classes after harvest; however, the color indexes for peaches grown in California and Carolina differed (Delwiche and Baumgardner 1983). Based on data from a tristimulus colorimeter (L* a* b*), the a* coordinate showed the largest rate of change in peach before harvest (Delwiche and Baumgardner 1985). Color development during ripening of peach (Thai and Shewfelt 1991b) and tomato (Thai and Shewfelt 1991c) was linearly related to the hue angle (H = tan-1 a*/b*) calculated from tristimulus color measurements. Sacks and Shaw (1994) used L* a* b* to distinguish among selections and cultivars of strawberry on the assumption that fruit color is critical for cultivar acceptance; however, they did not include consumer

44

J. ABBOTT, R. LU, B.

UPCHURCH, AND R. STROSHINE

evaluations. Thompson et al. (1996) used Munsell color space in a pecanbreeding program and found it necessary to use the combination of hue, value, and chroma to adequately describe the color of pecan kernels. While chromaticity coordinates are useful for describing color, reflectance at one or more wavelengths may be a more useful measurement for quality classification. Spectral composition of the body reflectance has been used as an indicator of maturity for fruits and vegetables. For apple, reflectance at 670 nm increased with maturation (Lott 1944; Bittner and Norris 1976). The increased reflectance in the 670 region corresponded to a decrease of chlorophyll. Bittner and Norris (1976) measured the body reflectance on the blushed side of peaches and reported an increase at 670 nm during maturation and a decrease at 500 nm due to the absorbance by red and yellow pigments. They suggested a reflectance ratio of R5S0 / R 620 as a maturity indicator for apple and peach and a ratio of R 670 / R 730 for pear. Since water is the main absorber in the near-infrared region and water content of the skin is not likely to change, the near-infrared region of the spectrum is less likely to provide a maturity index than the visible region (Bittner and Norris 1976). However, the near-infrared region may provide a reference wavelength for a multiwavelength index because of the absence of absorbers in that region. Long and Webb (1973) reported that a reflectance ratio of R 675 / Rsoo was less affected by the presence or absence of blush on peach. For maturity sorting of fresh peach, a reflectance ratio of R 67 01 Rsoo exhibited the largest differences between maturity classes (Delwiche et al. 1987b). "Universal" color indexes based on combination reflectance indexes at three wavelengths [(R650 - R9S0)/R9S0' (R 660 - RS20)/Rs20' and others] were developed for 'Ponkan' mandarin and tomato (Chen et al. 1990). Although the same indexes worked well with these fruits, Chen et al. recommended further research to verify the indexes on other fruits that exhibit a change in color from green to red or yellow during maturation. A reflectance index (R 670 - R 1150 )/R 1150 provided adequate separation of tomatoes for processing (Moini and O'Brien 1978). The effect of chlorophyll absorption is a major element in each of the reflectance indexes. Other terms are included to reduce the variability caused by instrumentation or geometric differences. Maturity of peach as defined by firmness was estimated by the reflectance ratio, R670/Rsoo (Upchurch et al. 1990b). Based on the reflectance ratio, fruit of four peach cultivars was separated into three maturity classes (immature, mature, and overmature) by predicting the firmness. It is apparent that the majority of maturity indexes have been based on pigment concentrations, that is, chlorophyll, anthocyanins, and carotenoids. However, with more sensitive optical systems and newer

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

45

chemometric tools, it is possible to detect carbohydrates, proteins, and fats that may provide useful maturity indexes in species with little pigment change during ripening, such as nuts or avocado (Fig. 1.7).

Surface Defects. Differences in the spectral composition of light reflected from various defects and from unblemished areas on fruit or vegetable surfaces have been used to identify wavelengths to distinguish blemishes. Gaffney (1976a) examined spectral reflectance curves from blemished and nonblemished citrus fruits. Since there is variability in reflectance from healthy tissue, a minimum difference of 15% from the normal reflectance is needed to adequately sort fruit (Gaffney 1976a). Wavelengths within the region from 540 to 700 nm appeared to be suitable for detecting defects on grapefruit, while a band of 580 to 650 nm was best for detecting defects in lemon. Radishes were scanned over the wavelength range of 300 to 2000 nm; however, differences between blemished and nonblemished areas occurred at wavelengths below 800 nm (Gaffney 1976b), and most of the defects were detected at either 550 or 675 nm. Moini et al. (1980) successfully sorted out tomatoes with mold and defects using the reflectance model, (R 670 + R960)IR960' A linear combination of reflectance values, 0.18 R 590 - 0.63 R 710 - 1. 73, was

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46

J. ABBOTT, R.

LV, B. UPCHURCH, AND R. STROSHINE

used as a quality index for separating tomatoes with black mold, gray mold, and sunscald from sound fruit (Ruiz and Chen 1982). By inspecting the difference spectra calculated by subtracting reflectance spectra for defective potatoes from those of sound ones, six wavelengths (630, 650, 710, 750, 830, 1410 nm) were identified for sorting potato into 14 classes (Porteous et al. 1981). Muir et al. (1982) successfully identified both latent and visible diseases on potato. Wavelengths selected were between 590 and 750 nm and in bands near 950, 1150, 1350, 1470, and 1850 nm. Wavelengths between 535 and 722 nm successfully identified dry rot, soft rot, black crown, and cavity spot on carrots (Howarth et al. 1990b). A three-wavelength index using 650,720, and 815 nm exhibited the largest class separation among five defects and nonblemished regions on peach (Miller and Delwiche 1991a). Four optical indexes with three wavelengths each were needed to distinguish among russet, bruises, and nonblemished regions on 'Empire' apple (Upchurch et al. 1991). 'Empire' apples have large areas of red and green; therefore, a tree structure classifier (similar in function to a binomial taxonomic tree) was required to sequentially distinguish russet, then the red and green areas, and finally bruises. Most of the optical indexes for detecting surface blemishes were in the visible region of the spectrum; however, most indexes for detecting bruises were in the near-infrared. Brown et al. (1974) and Reid (1976) showed that the reflectance from bruised apple tissue was less than the reflectance from unbruised regions. This decrease was attributed to the effects of filling the intercellular spaces with fluid (reducing scatter) and to chemical changes due enzymatic reactions. Bilanski et al. (1984), Pen et al. (1985), and Upchurch et al. (1990a) extended these observations and suggested specific wavelengths for detecting bruises on apple. Wavelengths in the visible region, 356 to 458 nm, worked best for peeled apples (Bilanski et al. 1984); while longer wavelengths, 720 to 840 nm, were more appropriate for unpeeled apples (Upchurch et al. 1990a). Miller and Delwiche (1991a) recommended wavelengths between 760 and 1115 nm for detecting bruises on peach. The anthocyanin and/or chlorophyll in the outer skin of unpeeled fruits has been a major hindrance when attempting to detect bruises. The measurement requires detecting the reflectance from the damaged tissue and not the reflectance from the skin since the skin is not damaged. Most of the research to date has limited the investigations to bruises that were 24 h old. In a recent study, the near-infrared reflectance from bruised apple tissues varied with time, bruise type and severity (Upchurch et al. 1994b). These results agreed with earlier studies that found a decrease in reflectance at 24 h. However, this recent study showed that the reflectance from a

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

47

bruised region increased within one month and continued to increase. This increase was caused by an increase in light scatter as the tissue became dehydrated over time. 2. Transmittance. Valid measurements of light transmission through optically dense materials such as fruits and vegetables are difficult. With optically dense materials, the amount of light reaching the detector on the opposite side is very low. Using wedge interference filters to construct a scanning monochromator, a transmission spectrophotometer was developed for scanning from 400 to 720 nm (Birth and Norris 1958). An integrating sphere collected all the energy transmitted through the sample; therefore, an average characteristic of the sample was measured. Birth and Norris (1965) described a simple instrument for measuring the internal quality of fruits and vegetables. Instead of measuring the full spectrum, their instrument measured optical-density differences at two to four given wavelengths obtained by using optical filters. The specific wavelengths for a particular application were identified from full spectral scans of samples of the product made on a spectrophotometer. A computer-controlled spectrophotometer was designed for scanning from 390 to 880 nm (Massie and Norris 1975). This unit used two separate light sources and detectors to cover the spectral range. It also used paired filters to ensure that the detector received only the wavelengths used for illumination, thereby eliminating confounding by unwanted fluorescence (see later section on fluorescence). Since the intensity of the light reaching the detector is very low, the amount of stray light through the optical components and around the sample must be minimized to minimize the noise to signal ratio.

Maturity. Transmittance has been used to objectively assess the maturity of fruits and vegetables. Since the light transmitted through the product is affected by both light scatter and absorption, researchers often report changes in transmittance as a difference in optical density between two wavelengths, ~OD(Al - Az). Changes in chlorophyll content affected the transmission spectra for tomato (Birth et al. 1957; Worthington et al. 1976), peach (Sidwell et al. 1961), and apple (Yeatman and Norris 1965). Sidwell et al. (1961) recommended ~OD(700 - 740 nm) for measuring the chlorophyll content in peach, while Yeatman and Norris (1965) suggested ~OD(740 - 695 nm) for apple. Olsen et al. (1967) used ~OD(690 - 740 nm) to sort apple into five ripeness categories according to chlorophyll content. They observed differences in firmness, soluble solids (SS), and acidity among groups. In an extensive three-year study involving taste panels, Aulenbach et al. (1972) recommended

48

J. ABBOTT, R. LU, B. UPCHURCH, AND R.

STROSHINE

i10D (700 - 740 nm) as a reliable indicator of quality in 'Delicious' apple, but found that the relationship to firmness varied from year to year. A i10D(510 - 600 nm) was effective for classifying green tomatoes into maturity classes, while a i10D(600 - 690 nm) was more effective for classifying tomatoes from the breaker stage through red ripe (Worthington 1974). A later report by Worthington et al. (1976) used a i10D(510600 nm) to predict the ripening time for tomatoes; however, over 60% of the green tomatoes were too dense for transmittance to be measured. Yeatman et al. (1961) used a i10D(540 - 612 nm) to measure the anthocyanin content in red tart cherry. They recommended using i10D(590620 nm) in a portable instrument because the anthocyanin absorbed most of the light energy that was transmitted through the fruit. For various blueberry cultivars, i10D(740 - 800 nm) or i10D(760 - 800 nm) provided the best measure of anthocyanin concentration (r = 0.92 to 0.97) (Dekazos and Birth 1970; Hamann et al. 1973). A i10D(630 - 690 nm) correlated with log of anthocyanin content (r = 0.98) for macerated grapes; intact fruit were not tested (Watada and Abbott 1975). A large portion of the grade for dates is based on moisture. Dull et al. (1991) found that NIR transmittance at two wavelength bands accurately predicted moisture of dates and suggested that sorting could be automated using a rapid-scanning spectrometer.

Internal Defects. Light transmittance has been explored for detecting internal disorders by measuring either a change in light intensity after passing through the object or absorption of light energy at specific wavelengths. Birth (1960) suggested an optical density difference between 800 and 710 nm for detecting hollow heart in potato. The brown tissue usually associated with the void exhibited an absorption band at 710 nm. Transmittance between 725 and 800 nm was attenuated by internal browning in apple, while the longer wavelengths exhibited a decrease in attenuation (Upchurch et al. 1995). Selecting a classifier based on the ratio between the transmittance at 720 and 810 nm, 90% of the apples with internal browning were correctly classified. Using ai10D(600 - 740 nm), 'Delicious' apples with moderate to severe internal browning were separated from sound fruit (Fukuda et al. 1979). The i10D(600 - 740 nm) reported by Fukuda et al. (1979) was unable to identify apples with slight internal browning; however, the transmission ratio, Tno/Tslo that was reported by Upchurch et al. (1995) detected apples with slight browning. Fluid filling the intercellular spaces in watercored apples reduces the light scattering within the tissues; therefore, more light is transmitted through the watercored apple than through nonwatercored apples. Birth and Olsen (1964) used an optical density difference between 760

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

49

and 810 nm as a nondestructive technique for detecting watercore. Fruit size and temperature affected the i10D(760 - 810 nm) technique (Fukuda et al. 1979). Although light transmittance was successful for segregating watercored apples, the measurement was affected by the length of time in storage before inspection (Upchurch and Throop 1994). Transmitted light decreased as storage time increased and made the transmission measurement unable to detect apples with watercore after four weeks in storage. Miller et al. (1995) used a commercial watercore tester to evaluate the internal quality of pickling cucumbers. The watercore tester used i10D(760 - 810 nm) as a quality index and was able to detect watersoaked lesions in the tissue when pickling cucumbers were bruised. 3. Interactance. Light transmittance has several disadvantages when measuring optically dense materials such as fruits and vegetables. Fruit size affects the transmission readings. The intensity of the light decreases logarithmically with distance from the source (Birth 1983). Fruits and vegetables are highly scattering materials so the path through the product is much greater than a straight line, and the light level transmitted through the fruit is very low in intensity. Therefore, these measurements require extreme care to minimize stray light. To overcome some of the problems with transmission measurements, an alternative technique measures the light transmitted through a small region of the intact fruit and is known as body transmittance or interactance. A ratio between the interactance at 620 and 670 nm was an indicator of red color in tomatoes and was highly correlated with the Hunter a * /b * ratio (Birth et al. 1957). To separate green tomatoes with an amber interior color from wholly green tomatoes, a ratio of 520 and 545 nm was successfully applied (Birth et al. 1957). Mounting the detector 2.54 em from the incident beam, Nattuvetty and Chen (1978) studied nine wavelengths between 550 and 730 nm for sorting tomatoes. Variety of tomato affected the optimum wavelength selected for the optical density measurement. OD 615 had the highest correlation for two varieties, while OD 670 and OD 730 were optimum for two other varieties. Interactance measurements at 620 and 588 nm were highly correlated (r = 0.97) with the chlorophyll content in papaya (Birth et al. 1984). In addition to chlorophyll, measurements at 643 and 520 nm were correlated (r= 0.94) with concentration of carotenoids, while measurements at 714 and 582 nm were correlated (r= 0.90) with SS. A difference in absorbance at 710 and 780 nm, M(710-780 nm), was highly correlated (r = 0.98) with chlorophyll content in tomatoes (Watada et al. 1976). Concentrations of lycopene and fJ-carotene were highly correlated with an optical measurement of M(570-780 nm) and M(550-580 nm), respectively.

50

J. ABBOTT, R. LU, B. UPCHURCH, AND R.

STROSHINE

Composition of fruits and vegetables changes during development, and these changes are important in defining the total quality of the product. In addition to changes in pigment, other compounds increase or decrease during maturation. These compounds include soluble sugars, acids, phenolics, lipids, vitamins, and several volatile aromatic compounds (Gortner et al. 1967; Charron et al. 1995). Most procedures for measuring these quality indexes involve destructive methods. Nondestructive techniques for measuring quality have been reviewed by Gunasekaran et al. (1985), Chen and Sun (1991), and Tollner et al. (1993). Development of nondestructive techniques for measuring the positive factors in peach, pear, mandarin, and apple is reviewed by Kawano and Iwamoto (1991), Kawano et al. (1994), Kawano (1994), and De Baerdemaeker (1994). Some chemical compounds that change during maturation of fruits and vegetables exhibit absorption at specific wavelengths. Using interactance, data at 906 nm were highly correlated (r = 0.99) with the percent dry matter in onion (Birth et al. 1985). Absorption at 906 nm is close to an absorption band associated with carbohydrates; therefore, Birth et al. (1985) suggested that the prediction equation developed to measure percent dry matter in onion could also be used to measure the concentration of carbohydrates in onion. Dull (1984) selected a ratio between 1701 and 1672 nm for predicting sucrose concentrations in cantaloupes, and later (Dull et al. 1989b) recommended 884 and 913 nm. A ratio between 884 and 834 nm was used for peach (Slaughter 1995). SSs has been measured in apple (Davenel et al. 1987; Murakami et al. 1994; Bellon-Maurel and Vigneau 1995), cantaloupe (Dull et al. 1989b), honeydew melon (Dull et al. 1992), peach (Kawano et al. 1992; Kawano 1994; Slaughter 1995), mandarin (Kawano et al. 1993), mango (Saputra et al. 1995), and tomato (Slaughter et al. 1996). A single calibration equation for predicting the moisture, sugar, and acid content in apple was developed by Murakami et al. (1994). There was no significant difference between a single equation and individual calibration equations for each component. Katayama et al. (1996) measured starch, sugar, and moisture using spectra from slices of sweet potato. Their prediction equations required small bias adjustments from year to year. Saputra et al. (1995) developed separate equations for predicting malic acid and sucrose in mango. Both equations required nine wavelengths between 1400 and 1900 nm. Slaughter et al. (1996) measured interactance of tomato from 400 to 1100 nm to determine SS content. They found that the wavelength range 800 to 1000 nm contained the most useful information and used between 6 and 10 partial least squares factors to predict SS (r = 0.92, std. error of calibration = 0.27% SS).

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

51

The major advances in spectral analysis in recent years have been in statistical methods. Early analyses used multiple linear regression of raw, first difference, or second difference spectra (Hruschka 1987). Later methods used various forms of data reduction such as principal component or partial least squares coupled with regression (Martens and Naes 1987). Present investigations focus on artificial neural networks and wavelets for data reduction (Resnikoff 1992; Strang 1994). There are advantages and disadvantages to each approach. Optical-filter instruments or multispectral cameras require wavelength selection rather than full-spectrum scanning. Rapid-scanning spectrophotometers are available and permit the use of all or large parts of the spectrum. Major advances in machine vision and image processing are enabling application of optical measurement on-line for sorting operations at commercially acceptable speeds. Real-time imaging now requires selection of one or a very few wavelengths to reduce the number of images that must be processed. 4. Machine Vision. Machine vision provides information about the spa-

tial distribution of the intensity as well as the spectral content of the light. Coupling a camera with a computer enables machines to automatically perform visual-based inspection tasks. The various functions performed by a machine vision system include image capture, image processing, and pattern recognition. There are several components that form a machine vision system (Fig. 1.8). The fruit is illuminated with light. Generally, tungsten halogen lamps are used for monochromatic vision systems. Fluorescent lamps with a high color rendering index are used in color inspection systems. To reduce the specular reflections from the glossy surfaces common to fruits and vegetables, a diffusing material is placed between the lamps and product. Lamp location is critical when viewing a spherical object such as a fruit. Light reflected from the object is measured by a solid-state camera (Fig. 1.8). The photodetector inside the camera is usually a charge-coupled device (CCD) with an array of 512 x 512 pixels (picture elements) (Fig. 1.9). Some systems use a line-scan camera in which a single narrow line is continuously scanned while the product moves or is rotated beneath the camera, building up an image similar to that on a television screen or computer monitor (Fig. 1.10). Cameras are available with analog or digital outputs; however, at present, most applications use analog cameras. The signal from the camera is digitized by a frame grabber within the computer. Typically, each pixel within the image is digitized to 8 bits which allows a gray-level resolution of 256 levels. After digitizing and placing the image in memory, a program processes the image to enhance the contrast of the area of interest. Pattern recog-

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Frame grabber

LU, B. UPCHURCH, AND R. STROSHINE

Micro computer

Fig. 1.8. A machine vision system consists of several subsystems that must be designed and integrated into a system to achieve acceptable performance.

nition routines are executed to identify clusters of pixels in the image. These routines can process grayscale or binary images. A binary image is generated by applying a threshold where each pixel can have only one of two values, that is, black or white. Other processing algorithms use the grayscale information and evaluate the spatial and intensity relationships among pixels.

Shape and Size. Digital image processing has increased the ease and accuracy of defining geometric shapes and measuring sizes of fruits and vegetables. Shape features such as minimum and maximum diameter, area, perimeter, curvature, and others were used to characterize the shape of potato (Howarth and McClure 1987; Marchant et al. 1988; Sistler et al. 1984). Using a single view, Marchant et al. (1988) described a system for sorting potatoes at 20 per second. Fourier frequency coefficients (spatial frequency) effectively characterized the shape of potato and could easily be extended to other irregularly shaped objects (Tao et al. 1995b). Twelve shape features were developed for grading asparagus based on straightness, roundness, and head compactness (Rigney and Brusewitz 1992). Algorithms that measured the length, width, curvature, blunted/broken tips, and tip shape were developed for sorting carrots (Howarth et al. 1990a; Howarth and Searcy 1989). Wolfe and Swaminathan (1987) developed the paired gradient and medial axis techniques

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Fig. 1.9. The photodetector inside the camera is usually a charge-coupled device (CCD) with an array of 512 x 512 pixels (picture elements). Images of a 'Crispin' apple at 750 nm (top) and at 1030 nm (center) (20-nm bandwidths). Bitterpit damage has reduced reflectance at 750 nm (dark spots) and increased reflectance at 1030 nm (barely visible bright spots). Subtracting the top image from the center image shows just the bitterpit damage (lower). (Courtesy ofD. J. Aneshansley and J. A. Throop, Cornell University, NY.)

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Fig. 1.10. Some systems use a linescan camera in which a single narrow line is continuously scanned while the product moves or is rotated beneath the camera, building up an image similar to that on a television screen or computer monitor. Images are 350 x 105 pixel line scans at wavelengths from 870 to 1000 nm of two 'Delicious' apples with a 1-day-old bruise (dark areas) and a 50-day-old bruise (light areas). Reflectance ofthe bruise increases with storage time due to dehydration of the damaged tissues. (Courtesy of D. J. Aneshansley and J. A. Throop, Cornell University, NY)

for identifying the irregular shape of bell peppers. For shape classification, locations of the stem and blossom ends were identified with the Hough transform on six orthogonal views (stem end, blossom end, and four sides). Machine vision systems for shape and sizing are being installed on commercial packing lines and are replacing the methods currently employed to size fruit. Present systems include sizing belts, diverging

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rollers, weight sizers, and optical sizers. Fruit with a diameter less than a minimum are removed as they pass through sizing belts. These belts have certain diameter openings, and any fruit larger than the opening continues in the process. Diverging rollers are used to achieve more size categories and allow the packer more flexibility in establishing size classes. As fruit pass along the rollers, they drop between the rollers when distance between rollers becomes greater than the minimum diameter of the fruit. To reduce damage and the number of transfers, newer machines are equipped with strain gages (weight scales) to measure the weight of each fruit as it passes over the scale. Most weight sizers can measure each fruit to within 1.5 g. Optical sizing is still in its early stages of application. Some citrus crops are sized optically, but the applications are very limited. From multiple projections through the centroid of the object, size, shape, and minimum and maximum diameters can be measured. One of the problems with optical sizing is the need to orient or know the orientation of the fruit during the operation. Another is that the packer must ensure that the net weight of all the fruit in the carton meets weight-per-package requirements. If the actual mass is greater than the estimated mass, the package will be overweight. Any excess weight represents economic loss. If the mass estimated from the volume is less than the actual mass, the package contains less product by weight than required and an additional fruit must be added; the package then becomes overweight.

Maturity. To the extent that chlorophyll content is indicative of maturity, imaging may be applicable for maturity sorting, although a sensor that integrates reflectance from a relatively large area of the product may be as useful. Lin et al. (1993) imaged reflectance at 550 nm to estimate color of cucumber. The relationship between chlorophyll content and grayscale level differed between harvest times (chlorophyll concentrations), but they suggest that it has potential for product sorting. Commercial color sorters using imaging are in use in many packing facilities with varying levels of accuracy. We were unable to find published research reports on their accuracy or comparisons among brands. They appear to be more satisfactory for products with fairly uniform color distribution, such as citrus and most apple cultivars, than for those with color extremes such as 'Empire' apple and nectarine. The amount of light scatter within a product is affected by air spaces and tissue density. Scatter may change with ripening of some products. The image size of laser light scattered near the point of incidence was negatively correlated to firmness of apple (Duprat et al. 1995). The number of pixels above a threshold was related to maturity stage of tomato

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and to sonic stiffness (firmness) changes in apple over 8 days (Tu et al. 1995). Skin coloration affected the results in apple (Tu et al. 1995), but selection of a different wavelength laser may overcome that problem.

Surface Defects. Extensive research effort has been expended to develop image processing techniques for detecting defects on fruits and vegetables. Imaging permits not only detection of relatively small abnormal areas that would not be detectable in a single-sensor measurement due to averaging, it also permits quantification of the area affected and, in some processing operations, could facilitate automated trimming of the damaged portion. Defects on a product can occur anywhere; therefore, detection of these flaws requires inspecting most of the surface area. Sarkai' and Wolfe (1985a) used a gradient profile to detect stemand blossom-end defects on tomatoes with 92% accuracy. Various techniques have been explored for detecting blemishes and bruises on apple. These approaches include thinness ratio of segmented clusters (Rehkugler and Throop 1989; Throop et al. 1995b), structured lighting (Yang 1993a,b), selective thresholding within zones (Davenel et al. 1988), pixel variations due to concavity of the area (Yang 1994), and cooccurrence texture analysis (Throop et al. 1994b, 1995a). Yang used a monochromatic camera without an optical filter. Rehkugler and Throop used a longpass filter to create an NIR image, while Davenellimited his investigation to the 550-nm wavelength band. Morphological features for clusters within regions after segmentation were evaluated for detecting defects on peach (Miller and Delwiche 1991b; Singh and Delwiche 1994; Crowe and Delwiche 1994). The correlations between the predicted and measured areas were 0.75 and 0.72 for bruises and scars, respectively (Singh and Delwiche 1994). Error rate associated with the classifier performance was less for a near-infrared system than for a color system (Miller and Delwiche 1991b). The number of pixels above a set gray level in a linescan image was effective for classifying defects on prunes with an error rate of less than 2% (Delwiche et al. 1990). Rigney et al. (1992) developed classification algorithms for detecting scars, cracks, and spreading tips for asparagus. Internal Defects. Generally, machine vision using reflectance measurements is not adequate for detecting internal defects; however, transmittance is useful for detecting some internal disorders. Throop et al. (1989) compared machine vision methods for detecting watercore by apple weight density and by light transmittance. By viewing the stem end with a camera while illuminating the calyx of each apple, all of the apples with watercore were correctly classified. Classification by sever-

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ity of watercore was camera dependent: A more sensitive camera was needed to separate slight and moderate classes, while a less sensitive camera was required for moderate and severe classes (Throop et al. 1994a). This technique requiredorientation of the apple so that the light energy entered the calyx end of the apple. 5. Multispectral Imaging. Multispectral imaging provides spectral information at two or more wavelengths in addition to spatial information. Color vision is a special case of multispectral imaging, which uses broad bandwidth signals. A color image is acquired by digitizing the three video signals (red, green, and blue) from the camera. Multispectral imaging is not limited to color; multiple images can be captured at different wavelengths in the visible and near-infrared regions. Generally, an interference filter on the lens of the camera allows an image to be acquired at a specific wavelength (narrow band); however, a filter wheel or multiple cameras are required when more than one wavelength is specified. More advanced multispectral imaging systems use acoustical optical tunable filters (AOTF) and liquid crystal tunable filters (LCTF). The waveband passing through the filter can be changed under computer control and can switch between wavebands in less than 40 ms. There are advantages and disadvantages for each device.

Surface Defects. Machine vision systems incorporating color information are feasible solutions for the inspection of fruits and vegetables. Color information about the product is captured using a color camera with three video signals: red (R), green (G), and blue (B). Using the R-G color space, peaches were sorted into six maturity categories (Singh et al. 1993). Comparing system performance with manual classification, 46% of the peaches matched manual placement and 75% of the fruit were placed within one class of the correct one. It is difficult to describe color in RGB color space; therefore, several researchers applied color transformations to convert from the RGB color space into HSI (hue, saturation, and intensity). When the quality feature of interest was color, hue information was used successfully to sort bell pepper (Shearer and Payne 1990), apple (Tao et al. 1995a; Heinemann et al. 1995), potato (Tao et al. 1995a), and tomato (Choi et al. 1995). Although classification accuracies as high as 100% were reported, most of these projects limited the number of color classes to two or three. Throop et al. (1993) demonstrated a color difference between bruised and nonbruised regions on 'Golden Delicious' apples. Hue in the HSI color space and red and green in RGB color space were most effective for distinguishing bruised from nonbruised regions on 'Golden Delicious' apples. Muir et al. (1989) used

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spatial information at eight wavelengths to detect 12 to 15 kinds of blemishes on potato.

Automatic Inspection Systems. Advances in microelectronics have increased the performance of machine vision systems while reducing the costs. On-line inspection systems for fruits and vegetables must be able to inspect most of the product's surface at line speeds of 4 to 10 fruit/silane. Sarkar and Wolfe (1985b) described an inspection system for sorting tomato by color and defects. The system was slow (6 s/fruit); however, no attempts were made to optimize the algorithm to run faster. Rehkugler and Throop (1986) described a handling system and sorting device for apple. With a spindle device to pick up and rotate each fruit, the system had a throughput of 30 apples/min. Both of the previously mentioned systems are far from the needed line speeds. Davenel et a1. (1988) developed a system with a throughput of five fruit/s for detecting defects on 'Golden Delicious' apples; however, apples were hand placed in a known orientation. For apples (and many other products) on a commercial packing line, orientation is a problem because the stem and calyx appear as defects during the inspection process. Orientation of apples is very difficult; therefore, structured lighting techniques for distinguishing the stem and calyx from blemishes are being developed (Yang 1993a,b; Crowe and Delwiche 1995). Structured lighting involves the projection of narrow, parallel bands of light over the product and analyzing the deviations from straightness of the lines in the image to determine the surface contour of the product. Blemishes often occur on convex portions of the product (cheeks) and the areas immediately around the stem and calyx are usually concave, so contours are useful in locating the stem and calyx. Development also continues on mechanical systems for orientation (Delwiche et a1. 1993; Throop et a1. 1995c; Rigney et a1. 1996); but accuracy of orientation is often dependent on species and cultivar. B. Fluorescence and Delayed Light Emission Fluorescence is the result of excitation of a molecule by high-energy light (short wavelength) and its subsequent instantaneous relaxation with the emission of lower-energy light (longer wavelength). Many agricultural materials fluoresce. However, nearly all applications of fluorescence reported in this chapter involve chlorophyll; therefore, unless otherwise qualified, the term fluorescence refers hereafter to that from chlorophylL Fluorescence and delayed light emission (DLE) are responses of the

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chlorophyll contained in fruits, vegetables, or leaves to excitation by electromagnetic radiation. Note that fluorescence and DLE do not directly involve transmitted or reflected light, but light emitted by the chlorophyll. DLE was formerly termed delayed fluorescence and, while very different in some ways, it shares several characteristics with ordinary chlorophyll fluorescence. Peak excitations of chlorophyll are induced by wavelengths around 420 nm (blue) or 660 to 6S0 nm (red) (Gibbons and Smillie 19S0; Walker 1990). However, excitation can be achieved using light over a broad range of wavelengths (Jacob et al. 1965; Walker 1990). Most energy of these wavelengths that strikes photosynthetically competent tissue is used in photosynthesis, but a small portion-3 to 5% in leaves (Walker 1990)-is dissipated as fluorescence. A much smaller proportion may be emitted as DLE. After excitation, radiation is emitted by the chlorophyll over a range of 650 to SOO nm (Butler and Norris 1963; Schreiber et al. 1975; Gibbons and Smillie 19S0; Walker 1990). An emission spectrum for a barley leaf at O°C is shown in Fig. 1.11; fruit have similar spectra.

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60

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ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

The peak emission occurs near 690 nm and there is a "shoulder" between 720 and 750 nm. Above 750 nm, emission declines smoothly until it reaches insignificant levels. Fluorescence in leaves has a lifetime of about 0.7 ns at 25°C (Butler and Norris 1963). However, it can be detected during continuous illumination by using optical filters to shield the detector from the wavelengths used to excite the sample. When the chlorophyll resides in functional chloroplasts, the fluorescence shows characteristic kinetics because it is influenced by the dynamic oxidationreduction balance in the chloroplast and photosystem II activity (Schreiber et al. 1975; Walker 1990). Therefore, fluorescence is typically monitored for at least 1 s (Fig. 1.12)(Schreiber et al. 1975). Reproducible measurements of fluorescence or DLE are obtained only when excitation is preceded by a dark period; the optimum dark time varies with the species and is typically 10 min or longer. It is therefore necessary to work in the dark or in very dim green light (the chlorophyll excitation minimum) before the initiation of measurement. Delayed light emission is excited by reverse reactions along the photosynthetic pathway and occurs only in intact, functional chloroplasts. The chlorophyll molecules are stimulated to initiate photosynthesis, but the process does not go to completion and some of the energy is transferred back through reverse reactions to reexcite chlorophyll with the resultant emission of energy. The excitation and emission spectra are similar to those of fluorescence, but the timing differs. A DLE emission spectrum for a green peach (Jacob et al. 1965) is shown in Fig. 1.13. The peak occurs at about 680 nm and there is no "shoulder" at 720 nm as there is in fluorescence, although spectra may vary slightly among species and types of tissue. DLE can be detected for times up to an hour after a single illumination with sufficiently sensitive instrumentation (J. A. Abbott, pers. comm.). There is evidence of at least three energy pools being involved in DLE, each contributing to the emission at different times. In contrast to fluorescence, DLE is detectable only in the dark. After a single illumination, DLE intensity decays exponentially and it persists for time periods ranging from milliseconds to several minutes. However, the energy levels of DLE are much lower than those of fluorescence, and DLE measurements require more complicated equipment than fluorescence measurements. Chlorophyll content and its photosynthetic capacity are often related to maturity and to certain defects or injuries. Fluorescence and DLE have been studied as possible methods for evaluating maturity in fruits and vegetables that lose chlorophyll as they ripen or mature. This allows the measurements to be correlated to maturity or to changes in internal composition (such as increases in SS) which accompany maturation.

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Fig. 1.12. Chlorophyll fluorescence over time for a dark adapted green mango. Upper: Fluorescence during the first second after illumination. Lower: Fluorescence for 10 s after illumination. Fluorescence rises almost instantaneously to F 0, levels off for several tenths of a second at an intermediate peak, increases to a peak value Fp , and then declines. Difference between Fo and Fp is variable fluorescence, Fv . FR is rate of rise to Fp . (Redrawn from Smillie et al. 1987, with permission.)

J. ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

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Physiological stresses that affect chloroplasts or photosynthesis, such as temperature, salinity, moisture, and atmospheric pollutants, can also affect fluorescence and DLE. Although these stresses can be a source of error in maturity measurements, the sensitivity to at least one of these stresses, temperature, creates the potential for using fluorescence and DLE to detect injury caused by chilling or heat treatment. 1. Fluorescence. Fluorescence measurements of chlorophyll-containing tissue are routinely used for investigations of photosynthetic activity in plant leaves. Melcarek and Brown (1977a,b) evaluated relative chilling susceptibility of various tree species by measuring the fluorescence of leaves as their temperature was sequentially lowered. Smillie and Nott (1979) used fluorescence to investigate photosynthetic activity of chilled leaves from two cultivars and two wild forms of tomato, one of which was adapted to high altitudes (low temperature and high light intensity). Sets of leaves from each plant were subjected to two temperatures, O°C

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or 10°C. Photoreductive activity decreased with time of storage at O°C for both tomato cultivars, but it decreased more rapidly in the plants that were not adapted to high altitudes. Smillie and Nott were perhaps the first to make fluorescence measurements on fruit. They found that the photoreductive activity responded to temperature stress similarly in mature green tomatoes and in leaves from the same plants. In their discussion, Smillie and Nott stated that, since the skin and sometimes the flesh of many tropical fruits are green at some stage in their development, the results obtained with tomato fruit suggest that the assay could be used to determine the susceptibility of many fruits to chilling injury. Smillie and coworkers subsequently related changes in fluorescence to both ripening and chilling injury (Smillie et al. 1987). They illuminated banana and mango with blue light and then detected fluorescence with a photomultiplier protected by filters. Fruit were kept in the dark for at least one hour prior to testing. Fig. 1.12 shows their measurements of fluorescence of a green mango as a function of time. Two time scales are shown to illustrate the overall response as well as the behavior during the first second. After an initial rapid (within 1 ms) increase to Fo , fluorescence leveled off to an intermediate peak at approximately 0.4 s, then began to increase almost linearly until 4 or 5 s after illumination when it reached a maximum, F p • Two parameters can be calculated from fluorescence curves: F v , the variable fluorescence which is defined as Fp - F0; and FR , the maximum rate of rise between the intermediate and maximum peaks. A measurement of F R requires 1 to 2 s. Smillie and coworkers evaluated chilling injury on banana stored at O°C, 13°C, or 20°C (Smillie et al. 1987). As green bananas ripened at 20°C, F o and FR/F0 decreased as a result of both loss of chlorophyll content and decrease in photosynthetic competence per unit chlorophyll. F R decreased exponentially with time in storage at O°C and the decrease was much more rapid than that at 20°C. However, F R did not decrease in bananas stored at 13°C. In the same paper, Smillie and coworkers describe tests on mango stored at temperatures between O°C and 15°C for four weeks. Decreases in Fv/F0 corresponded to ripening in fruit stored at 15°C, as indicated by changes in skin color. However, Fv/Fo also decreased in fruit stored at 5°C, where ripening was largely suppressed; that decline was attributed to chilling injury. The authors concluded that it was not possible to use Fv/F0 to distinguish between ripening and chilling injury. However, there was no variable fluorescence, F v , in fruit stored at O°C. The authors interpreted this as an indication of severe chilling injury and concluded that the method could be used to screen cultivars for chilling tolerance. Superficial scald of apple appears to be a chilling response (Watkins et al. 1995). DeEll et al. (1996)

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reported that F v measured at harvest was correlated with the development of superficial scald during storage (r = 0.43 to 0.50) in an early harvest of' Sturdeespur Delicious' apple but not in subsequent harvests. F v at harvest was not related to scald development in 'Imperial Delicious' apples, even though there was an unusually high incidence of scald in apples from the second harvest of that cultivar. Fluorescence sensing can be used to detect superficial scald on apple at packout because the necrotic tissues do not flouresce (J. A. Abbott, pers. comm.). Results of a study on broccoli indicated that fluorescence could be used for nondestructive monitoring of the early stages of deterioration during the storage of green vegetables. Toivonen (1992) reported that F v declined as a result of loss of chlorophyll function in stored broccoli although no chlorosis (yellowing) was apparent. When he compared F v measurements to two other established methods of assessing changes in tissue condition, he found that F v was more closely correlated to respiration measurements (r = 0.83) than to vitamin C concentration (r = 0.42). Several relatively recent studies have demonstrated the use of fluorescence to monitor damage caused by hot water treatment (HWT). HWTs are used for insect disinfestation or to delay senescence and involve maintaining the temperature of the core of the fruit at a specified level for a specified time. Tian et al. (1996) reported that HWT of broccoli heads to reduce yellowing in storage reduced the ratio Fv/Fp . They observed that fluorescence was clearly affected by the HWT and suggested that it has the potential to discriminate between beneficial heat treatments that maintain green color and excessive treatments that result in damage. Joyce and Shorter (1994) heated mango from 22°C to 37°C over 7 h using air at 37°C and then held this temperature for 0 to 12 h (preconditioning). Their HWT consisted of bringing the core to 47°C and holding that temperature for 25 min. They found that fluorescence was affected by HWT and that preconditioning did not ameliorate the effects of HWT on fluorescence. Jacobi et al. (1995) used a preconditioning temperature of 39°C and HWT temperature of 45°C on mango. HWT, with or without preconditioning, reduced fluorescence to approximately 60 to 70% of the value for untreated fruit. Jacobi and coworkers found that fluorescence did not provide a measure of subsequent skin scalding. They did not give correlations between fluorescence and other quality parameters. However, based on the graphs they presented, only colorimeter measurements and subjective color ratings were related to fluorescence. A relatively new fluorescence technique is pulse amplitude modulated (PAM) fluorometry. Previous methods have detected initial fluorescence (F0) which is related to the amount of chlorophyll present,

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maximum fluorescence (Fp ), and variable fluorescence (F y ), which is responsive to changes in photosystem II. PAM fluorometry also enables the investigator to determine features related to quenching due to electron transport, proton pumping of ATPase, and pH gradients in the thylakoid membrane. In PAM fluorescence a weak modulated measuring light (1.6 kHz at 650 nm) is applied to the fruit. This is followed by a saturating light pulse applied for 600 ms, and then illumination with an actinic red light (650 nm). The saturating pulses continue at intervals of 5 s after the actinic red light has been applied. Lurie et al. (1994) used PAM fluorescence to follow development of chilling injury in whole green peppers, correlating it to potassium leakage from fruit disks (a standard measure of chilling injury) and development of visible symptoms of injury. They reported that F y responded to chilling temperatures even before the tissue became susceptible to chilling injury and recovered significantly when the fruit were returned to nonchilling temperature. However, nonphotochemical quenching decreased simultaneously with increases in membrane leakage and other signs of injury. The authors concluded that PAM fluorometry has the potential to estimate rapidly and nondestructively the chilling tolerance of chloroplast-containing fruit. Total measurement time was not stated in the article, but it appeared to be approximately 1 min (Lurie et al. 1994). Woolf and Laing (1996) used PAM fluorescence to study HWTs and pretreatments of avocado. They reported that fluorescence responded immediately to the heat treatments, but that pretreatment conditioning that eliminated visible heat damage responses did not ameliorate the heat damage to the chloroplasts. They concluded that fluorescence reflects the effect of heat on the photosynthetic system in avocado fruit but does not necessarily indicate the overall health of the skin. Several studies have used fluorescence photography to detect damage to plant tissue which contained chlorophyll. Gibbons and Smillie (1980) used a 35-mm camera to photograph barley leaves exposed to O°C or 21°C for 24 h and then illuminated with blue light. Abbott and Massie (Abbott et al. 1994b) used an image-intensified video camera to obtain images of cucumber fluorescence excited by red light, showing chilling injury. In Fig. 1.14, fluorescence images demonstrate distribution of applied wax, surface injury, and decay on apple. Several common pathogens that affect fruits or vegetables are known to fluoresce. Most have distinctly different excitation and emission spectra from those of chlorophyll. As noted in the previous section on machine vision, visible and near-infrared imaging is currently used in some sorting applications. Advances in image analysis hardware and software may permit fluorescence imaging to be used for real-time detection and location of

0) 0)

Fluorescence images of 'Granny Smith' apples. Left: Wax coating over healthy tissue; blue excitation. Light streaks are fluorescence from applied wax. Center: Moderate storage scald; red excitation. Dark areas indicate necrotic tissue. Right: Decay lesions; red excitation. Light areas bounded by dark are fluorescence from decay organisms; other light areas (e.g., upper right) are due to saturation of the camera (imaging conditions were not optimized for spherical objects). (Courtesy of J. Abbott, J. McMurtrey, M. Kim, and D. Massie, USDA, Allricultural Research Service.)

Fig. 1.14.

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damage on fruits and vegetables with significant amounts of chlorophyll in their epidermal tissue. Chlorophyll fluorescence measurements have been used primarily as laboratory procedures for evaluating damage to chloroplasts and the photosynthetic system. An application not based on chlorophyll is the work of Uozumi et al. (1987). They detected mechanical injury of 'Satsuma' orange rind by using fluorescence to measure oils that leaked from damaged oil cells. While we recognize that expression of juice and chemical extractions are certainly not nondestructive, there have been several studies of extracted fluorescent compounds (other than chlorophyll) that develop during ripening and senescence of fruits and vegetables. These suggest possibilities for future development of nondestructive quality assessment methods. Maguire and Haard reported increases in fluorescent lipid peroxidation products in solvent extracts of cell membranes of ripening pear (1975) and banana pulp and peel (1976). Meir and coworkers reported similar products extracted from ripening avocado peel, but not from flesh (1991b), and from senescing parsley (1991a). The compounds had excitation peaks at 300 and 355 nm and peak emission occurred at 460 nm. Seiden et al. (1996) related fluorescence of pasteurized apple juice to traditional harvest indexes. They excited the samples using 265 and 315 nm and measured emission spectra from 275 to 560 nm. Their two cultivars could be distinguished using principal component analysis; and SS content, but not acidity, could be predicted (r = 0.80 and 0.75). The authors point out that sugars, which constitute most of the SS, do not fluoresce, but sugars apparently develop in parallel with other compounds that do fluoresce. They did not attempt to identify the fluorescencing compounds. Studies by Smillie et al. (1987) and Jacobi et al. (1995) indicate the potential of chlorophyll fluorescence as a means of detecting thermal injury. Although chlorophyll fluorescence was able to detect whether mango and avocado received heat treatment, it appeared to be incapable of detecting changes in the quality of these fruits caused by the treatment. At its present stage of development, chlorophyll fluorescence appears to be practical for quality monitoring of incoming fruit shipments where fruit are sampled and tested in a laboratory. It can detect damage several hours after it occurs. PAM fluorescence provides additional information at the expense of additional measurement time. It may also be of use in quality control. Additional work is needed to determine whether fluorescence measurements on one or more regions of the fruit surface could be used for on-line sorting for chilling injury. Advances in electronics and data processing may permit fluorescence imaging of

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the entire fruit and subsequent quantification of the extent of damage. Additionally, there are other fluorescing compounds in fruit which may provide information on the state of ripeness or senescence, but nondestructive methods to detect these have not been reported. Smillie et al. (19S7) stated that fluorescence measurement permitted rapid (1 to 2 s) and nondestructive measurement compared with alternative methods but had the disadvantages of a dark equilibration requirement and a need to work in dim green light to minimize unwanted excitation before measurement. The disadvantages have been partially overcome by modulated chlorophyll fluorescence fluorometers, capable of measuring fluorescence in ambient light, now on the market. They listed the following potential uses for fluorescence: measuring ripening in fruit with green peel or flesh, detecting pre- or postharvest chilling or heat injury, and assessing fruit condition during or after high temperature treatments used for destroying insects. Advances in image analysis hardware and software may permit fluorescence imaging to be used for real-time detection and location of damage on fruits and vegetables with significant amounts of chlorophyll in their epidermal tissue. 2. Delayed Light Emission. Jacob et al. (1955) conducted one of the first evaluations of DLE as a method for maturity sorting of tomato, oranges, and lemons. They studied several of the basic factors affecting DLE and built a prototype which they eventually mounted on a tomato harvester. When a specimen which has been in the dark is illuminated, the intensity of the subsequent DLE rapidly increases and then decays exponentially (Fig. 1.15, lower). Within 2 s after illumination, Jacob and coworkers recorded initial DLE intensities of the order of 10-3 watts/cm 2 and 10-12 watts/cm 2 for green and mature specimens, respectively. Although the intensity of the initial DLE was affected by the time and intensity of the illumination (Fig. 1.15, upper), for levels in excess of 1,OSO lumens/m 2 the initial DLE reached saturation in 1 s or less. Furthermore, DLE was essentially a surface phenomenon with tissue 2.5 mm or more from the surface making no contribution to the emission. Depth of tissue contributing to DLE (or fluorescence) depends on light scattering by the tissue and concentration of absorbing pigments; for example, red pigments in eggplant skin absorb the red wavelengths emitted by the chlorophyll and thus prevent detection of DLE (J. A. Abbott and K. H. Norris, pers. comm.). Several investigators have developed equipment which was more suitable for rapid field measurements of DLE. Chuma et al. (19S2a,b,c,d) used a 50-watt ring-type flash lamp for excitation instead of the tungsten lamp and shutter used in their laboratory studies. The half-width of the flash duration was 24 )1s. They stated that this mech-

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Fig. 1.15. Intensity of delayed light emission (DLE). Upper: Effect of time of illumination on the initial intensity ofDLE for an illumination of 300 lumens/m2 • Lower: DLE intensity versus time for lemons illuminated with a 100-watt incandescent lamp located 1.2 m from the specimen. (Redrawn from Jacob et al. 1965, with permission.)

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anism would be more suitable for practical sorting work because it has fewer moving mechanical parts, such as the shutter, and it could hold up under continuous operation. For this equipment, they found that the minimum dark period was 5 min when delay times before DLE measurement were 0.3 s or longer. For temperatures between 11°C and 26°C, temperature had no effect on DLE providing the delay period exceeded 0.2 s. Forbus et al. (1985a) developed a relatively simple and flexible DLE meter which used a 100-watt tungsten lamp. They (1985b) used the meter for DLE measurements on tomato and found that 0.25 s after illumination terminated, values from red and breaker fruit were 0.07 and 0.44 times those of green tomatoes. Jacob and coworkers found that a delay of 5 to 10 s between illumination and measurement produced a substantial difference in DLE among fruit with different maturity levels. The disappearance of DLE with time for light green and "silver" lemon is shown in Fig. 1.15 (lower); note the difference in DLE 10 s after illumination. Although DLE was affected by fruit temperature, in field tests on tomato there were no difficulties attributable to temperature. DLE is proportional to area and Jacob and coworkers chose to examine only a single spot on each fruit, which prevented them from obtaining an average for the entire fruit. This is unsatisfactory for fruit having uneven distribution of chlorophyll over the surface; however it should be possible to compensate for area or to use imaging techniques to overcome the effect of size. In 1966, the USDA (J. N. Yeatman; pers. comm.) built an instrument to sort tomato for maturity and green shoulders. To overcome the problem of uneven size and pigment distribution, fruit were tumbled past three photomultiplier tubes which detected the DLE from different sites; the fruit were sorted according to the summed DLE. Chuma and coworkers conducted several studies in which they evaluated factors such as dark equilibration prior to illumination; excitation time, intensity, and area; fruit temperature; peel surface treatment; and storage conditions. One of their first studies was conducted on 'Satsuma' orange, for which peel color is an important grading factor (Chuma et al. 1977). After a 20-min dark period, they found that DLE intensity reached a maximum when excitation energy was at least 2,750 lumens/m 2 for 4 to 7 s. The DLE intensity at 0.75 s after illumination increased as peel temperature was increased to 30°C, but then declined to almost nothing when temperature reached 41°C. DLE was also affected by storage time and temperature. DLE from oranges stored in the light at ambient conditions for two weeks decreased to 2% of the initial intensity. However, for oranges stored at either 2°C or in the dark at ambient temperature, the DLE after two weeks was 60 to 70% of the initial intensity. In tests

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on banana, Chuma et al. (1980) found that DLE intensity 0.7 s after illumination decreased as SS content increased from 2 to 17%. Optimum DLE results were obtained after at least a 10-min dark period and 1,375 lumens/m 2 illumination for 1 to 2 s. The temperature response was different from that of orange. DLE gradually increased as peel temperature increased to a maximum at 26°C and then gradually declined as peel temperature continued to increase to 35°C. Chuma et al. (1982b) obtained maximum DLE with excitation at 5,500 lumens/m 2 for 3 to 6 s; note that this is a much higher level of illumination than was used for banana. They obtained maximum DLE from tomato at flesh temperatures of 13°C to 17°C and they reported a linear relationship between DLE and chlorophyll content. A number of investigators have evaluated DLE as a method for determining the maturity of fruits and vegetables. Forbus and coworkers evaluated the use of DLE for maturity selection in muskmelon (Forbus and Senter 1989; Forbus et al. 1991a), canary melon (Forbus et al. 1992), peach (Forbus and Dull 1990), and persimmon (Forbus et al. 1991b). In many cases, maturity sorting by DLE is complicated by the fact that DLE may vary significantly from one region of an individual fruit to another. In their evaluation of papaya maturity, Forbus et al. (1987) found that DLE measurements on the abaxial side of the fruit were lower than those on the adaxial side even though chlorophyll content was higher on the abaxial side. They attributed this to differences in photosynthetic rate caused by differences in exposure to direct sunlight. DLE measurements from the blossom end were most effective in predicting maturity. However, immature papaya had lower DLE values than mature green fruit. The authors suggested that, in a sorting operation, the immature green fruits would have to be removed prior to DLE interrogation. In their study of DLE of peach, Forbus and Dull (1990) measured peaches in three locations: blossom end, blush, and ground areas. They observed that neither DLE nor chlorophyll content varied significantly among the three positions on the peach and concluded that it would be easier to adapt DLE to automated sorting of peach because orientation of the fruit would not be necessary. Several investigators have evaluated the use of DLE for detection of injuries to fruits and vegetables caused by chilling or excessive heating. It is possible to optically detect the physical effects of some of these injuries. However, such changes usually do not show up until several days after the actual damage occurs. Chilling injury results from exposure of susceptible products to temperatures below about 10°C but above freezing, as may occur with poor temperature control in refrigerated storage or when susceptible fruits are shipped with fruits requiring relatively

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low storage temperatures. Heating injury can result from exposure to the sun during holding or transport or from thermal disinfestation of fruits such as mango, papaya, and avocado. Such heat treatments are alternatives to treatment with pesticides. Typically, the fruits are exposed to hot water at temperatures around 45°C for approximately 30 min. Heat damage is sometimes reduced by preconditioning at a lower temperature, such as 37°C for 30 to 420 min. Chan and Forbus (1988) investigated the heat of inactivation of the papaya's DLE system by measuring the DLE of papaya heated to 42°C to 51°C for times between 0 and 100 min. There appeared to be three different luminescence components in papaya. They concluded that the DLE system was not as sensitive to deleterious effects of heat as was the ethylene-forming enzyme system. However, DLE measurements are nondestructive and the effects of heat on DLE can be measured immediately after treatment. It may be possible to develop DLE imaging devices for the purpose of locating and quantifying chilling or heating damage on individual fruits and vegetables. Sundbom and Bjorn (1977) photographed delayed light emission from leaves using an image intensifier. No references to DLE imaging of fruit were found in our literature search, but it should be possible using flash excitation, image intensified cameras, and electronic gating. Abbott and Massie (1985) developed a refreshed delayed light emission (RDLE) system for detection of chilling injury. The system used monochromatic light (ca. 655 nm) to minimize the heat buildup in the sample chamber that normally occurs when white light is used. Rotating shutters provided an alternating pattern of illumination followed by measurement of DLE. The illumination and measurement periods were each 7 ms in duration, followed by a delay of 0.5 ms. DLE curves were obtained by monitoring RDLE through 1,000 illumination-measurement cycles, a total of 15 s. Abbott and Massie (1985) used their system to measure RDLE from cucumbers and bell peppers exposed to storage temperatures between 2.5°C and 12.5°C for times varying from 12 to 288 h. The DLE from fruit that had not been chilled (e.g., kept at 12.5°C) showed a gradual rise in DLE to a maximum after about 500 cycles (7.5 s) and then a gradual decline. Fruit with chilling injury (e.g., stored at 2.5°C for 36 h) demonstrated a rapid rise in DLE for the first 50 cycles (0.75 s) followed by a gradual rise to a maximum that was approximately 50% of the maximum DLE observed for undamaged fruit. Maximum RDLE tended to decrease with time of exposure to chilling temperature. Injury scores for fruits were determined by rating severity of pitting, watersoaking, shriveling, and decay. Correlations between pitting and watersoaking scores and RDLE were relatively low; however, injury scores

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were based on evaluations of the entire fruit, whereas RDLE was measured on one 2.5-cm-diameter area. Abbott and Massie concluded that RDLE values at 6 to 7.5 s or the slopes of the RDLE curves around 2.7 to 4.5 s would provide a nondestructive and rapid indication of chilling exposure for cucumber and bell pepper. In an abstract describing RDLE work on tomato, Abbott et al. (1986) reported that maximum RDLE was correlated with visual ripeness of nonchilled (Le., nonstressed) (r = -0.88) but not of chill-stressed fruit (Fig. 1.16). Abbott and coworkers demonstrated suppression of fluorescence and DLE following cold exposure of chilling-susceptible coleus (1987) and African violet plants (1994a), cucumber (1985, 1991) and bell pepper (1985) fruits, and no suppression in cold-hardy Stokesia plants (1994a). Abbott et al. (1991) compared RDLE values to the amount and type of mechanical injury to pickling cucumbers. Fruit were damaged by either dropping them or rolling them between two boards to simulate damage incurred during mechanical harvesting. The damaged cucumbers were stored for up to 48 h at 2.5°C to 38°C. Mechanical damage reduced the amount of RDLE throughout the entire 15-s measurement period. They concluded that RDLE could be used to discriminate between mechani-

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cally stressed and nonstressed cucumbers within about 1 h after the damage occurred and differences persisted for at least one day. The abundance of work demonstrating a reasonable correlation between DLE and maturity indicates that it has considerable potential for commercial maturity sorting. It should be possible to make measurements on several fruit per second; however, this has not been tested on a sorting-line prototype. Obstacles to implementation of the technique appear to be the variability in DLE caused by prior storage of the fruits, the necessity for orientation of most types of fruit, and the requirement for holding fruit in the dark for a period of time (e.g., 10 min) prior to testing. The work by Jacob and Chuma and their coworkers illustrates that fruit temperature effects on DLE vary among fruit types. Variations caused by this and other factors suggest that fruit temperature would have to be controlled (or measurements would have to be adjusted for temperature) and that methods and equipment would have to be optimized for each type of fruit. DLE is closely related to chlorophyll content and is therefore an indirect, although relatively accurate, measurement of maturity. This was demonstrated by the high correlations which Chuma and Forbus and their coworkers obtained between DLE and maturity indexes. However, Forbus indicated only moderate correlations between DLE and two other important quality attributes, firmness and 55. For some fruits, the latter quality attributes may be more important to the consumer than chlorophyll content. An additional complicating factor is the effect of chilling or heating injury on DLE. In some fruit, it could be difficult to distinguish effects of thermal injury from effects of maturity level. It should be noted here that there are alternative methods for determining chlorophyll content or other indicators of ripeness, such as firmness or 55 content (see earlier sections on mechanical or optical properties). Abbott et al. (1991) summarized some of the difficulties associated with using DLE for detecting chilling injury of pickling cucumber; these difficulties would apply to using DLE for evaluating stress levels in most fruits and vegetables. They listed variability among cucumbers, their relatively low individual value, and the requirement for dark equilibration as reasons why there was relatively low potential for use ofDLE for on-line sorting. However, they proposed that the method could be used as an inspection tool to detect loads of cucumbers that had been subjected to excessive stress. Furthermore, individual inspection could conceivably be used for high-value fruits such as papaya. It is possible that either fluorescence or DLE imaging systems may be developed for the purpose of first locating and then quantifying areas on fruits and vegetables where there is damage caused by chilling or excessive heating.

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The potential of RDLE has not been investigated fully and appears to merit further investigation.

c.

X-Ray and Gamma Ray

X-ray and gamma ray have been explored for inspecting the interior of agricultural commodities. Both are electromagnetic radiation with a short wavelength. X-rays result from a loss of energy of excited orbital electrons whereas gamma rays are produced by a nuclear transformation. The basic equations governing the absorption of energy apply to both sources. Beer's Law, 1 = 10 e-f.1p t, is used to describe the absorption of Xray energy through a product. The intensity 1 of the energy exiting the product is dependent upon the incident energy 10 , absorption coefficient fl, density of the productp, and sample thickness t. Due to the high moisture content in fruits and vegetables, water dominates the absorption. Anatomical and physiological changes within the tissue of fruits and vegetables affect the quality of the product. Some of the changes such as cell breakdown, hydration, dehydration, chemical conversion, internal decay, softening, and insect injury have negative effects on quality. Internal disorders cited in grade standards include cork spot, bitter pit, watercore, and brown core for apple; blossom-end decline, membranous stain, black rot, seed germination, and freeze damage for citrus; and hollow heart, bruises, and black heart for potato. Changes in the density of the internal tissue are usually associated with these defects. Two techniques employing X-ray sources have been described for measuring the interior quality of fruits and vegetables. The most common radiography uses a linescan which is similar to the inspection systems used in airports for inspecting baggage. This method passes the product through a plane of X-ray energy. The intensity of each beam at the detector is an integration (or summation) of the energy absorbed along the path from the emitter to detector. The pathlength increases from the edge toward the center of spherical or cylindrical products, so the apparent intensity is lowest at the center. An example of an X-ray linescan is shown in Fig. 1.17. The X-ray absorption properties of a product are also measured with X-ray computed tomography (CT). ACT scanner measures the X-ray beams over several different paths through the object, computes a three-dimensional projection, and then computes a "slice" of that projection. The result of a CT scan is a two-dimensional image of the X-ray absorption through that slice of the product (Fig. 1.18).

Absorbance of X-ray and gamma ray is associated with tissue density and water content, and the technique has possibilities for measuring the

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internal quality of fruits and vegetables. As a lettuce head matured, the attenuation of X-ray and gamma ray through the head increased and was used to distinguish mature from immature lettuce heads (Lenker and Adrian 1971; Garrett and Talley 1970; Schatzki and Witt 1981a, 1981b). Researchers have investigated X-ray techniques for detecting hollow heart in potato (Nylund and Lutz 1950; Finney and Norris 1973), bruises on apple (Ziegler and Morrow 1970; Diener et al. 1970), split pit in peach (Bowers et al. 1988), and freeze damage in citrus (Johnson 1985). To improve the contrast between sound tissue and defective areas, Finney and Norris (1973) and Ziegler and Morrow (1970) recommended placing the product in water during the inspection process. X-ray computed tomography was used to evaluate density changes in the locules of tomato fruit during maturation (Brecht et al. 1990) and in watercored apple tissue (Fig. 1.18) (Tollner et al. 1992). With limited success, internal sprouting and ring separations due to microbial rot in onion were detected with X-ray linescan (Fig. 1.17) (Tollner et al. 1995). Keagy et al. (1996) are investigating the detection of insect infestations using imaging of multiple nuts. They reported that it was possible to identify most, but not all, insect-infested pistachio nuts. Schatzki et al. (1996) reported on the detection of insects, decay, and bruising in fruits. There are two applications where X-ray inspection systems are now used to detect internal defects on-line. Inspection systems using X-ray are employed to detect potatoes with hollow heart in commercial packinghouses. To reduce the operating cost, the detector is switched on or off depending on a density estimate made on the potato prior to the X-ray

Fig. 1.17. X-ray line scanimage for detecting the ring separations in onions. Left: Original image. Right: Image processed with Sobel filter to enhance detail. (Images courtesy of E. W. Tollner, University of Georgia.)

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Fig. 1.18. An X-ray computed tomography (CT) image of apples with and without watercore and bruises. (Courtesy of E. W. Tollner, University of Georgia.)

station. Dehydration of the pulp occurs when a citrus crop experiences freeze damage. This change in the pulp affects the X-ray density of the fruit and is detected with an X-ray system. The potential for X-ray inspection will increase. Components in X-ray systems are improving. With improved signal-to-noise ratio in the detectors, energy levels required at the source are decreasing; therefore, sheet metal is an adequate material for shields. The lifetime of the detectors is increasing. Manufacturers are supplying detectors with a lO-year lifetime which is a dramatic increase over the previous one-year lifetime. D. Magnetic Resonance and Magnetic Resonance Imaging Magnetic resonance (MR) sensing is based on the fact that certain nuclei, including lH, l3C, 3lp, and 23Na, interact with electromagnetic radiation in the radio frequency (RF) range in the presence of an externally applied magnetic field. One analogy frequently used to describe the phenome-

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non is that the nuclei act as tiny bar magnets which align with the external field. When RF radiation is applied to the aligned nuclei, they are excited to a higher energy level. This can be visualized as a reorientation of the nuclei (the tiny bar magnets in our analogy). When the RF excitation is removed, the nuclei return to their original energy state, releasing energy and inducing an RF signal in a receiver coil. For our bar magnet analogy, this means that the reoriented magnets return to their original alignment. The realignment, called relaxation, occurs at a constant rate. During reorientation, the net magnetic field which the nuclei create rotates within the coil, inducing a current in it. The signal induced in the coil, which is in the range of microvolts, is amplified, digitized, and can be used to generate images. It should be noted that, in order to achieve excitation, the RF energy must have a specific "resonance" frequency (typically in the range of 2 to 200 MHz) which is dependent on the type of nucleus being excited and directly proportional to the strength of the externally applied magnetic field. In pulsed MR, the RF energy is applied in the form of one or more pulses of specified duration. A single pulse of the proper length produces a maximum free induction decay (FID) signal. Sequences of pulses of precise length and spacing may be used to determine the two relaxation parameters: the spin-lattice relaxation time, T 1 , and the spin-spin relaxation time, For a liquid sample, the FID and T 1 and T z time constants are normally several hundreds of milliseconds or more. For example, free water at room temperature has a T 1 of approximately 3.2 sand a T z of approximately 2.3 s. The relaxations are exponential (as opposed to linear) and are affected by the chemical state of the nuclei. Thus, hydrogen nuclei in water, sugar, and oil molecules have slightly different relaxation rates, and water that is bound has a much faster relaxation than free water. There are a number of books and articles which give indepth discussions of MR including those by Fukushima and Roeder (1981), Pykett (1982), Smith and Ranallo (1989), Keller (1991), Chakeres and Schmalbrock (1992), and de Certaines et al. (1992). Hydrogen is a major component of fruits and vegetables where it is found in water, sugars, and oils. Furthermore, hydrogen nuclei produce one of the strongest MR signals. For these reasons, proton magnetic resonance (lH-MR), as opposed to 13C-MR or 31P-MR, has been evaluated for use in nondestructive sensing of internal composition and other quality factors of fruits and vegetables. This review is limited to some of the first studies conducted on fruit and subsequent studies directed at development of equipment for examining whole fruits and vegetables. For examples of recent fundamental studies on fruit tissue which used high resolution MR spectroscopy, see Ni and Eads (1992, 1993a,b).

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Hinshaw et al. (1979) may have been the first to publish results of an MR study of fruit. They obtained images of an apple, an orange, and a plum in a 30-MHz magnetic resonance imaging (MRI) system. Total imaging time was 6.3 min. The purpose of their study was to demonstrate the resolution that could be obtained with biological tissue. For example, they were able to distinguish the fine septa (0.5 mm thick) which separated the flesh segments of the orange. Rollwitz (1985) was one of the first to advocate MR as a technique for monitoring the quality of agricultural products. Three subsequent MRI studies demonstrated the potential for H-MR sensing of fruit and vegetable quality. Wang et al. (1988) examined 'Delicious' apples with diameters up to 7.5 cm using an RF frequency of 20 MHz and a 0.5-tesla superconducting magnet. Total imaging time for a scan was 8.4 min. Variations in signal intensity in a cross-sectional "slice" 3 mm thick revealed detailed features of the internal structure including the petal bundle, endocarp, outer limit of the carpel, the dorsal bundle of the carpel, and the pith with seeds. Areas affected by watercore produced a stronger MR signal (were brighter) than non-affected areas because watercored tissue contained more free water than non-affected areas. Wang and Wang (1989) used the same system to illustrate differences in extent and rate of core breakdown in pears stored at DOC in either air or 1 % oxygen. Core breakdown was characterized by increasing free water in affected tissues (brighter areas in MR images), followed by development of voids (areas appearing dark in MR images due to lack of hydrogen ions). Epidermal regions of zucchini squash (Fig. 1.19) subjected to chilling showed highter MR intensity and shorter T 1 and longer T z relaxation times than did similar regions of non-chilled squash, suggesting increased water mobility and diffusion in response to chilling (Wang and Wang 1992). Chen et al. (1989) used a General Electric CSI-2 Fourier Transform NMR spectrometer which had a 2-tesla superconducting magnet and operated at 85.53 MHz. They obtained MR images of apple, peach, Asian pear, onion, olive, fresh prune, orange, tomato, pineapple, avocado, and cucumber. Factors they could detect in one or more of the images included location of seeds or pits, and the presence of voids, worm damage, bruises, and dry regions. They also detected increases in free water that accompanied ripening of tomato and pineapple and increases in oil content associated with ripening of avocado. They reported that adjustments to experimental parameters such as echo delay and the thickness of the scanning slice enabled enhancement of specific features. One approach to utilizing lH-MR for determination of quality of fruits and vegetables involves interrogating a region within the specimen.

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Although this neglects variations that can occur at various points within the fruit and limits the amount of information that can be obtained, it reduces the complexity and cost of the sensor needed to perform the interrogation. The possibilities of this technique were demonstrated by Tollner (1990), Heil et al. (1992), and Cho et al. (1991). Tollner (1990) tested halves of 'Montmorency' red sour cherries in a spectrometer operating at 25.7 MHz to determine whether the presence of the pit could be detected. The maximum diameter of the sample was 16 mm. He found that pits reduced the FID peak height and affected the shape of the FID curve. Heil et al. (1992) used a 10-MHz spectrometer to evaluate slices of pears weighing 5 to 20 g. They reported significant correlations for T 1 versus firmness (r = -0.56), total acidity (r = 0.42), and 55 to acid ratios (r = -0.48). Tz was correlated with 55 (r = -0.33) and 55 to acid ratio (r = -0.33). Cho et al. (1991) examined cylindrical tissue samples of muskmelon using a high resolution spectrometer operating at a frequency of 200 MHz. The linear correlation for T 1 of water versus 55 was -0.76. When a water suppression technique was used to suppress the water peak from frequency domain signals (Fourier transformed from the time domain), the correlation coefficients (r) of the linear and polyno-

Fig. 1.19. Nuclear magnetic resonance images of computed transverse sections of two zucchini squash after storage for three days. Left: Squash stored at nonchilling temperature (12.5°C). Right: Squash stored at chilling temperature (2.5°C). Epidermal region of chilled squash shows a thicker, less distinct, high-intensity (bright) layer extending into the cortex and vascular bundles, indicating greater mobility of water and greater membrane permeability of those tissues. No visual differences between the squash were apparent when these images were made. however, such diffusion of free water eventually results in the water-soaked appearance typical of chilling injury. (Reproduced from Wang and Wang (1992) with permission.)

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mial regression coefficients of the height of the SS peak versus SS of the sample were 0.94 and 0.98, respectively. At the present time, high resolution spectrometers such as those used by Cho et al. (1991) are not practical for routine quality testing on fruits and vegetables. They require high magnetic field strengths. These are more difficult to achieve in a unit with an air gap large enough to accommodate whole fruits. The cost of the magnet increases with the cube of the air gap and therefore such a unit would also be very expensive. Furthermore, a high resolution unit would be very sensitive to temperature and might not perform effectively in environments where temperatures fluctuate, such as are found in packinghouses. A low field magnet, which would be lower in cost and more suitable for operation in a commercial environment, was designed by Cho et al. (1990). The magnet produced a 1200-gauss field in a spherical region with a diameter up to 30 mm. Some of the first tests with this system were conducted on sweet cherries and grapes. Stroshine et al. (1991) and Cho et al. (1993) tested whole sweet cherries and grapes in a spectrometer which operated at 10 MHz. Correlation coefficients for Tz values versus SS ranged from -0.61 to -0.66. Cho et al. (1993) also gave a theoretical justification for the observed linear relationship. Wai et al. (1995) first proposed use of a static gradient technique for measurement of SS in fruits. A gradient in magnetic field strength was imposed upon the sample, and a modification of the Hahn spin echo sequence was developed which produced two echo peaks. The ratio of the heights of these peaks (EP) was related to the diffusion coefficient of the water in sucrose solutions. This ratio was in turn related to the percent of sucrose in the sample. Li et al. (1992) reported correlation coefficients of 0.95 for adjusted EP versus SS for sweet cherries. Ray et al. (1993), using the same equipment after implementation of design improvements, reported a slightly higher correlation between EP and SS (r = 0.97) for sweet cherries. They also reported a slight correlation (r ranging from 0.37 to 0.48) between T z and cherry firmness as measured by compression tests. Research with a low field sensor, which began with tests on sweet cherries and grapes, has continued with larger fruits. Stroshine et al. (1993) built a magnet assembly capable of testing fruits with diameters up to 8 cm and used it with a 5.35-MHz system to test whole avocados and oranges. Using the smaller magnet assembly previously used for cherries, they also tested segments from oranges and cylindrical samples approximately 25 mm in diameter and 25 mm long which had been cut from nectarines. For echo ratio adjusted for (termed "AER") versus SS, correlations were 0.73 for nectarines, 0.89 for orange segments, and 0.67 for whole oranges. The r value for T z versus dry weight for avoca-

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dos was -0.86. Stroshine et al. (1994) reported similar correlations for whole apples, oranges, and tomatoes. For tomato, they reported an r of 0.57 for a linear regression of AER versus SS. The pulse sequence they used to determine AER (Wai et al. 1995) was developed specifically to increase the speed of measurement and reduce the effects of sample size and shape. Chen et al. (1993b) adapted a magnet normally used with an imaging system to IH-MR tests in which they used a surface coil to interrogate only a portion of the fruit. The coil and fruit were placed inside the magnet of a General Electric CSI-2 MR spectrometer which operated at a frequency of 85.5 MHz. The coil received a signal from the region located above the center of the coil at a depth into the fruit of approximately half the coil diameter. An FID signal was obtained and transformed from the time domain to the frequency domain using Fourier transformation techniques. This gave Lorentzian curves for components in the sample such as water, SS, and oil. In tests with avocado, they found an r of 0.98 for dry weight (which is directly proportional to oil content) versus the ratio of oil to water peak heights. Zion et al. (1995) used the equipment for measurements of SS content of fresh (not dried) 'sugar' prunes. The separation between the water and sugar peaks was 1 ppm, which is less than a 100-Hz difference in frequency for the 2-tesla system they were using. Nevertheless, their equipment was sufficiently accurate to enable them to resolve the water and sugar spectral peaks using the surface coil. The value of r for SS versus the ratios of the sugar to water peak heights was 0.91. They also measured firmness by puncturing the fruits with a 7.9-mm MT plunger and found that SS was correlated to firmness (r= -0.90). The major disadvantage they cited was the relatively high cost of spectrometers with 2-tesla magnetic fields. However, they anticipated that improvements in magnetic resonance sensing technology could reduce these costs significantly, thereby making the method feasible. Many investigators have questioned whether IH-MR sensing of fruit quality can be performed on fruits moving on a belt. Chen et al. (1995) modified the system previously used for avocado and prune (see above) by adding a variable-speed conveyor which transported fruits through the magnet. For speeds up to 250 mm/s, motion had very little effect on the shape of FID curves or the heights of the Fourier transformed water and oil peaks acquired from avocado. For 12 avocados, the values of r for linear regressions of the dry weight versus the ratio of oil to water peak heights were 0.986 and 0.991, respectively, for belt speeds of 50 and 250 mm/s. They also reported that slight misalignment of the fruit appeared to have relatively little effect on performance. In tests of avocados resting in a plexiglass holder, the r for echo-peak ratio versus dry

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weight was 0.94 when the fruit center was misaligned by 4 mm from the center of the coil, compared with r = 0.95 with 0 mm of misalignment. Assuming that signal processing could keep pace with interrogation and signal acquisition, the results of their study suggest that high-speed sensing of internal quality of fruits is technologically feasible. An MR system which operates at 20 MHz was developed by Pearson and Job (1994). It utilizes a magnet which is relatively light because it does not require the heavy steel yoke used by the conventional magnets. They reported preliminary relaxation measurements which correlated with the sugar content of onions. In addition, T z values for a ripe and a green tomato were measured as 0.62 sand 0.58 s, respectively. They attributed the difference in T z to a difference in firmness. Two MR studies looked specifically at measurement of tomato maturity. Saltveit (1991) used the 85-MHz system previously used by Chen et al. (1989) to examine the free water content of tomato in the mature green (MG) to red-ripe stages of maturity. Two of his images for mature green fruit are shown in Fig. 1.20. The liquification of the locular tissue that occurs in the MG-3 fruit is indicated by the increased MR intensity in that region. Saltveit concluded that MRI could be used to nondestructively differentiate among MG ripeness classes. However, he noted that, for the current (1991) state ofMRI technology, the time required and expense involved made the technique impractical. A more recent MRI study by Kim et al. (1994) used the same MRI hardware for measurements of T z and T 1 of whole tomato with widely varying maturities. Like Pearson and Job, Kim found that T z of a firm tomato was shorter than the of a soft (ripe) tomato. Correlation coefficients for l/Tz versus moisture content and l/Tz versus firmness (measured with a Hunter spring gauge) were 0.78 and 0.92, respectively. Correlations were slightly higher for second-order regression of T z versus moisture content (r = 0.82) and In(Tz) versus firmness (r = 0.94). They also compared the faster gradient recalled echo (GRE) technique with the more conventional spin echo (SE) technique and the results of that portion of their study are described later in this section. Eustace and Jordan (1995) circumvented the need for a permanent magnet by using the earth's magnetic field. Their system operated at 2.220 kHz, less than one-thousandth the frequency of the other systems described in our summary. They reported tests on aqueous solutions containing 0 to 20% sugar by weight. The r values for T 1 and T z versus percent sugar were -0.996 and -0.986, respectively. Their analysis included development of multiexponential models of the T 1 and T z relaxation. They stated that the technique was only suitable for experimental studies because of difficulties in locating the apparatus away

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Fig. 1.20. Nuclear magnetic resonance images of computed transverse sections (large) and photographs of actual slices (small) through L~e equator of tomato fruit at two mature-green stages of development. Upper: MGl stage where, in the MR image, the locular tissue has an appearance similar to that of the pericarp wall. Lower: MG3 stage where, in the MR image, the intensity of the locular region has increased. (Photograph courtesy of M. E. Saltveit, University of California, Davis.)

from large ferrous objects and the system's sensitivity to interference from AC electrical power. In a study described in an earlier publication, Jordan and Eustace (1993) reported preliminary tests on apple and kiwifruit. Magnetic resonance equipment is expensive. Units made for laboratory use that operate at 10 MHz and accommodate samples up to 25 mm in diameter usually cost at least $50,000. An MRI system and magnet cost approximately $250,000 to $500,000. A second major concern is keeping the system "on resonance" so that maximum signal strength can be attained. lH-MR systems which use permanent magnets are usually very sensitive to temperature. A change of 1°C in magnet temperature can

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change the field strength 0.10%, and this is sufficient to seriously attenuate the signal. Furthermore, Eustace and Jordan (1995) reported that the change in T 1 and T z caused by a 1°C change in sample temperature was similar to that caused by a 1% reduction in sugar concentration. Three other factors must be considered when adapting MR for on-line systems. Conducting metals and certain materials containing hydrogen can interfere with the signal and must be kept away from the sensors. These materials could not be used in the mechanism which conveys fruit through the sensor. Free water on components or fruit surfaces would seriously affect results. Second, the receiver coil must be thoroughly shielded from external electromagnetic noise by means of a copper or aluminum box. Finally, according to theory, the sample should be "magnetized" for several seconds (perhaps as much as 10 s) before it is interrogated by the RF. However, it should be noted that the results reported by Chen et al. (1995) suggest that a long premagnetization time may not be needed. Some of the studies cited previously used MRI to detect differences in quality of fruits and vegetables. This technique allows the investigator to "focus" on a small region of the fruit or vegetable, thereby pinpointing regions where abnormalities occur. Obtaining a twodimensional (2-D) image involves collection of a relatively large amount of data and additional processing time. One approach to simplifying the procedure is to use a one-dimensional (l-D) image. This approach was taken for MRI tests which accompanied the previously cited study with 5- to 20-g pear slices placed in a 10-MHz spectrometer (Heil et al. 1992). Their correlations for the average values of T z and T1 versus firmness, SS, and SS to acid ratio were lower when 1-D imaging was used. The authors attributed the drop to the variation in the attributes within the pears. The magnitude of these variations may depend on the type of fruit being tested. Note that in the previously discussed study by Chen et al. (1993b), good correlations were achieved when a surface coil was used to interrogate a single region of the avocado. Zion et al. (1994) evaluated the 1-D imaging approach as a method for detecting pits in cherries. They used a birdcage coil capable of accommodating three cherries at a time to acquire 1-D projections of cherries with and without pits. The data analysis techniques they developed could distinguish between projections of cherries with and without pits. When cherries were oriented with the pitting holes out of the excited plane, they were able to correctly classify 29 out of 30 cherries. Two recent investigations, one on bruising of apple and the other on firmness of tomato, have used 2-D MRI. Both used the gradient recalled echo (GRE) imaging method, which is faster than spin echo (SE) imag-

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ing and therefore more suitable for real-time processing of images. McCarthy et al. (1995) compared GRE and SE images of apples acquired before and immediately after bruising. Their objective was to investigate the cause of the contrast between bruised and firm apple tissue and why this contrast changes over time. The increase in brightness of bruised regions appeared to be caused by the destruction of the cellular structure and displacement of intercellular liquids into large intercellular spaces. They also observed that when short echo delays were used with the pulse sequence (several ms), the contrast between bruised and firm regions was greater in the GRE images. In the previously cited study by Kim et al. (1994), the SE and GRE techniques were used to image whole tomatoes varying widely in maturity. The correlation between parameters acquired from the SE and GRE tomato images and the firmness of the tomatoes had r values of 0.84 and 0.85, respectively. The authors concluded that the GRE method of acquiring images was faster and better than the SE method. MRI has also been used for fundamental studies of the development, structure, and quality of mature fruits and vegetables. Faust et al. (1997) reviewed horticultural applications of MRI in a chapter in this volume. Clark et al. (1997) reviewed the application of MRI to the pre- and postharvest evaluation of fruits and vegetables. We will briefly summarize several fundamental studies involving fruits. Goodman et al. (1992) used MRI to study the progression of a fungal pathogen in a red raspberry fruit. Maas et al. (1992, 1993) examined the distribution of water in developing and mature strawberries and vascular tissue in developing strawberries. They noted that bruising was quite apparent. The distributions of water, lipid, and soluble carbohydrates (glucose and fructose) in whole grape berries were evaluated by Pope et al. (1993). Gamble (1994) used MRI to map the distribution of water and sugar in fresh, frozen, and thawed blueberries and found that freezing changed the distribution of sugar and water. MacFall and Johnson (1994) published two- and three-dimensional images showing the arrangement of vascular elements in mature fruits and vegetables, including apple, fig, okra, kiwifruit, and potato. Although MRI has great potential for evaluating the quality of fruits and vegetables, at the present time, commonly available high resolution spectrometers are not practical for routine quality testing. Commercial applications most likely will be based on low-field magnets and 1-D imaging whenever possible. Assuming that signal processing could keep pace with interrogation and signal acquisition, high-speed sensing of internal quality of fruits is technologically feasible. Incorporation of new, more powerful, imaging techniques such as GRE, could also have a major impact.

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E. Dielectric and Electrical Properties

Although several investigations have evaluated the resistivity or conductivity of fruit tissue (Holcomb et al. 1977; Rotz and Mohsenin 1978; Blahovec and JancH 1987; Kato 1987), most of the work on electrical properties of fruits and vegetables has focused on two properties which characterize the interaction of materials with electromagnetic radiation: the dielectric constant and the dielectric loss factor. Values of these two properties, which are frequency dependent, have been reported for frequencies varying from 1 kHz to 20 GHz. This range includes radiation used in radio, radar, and microwave applications (Table 1.2). The dielectric constant, usually designated by E', influences the electric field distribution within the material and the phase of waves which travel through it. The dielectric loss factor, usually designated as E", characterizes the energy absorption by the material. The value of E" is important in microwave heating. As E" increases, the rate of heating increases and the penetration depth decreases. Thus, high values of E" lead to increased heating near the surface of the material and decreased heating in the interior. Water has relatively large E' and E" values in comparison to the dry matter in biological materials. Therefore, both E' and E" are highly moisture dependent. Nelson (1973) gave a comprehensive summary of electrical properties of agricultural materials which included values for E', E", and the conductivity of fruits and vegetables. However, most of the fruit and vegetable data were not for fresh products. Several investigators have made dielectric measurements on fresh fruit and then examined the relationship of E' and E" to moisture content or maturity indicators. Preparation of the samples for the basic studies has usually been destructive, but nondestructive sensing techniques could be applied if bases were found for distinguishing quality differences. Measurements have been made at several different frequencies. Nelson (1980) measured the dielectric properties of fresh fruits and vegetables at 2.45 GHz, a frequency commonly used in microwave heating. He prepared cylindrical samples of peach, sweet potato, apple, cantaloupe, carrot, and potato with a special cutting tool. Nelson emphasized that the samples had to fit snugly into a coaxial-line sample holder and that the upper and lower surfaces of the cylinder had to be perpendicular to the cylinder axis. After errors caused by poor fitting of the samples were eliminated, the coefficients of variation for measurements of E' and E" were 5% and 10 to 20%, respectively. When measurements on all the fruits and vegetables were grouped together, moisture content was correlated to E' (r = 0.41). Structural differences, particularly density, were apparently responsible for differences in E' among samples which had nearly identical moisture contents. In a subsequent study, Nelson

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(1983) measured e' and e" for potato, sweet potato, peach, watermelon, cantaloupe, and cucumber at 2.45, 11.7, and 22.0 GHz. Values of e' decreased slightly between 2.45 and the higher frequencies. In general, correlations between dielectric properties and measures of maturity, such as SS level, have been low. Nelson (1983) observed that in both the 1980 and 1983 studies there was no correlation between dielectric properties and SS content and that the method appeared to hold little promise for maturity detection by measurements at a single frequency. McClendon and Brown (1971) conducted one of the few comparisons between dielectric constant and maturity of two varieties of peach. Although the dielectric measurements (made at 0.5 to 5 kHz) were related to firmness, the correlations were relatively low. Bothe' and E" increased as the peaches approached maturity (became softer). The r for linear regression of E' on puncture force ranged from 0.72 to 0.83 for the firmer variety and from 0.37 to 0.60 for the softer variety. Correlations decreased with increasing time after harvest. The authors attributed the higher correlation for the firmer variety to the greater range in firmness of that variety. Similarly, the range in puncture force decreased drastically between 6 and 48 hours after harvest, and correlations for peaches stored 48 hours ranged from 0.15 to 0.44. The results of McClendon and Brown appear to agree with Nelson's (1980) observation that dielectric measurements at a single frequency are unsuitable for use in measuring maturity of high water content fruits. The equipment used to measure dielectric properties can affect the values obtained because measurement errors in certain frequency ranges vary with the technique used. Nelson (1980, 1983) made measurements with samples in a coaxial line and rectangular wave guide. As mentioned previously, he expended significant effort in cutting his samples and ensuring that they fit snugly in the sample holder. Poor fits increased measurement variability. Tran et al. (1984) used an open-ended coaxialline probe in combination with an automatic network analyzer to measure dielectric properties of raw fruits and vegetables in the frequency range of 100 to 10,000 MHz. Their system permitted multiple measurements on the sample. Cylindrical samples were cut from carrot, potato, apple, pear, and peach. Care had to be taken to ensure that the ends of the cylinders were perpendicular to the cylinder axis. Values of E' and e" for potato and peach at 2.40 GHz were approximately 1.1 and 1.2 times the values reported by Nelson (1983) at 2.45 GHz. Tran and coworkers reported favorable agreement between their measurements of E' and E" and values calculated from Maxwell's dielectric mixture formula. Seaman and Seals (1991) used equipment similar to that used by Tran et al. They made in situ measurements of dielectric properties of

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fruit skin and fruit pulp at frequencies between 150 and 6400 MHz using an open-ended coaxial cable probe with an outer diameter of 2.2 mm. Values for £' and £" for peach and apple were approximately 1.5 times those reported by Nelson (1980, 1983). Due to lower moisture content, the dielectric constant for the skin was lower than that of the pulp. In a more recent study using an open-ended coaxial-line probe, Nelson et al. (1993) measured £' and £" for cylindrical samples of 23 kinds of common fresh fruits and vegetables. Measurements could be made relatively quickly with this equipment, permitting determinations at many frequencies. They measured 41 frequencies between 200 MHz, 20 GHz and included a table which gives values of £' and £" for all the fruits at frequencies of 200 MHz, 20 GHz, and four intermediate frequencies. Nelson and coworkers observed that their data agreed quite favorably with most of the data presented by Tran et al. (1984) and some of the data presented by Seaman and Seals (1991). In their curves, £' decreased monotonically as frequency increased while £" decreased from 200 MHz to 1 GHz, showed a broad minimum at 1 to 3 GHz, and then increased with frequency up to 20 GHz. They attributed the shape of the £" versus frequency curve to ionic conductivity at the lower frequencies, bound water relaxation, and relaxation of free water near the top of this frequency range. Similar measurements of the dielectric properties from 200 MHz to 20 GHz were made on tree-ripened peaches by Nelson et al. (1995). Differences were found in the dielectric properties of peaches first picked for the fresh market and those of the same three cultivars picked two and four weeks later. Differences at 200 MHz and at 10 GHz were combined to form a maturity index based on the dielectric properties that had reasonable correlations with firmness and delayed light emission. They concluded that further effort would be required to develop a reliable practical maturity index. Based on results of these studies, it is presently questionable whether dielectric properties can provide enough sensitivity for accurate measurements of maturity or SS contents of fruits and vegetables. The openended coaxial probe would be best suited for measurements on whole fruits. However, measurements on unpeeled fruit are strongly dependent on the properties of the skin. Seaman and Seals (1991) state that the fields created by their probe extended to depths of 1 to 5 mm. Thus, only the properties very near the surface of the fruit are measured. Even if the open-ended coaxial probe could be used for measurements, it would be difficult to achieve good contact between the fruit and the probe when the fruit is moving rapidly through a sensor. Kato (1987) developed a real-time, on-line system using nondestructive contact methods and

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examined sound, mechanically damaged, and rotted fruit. He tested contacts made of felt saturated with salt water, but suggested that a conductible rubber would be more suitable. Relatively little work has been done on defect detection by means of dielectric measurements. Nelson (1980) reported that there were no discernible differences between sweet potatoes stored under normal conditions and those subjected to storage conditions which cause chilling injury. The limitations on the depth of penetration of the electric field created by the more practical open-ended coaxial probe would make it difficult to develop a nondestructive sensor which would detect internal defects. Kato (1987) demonstrated differences in resistance due to rot or damage. Dielectric properties show more potential for use in measurement of moisture content of dry fruits such as dates. Nelson and Lawrence (1992a,b) measured the impedance of dried dates having moistures between 13 and 30% at frequencies of 1 and 5 MHz. Although moisture content is not a specific factor in grading dates, it has an important influence on the physical properties of the dates and therefore influences the grade determination. The authors placed individual dates between two plates. When the date is thus incorporated into an alternating current circuit, it behaves like a resistor and capacitor in parallel (RC circuit). Using an impedance analyzer, the authors measured electrical properties associated with an RC circuit such as conductance, susceptance, capacitance, phase angle, and dissipation factor. Using stepwise regression, they determined those factors which, in combination, gave the best correlation with moisture content. For data collected during the 1988 and 1989 crop years, values of r for the one-term models for whole and pitted dates were 0.972 and 0.970, respectively. Those for two-term models were 0.979 and 0.977, respectively. The standard errors of calibration were 1.0% moisture for whole dates and 1.2% moisture for pitted dates. This method shows potential for on-line sorting in which multiple channels would be used to make RF impedance measurements. A major obstacle to implementation of this technique would be development of suitable mechanical singulation equipment to present dates to the electrodes. Results indicate that single-frequency measurements of dielectric properties would not be sensitive enough for accurate assessment of maturity or SS contents of fruits and vegetables; however, multiplefrequency measurements might be useful. Structural differences among samples can have a considerable effect on the accuracy of the data and the equipment used to measure dielectric properties can affect the values obtained. A probe such as used by Tran (1984) would be best suited

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for nondestructive measurement of whole fruits and vegetables; unfortunately, because the fields produced by a suitable probe would only penetrate to a very shallow depth, such a system would not be effective for detecting internal defects. It would also be difficult to achieve good contact between the fruit and the probe when the fruit is moving rapidly along a sorting line. Measurements of dielectric properties would appear to have greater potential for the assessment of the moisture content of dry fruits such as dates. V. ELECTROCHEMICAL PROPERTIES

As fruits and vegetables ripen, they produce volatiles which can be detected by the sensitive instruments currently available to the scientific community (Charron et al. 1995). The concentration of volatiles within a fruit increases as it ripens, and their release to the surrounding atmosphere is responsible for the fruit's pleasing aroma. Both nonaromatic and aromatic volatiles are released, including ethylene, ethyl esters, acetaldehyde, ethanol, and acetate esters (Buttery et al. 1971; Kitamura et al. 1976; Sapers et al. 1977; Horvat and Senter 1985; Douillard and Guichard 1990; Lee and Lee 1992; Yahia 1994). Benady et al. (1992, 1995) developed and tested an electronic sensor which measured the concentration of some of the volatile compounds released. The sensor utilizes a commercially available semiconductor gas detector positioned within a small flexible rubber cup that is placed on the fruit surface. Volatiles emitted by the fruit accumulate in the cup and cause a decrease in the electrical resistance of the sensor. A computer-based data acquisition system calculates R s ' the resistance at a given time after application of the sensor to the fruit, and divides it by R o, the initial resistance of the sensor. Division by Ro recalibrates the sensor at the beginning of each measurement, eliminating the effects of variations in resistance caused by temperature, relative humidity, and volatiles in the ambient atmosphere. Devices such as this are being used with increasing frequency by the food industry. In some applications, they may be able to discriminate among types of volatiles present. For example, Gardner et al. (1992) found that an array of 12 tin oxide sensors could classify coffee aromas according to blend and roasting time with an overall success rate of 88% or better. Shurmer et al. (1989) claimed success in discriminating volatiles from different blends of alcohol (methanol and proponal) and smoke from the burning of different blends of tobacco. Benady et al. (1992, 1995) tested their sensor as a means of measuring melon maturity. Over a period of 30 s, they observed that there was

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very little decrease in R/Ro for melons visually classified as unripe. However, the ratio decreased exponentially in half-, full-, and overripe melons with the rate of decrease being stronger as ripeness increased. They visually classified melon ripeness on the basis of the appearance of the abscission zone around the stem and, in the case of overripe melons, the observation of external fruit color changes and cracking along the stem detachment section prior to severe external physical breakdown. Additional maturity measurements included a visual rating of external color, collection of volatiles from the headspace of melons placed in the laboratory in sealed glass containers, and measurements of stem detachment force, flesh firmness, and SS. Analysis of the volatiles by gas chromatography with verification by mass spectrometry revealed the presence of CO 2 and nine aromatic volatile compounds, all of which increased with ripening. Statistical analysis of the results indicated that the accuracy for classification into four ripeness stages using any of the measured indexes was relatively low (40 to 64%). In two of the three cultivars tested, there was no statistically significant difference between full-ripe and overripe melons. For each of the maturity-related measurements, the classification accuracy increased as the number of classes was decreased to three and then two. With one exception, the electronic sniffer gave the best separation. For classification into two categories, external color had an accuracy of 97% while the accuracy of the electronic sniffer was 93 %. Electronic sniffer tests were conducted on some of the melons while they were still in the field and then subsequently in the laboratory. The correlation between the two measurements was very good (r = 0.92). The authors stated that, although R/Ro was measured for 30 s, only 1 s was needed to achieve response. They also stated that subsequent samples could be measured after less than 30 s, and that this time could be reduced even further by flushing with forced air. The electronic sniffer has also been tested on strawberry and blueberry. Hetzroni et al. (1994) determined R/Ro at measurement times of 1,5, 10, and 30 s on strawberries that had been visually sorted into six ripeness classes (green, white, pink, half-ripe, ripe, and overripe). They also took chromameter measurements on each sample and determined average firmness, SS, titratable acidity, and pH. For all measurement times, the sniffer was able to differentiate half-ripe, ripe, and overripe fruit. However, it was not able to separate immature fruit (green, white, and pink). Canonical discrimination analysis suggested that, although separation of ripe and overripe fruit was possible, a high classification error could be expected. This would mean that fruits could be exchanged between the two groups. No single index was able to accurately classify

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strawberries into the six classes. However, the authors concluded that a combination of nondestructive assessment of aromatic volatiles and color measurement enabled accurate classification of strawberries into six ripeness classes. Simon et al. (1996) tested the ability of the sniffer to distinguish maturity levels among lots of blueberry. Expert graders separated the blueberries into five ripeness classes: mature-green, green-pink, blue-pink, blue, and ripe. The sniffer response, R/Ro, was determined by placing the containers in a chamber with two tin oxide gas sensors mounted in the lid. They also determined color (L, a, b values), firmness by a destructive compression test, SS, pH, and titratable acidity. They used gas chromatography to determine aromatic volatiles collected from the headspace of berries and solvent-extracted volatiles from ripe berries. Firmness by compression was most efficient in separating the more immature fruit (mature-green versus green-pink versus blue-pink). The electronic sniffer was able to distinguish the three classes of mature fruit (blue-pink, blue, and ripe) but placed the mature-green and green-pink berries in one class. The difficulty with immature fruits was linked to their low levels of volatile emissions. Discriminant analysis revealed that combining color and sniffer measurements would give a 90% accuracy in fruit classification. Simon et al. (1996) concluded that the sniffer can operate across a wide range of blueberry cultivars, and that baseline data for each cultivar are required. In their studies with damaged berries, sniffer response increased as the percentage of lower-quality berries increased. Simon and coworkers suggested that a matrix of gas sensors might be able to distinguish among individual volatile constituents, thereby enhancing the ability of the sniffer to discriminate berries of varying quality and ripeness. Although ethylene production alone cannot distinguish ripeness of fruits (Abeles et al. 1992, and pers. corr.; Blankenship and Unrath 1988), results with the electronic sniffer (Hetzroni et al. 1994; Benady et al. 1995; Simon et al. 1996) suggest that measurements of total volatiles may be able to accurately classify a number of fruits into ripeness categories. The semiconductors in their sniffer are not sensitive to ethylene but are very sensitive to the classes of compounds that include the aromatic volatiles. The sensor is relatively low in cost and can be taken to the field for sensing of selected fruits prior to harvest. A major disadvantage is the time required to perform a measurement and then purge the sensor for the next measurement. Although this would not be an obstacle for quality control situations, it would make it difficult to use the sensor for online sorting. It may also be necessary to calibrate the sensor for different cultivars of the same fruit. As mentioned previously, Simon et al. (1996)

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found differences in both volatile emission and sensor response among blueberry cultivars. In their study of melons, Kitamura et al. (1976) made a similar observation with respect to emission of volatiles. They found that subsequent volatile production by fruit that had been stored for up to 7 days was affected by factors such as cultivar and the number of days after anthesis at which the melons were harvested. VI. STATISTICAL METHODS

Major advances in computers in recent years have greatly increased the ability to collect and process signals from various kinds of sensors. Significant advances have been made in the practical application of classical statistical methods and in the development of new methods. It is outside the scope of this paper to do more than mention these important techniques. Statistical methods are used for data reduction-the selection of measurement variables, such as specific wavelengths, for predicting quality-and for product classification. Data reduction techniques include first- and second-difference spectral treatments; fast Fourier transformation (FFT); multiple linear regression; partial least squares, principal components, and factor analyses; artificial neural networks; and wavelet analysis. Advances in machine vision and image processing require statistical pattern recognition to reduce the number of data fed into decision-making algorithms. Image processing involves histogram analysis, averaging, smoothing, edge recognition, thresholding (often dynamic), texture analysis, and numerous other pattern recognition techniques. There is growing recognition that quality is a multifaceted attribute, and there is increasing interest in "sensor fusion" or combining several inputs-different measurements from a single sensor, measurements on different parts of a product (views), or measurements from different sensors-into a classification decision regarding the quality of the product. Some of the inaccuracies of the instrumentation and difficulties such as variation among cultivars could be circumvented by development of an appropriate classifier which uses data from two or more sensors that measure and quantify attributes such as color, firmness, size, and defects. In addition to classical discriminant, cluster, and principal component analyses, researchers are investigating newer computer-intensive multivariate statistical methods such as recursive classification trees and artificial neural networks. Thai et al. (1990) combined SS, two firmness values, and five sensory scores to model consumer preferences using neural networks. Thai and Shewfelt (1991a) combined color measurements and sensory responses in a similar study.

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In both studies, they found that consumer preferences tended to be linear. They suggested that neural nets be used to select the basic features to apply in classical regression analysis for predicting preference. Bochereau et al. (1992) briefly explained artificial neural networks and applied the method to predicting apple quality from near-infrared spectra. Guedalia and Edan (1994) used neural networks to combine multiple visual scores with shape, color, surface defects, and bruises measured with an image processing system to classify tomato. Ozer et al. (1995) found that excellent results could be achieved in the classification of cantaloupes using a recurrent autoassociative memory neural network (also known as a Hopfield net). The system could be rapidly trained using small numbers of samples, and it could also classify different cultivars of cantaloupe without additional training. To develop an automated apple grading station, Heinemann et al. (1995) developed separate classification algorithms to classify color, shape, size, and surface defects from machine vision images. They then input either the quantitative data or the classification decision for each attribute into a neural network for integration into an overall grade decision. VII. OVERVIEW AND CONCLUSIONS Quality is not a single, well-defined attribute but comprises many properties or characteristics that relate to appearance, texture, flavor, and wholesomeness. Anatomical and physiological changes within the tissue of fruits and vegetables affect the quality of the product. Changes such as cell breakdown, chemical conversion, softening, and internal decay have negative effects on quality. Additionally, freedom from defects, decay, and insect infestation is critical. Sorting and classifying by size, color, or defects adds value to the product. In selecting one or more attributes to measure, it is important that there is a close, consistent relationship between the property to be measured and the quality attribute. Care must be taken to ensure that sensor testing and calibration are done over as wide a range of conditions as possible and that what is really being detected is understood. We leave it to the user to decide which quality attributes are important, what sensor technology to use, and what values are acceptable for their particular application. Mechanical properties, particularly firmness, are related to ripeness, to physiological and pathological condition, to resistance to mechanical damage, and-perhaps the most important-to culinary quality. Density is indirectly related to firmness and to certain defects. Although density sorting has some potential for quality segregation of fruits and vegeta-

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bles, it is relatively crude. Density changes associated with maturity or other quality parameters are often so small as to make it difficult to sort the product effectively. Many mechanical measurement methods have been developed for nondestructive evaluation of firmness. However, none is presently in common commercial use. Among the methods that have been investigated are direct mechanical force-deformation methods, impact testing, sensing deformation resulting from an air puff, and sonic vibration. Many are not directly related to storage life, resistance to injury, or consumer evaluations of texture. Despite significant research on mechanical measurement of fruit and vegetable firmness, most methods reported are not suitable for on-line sorting. An inherent problem with many of these methods is that they measure local mechanical properties and, in many fruits and vegetables, there are large variations in mechanical properties within the product. This variation may limit the usefulness of these methods for nondestructive firmness measurement. Although they measure primarily local mechanical properties, impact testers and the laser-puff firmness detector appear to have potential for real-time firmness sorting of some horticultural products. The sonic vibration method is nondestructive and generally represents the mechanical properties of whole fruit rather than local tissues. Despite considerable research in the past, sonic vibration has not been used for on-line sorting of horticultural products (except for detecting internal voids in watermelon). Appearance is one of the major factors the consumer uses to evaluate the quality of fruits and vegetables, and measurement of optical properties has been one of the most successful nondestructive techniques for assessing quality. Optical properties can be based on reflectance, transmittance, absorbance, or scatter of polychromatic or monochromatic radiation in the ultraviolet (UV), visible, and near-infrared (NIR) regions of the electromagnetic spectrum. Many maturity indexes have been based on pigment concentrations; however, present optical systems, especially in the NIR region, and newer software make it possible to detect carbohydrates, proteins, and fats that may provide better maturity or quality indexes. Machine vision provides information about the spatial distribution of the intensity as well as the spectral content of the light. Machine vision is suitable for detecting surface phenomena, which permits measurement of size, shape, and color and the detection, quantification, and location of surface defects. Generally, machine vision using reflectance measurements is not adequate for detecting internal defects; however, transmittance is useful for detecting some internal disorders. Multispectral imaging provides spectral information at two or more wavelengths in addition to spatial information. Color vision is one form of multispectral imaging. Fast color sorters are on-line in many

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pacldng plants, being used with great success on some commodities and with less success on others. The speed of data processing places some limitations on optical sorting, but systems that process many hundreds of pieces per second are used in food processing to detect simple color defects. A major limitation to sorting speed for fresh fruits and vegetables is the speed with which the product can be handled without damage. Despite the potential of fluorescence or delayed light emission (DLE), a related phenomenon, for detecting physiological condition, there has been little effort to exploit either for quality measurement. Fluorescence is routinely measured in the laboratory as a nondestructive measurement of chlorophyll quantity and function. It is relatively fast (1 to 3 s). It requires dark equilibration but this has been partially overcome by modulated chlorophyll fluorescence fluorometers, capable of measuring fluorescence ldnetics in ambient light. Chlorophyll fluorescence and DLE are useful for measuring ripening in fruit with green peel or flesh, detecting pre- or postharvest chilling or heat injury, and assessing fruit condition during or after high temperature treatments used for destroying insects or inhibiting senescence. Because chlorophyll fluorescence measurements are widely used by physiologists, because other industries measure the fluorescence of other compounds, and because chlorophyll fluorescence is much stronger than DLE, it is likely that fluorescence measurements will be further developed for quality measurements. Sugars (or soluble solids), acids, and aromatic compounds contribute significantly to the pleasing flavor of fruits and vegetables and are generally indicative of the physiological condition of the product. It is likely that on-line near-infrared or magnetic resonance sensing of soluble solids will be available in the near future. However, nondestructive measurement of acids is proving to be more difficult. Electronic sniffers based on the response of semiconducting polymers to various volatiles may be able to accurately classify a number of fruits into ripeness or aroma quality categories. The semiconductors are very sensitive to the classes of compounds that include the aromatic volatiles of fruits and vegetables. Results indicate that measurements of dielectric properties at single frequencies are not sensitive enough for accurate assessment of maturity or soluble solids content of fruits and vegetables. Nevertheless, work continues on development of practical probe configurations for nondestructive sensing of produce. Because fields of a suitable probe would likely penetrate to only a shallow depth, such a system would not be effective for detecting internal defects. Measurements of dielectric properties appear to have greatest potential for assessing the moisture content of dry fruits such as date, fig, and raisin. Shape, bruises, cuts, punctures, insect damage, decay, and other inter-

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nal and external disorders are important to fresh produce packers and to processors. Brown and Sarig (1994), in the proceedings ofa workshop on sensors for nondestructive quality evaluation of fruits and vegetables, concluded that nondestructive sensing technologies are commercially available and are being used to sort for size, shape, surface texture, and surface color for many commodities, but that sensors are not available for most other quality attributes. X-ray inspection systems are now used to detect internal defects on-line in some limited applications, but the increasing sensitivity of the equipment and the development of rapid image processing could soon make this technology more available for detecting internal defects. Magnetic resonance sensors and magnetic resonance imaging have great potential for evaluating the quality of fruits and vegetables. However, the commonly available, high resolution spectrometers that are presently available are not practical and are prohibitively expensive for routine quality testing. Commercial applications most likely will be based on low-field magnets used with magnetic resonance sensors or with simplified (e.g., i-D) imaging systems. The magnetic resonance sensors could provide a signal that reflects the average properties of the sample or the properties of a subregion a predetermined distance below the surface. Two-dimensional imaging is considerably more sophisticated, expensive, and computationally intensive. A simplified 1-D imaging system would indicate spatial variation of the desired property along a single axis, most likely perpendicular to the plane of the belt on which the product travels through the magnet. Rapid imaging for sensing internal quality of fruits and vegetables appears to be technologically feasible, providing signal processing can be accelerated so that it can keep pace with interrogation and signal acquisition. Incorporation of new, more powerful imaging techniques, such as gradient recalled echo (GRE), could also have a major impact. The major sensor advances in recent years are largely due to developments in computing. Image processing, whether of multispectral optical, X-ray, or magnetic resonance images, requires extensive computing power and speed. Faster, more robust, and more specific image processing methods need to be developed to classify the extent and type of defect. Improved methods for combining the inputs from many measurements (sensor fusion) into classification algorithms are being developed. Research is still needed to develop practical methods for sensing firmness, internal condition, and composition of quality-related constituents and volatiles. Nondestructive sensing of firmness remains an elusive, high-priority need; so extensive research and development are being undertaken on firmness sensing. Commercial machine vision systems

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are beginning to be installed in packinghouses to detect surface defects and, in some cases, for sizing. Product orientation, defect identification specificity, and speed of these systems continue to be research topics. Considerable research activity is in progress to improve light transmittance, X-ray, and magnetic resonance technologies for detecting internal defects such as phyiological disorders, bruising, pathological infection, and insect infestation. Improvements are needed in specificity' cost, speed, and safety of these sensors. Methods to detect compositional factors, particularly those that relate to ripeness, aroma, or flavor, are under development. Each sensor method is based on the measurement of a given constituent or property; therefore, its relationship to quality is only as good as the relationship of that constituent or property to quality as defined for a particular purpose. The inherent variability in composition, maturity, and structure among individual fruits and vegetables within a lot and the differing definitions of quality make it difficult to establish such correlations. Additional research is needed to establish such relationships. Methods for combining the measurements from several sensors into an overall quality classification are being investigated. New methods and applications for nondestructive quality evaluation continually being published in the horticultural, food science, and agricultural engineering literature indicate the activity and importance of this field.

LITERATURE CITED Abbott, J. A. 1994. Firmness measurement of freshly harvested 'Delicious' apples by sensory methods, sonic transmission, Magness-Taylor, and compression. J. Am. Soc. Hort. Sci. 119:510-515. Abbott, J. A., H. A. Affeldt, and L. A. Liljedahl. 1992. Firmness measurement of stored 'Delicious' apples by sensory methods, Magness-Taylor, and sonic transmission. J. Am. Soc. Hort. Sci. 117:590-595. Abbott, J. A., G. S. Bachman, N. F. Childers, J. V. Fitzgerald, and F. J. Matusik. 1968a. Sonic techniques for measuring texture of fruits and vegetables. Food Technol. 22:101-112. Abbott, J. A., T. A. Campbell, and D. R. Massie. 1994a. Delayed light emission and fluorescence responses of plants to chilling. Remote Sensing Environ. 47:87-97. Abbott, J. A., N. F. Childers, G. S. Bachman, J. V. Fitzgerald, and F. J. Matusik. 1968b. Acoustic vibration for detecting textural quality of apples. Proc. Am. Soc. Hort. Sci. 93:725-737. Abbott, J. A., W. R. Forbus, Jr., and D. R. Massie. 1994b. Temperature damage measurement by fluorescence or delayed light emission from chlorophyll. p. 44-49. In: G. Brown and Y. Sarig (eds.), Nondestructive technologies for quality evaluation of fruits and vegetables. Proc. Int. Workshop, U.S.-Israel BARD Fund. Spokane, WA, June 15-19, 1993. Am. Soc. Agr. Eng., St. Joseph, MI. Abbott, J. A., D. T. Krizek, P. Semeniuk, H. E. Moline, and R. M. Mirecki. 1987. Refreshed

100

J. ABBOTT, R

LU, B. UPCHURCH, AND R STROSHINE

delayed light emission and fluorescence for detecting pretreatment effects on chilling injury in coleus. J. Am. Soc. Hort. Sci. 112:560-565. Abbott, J. A, and L. A Liljedahl. 1994. Relationship of sonic resonant frequency to compression tests and Magness-Taylor firmness of apples during refrigerated storage. Trans. Am. Soc. Agr. Eng. 37:1211-1215. Abbott, J. A., and D. R Massie. 1985. Delayed light emission for early detection of chilling in cucumber and bell pepper fruit. J. Am. Soc. Hort. Sci. 1:42-47. Abbott, J. A., and D. R Massie. 1993. Nondestructive firmness measurement of apples. Am. Soc. Agr. Eng. Paper 93-6025. Abbott, J. A, and D. R Massie. 1995. Nondestructive dynamic force/deformation measurement of kiwifruit firmness (Actinidia deliciosa). Trans. Am. Soc. Agr. Eng. 38:1809-1812. Abbott, J. A., A R Miller, and T. A. Campbell. 1991. Detection of mechanical injury and physiological breakdown of cucumbers using delayed light emission. J. Am. Soc. Hort. Sci. 116:52-57. Abbott, J. A., K. H. Norris, and A. K Watada. 1986. Maturity and chilling stress of tomato fruit measured by nondestructive DLE and optical techniques. HortScience 21:128. Abbott, J. A, A K Watada, and D. R Massie. 1976. Effe-gi, Magness-Taylor, and Instron fruit pressure testing devices for apples, peaches, and nectarines. J. Am. Soc. Hort. Sci. 101:698-700. Abeles, F. B., P. W. Morgan, and M. K Saltveit, Jr. 1992. Ethylene in plant biology. Academic Press, San Diego, CA Affeldt, H. A, and J. A Abbott. 1989. Apple firmness and sensory quality using contact acoustic transmission. p. 3:2037-2045. In: V.A Dodd andP. M. Grace (eds.J, Agricultural engineering. Proc. 7th. Int. Congress on Agr. Eng., Dublin. A A Balkema, Rotterdam. Affeldt, H. A., and J. A Abbott. 1992. Nondestructive sonic texture measurement of agricultural commodities. U. S. Patent 5,152,401. Anon. 1976. Pilot model is 95% accurate. Am. Veg. Grower 24(6):38. Armstrong, P. R, R M. Beaudry, and G. K. Brown. 1993. Nondestructive firmness testing on the tree during maturation. Am. Soc. Agr. Eng. Paper 93-6540. Armstrong, P. R, and G. K. Brown. 1993. Non-destructive firmness measurement of apples. Am. Soc. Agr. Eng. Paper 93-6023. Armstrong, P. R, H. R Zapp, and G. K. Brown. 1990. Impulsive excitation of acoustic vibrations in apples for firmness determination. Trans. Am. Soc. Agr. Eng. 33:1353-1359. Aulenbach, B. A, and J. T. Worthington. 1973. New portable colorimeter to evaluate external fruit color of tomato and peach. HortScience 8:92-94. Aulenbach, B. A, J. N. Yeatman, and J. T. Worthington. 1972. Quality sorting ofred Delicious apple by light transmission. USDA, ARS Mkt. Res. Rpt. 936. Bajema, R, A. Baritelle, and G. M. Hyde. 1996. Comprehensive evaluation of potato tuber impact sensitivity. Am. Soc. Agr. Eng. Paper 96-6006. Ballinger, W. K, W. F. McClure, K P. Maness, W. B. Nesbitt, D. K Carroll, and R P. Rohrbach. 1978. Nondestructive quality evaluation from a horticulturist's point of view. J. Food Prot. 41:63-66. Bartsch, J. A., and K Askariaman. 1982. Impact characteristics of storage cabbage. Am. Soc. Agr. Eng. Paper 82-3080. Bayer, G. M. 1976. Simulation study of a vibratory tomato sorter. M.S. Thesis. Ohio State Univ., Columbus. Bellon, V., J. L. Vigneau, and M. Crochon. 1993. Non-destructive sensing of peach firmness. IV Int. Symp. on Fruit, Nut, and Vegetable Production Engineering, ValenciaZaragoza, Spain, March 22-26. Bellon-Maurel, V., and J. L. Vigneau. 1995. NIR fast spectrometer for fruit internal qual-

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

101

ity assessment. p. 471-476. Proc. harvest and postharvest technologies for fresh fruit and vegetables. Am. Soc. Agr. Eng., St. Joseph, M1. Benady, M., J. E. Simon, D. J. Charles, and G. E. Miles. 1992. Determining melon ripeness by analyzing headspace gas emissions. Am. Soc. Agr. Eng. Paper 92-6055. Benady, M., J. E. Simon, D. J. Charles, and G. E. Miles. 1995. Fruit ripeness determination by electronic sensing of aromatic volatiles. Trans. Am. Soc. Agr. Eng. 38:251-257. Bernstein, Z., and 1. Lustig. 1981. A new method of firmness measurement of grape berries and other juicy fruits. Vitis 20:15-21. Bilanski, W. K., C. L. Pen, and D. R Fuzzen. 1984. Apple bruise detection using optical reflectance parameters. Canadian Agr. Eng. 26:111-114. Birth, G. S. 1960. A nondestructive technique for detecting internal discolorations in potatoes. Am. Potato J. 87:53-60. Birth, G. S. 1976. How light interacts with foods. p. 6-11. In: J. J. Gaffney (ed.), Quality detection in foods. Am. Soc. Agr. Eng., St. Joseph, MI. Birth, G. S. 1983. Optical radiation. p. 9-01 to 9-25. In: B. W. Mitchell (ed.), Instrumentation and measurement for environmental sciences. Am. Soc. Agr. Eng., St. Joseph, M1. Birth, G. S., G. G. Dull, W. T. Renfroe, and J. Kays. 1985. Nondestructive spectrophotometric determination of dry matter in onions. J. Am. Soc. Hort. Sci. 110:297-303. Birth, G. S., G. G. Dull, and R G. Leffler. 1990. Nondestructive determination of the solids content of horticultural products. p. 10-15. In: J. A. DeShazer and G. E. Meyer (eds.), Optics in agriculture. Proc. Soc. Photo-Optical Instru. Eng. 1379. Birth, G. S., G. G. Dull, F. B. Magee, H. T. Chan, and C. G. Cavaletto. 1984. An optical method for estimating papaya maturity. J. Am. Soc. Hort. Sci. 109:62-66. Birth, G. S., and H. G. Hecht. 1987. The physics of near-infrared reflectance. p. 1-15. In: P. Williams and K. Norris (eds.), Near-infrared technology in the agricultural and food industries. Am. Assoc. Cereal Chemists, St. Paul, MN. Birth, G. S., and K. H. Norris. 1958. An instrument using light transmittance for nondestructive measurement of fruit maturity. Food Technol. 12:592-595. Birth, G. S., and K. H. Norris. 1965. The difference meter for measuring interior quality of foods and pigments in biological tissues. USDA, ARS Tech. Bul. 1341. Birth, G. S., K. H. Norris, and J. N. Yeatman. 1957. Non-destructive measurement of internal color of tomatoes by spectral transmission. Food Technol. 11:552-557. Birth, G. S., and K. L. Olsen. 1964. Nondestructive detection of watercore in Delicious apples. Proc. Am. Soc. Hort. Sci. 85:75-84. Bittner, D. R, and K. H. Norris. 1976. Optical properties of selected fruits vs maturity. p. 57-59. In: J. J. Gaffney (ed.), Quality detection in foods. Am. Soc. Agr. Eng., St. Joseph, MI. Blahovec, J., and R Janal. 1987. The strength and electrical conductivity of apple pulp. Sbornik UvTIZ-Zahradnictvi 14:252-258. Blankenship, S. M., and C. R Unrath. 1988. Internal ethylene levels and maturity of 'Delicious' and 'Golden Delicious' apples destined for prompt consumption. J. Am. Soc. Hort. Sci. 113:88-91. Blanpied, G. D., and V. A. Blak. 1977. A comparison of pressure tests, acid levels, and sensory evaluations of overripeness in apples. HortScience 12:73-74. Bochereau, L., P. Bourgine, and B. Palagos. 1992. A method for prediction by combining data analysis and neural networks: application to prediction of apple quality using near infra-red spectra. J. Agr. Eng. Res. 51:207-216. Bourne, M. C. 1965. Studies on punch testing of apples. Food Techol. 19:413-415. Bourne, M. C. 1968. Texture profile ofripening pears. J. Food Sci. 33:223-226. Bourne, M. C. 1976a. Interpretation of force curves from instrumental texture measurement. p. 244-274. In: J. M. DeMan, P. W. Voisey, V. F. Rasper, and D. W. Stanley (eds.), Rheology and texture in food quality. AVI, Westport, CT.

102

J. ABBOTT, R

LU, B. UPCHURCH, AND R STROSHINE

Bourne, M. C. 1976b. Texture of fruits and vegetables. p. 275-307. In: J. M. DeMan, P. W. Voisey, V. F. Rasper, and D. W. Stanley (eds.), Rheology and texture in food quality. AVI, Westport, CT. Bourne, M. C. 1979. Fruit texture-an overview of trends and problems. J. Texture Studies 10:83-94. Bourne, M. C. 1980. Texture evaluation of horticultural crops. HortScience 15:51-57. Bouyoucos, G. J., and R E. Marshall. 1950. A pressure tester for small fruits and fruit and vegetable tissues. Proc. Am. Soc. Hart. Sci. 57:211-213. Bower, D. R, and R P. Rohrbach. 1976. Application of vibrational sorting to blueberry firmness separation. Trans. Am. Soc. Agr. Eng. 19:185-191. Bowers, S. V., R B. Dodd, and Y. J. Han. 1988. Nondestructive testing to determine internal quality of fruit. Am. Soc. Agr. Eng. Paper 88-6569. Brecht, J. K., R 1. Shewfelt, J. C. Garner, and E. W. Tollner. 1990. Using X-ray computed tomography (X-ray CT) to nondestructively determine maturity of green tomatoes. HortScience 26:45-47. Breene, W. M. 1975. Application of texture profile analysis to instrumental food texture evaluation. J. Texture Studies 6:53-82. Breene, W. M., 1. J. Jeon, and S. N. Bernard. 1974. Observations on texture measurement of raw cucumbers with the fruit pressure tester. J. Texture Studies 5:317-327. Brown, G. K., 1. J. Segerlind, and R Summitt. 1974. Near-infrared reflectance of bruised apples. Trans. Am. Soc. Agr. Eng. 17:17-19. Brown, G. K., and Y. Sarig (eds.). 1994. Nondestructive technologies for quality evaluation of fruits and vegetables, Proc. Int. Workshop, US-Israel BARD Fund. Spokane, WA. June 15-19, 1993. Am. Soc. Agr. Eng., St. Joseph, M1. Brusewitz, G. H., T. G. McCollum, and X. Zhang. 1991. Impact bruise resistance of peaches. Trans. Am. Soc. Agr. Eng. 34:962-965. Bryant, C. W. 1942. Grading and seed selection of Nancy Hall sweet potatoes by flotation. J. Tenn. Acad. Sci. 7:184-194. Butler, W. 1., and K. H. Norris. 1963. Lifetime of the long-wavelength chlorophyll fluorescence. Biochimica Biophysica Acta 66:72-77. Buttery, R G., R M. Seifert, D. G. Guadagni, and 1. C. Ling. 1971. Characterization of additional volatile components of tomato. Agr. Food Chern. 19:524-529. Chakeres, D. W., and P. Schmalbrock. 1992. Fundamentals of magnetic resonance imaging. Williams Wilkins, Baltimore, MD. Chan, H. T., Jr., and W. B. Forbus, Jr. 1988. Delayed light emission as a biochemical indicator of papaya heat treatment. J. Food Sci. 53:1490-1492. Charron, C. S., D. J. Cantliffe, and R R Heath. 1995. Volatile emissions in plants. Hort. Rev. 17:43-72. Chen, H., and J. De Baerdemaeker. 1993a. Effect of apple shape on acoustic measurements of firmness. J. Agr. Eng. Res. 56:253-266. Chen, H., and J. De Baerdemaeker. 1993b. Modal analysis of the dynamic behavior of pineapples and its relation to fruit firmness. Trans. Am. Soc. Agr. Eng. 36:14391444. Chen, H., and J. De Baerdemaeker. 1993c. Finite-element-based modal analysis of fruit firmness. Trans. Am. Soc. Agr. Eng. 36:1827-1833. Chen, H., J. De Baerdemaeker, and V. Bellon. 1993a. Finite element study of the melon far nondestructive sensing of melon quality. Am. Soc. Agr. Eng. Paper 93-6599. Chen, P., M. J. McCarthy, andR Kauten. 1989. NMR for internal quality evaluation of fruits and vegetables. Trans. Am. Soc. Agr. Eng. 32:1747-1753. Chen, P., M. J. McCarthy, R Kauten, Y. Sarig, and S. Han. 1993b. Maturity evaluation of avocados by NMR methods. J. Agr. Eng. Res. 55:177-187.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

103

Chen, P., M. J. McCarthy, S. M. Kim, and B. Zion. 1996. Development of a high-speed NMR technique for sensing maturity of avocados. Trans. Am. Soc. Agr. Eng. 39:2205-2209. Chen, P., and V. R. Nattuvetty. 1980. Light transmittance through a region of an intact fruit. Trans. Am. Soc. Agr. Eng. 23:519-522. Chen, P., and Z. Sun. 1991. A review of nondestructive methods for quality evaluation and sorting of agricultural products. J. Agr. Eng. Res. 49:85-98. Chen, P., Z. Sun, and L. Huarng. 1992. Factors affecting acoustic responses of apples. Trans. Am. Soc. Agr. Eng. 35:1915-1920. Chen, S., D. S. Fon, and C. C. Chang. 1990. Universal color indices for maturity evaluations of fruits. Am. Soc. Agr. Eng. Paper 90-7054. Cheng, Y., and C. G. Haugh. 1994. Detecting hollow heart in potatoes using ultrasound. Trans. Am. Soc. Agr. Eng. 37:217-222. Chinnan, M. S., C. Thai, and P. Verma. 1988. Evaluation of fluidized bed separation technology in processing plants. Am. Soc. Agr. Eng. Paper 88-6084. Cho, S. 1., V. Bellon, T. M. Eads, R. L. Stroshine, and G. W. Krutz. 1991. Sugar content measurement in fruit tissue using water peak suppression in high resolution lH magnetic resonance. J. Food Sci. 56:1091-1094. Cho, S. 1., G. W. Krutz, H. G. Gibson, and K Haghighi. 1990. Magnet console design of an NMR-based sensor to detect ripeness of fruit. Trans. Am. Soc. Agr. Eng. 33:1043-1050. Cho, S. 1., R. L. Stroshine, 1. C. Baianu, and G. W. Krutz. 1993. Nondestructive sugar content measurements of intact fruit using spin-spin relaxation times (Tz) measurements by pulsed lH magnetic resonance. Trans. Am. Soc. Agr. Eng. 36:1217-1221. Choi, K, G. Lee, Y. J. Han, and J. M. Bunn. 1995. Tomato maturity evaluation using color image analysis. Trans. Am. Soc. Agr. Eng. 38:171-176. Chuma, Y., K Nakaji, and W. McClure. 1982a. Delayed light emission as a means of automatic color sorting of persimmon fruits. (Part 1) DLE fundamental characteristics of persimmon fruits. J. Fac. Agr., Kyushu Univ. 27:1-12. Chuma, Y., K Nakaji, and W. McClure. 1982b. Delayed light emission as a means of automatic color sorting of persimmon fruits. (Part 2) DLE characteristics as a means of color sorting. J. Fac. Agr., Kyushu Univ. 27:13-20. Chmna, Y, K Nakaji, and M. Ohura. 1980. Maturity evaluation of bananas by delayed light emission. Trans. Am. Soc. Agr. Eng. 23:1043-1047. Chuma, Y., M. Ohura, and A. Tagawa. 1982c. Delayed light emission as a means of sorting tomatoes, DLE characteristics of tomatoes excited by flash light. J. Faculty Agr., Kyushu Univ. 26:159-167. Chuma, Y., K Nakaji, and A. Tagawa. 1982d. Delayed light emission as a means of automatic sorting tomatoes. J. Faculty Agr., Kyushu Univ. 26:221-234. Chuma, Y., K. Sein, S. Kawano, and K Nakaji. 1977. Delayed light emission as a means of automatic selection of Satsuma oranges. Trans. Am. Soc. Agr. Eng. 20:996-1000. Clark, C. J., P. D. Hockings, D. C. Joyce, and R. A. Mazucco. 1997. Application of magnetic resonance imaging to pre- and post-harvest studies of fruit and vegetables. Postharv. BioI. Tech. 11: in press. Clark, H. L., and W. Mikelson. 1942. Fruit ripeness tester. U. S. Patent 2,277,037. Clark, R. L., and P. S. Shackelford, Jr. 1973. Resonance and optical properties of peaches as related to flesh firmness. Trans. Am. Soc. Agr. Eng. 16:1140-1142. Claypool, L. L., R. B. Fridley, and R. Johns. 1966. Precision in a pressure tester. Western Fruit Grower 20(7):18-19. Clydesdale, F. M. 1978. Colorimetry-methodology and applications. Crit. Rev. Food Sci. Nutr. 10:243-301. Clydesdale, F. M., and C. H. Podlesny, Jr. 1968. A computer program for the interconversion of color data. Color Eng. 6(3):55-56.

104

J. ABBOTT, R LU, B. UPCHURCH, AND R STROSHINE

Coleman, R L., C. J. Wagner, Jr., and R E. Berry. 1983. Mobile processing line for southeastern grapes-status and plans. Proc. Fla. State Hort. Soc. 96:338-340. Conway, J. M., K. H. Norris, and C. E. Bodwell. 1984. A new approach for the estimation of body composition: infrared interactance. Am. Soc. Clinical Nutr. 40:1123-1130. Cooke, J. R 1972. An interpretation of the resonant behavior of intact fruits and vegetables. Trans. Am. Soc. Agr. Eng. 15:1075-1080. Cooke, J. R, and R H. Rand. 1973. A mathematical study ofresonance in intact fruits and vegetables using a 3-media elastic sphere model. J. Agr. Eng. Res. 18:141-157. Corey, H. 1970. Texture in foodstuffs. Crit. Rev. Food Technol. 1:161-198. Crowe, T. G., and M. J. Delwiche. 1994. Real-time defect detection in fruit. Am. Soc. Agr. Eng. Paper 94-6028. Crowe, T. G., and M. J. Delwiche. 1995. Pipeline image processing for real-time defect detection in produce. Am. Soc. Agr. Eng. Paper 95-3174. Davenel, A., M. Crochon, F. Sevila, J. Pourcin, P. Verlaque, D. Bertrand, and P. Robert. 1987. Nondestructive fruit control: sugar content by near-infrared reflectance. Euro Food Chem.4:171-191. Davenel, A., C. Guizard, T. Labarre, and F. Sevila. 1988. Automatic detection of surface defects on fruit using a vision system. J. Agr. Eng. Res. 41:1-9. Davis, D. c., and M. J. Pitts. 1985. Measurement of cherry firmness. Postharvest Pomology Newsletter 3(2):10-11. De Baerdemaeker, J. 1989. The use of mechanical resonance measurements to determine fruit texture. Acta Hort. 258:331-339. De Baerdemaeker, J. 1994. Quality sensing for the European market of fruits and vegetables. p. 9-22. In: G. Brown and Y. Sarig (eds.), Nondestructive technologies for quality evaluation of fruits and vegetables. Proc. Int. Workshop, U.S.-Israel BARD Fund, Spokane, WA, June 15-19, 1993. Am. Soc. Agr. Eng., St. Joseph, MI. De Baerdemaeker, J., L. Lemaitre, and R Meire. 1982. Quality detection by frequency spectrum analysis of the fruit impact force. Trans. Am. Soc. Agr. Eng. 25:175-178. de Certaines, J. D., W. M. M. J. Bovee, and F. Podo (eds.). 1992. Magnetic resonance spectroscopy in biology and medicine. Pergamon Press, New York. DeEll, J. R, R K. Prange, and D. P. Murr. 1996. Chlorophyll fluorescence of Delicious apples at harvest as a potential predictor of superficial scald development during storage. Postharvest BioI. Technol. 9:1-6. Dekazos, E. D., and G. S. Birth. 1970. A maturity index for blueberries using light transmittance. J. Am. Soc. Hort. Sci. 95:610-614. Delwiche, M. J. 1987. Theory of fruit firmness sorting by impact forces. Trans. Am. Soc. Agr. Eng. 30:1160-1166, 1171. Delwiche, M. J., H. A. Affeldt, G. Birth, G. K. Brown, D. E. Guyer, A. Hetzroni, K. Peleg, and C. N. Thai. 1994. Surface color measurement of fruits and vegetables. p. 63-71. In: G. Brown and Y. Sarig (eds.), Nondestructive technologies for quality evaluation of fruits and vegetables. Proc. Int. Workshop, U.S.-Israel BARD Fund, Spokane, WA, June 15-19, 1993. Am. Soc. Agr. Eng., St. Joseph, MI. Delwiche, M. J., H Arevalo, and J. Mehlschau. 1993. A fruit firmness sorting system. Am. Soc. Agr. Eng. Paper 93-6024. Delwiche, M. J., and R A. Baumgardner. 1983. Ground color measurement of peach. J. Am. Soc. Hort. Sci. 108:1012-1016. Delwiche, M. J., and R A. Baumgardner. 1985. Ground color as a peach maturity index. J. Am. Soc. Hort. Sci. 110:53-57. Delwiche, M. J., T. McDonald, and S. V. Bowers. 1987a. Determination of peach firmness by analysis of impact forces. Trans. Am. Soc. Agr. Eng. 30:249-254.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

105

Delwiche, M. J., and Y. Sarig. 1991. A probe impact sensor for fruit firmness measurement. Trans. Am. Soc. Agr. Eng. 34:187-192. Delwiche, M. J., N. Singh, H. Arevalo, and J. Mehlschau. 1991. A second generation fruit firmness sorter. Am. Soc. Agr. Eng. Paper 91-6042. Delwiche, M. J., S. Tang, and J. J. Mehlschau. 1989. An impact force response fruit firmness sorter. Trans. Am. Soc. Agr. Eng. 32:321-326. Delwiche, M. J., S. Tang, and J. W. Rumsey. 1987b. Color and optical properties of clingstone peaches related to maturity. Trans. Am. Soc. Agr. Eng. 30:1873-1879. Delwiche, M. J., S. Tang, and J. F. Thompson. 1990. Prune defect detection by line-scan imaging. Trans. Am. Soc. Agr. Eng. 33:950-954. DeMan, J. M., P. W. Voisey, V. F. Rasper, and D. W. Stanley. (eds.) 1976. Rheology and texture in food quality. AVI, Westport, CT. Diener, R. G., J. H. Levin, and R. J. Wolthuis. 1969. An instrument for automatically measuring the firmness of red tart cherries. Food TechoL 23:125-127. Diener, R. G., J. P. Mitchell, and M. L. Rhoten. 1970. Using an X-ray image scan to sort bruised apples. Agr. Eng. 51:356-361. Diener, R. G., F. E. Sobotka, and A. E. Watada. 1971. An accurate, low cost firmness measuring instrument. J. Texture Studies 2:373-384. Douillard, c., and E. Guichard. 1990. The aroma of strawberry (Fragaria ananassa): characterisation of some cultivars and influence of freezing. J. Sci. Food Agr. 50:517-531. Drake, B. 1962. Automatic recording of vibrational properties of foodstuffs. J. Food Sci. 27:182-188. Dull, G. G. 1984. Measurement of cantaloupe quality with near infrared spectrophotometry. Am. Soc. Agr. Eng. Paper 84-3557. Dull, G. G., G. S. Birth, and R. G. Leffler. 1989a. Exiting energy distribution in honeydew melon irradiated with a near-infrared beam. J. Food QuaL 12:377-381. Dull, G. G., G. S. Birth, D. A. Smittle, and R. G. Leffler. 1989b. Near-infrared analysis of soluble solids in intact cantaloupe. J. Food Sci. 54:393-395. Dull, G. G., R. G. Leffler, G. S. Birth, and D. A. Smittle. 1992. Instrument for nondestructive measurement of soluble solids in Honeydew melons. Trans. Am. Soc. Agr. Eng. 35:735-737. Dull, G. G., R. G. Leffler, G. S. Birth, A. Zaltzman, and Z. Schmilovitch. 1991. The near infrared determination of moisture in whole dates. HortScience 26:1303-1305. Duprat, F., H. Chen, M. Grotte, D. Loonis, and E. Pietri. 1995. Laser light based machine vision system for nondestructive ripeness sensing of Golden apples. Proc. 1st IFRACICIGR/EURAGENG/ISHS Workshop Control applic. post-harv. process. tech. 1:58-93. Eustace, C. A., and R. B. Jordan. 1995. Sugar content evaluation using the earth's field proton magnetic resonance. Trans. Am. Soc. Agr. Eng. 38:1563-1571. Falk, S., C. H. Hertz, and H. 1. Virgin. 1958. On the relation between turgor pressure and tissue rigidity. PhysioL Plant. 11:802-817. Fan, S., S. E. Prussia, and Y. C. Hung. 1994. Evaluating the UGA laser-pufffood firmness detector. Am. Soc. Agr. Eng. Paper 94-6540. Farabee, L. M., and M. L. Stone. 1991. Determination of watermelon maturity with sonic impulse testing. Am. Soc. Agr. Eng. Paper 91-3013. Faust, M., P. C. Wang, and J. Maas. 1997. The use of magnetic resonance imaging in plant science. Hort. Rev. 20: 225-266. Fekete, A. 1993. Non-destructive method of fruit elasticity determination. IV Int. Symp. on Fruit, Nut, and Vegetable Production Engineering, Valencia-Zaragoza, Spain, March 22-26.

106

J. ABBOTT, R LU, B. UPCHURCH, AND R STROSHINE

Fekete, A. 1994. Firmness ranges of fruits and vegetables. Am. Soc. Agr. Eng. Paper 946601. Finney, E. E., Jr. 1967. Dynamic elastic properties of some fyuits during growth and development. J. Agr. Eng. Res. 12:249-256. Finney, E. E., Jr. 1970. Mechanical resonance within red Delicious apples and its relation to fruit texture. Trans. Am. Soc. Agr. Eng. 12:177-180. Finney, E. E., Jr. 1971a. Dynamic elastic properties and sensory quality of apple fruit. J. Texture Studies 2:62-74. Finney, E. E., Jr. 1971b. Random vibration techniques for nondestructive evaluation of peach firmness. J. Agr. Eng. Res. 16:81-87. Finney, E. E., Jr. 1972. Vibration techniques for testing fruit firmness. J. Texture Studies 3:263-283. Finney, E. E., Jr., and J. A. Abbott. 1978. Methods for testing the dynamic mechanical response of solid foods. J. Food Quality 2:55-74. Finney, E. E., Jr., J. A. Abbott, A. E. Watada, and D. R Massie. 1978. Nondestructive sonic resonance and the texture of apples. J. Am. Soc. Hort. Sci. 103:158-162. Finney, E. E., Jr., 1. Ben-Gera, and D. R Massie. 1967. An objective evaluation of changes in firmness of ripening bananas using a sonic techniques. J. Food Sci. 32 :642-646. Finney, E. E., Jr., and D. R Massie. 1975. Instrumentation for testing the response of fruits to mechanical impact. Trans. Am. Soc. Agr. Eng. 18:1184-1187, 1192. Finney, E. E., Jr., and K. H. Norris. 1968. Instrumentation for investigating dynamic mechanical properties of fruits and vegetables. Trans. Am. Soc. Agr. Eng. 11:94-97. Finney, E. E., Jr., and K. H. Norris. 1973. X-ray images of hollow heart potatoes in water. Am. Potato J. 50(1):1-8. Fluck, R, and E. M. Ahmed. 1973. Impact testing of fruits and vegetables. Trans. Am. Soc. Agr. Eng. 16:660-666. Forbus, W. R, Jr., and G. G. Dull. 1990. Delayed light emission as an indicator of peach maturity. J. Food Sci. 55:1581-1584. Forbus, W. R, Jr., G. G. Dull, and D. Smittle. 1991a. Measuring netted muskmelon maturity by delayed light emission. J. Food Sci. 56:981-984. Forbus, W. R, Jr., G. G. Dull, and D. A. Smittle. 1992. Nondestructive measurement of canary melon maturity by delayed light emission. J. Food Qual. 15:119-127. Forbus, W. R, Jr., G. A. Hardigree, and J. H. Adams. 1985a. Experimental delayed-light emission meter for horticultural crops. USDA, ARS-41. Forbus, W. R, Jr., J. A. Payne, and S. D. Senter. 1991b. Nondestructive evaluation of Japanese persimmon maturity by delayed light emission. J. Food Sci. 56:985-988. Forbus, W. R, and S. D. Senter. 1989. Delayed light emission as an indicator of cantaloupe maturity. J. Food Sci. 54:1094-1095,1097. Forbus, W. R, Jr., S. D. Senter, and H. T. Chan, Jr. 1987. Measurement of papaya maturity by delayed light emission. J. Food Sci. 52:356-360. Forbus, W. R, Jr., S. D. Senter, and R L. Wilson. 1985b. Measurement of tomato maturity by delayed light emission. J. Food Sci. 50:750-753. Francis, F. J. 1980. Color quality evaluation of horticultural crops. HortScience 15:14-15. Franke, J. E., and R P. Rohrbach. 1981. A nonlinear impact model for a sphere with a flat plate. Trans. Am. Soc. Agr. Eng. 24:1683-1686. Fridley, R B. 1969. Firmness tester for fruit. U. S. Patent 3,470,737. Friedman, H. H., J. E. Whitney, and A. S. Szczesniak. 1963. The texturometer-a new instrument for objective texture measurement. J. Food Sci. 28:390-396. Fukuda, H., T. Kubota, and T. Suyama. 1979. Nondestructive measurement of chlorophyll, watercore and internal browning disorders in apple fruits by light transmission. Bul. Fruit Tree Res. Sta., Iboraki Prefecture 6:27-54.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

107

Fukushima, E., and S. B. W. Roeder. 1981. Experimental pulse NMR, a nuts and bolts approach. Addison-Wesley, Reading, MA. Gaffney, J. J. 1976a. Reflectance properties of citrus fruits. p. 60-64. In: J. J. Gaffney (ed.), Quality detection in foods. Am. Soc. Agr. Eng., St. Joseph, MI. Gaffney, J. J. 1976b. Light reflectance of radishes as a basis for automatic grading. p. 75-79,85. In: J. J. Gaffney (ed.), Quality dectection in foods. Am. Soc. Agr. Eng., St. Joseph, MI. Galili, N., A. Mizrach, and G. Rosenhouse. 1993. Ultrasonic testing of whole fruit for nondestructive quality evaluation. Am. Soc. Agr. Eng. Paper 93-6026. Gamble, G. 1994. Non-invasive observation of freezing effects in fruit tissue by magnetic resonance imaging. J. Food Sci. 59:571-573,610. Gardner, J. W., H. V. Shurmer, and T. T. Tan. 1992. Application of an electronic nose to discrimination of coffees. Sensors Actuators B 6:71-75. Garrett, R E. 1970. Velocity of propagation of mechanical disturbances in apples. Ph.D. Thesis, Cornell Univ., Ithaca, NY. Garrett, A. W., N. W. Desrosier, G. D. Kuhn, and M. L. Fields. 1960. Evaluation of instruments to measure firmness of tomatoes. Food Technol. 14:562-564. Garrett, R E., and R B. Furry. 1972. Velocity of sonic pulses in apples. Trans. Am. Soc. Agr. Eng. 15:770-774. Garrett, R E., and W. K. Talley. 1970. Use of gamma ray transmission in selecting lettuce for harvest. Trans. Am. Soc. Agr. Eng. 13:820-823. Gibbons, G. c., and R M. Smillie. 1980. Chlorophyll fluorescence photography to detect mutants, chilling injury, and heat stress. Carlsburg Res. Commun. 45:269-282. Goldy, R G. 1992. Breeding muscadine grapes. Hort. Rev. 14:357-405. Goodman, B. A., B. Williamson, and J. A. Chudek. 1992. Non-invasive observation of the development of fungal infection in fruit. Protoplasma 166:107-109. Gorini, F. L., and A. Testoni. 1990. The relation between colour and quality of vegetables. Acta Hort. 259:31-60. Gortner, W. A., G. G. Dull, and B. H. Krauss. 1967. Fruit development, maturation, ripening, and senescence. HortScience 2:1-4. Guedalia, D., and Y. Edan. 1994. A dynamic artificial neural network for coding and classification of multi-sensor quality information. Am. Soc. Agr. Eng. Paper 94-3053. Gunasekaran, S., M. R Paulsen, and G. C. Shove. 1985. Optical methods for nondestructive quality evaluation of agricultural and biological materials. J. Agr. Eng. Res. 32:209241. Gutterman, R P. 1976. Mass flow density sorter for fruits and vegetables. p. 211-213. In: J. J. Gaffney (ed.), Quality detection in foods. Am. Soc. Agr. Eng., St. Joseph, MI. Haller, M. H. 1941. Fruit pressure testers and their practical applications. USDA Circ. 627, Washington, DC. Hamann, D. D. 1970. Analysis of stress during impact of fruit considered to be viscoelastic. Trans. Am. Soc. Agr. Eng. 13:893-899. Hamann, D. D., and D. E. Carroll. 1971. Ripeness sorting of muscadine grapes by use of low-frequency vibrational energy. J. Food. Sci. 36:1049-1051. Hamann, D. D., L. J. Kushman, and W. E. Ballinger. 1973. Sorting blueberries for quality by vibration. J. Am. Soc. Hort. Sci. 98:572-576. Hamson, A. R 1952. Measuring firmness of tomatoes in a breeding program. J. Am. Soc. Hort. Sci. 60:425-433. Harker, F. R, J. H. Maindonald, and P. J. Jackson. 1996. Penetrometer measurement of apple and kiwifruit firmness: operator and instrument differences. J. Am. Soc. Hort. Sci. 121:927-936. Heil, J. R, P. Chen, F. G. Mitchell, B. Zion, and M. J. McCarthy. 1992. Testing of Bartlett

108

J. ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

pear maturity and ripening characteristics using magnetic resonance procedures. Ann. Res. Rpt. California Tree Fruit Agreement, Reedly, CA. Heinemann, P. H., Z. A. Varghese, C. T. Morrow, H. J. Sommer, and R. M. Crassweller. 1995. Machine vision inspection of Golden Delicious apples. Applied Eng. Agr. 11:901-906. Hetzroni, A., J. E. Simon, M. Benady, and B. Bordelon. 1994. Electronic sensing of aromatic volatiles for quality sorting in strawberries. Am. Soc. Agr. Eng. Paper 94-6029. Hinshaw, W. S., P. A. Bottomley, and G. N. Holland. 1979. A demonstration of the resolution ofNMR imaging in biological systems. Experientia 35:1268-1269. Holcomb, D. J., R. Cooke, and P. L. Hartman. 1977. A study of electrical, thermal, and mechanical properties of apples in relation to bruise detection. Am. Soc. Agr. Eng. Paper 77-3512. Holmes, R. G. 1979. Vibratory sorting of process tomatoes. Am. Soc. Agr. Eng. Paper 796543. Horvat, R. J., and S. D. Senter. 1985. Comparison of the volatile constituents from rabbiteye blueberries (Vaccinium ashei) during ripening. J. Food Sci. 50:429-431, 436. Howarth, M. S., J. R. Brandon, S. W. Searcy, and N. Kehtarnavaz. 1990a. Estimation of tip shape for carrot classification by machine vision. Am. Soc. Agr. Eng. Paper 90-3530. Howarth, M. S., and W. F. McClure. 1987. Agricultural product analysis by computer vision. Am. Soc. Agr. Eng. Paper 87-3043. Howarth, M. S., and S. W. Searcy. 1989. Algorithms for grading carrots by machine vision. Am. Soc. Agr. Eng. Paper 89-7502. Howarth, M. S., S. W. Searcy, and G. S. Birth. 1990b. Reflectance characteristics of freshmarket carrots. Trans. Am. Soc. Agr. Eng. 33:961-964. Hruschka, W. R. 1987. Data analysis: wavelength selection methods. p. 35-55. In: P. Williams and K. Norris (eds.), Near-infrared technology in the agricultural and food industries. Am. Assoc. Cereal Chemists, St. Paul, MN. Hunter, R. S., andR. W. Harold. 1987. The measurement of appearance. Wiley, New York. Huarng, L., P. Chen, and S. Upadhyaya. 1993. Determination of acoustic vibration modes in apples. Trans. Am. Soc. Agr. Eng. 36:1423-1429. Hung, Y. c., and S. E. Prussia. 1995. Firmness measurement using a nondestructive laserpuff detector. p.145-154. Proc. Food Processing Automation IV Conf., Am. Soc. Agr. Eng., St. Joseph, MI. Hyde, G. M., G. K. Brown, E. J. Timm, and W. Zhang. 1992. Instrumented sphere evaluation of potato packing line impacts. Trans. Am. Soc. Agr. Eng. 35:65-69. Jackman, R. L., A. G. Marangoni, and D. W. Stanley. 1990. Measurement of tomato fruit firmness. HortScience 25:781-783. Jacob, R. c., R. J. Romani, and C. M. Sprock. 1965. Fruit sorting by delayed light emission. Trans. Am. Soc. Agr. Eng. 8:18-19,24. Jacobi, K., J. Giles, E. MacRae, and T. Wegrzyn. 1995. Conditioning 'Kensington' mango with hot air alleviates hot water disinfestation injuries. HortScience 30:562-565. Johnson, M. 1985. Automation in citrus sorting and packing. p. 63-68. Proc. Agri-Mation Conf. Expo. Am. Soc. Agr. Eng. St. Joseph, MI. Jordan, R. B., and C. A. Eustace. 1993. Sugar content evaluation using earth's field NMR. Am. Soc. Agr. Eng. Paper 93-3584. Joyce, D. c., and A. J. Shorter. 1994. High-temperature conditioning reduces hot water treatment injury of 'Kensington Pride' mango fruit. HortScience 29:1047-1051. Judd, D. B., and G. Wyszecki. 1963. Color in business, science, and industry. Wiley, New York. Katayama, K., K. Komaki, and S. Tamiya. 1996. Prediction of starch, moisture, and sugar in sweetpotato by near infrared transmittance. HortScience 31:1003-1006.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

109

Kato, K. 1987. Nondestructive measurement of fruits quality by electrical impedence (Part 1) Relation between impedence parameters and freshness. Japan. Soc. Agr. Mach. 61:29-32. Kattan, A. A. 1957. Changes in color and firmness during ripening of detached tomatoes, and the use of a new instrument for measuring firmness. Proc. Am. Soc. Hort. Sci. 70:379-383. Kattan, A. A., R H. Benedict, G. A. Albritton, H. F. Osborn, and C. Q. Sharp. 1968. Mass grading machine-harvested tomatoes. Arkansas Farm Res. 17(1):5. Kattan, A. A., and C. Q. Sharp. 1970. Mass grading of snap beans. Arkansas Farm Res. 19 (5):10. Kattan, A. A., C. Q. Sharp, and J. R Morris. 1969. Mass grading of sour cherries. Arkansas Farm Res. 18(4):12. Kawano, S. 1994. Present condition of nondestructive quality evaluation of fruits and vegetables in Japan. Japan. Agr. Res. Quart. 28:212-216. Kawano, S., H. Abe, and M. Iwamoto. 1994. Novel applications of nondestructive techniques for quality evaluation of fruits and vegetables. p. 1-8. In: G. Brown and Y. Sarig (eds.), Nondestructive technologies for quality evaluation of fruits and vegetables. Proc. Int. Workshop, US-Israel BARD Fund. Spokane, WA. June 15-19, 1993. Am. Soc. Agr. Eng., St. Joseph, ML Kawano, S., and M. Iwamoto. 1991. Advances in R&D on near infrared spectroscopy in Japan. p. 2-9. In: J.A. DeShazer and G.E. Meyer (eds.), Optics in agriculture. Proc. Soc. Photo-Optical Instru. Eng. 1379. Kawano, S., T. Fujiwara, and M. Iwamoto. 1993. Nondestructive determination of sugar content in Satsuma mandarin using near infrared (NIR) transmittance. J. Japan. Soc. Hort. Sci. 62:465-470. Kawano, S., H. Watanabe, and M. Iwamoto. 1992. Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode. J. Japan. Soc. Hort. Sci. 61:445-451. Keagy, P. M., B. Parvin, and T. F. Schatzki. 1996. Machine recognition of navel orange worm damage in X-ray images of pistachio nuts. Lebensm.-Wiss. U.-TechnoL 29:140-145. Keller, P. J. 1991. Basic principles of magnetic resonance imaging. General Electric Medical Systems, Milwaukee, WI. Kenworthy, A. 1., and 1. Silsby. 1974. Red tart cherry fruit quality: measuring fruit firmness objectively. Mich. Agr. Exp. Sta. Res. Rpt. 253. Kim, S. M., M. J. McCarthy, and P. Chen. 1994. Feasibility of tomato quality grading and sorting using magnetic resonance. Am. Soc. Agr. Eng. Paper 94-6519. Kimmel, E., K. Peleg, and S. Binga. 1992. Vibration modes of spheroidal fruits. J. Agr. Eng. Res. 52:201-213. Kitamura, T., T. Umemoto, T. Iwata, and T. Akazawa. 1976. Studies on the storage of melons, ill: varietal differences in the fluctuation of the amount of volatile constituents produced during ripening. J. Japan. Soc. Hort. Sci. 44:417-421. Koster, E. P. 1990. Sensory analysis and consumer research in product development. Acta Hort. 259:13-23. Kramer, A., and A. S. Szczesniak, eds. 1973. Texture Measurements of Foods. Reidel PubL Co., Boston. Kramer, A., and B. A. Twigg. 1970. Quality control for the food industry. p. 10. VoL 1: Fundamentals, ed. 3. AVI, Westport, CT. Krautkramer, J., and H. Krautkramer. 1990. Ultrasonic testing of materials , 4th ed. SpringerVerlag, New York. Kunkel, R, P. F. Gifford, A. D. Edgar, and A. M. Binkley. 1952. The mechanical separation of potatoes into specific gravity groups. Colorado Agr. Expt. Sta. BuL 422-A.

110

J. ABBOTT, R LU, B. UPCHURCH, AND R STROSHINE

Lee, F. F., and R P. Rohrbach. 1983. Firmness modeling and rapid relaxation modulus determination in blueberries. Trans. Am. Soc. Agr. Eng. 26:1654-1660, 1664. Lee, Y. c., and S. H. Lee. 1992. Flavor quality of concentrated strawberry pulp with aroma recovery. J. Food Qual. 15:321-332. Lehman-Salada, 1. 1996. Instrument and operator effects on apple firmness readings. HortScience 31:994-997. Lenker, D. H., and P. A. Adrian. 1971. Use of X-ray for selecting mature lettuce heads. Trans. Am. Soc. Agr. Eng. 14:894-898. Lewis, C. E. 1978. The maturity of avocados-a general review. J. Sci. Food Agr. 29:857-866. Lewis, C. 1., A. E. Murneek, C. C. Cate. 1919. Pear harvesting and storage investigations in Rogue Valley (2nd report). Oreg. Agr. Expt. Sta. Bul. Li, Y., W. K. Wai, R 1. Stroshine, and G. W. Krutz. 1992. Nondestructive sugar content sensing of intact fruit using low field and low resolution pulsed magnetic resonance. Am. Soc. Agr. Eng. Paper 92-6598. Lichtensteiger, M. J., R G. Holmes, M. Y. Hamdy, and J. 1. Blaisdell. 1988. Impact parameters of spherical viscoelastic objects and tomatoes. Trans. Am. Soc. Agr. Eng. 31:595-602. Liljedahl, L A., and J. A. Abbott. 1994. Changes in sonic resonance of 'Delicious' and 'Golden Delicious' apples undergoing accelerated ripening. Trans. Am. Soc. Agr. Eng. 37:907-912. Lin, W. c., J. W. Hall, and A. Klieber. 1993. Video imaging for quantifying cucumber fruit color. HortTechno!' 3:436-439. Lipton, W. J. 1980. Interpretation of quality evaluations of horticultural crops. HortScience 15:64-66. Little, A. C. 1976. Physical measurements as predictors of visual appearance. Food Techno!' 30(10):74-82. Liu, F. W., and M. M. King. 1978. Consumer evaluations of 'McIntosh' apple firmness. HortScience 13:162-163. Long, J. F., and B. K. Webb. 1973. Correlations ofreflectance ratios to maturity of whole peaches. Trans. Am. Soc. Agr. Eng. 16:922-925. Lott, R V. 1944. A spectral analysis of color changes in flesh and skin of maturing Grimes Golden and Stayman Winesap apples. Proc. Am. Soc. Hort. Sci. 44:157-171. Lu, R, and J. A. Abbott. 1996. Finite element analysis of modes of vibration in apples. J. Texture Studies 27:265-286. Luan, J., and R P. Rohrbach. 1989. Measurement of contact pressure during fruit impact. Am. Soc. Agr. Eng. Paper 89-6025. Lurie, S., R Ronen, and S. Meier. 1994. Determining chilling injury induction in green peppers using nondestructive pulse amplitude modulated (PAM) fluorometry. J. Am. Soc. Hort. Sci. 119:59-62. Lustig, 1., and Z. Bernstein. 1987. An improved firmness tester for juicy fruit. HortScience 22:653-655. Maas, J. 1., M. J. Line, M. M. Millard, and G. J. Galleta. 1992. Nuclear magnetic resonance imaging: a different view of strawberry fruit. Adv. Strawberry Res. 11:64-66. Maas, J. 1., M. M. Millard, M. J. Line, and G. J. Galleta. 1993. Nuclear magnetic resonance microimaging of strawberry fruit. Acta Hort. 348:375-377. MacFall, J.S., and G. A. Johnson. 1994. The architecture of plant vasculature and transport as seen with magnetic resonance microscopy. Canadian J. Bot. 72:1561-1573. Macquarrie, P. M. 1993. Design and characteristics of a two-phase fluid density separation system for sorting watercored apples. M.S. Thesis. Washington State Univ., Pullman.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

111

Magness, J. R, and G. F. Taylor. 1925. An improved type of pressure tester for the determination of fruit maturity. USDA Circ. 350. Maguire, Y. P., and N. F. Haard. 1975. Fluorescent product accumulation in ripening fruit. Nature (London) 258:599-600. Maguire, Y. P., and N. F. Haard. 1976. Isolation oflipofuscin-like fluorescence products from ripening banana fruit. J. Food Sci. 41:551-554. Marchant, J. A, C. M. Onyango, and M. J. Street. 1988. High speed sorting of potatoes using computer vision. Am. Soc. Agr. Eng. Paper 88-3540. Martens, H., and T. Naes. 1987. Multivariate calibration by data compression. p. 57-87. In: P. Williams and K Norris (eds.), Near-infrared technology in the agricultural and food industries. Am. Assoc. Cereal Chemists, St. Paul, MN. Massie, D. R, and K H. Norris. 1975. A high-intensity spectrophotometer interfaced with a computer for food quality measurement. Trans. Am. Soc. Agr. Eng. 18:173-176. Mattus, G. E. 1965. Mechanical thumb tests of apple firmness. J. Am. Soc. Hort. Sci. 87:100-103. McCarthy, M. J., B. Zion, P. Chen, S. Ablett, A. H. Darke, and P. J. Lillford. 1995. Diamagnetic susceptibility changes in apple tissue after bruising. J. Sci. Food Agr. 67:13-20. McClendon, B. D., and R H. Brown. 1971. Dielectric properties of peaches as a maturity index. Am. Soc. Agr. Eng. Paper 71-332. McClure, J. R, R G. Holmes, and M. Y. Hamdy. 1979. Inclined vibrating plate tomato sorter. Am. Soc. Agr. Eng. Paper 79-6008. McGlone, V. A., and P. N. Schaare. 1993. The application of impact response analysis in the New Zealand fruit industry. Am. Soc. Agr. Eng. Paper 93-6537. Mehlschau, J. J., P. Chen, 1. 1. Claypool, and R B. Fridley. 1981. A deformeter for nondestructive maturity detection of pears. Trans. Am. Soc. Agr. Eng. 24:1368-1371, 1375. Meir, S., S. Philosoph-Hadas, and N. Aharoni. 1991a. Ethylene-increased accumulation of flourescent lipid-peroxidation products during senescence of parsley leaves. J. Am. Soc. Hort. Sci. 117:128-132. Meir, S., S. Philosoph-Hadas, G. Zauberman, Y. Fuchs, M. Akerman, and N. Aharoni.. 1991b. Increased formation of fluorescent lipid-peroxidation products in avocado peels precedes other signs of ripening. J. Am. Hort. Sci. 116:823-826. Melcarek, P. K, and Brown, G. N. 1977a. The effects of chilling stress on the chlorophyll fluorescence of leaves. Plant Cell Physiol. 18:1099-1107. Melcarek, P. K, and Brown, G. N. 1977b. Effects of chill stress on prompt and delayed chlorophyll fluorescence from leaves. Plant Physiol. 60:822-825. Meredith, F. 1., R G. Leffler, and C. E. Lyon. 1990. Detection of firmness in peaches by impact force response. Trans. Am. Soc. Agr. Eng. 33:186-188. Miller, A R, T. J. Kelley, and B. D. White. 1995. Nondestructive evaluation of pickling cucumbers using visible-infrared light transmission. J. Am. Soc. Hort. Sci. 120:1063-1068. Miller, B. K, and M. J. Delwiche. 1991a. Spectral analysis of peach surface defects. Trans. Am. Soc. Agr. Eng. 34:2509-2515. Miller, B. K, and M. J. Delwiche. 1991b. Peach defect detection with machine vision. Trans. Am. Soc. Agr. Eng. 34:2588-2597. Miller, W. M., K Peleg, and P. Briggs. 1988. Automated density separation for freezedamaged citrus. Appl. Eng. Agr. 4:344-348. Miller, W. M., and W. 1. Verba. 1987. Automatic density measurements for quality sorting of 'Marsh' grapefruit. Am. Soc. Agr. Eng. Paper 87-6518. Mizrach, A, N. Galili, and G. Rosenhouse. 1989. Determination of fruit and vegetable properties by ultrasonic excitation. Trans. Am. Soc. Agr. Eng. 32:2053-2058. Mizrach, A., N. Galili., G. Rosenhouse, and D. C. Teitel. 1991. Acoustical, mechanical, and

112

J. ABBOTT, R

LU, B. UPCHURCH, AND R STROSHINE

quality parameters of winter-grown melon tissue. Trans. Am. Soc. Agr. Eng. 34:21352138. Mizrach, A, D. Nahir, and B. Ronen. 1992. Mechanical thumb sensor for fruit and vegetable sorting. Trans. Am. Soc. Agr. Eng. 35:247-259. Mohsenin, N. N. 1986. Physical properties of plant and animal materials, 2nd. ed. Gordon Breach Science, New York. Mohsenin, N. N., and H. Gohlich. 1962. Techniques for determination of mechanical properties of fruits and vegetables as related to design and development of harvesting and processing machinery. J. Agr. Eng. Res. 7:300-315. Mohsenin, N. N., V. K. Jindal, and A N. Manor. 1978. Mechanics of impact of a falling fruit on a cushioned surface. Trans. Am. Soc. Agr. Eng. 21:594-600. Moini, S., and M. O'Brien. 1978. Tomato color measurement versus maturity. Trans. Am. Soc. Agr. Eng. 21:797-800. Moini, S., M. O'Brien, and P. Chen. 1980. Spectral properties of mold and defects of processing tomatoes. Trans. Am. Soc. Agr. Eng. 23:1062-1064. Montejano-Gaitan, J. G., D. D. Hamann, and F. G. Giesbrecht. 1982. Vibration sorting of simulated small fruit. Trans. Am. Soc. Agr. Eng. 25:1785-1791. Morris, O. M. 1925. Studies in apple storage. Wash. Agr. Expt. Sta. BuL 193. Muir, A Y, R L. Porteous, and R L. Wastie. 1982. Experiments in the detections ofincipient diseases in potato tubers by optical methods. J. Agr. Eng. Res. 27:131-138. Muir, A Y., 1. D. G. Shirlaw, and D. C. McRae. 1989. Machine vision using spectral imaging techniques. Agr. Eng. 44:79-81. Murakami, M., J. Himoto, and K. Itoh. 1994. Analysis of apple quality by near infrared reflectance spectroscopy. J. Faculty. Agr., Hokkaido University, Sapporo 66(1):51-61. Nahir, D., Z. Schmilovitch, and B. Ronen. 1986. Tomato grading by impact force response. Am. Soc. Agr. Eng. Paper 86-3028. Nametre Co. (Abbott, J. A., G. S. Bachman, N. F. Childers, J. V. Fitzgerald, and F. J. Matusik). 1966. Sonic techniques for measuring texture of fruits and vegetables. Report 5, U.S. Dept. Agr., Res. Svc. Contract 12-14-100-8280 (51). Nattuvetty, V. R, and P. Chen. 1978. Maturity sorting of green tomatoes based on light transmittance through regions of the fruit. Am. Soc. Agr. Eng. Paper 78-3550. Nelson, S. O. 1973. Electrical properties of agricultural products-a critical review. Trans. Am. Soc. Agr. Eng. 16:384--400. Nelson, S. O. 1980. Microwave dielectric properties of fresh fruits and vegetables. Trans. Am. Soc. Agr. Eng. 23:1314-1317. Nelson, S. O. 1983. Dielectric properties of some fresh fruits and vegetables at frequencies of 2.45 and 22 GHz. Trans. Am. Soc. Agr. Eng. 26:613-616. Nelson, S. 0., W. R Forbus, Jr., and K. C. Lawrence. 1993. Microwave permittivities of fresh fruits and vegetables from 0.2 to 20 GHz. Am. Soc. Agr. Eng. Paper 93-3079. Nelson, S. 0., W. R Forbus, Jr., and K. C. Lawrence. 1995. Assessment of microwave permittivity for sensing peach maturity. Trans. Am. Soc. Agr. Eng. 38:579-585. Nelson, S. 0., and K. C. Lawrence. 1992a. Sensing moisture contents in dates by RF impedance measurements. Trans. Am. Soc. Agr. Eng. 35:591-596. Nelson, S. 0., and K. C. Lawrence. 1992b. RF impedance sensing of moisture content in individual dates. Am. Soc. Agr. Eng. Paper 92-6507. Ni, Q. X., and T. M. Eads. 1992. Low-speed magic-angIe-spinning carbon-13 NMR of fruit tissue. J. Agr. Food Chem. 40:1507-1513. Ni, Q. X., and T. M. Eads. 1993a. Analysis by proton NMR of changes in liquid-phase and solid-phase components during ripening of banana. J. Agr. Food Chem. 41:1035-1040. Ni, Q. X., and T. M. Eads. 1993b. Liquid-phase composition of intact fruit tissue measured by high-resolution proton NMR. J. Agr. Food Chem. 41:1026-1034.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

113

Nicholas, R. C. 1960. Some observations on the use of fruit pressure testers. Mich. Agr. Expt. Sta. Quart. Bul. 43:312-326. Nielse, J. P., H. Campbell, C. S. Bohart, and M. P. Masure. 1947. Degree of maturity influences the quality of frozen peas. Food Industry 19:305-308, 432, 434, 436, 479-482,580. Norris, K. H. 1983. Instrumental techniques for measuring quality of agricultural crops. p. 471-484. In: M. Lieberman (ed.), Post-harvest physiology and crop preservation. Plenum, New York. Nybom, N. 1962. A new principle for measuring firmness of fruits. Hort. Res. 2:1-8. Nylund, R. E., and J. M. Lutz. 1950. Separation of hollow heart potato tubers by means of size grading, specific gravity, and x-ray examination. Am. Potato J. 27:214-222. Olsen, K. L., H. A. Schomer, and R. D. Bartram. 1967. Segregation of Golden Delicious apples for quality by light transmission. Proc. Am. Soc. Hort. Sci. 91:821-828. Ourecky, D. K., and M. C. Bourne. 1968. Measurement of strawberry texture with an Instron machine. J. Am. Soc. Hort. Sci. 93:317-325. Ozer, N., B. Engel, and J. Simon. 1995. Fusion classification techniques for fruit quality. Trans. Am. Soc. Agr. Eng. 38:1927-1934. Parker, R. E., J. H. Levin, andR. T. Whittenberger. 1966. Aninstrliment for measuring firmness ofred tart cherries. Mich. Agr. Expt. Sta. Quart. Bul. 48:471-482. Patel, N., V. A. McGlone, P. N. Schaare, and H. Hall. 1993. "BerryBounce": a technique for the rapid and nondestructive measurement of firmness in small fruit. Int. Soc. Hort. Sci. 352:189-198. Pazlaff, A. W. 1980. Hydrodynamic blueberry sorting. U. S. Patent 4,225,424. Pearson, R. M., and C. Job. 1994. Small nuclear magnetic resonance (NMR) spectrometers in process control. p. 366-375. Proc. Food Processing Automation III Conf., Am. Soc. Agr. Eng., St. Joseph, MI. Peleg, K. 1989. Method and apparatus for automatically inspecting and classifying different objects. U. S. Patent 4,884,696. Peleg, K., U. Ben-Hanan, and S. Hinga. 1990. Classification of avocado by firmness and maturity. J. Texture. Studies 21:123-129. Peleg, K., and S. Hinga. 1989. Firmness indexes of viscoelastic bodies by vibration testing. J. Rheology 33:639-657. Peleg, M. 1974. Determination of fresh papaya's texture by penetration tests. J. Food Sci. 39:156-159.

Pen, C. L., W. K. Bilanski, and D. R. Fuzzen. 1985. Classification analysis of good and bruised apple tissue using the measured optical reflectance. Trans. Am. Soc. Agr. Eng. 18:326-330.

Perry, J. S. 1977. A nondestructive firmness (NDF) testing unit for fruit. Trans. Am. Soc. Agr. Eng. 20:762-767. Perry, R. L., and R. M. Perkins. 1968. Separators for frost damaged oranges. Calif. Citrog. 53:305-312.

Pflug, 1. J, F. M. Joffe, and R. C. Nicholas. 1960. A mechanical recording pressure tester. Mich. Agr. Expt. Sta. Quart. Bul. 43(1):117-121. Pitt, R. E. 1982. Models for the rheology and statistical strength of uniformly stressed vegetative tissue. Trans. Am. Soc. Agr. Eng. 25:1776-1784. Pitts, M. J., R. P. Cavalieri, and S. Drake. 1991. Evaluation of the PFT apple firmness sensor. Am. Soc. Agr. Eng. Paper 91-3017. Pope, J. M., D. Jonas, and R. R. Walker. 1993. Applications of NMR micro-imaging to the study of water, lipid and carbohydrate distribution in grape berries. Protoplasma 173:177-186.

Porritt, S. W., A. D. McMechan, and K. Williams. 1963. Note on a flotation method for segregation of watercore apples. CanadianJ. Plant Sci. 43:600-602.

114

J. ABBOTT, R. LU, B. UPCHURCH, AND R. STROSHINE

Porteous, R. L., A. Y. Muir, and R. L. Wastie. 1981. The identification of diseases and defects in potato tubers from measurement of optical spectral reflectance. J. Agr. Eng. Res. 26:151-160. Povey, M. J. W. 1989. Ultrasonics in food engineering. Part II: Applications. J. Food Eng. 9:1-20.

Prussia, S. E., J. J. Astleford, B. Hewlett, and Y. C. Hung. 1994. Non-destructive firmness measuring device. U. S. Patent 5,372,030. Pykett, I. L. 1982. NMR imaging in medicine. Scientific Am. 246:78-88. Ray, J., R. L. Stroshine, G. W. Krutz, and W. K. Wai. 1993. Quality sorting of sweet cherries using magnetic resonance. Am. Soc. Agr. Eng. Paper 93-6598. Rehkugler, G. E., and J. A. Throop. 1986. Apple sorting with machine vision. Trans. Am. Soc. Agr. Eng. 29:1388-1397. Rehkugler, G. E., and J. A. Throop. 1989. Image processing algorithm for apple defect detection. Trans. Am. Soc. Agr. Eng. 32:267-272. Reid, W. S. 1976. Optical detection of apple skin, bruise flesh, stem and calyx. J. Agr. Eng. Res. 21:291-295. Resnikoff, H. L. 1992. A guide to wavelets and their application. p. 13-22. In: Curves and surfaces in computer vision and graphics. Soc. Photo-Optical Instru. Eng. 1830. Rigney, M. P., and G. H. Brusewitz. 1992. Asparagus shape features for quality assessment. Trans. Am. Soc. Agr. Eng. 35:1607-1613. Rigney, M. P., G. H. Brusewitz, and G. A. Kranzler. 1992. Asparagus defect inspection with machine vision. Trans. Am. Soc. Agr. Eng. 35:1873-1878. Rigney, M. P., G. H. Brusewitz, and M. L. Stone. 1996. Peach physical characteristics for orientation. Trans. Am. Soc. Agr. Eng. 39:1493-1497. Rohrbach, R. P. 1981. Sorting blueberries to improve fresh market shelf life. Am. Soc. Agr. Eng. Paper 81-1501. Rohrbach, R. P., J. E. Franke, and D. H. Willits. 1982. A firmness sorting criterion for blueberries. Trans. Am. Soc. Agr. Eng. 25:261-265. Rohrbach, R. P., and S. W. Glass, III. 1980. Driving point mechanical impedance of blueberries. Trans. Am. Soc. Agr. Eng. 23:298-302. Rohrbach, R. P., and C. M. Mainland. 1993. Accurate low cost measurement of blueberry firmness for research workers. Acta Hort. 346:338-353. Rollwitz, W. L. 1985. Radiofrequency spectroscopy: versatile sensors for agriculture applications. Agr. Eng. 66:12-14. Rood, P. 1957. Development and evaluation of objective maturity indices for California freestone peaches. Proc. Am. Soc. Hort. Sci. 70:104-112. Rosenfeld, D., I. Shmulevich, and G. Rosenhouse. 1992. Three-dimensional simulation of the acoustic response of fruit for firmness sorting. Trans. Am. Soc. Agr. Eng. 35:1267-1274. Rosenfeld, D., 1. Shmulevich, and G. Rosenhouse. 1994. Simulation of the dynamic response of hollow and stone fruit. Am. Soc. Agr. Eng. Paper 94-6019. Ross, E. 1949. A quantitative hardness tester for food products. Science 109:204. Rotz, C. A., and N. N. Mohsenin. 1978. A note on potential applications of physical properties of bruised tissue of apples for automated sorting. Trans. Am. Soc. Agr. Eng. 21:790-792.

Ruiz, M., and P. Chen. 1982. Use of first derivative of spectral reflectance to detect mold on tomatoes. Trans. Am. Soc. Agr. Eng. 25:759-762. Sacks, E. J., and D. V. Shaw. 1994. Optimum allocation of objective color measurements for evaluating fresh strawberries. J. Am. Soc. Hort. Sci. 119:330-334. Saltveit, M., Jr. 1991. Determining tomato fruit maturity with nondestructive in vivo nuclear magnetic resonance imaging. Postharv. BioI. TechnoI. 1:153-159.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

115

Saltveit, M. K, and M. K Mangrich. 1996. Using density measurements to study the effect of excision, storage, abcisic acid, and ethylene on pithiness in celery petioles. Am. Soc. Hort. Sci. 121:137-141. Saltveit, M. K, S. K. Upadhyaya, J. F. Happ, R Caveletto, and M. O'Brien. 1985. Maturity determination of tomatoes using acoustic methods. Am. Soc. Agr. Eng. Paper 85-3536. Sapers, G.M., J.A Abbott, D.R. Massie, AK Watada, and KK Finney, Jr. 1977. Correlation of volatiles in apple juice with objective indices of maturity and ripeness of apples. J. Food Sci. 42:44-47. Saputra, D., I. W. Budiastra, and H. K. Purwadaria. 1995. Classification of mango by near infrared diffuse reflectance. p. 197-203. Proc. Food Processing Automation Conf. IV, Am. Soc. Agr. Eng., St. Joseph, MI. Sarkar, N., and R. R Wolfe. 1983. Potential of ultrasonic measurements in food quality evaluation. Trans. Am. Soc. Agr. Eng. 26:624-629. Sarkar, N., and R. R Wolfe. 1985a. Feature extraction techniques for sorting tomatoes by computer vision. Trans. Am. Soc. Agr. Eng. 28:970-974. Sarkar, N., and R. R. Wolfe. 1985b. Computer vision based system for quality separation of fresh market tomatoes. Trans. Am. Soc. Agr. Eng. 28:1714-1718. Schatzki, T. F.,R. P. Haff, R. Young, I. Can, L. C. Le, and N. Toyofuku. 1996. Defect detection in apples by means of X-ray imaging. Soc. Photo-Optical Instru. Eng. 2907:176-185. Schatzki, T. F., S. C. Witt, D. K Wilkins, and D. H. Lenker. 1981a. Characterization of growing lettuce from density contours-I. head selection. Pattern Recognition 13:333-340. Schatzki, T. F., S. C. Witt, D. K Wilkins, and D. H. Lenker. 1981b. Characterization of growing lettuce from density contours-II. statistics. Pattern Recognition 13:341-346. Schmilovitch, A, A Zaltzman, A. Hoffman, and Y. Edan. 1995. Firmness sensor and system for date sorting. Applied Eng. Agr. 11:555-560. Schomer, H. A, and K. L. Olsen. 1962. A mechanical thumb for determining firmness of apples. Proc. Am. Soc. Hort. Sci. 81:61-66. Schreiber, U., L. Groberman, and W. Vidaver. 1975. Portable, solid-state fluorometer for the measurement of chlorophyll fluorescence induction in plants. Rev. Scientific Instrumentation 46:538-542. Seaman, R, and J. Seals. 1991. Fruit pulp and skin dielectric properties for 150 MHz to 6400 MHz. J. Microwave Power Electromagnetic Energy 26:72-80. Seiden, P., R. Bro, L. Poll, and L. Munck. 1996. Exploring fluorescence spectra of apple juice and their connection to quality parameters by chemometrics. J. Agr. Food Chern. 44:3202-3205. Self, G. K., K Ordozgoiti, M. J. W. Povey, and H. Wainwright. 1994. Ultrasonic evaluation of ripening avocado flesh. Postharv. BioI. TechnoI. 4:111-116. Shafshak, S. A, and G. W. Winsor. 1964. A new instrument for measuring the compressibility of tomatoes, and its application to the study offactors affecting fruit firmness. J. Hort. Sci. 39:284-297. Shearer, S. A, and F. A. Payne. 1990. Machine vision sorting of bell peppers. p. 289-300. Proc. Food Automation Conf., Am. Soc. Agr. Eng. St. Joseph, MI. Sherman, P. (ed.) 1979. Food texture and rheology. Academic Press, New York. Shmulevich, I., N. Galili, and N. Benichou. 1995. Development of a nondestructive method for measuring the shelf-life of mango fruit. p. 275-287. Proc. Food Processing Automation IV Conf., Am. Soc. Agr. Eng., St. Joseph, MI. Shmulevich, I., N. Galili, and D. Rosenfeld. 1993. Acoustic excitation for quality control of fruit. Am. Soc. Agr. Eng. Paper 93-6022. Shmulevich, I., N. Galili, and D. Rosenfeld. 1994. Firmness testing device based on fruit acoustic response. AGENG Paper 94-G-080, Milano, Italy.

116

J. ABBOTT, R. LU, B. UPCHURCH, AND R.

STROSHINE

Shurmer, H. V., J. W. Gardner, and H. T. Chan. 1989. The application of discrimination techniques to alcohols and tobaccos using tin-oxide sensors. Sensors Actuators 18:361-371. Sidwell, A. P., G. S. Birth, J. V. Ernest, and C. Golumbic. 1961. The use oflight transmittance techniques to estimate the chlorophyll content and stage of maturation of Elberta peaches. Food Technol. 15:75-78. Simon, J. K, A. Hetzroni, B. Bordelon, G. K Miles, and D. J. Charles. 1996. Electronic sensing of aromatic volatiles for quality sorting of blueberry fruit. J. Food Sci. 61:967-969, 972. Singh, N., and M. J. Delwiche. 1994. Machine vision methods for defect sorting stonefruit. Trans. Am. Soc. Agr. Eng. 37:1989-1997. Singh, N., M. J. Delwiche, and R. S. Johnson. 1993. Image analysis methods for real-time color grading of stonefruit. Computer Electronics Agr. 9:71-84. Sistler, F. K, M. K Wright, and R. M. Watson. 1984. Measurement of physical properties of biological products with a video analyzer. p. 646-651. Proc. Natl. Conf. on Agr. Electronic Applications, Am. Soc. Agr. Eng. St. Joseph, MI. Slaughter, D. C. 1995. Nondestructive determination of internal quality in peaches and nectarines. Trans. Am. Soc. Agr. Eng. 38:617-623. Slaughter, D. c., D. Barrett, and M. Boersig. 1996. Nondestructive determination of soluble solids in tomatoes using near infrared spectroscopy. J. Food Sci. 61:695-697. Slaughter, D. c., and R. P. Rohrbach. 1984. Developing a blueberry firmness standard. Am. Soc. Agr. Eng. Paper 84-6012. Smillie, R. M., S. K Hetherington, R. Nott, G. R. Chaplin, and N. 1. Wade. 1987. Applications of chlorophyll fluorescence to the postharvest physiology and storage of mango and banana fruit and the chilling tolerance of mango cultivars. ASEAN FoodJ. 3:55-59. Smillie, R. M., and R. Nott. 1979. Assay of chilling injury in wild and domestic tomatoes based on photosystem activity of the chilled leaves. Plant Physiol. 63:796-801. Smith, H. J., and R. N. Ranallo. 1989. A non-mathematical approach to basic MRl. Medical Physics Publ. Corp., Madison, WI. Stephenson, K. Q., R. K. Byler, and M. A. Wittman. 1973. Vibrational response properties as sorting criteria for tomatoes. Trans. Am. Soc. Agr. Eng. 16:258-260, 265. Stone, M. 1., X. Zhang, G. H. Brusewitz, and D. D. Chen. 1994. Watermelon maturity determination in the field with acoustic impedance techniques. Am. Soc. Agr. Eng. Paper 94-6024. Strang. G. 1994. Wavelets. Am. Scientist 82:250-255. Stroshine, R. 1., S. I. Cho, W. K. Wai, G. W. Krutz, and I. C. Baianu. 1991. Magnetic resonance sensing of fruit firmness and ripeness. Am. Soc. Agr. Eng. Paper 91-6565. Stroshine, R. 1., W. K. Wai, K. M. Keener, and G. W. Krutz. 1994. New developments in fruit ripeness sensing using magnetic resonance. Am. Soc. Agr. Eng. Paper 94-6539. Stroshine, R. 1., W. K. Wai, and G. W. Krutz. 1993. Non-destructive ripeness sensing of intact avocado, nectarine, and orange. Am. Soc. Agr. Eng. Paper 93-6535. Studman, C. J., and Yuwana. 1992. Twist test for measuring fruit firmness. J. Texture Studies 23:215-227. Sugiyama, J., K. Otobe, S. Hayashi, and S. Usui. 1994a. Firmness measurement of muskmelons by acoustic impulse transmission. Trans. Am. Soc. Agr. Eng. 37:1235-1241. Sugiyama, J., K. Otobe, and Y. Kikuchi. 1994b. A novel firmness meter for fruits and vegetables. Am. Soc. Agr. Eng. Paper 94-6030. Sundbom, K, and 1. Bjorn. 1977. Photoluminography: imaging plants by delayed light emission. Physiol. Plant. 40:39-41. Szczesniak, A. S. 1963. Classification oftextural characteristics. J. Food Sci. 28:385-389.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

117

Szczesniak, A. S. 1983. Physical properties of foods: what they are and their relation to other food properties. p. 1-41. In: M. Peleg and KB. Bagley (eds.), Physical properties of food. AVI, Westport, CT. Takao, H., and S. Ohmori. 1994a. Development of device for nondestructive evaluation of fruit firmness. Japan. Agr. Res. Quart. 28:36-43. Takao, H., and S. Ohmori. 1994b. Nondestructive hardness meter for fruit. p. 34-51. In: G. G. Dull, M. Iwamoto, and S. Kawano (eds.), Nondestructive quality evaluation ofhorticultural crops. Proc. 24th Int. Hort. Cong., Kyoto, Japan. Saiwai Shobou PubL, Tokyo. Tao, Y., P. H. Heinnemann, Z. Varghese, C. T. Morrow, and H. J. Sommer. 1995a. Machine vision for color inspection of potatoes and apples. Trans. Am. Soc. Agr. Eng. 38:1555-1561. Tao, Y., C. T. Morrow, P. H. Heinemann, and H. J. Sommer. 1995b. Fourier-based separation technique for shape grading of potatoes using machine vision. Trans. Am. Soc. Agr. Eng. 38:949-957. Thai, C. N. 1994. Soft transducer for firmness measurement. Am. Soc. Agr. Eng. Paper 946541. Thai, C. N., A. V. A. Resurreccion, G. G. Dull, and D. A. Smittle. 1990. Modeling consumer preferences with neural networks. Am. Soc. Agr. Eng. Paper 90-7550. Thai, C. N., and R. L. Shewfelt. 1991a. Modeling sensory color quality oftomato and peach: neural networks vs. statistical regression. Trans. Am. Soc. Agr. Eng. 34:950-955. Thai, C. N., and R. L. Shewfelt. 1991b. 'Redglobe' peach color kinetics under step-varying storage temperatures. Trans. Am. Soc. Agr. Eng. 34:212-216. Thai, C. N., and R. L. Shewfelt. 1991c. Seasonal variability of tomato color thermal kinetics. Trans. Am. Soc. Agr. Eng. 34:1830-1835. Thompson, T. K, L. J. Grauke, and K F. Young, Jr. 1996. Pecan kernel color: standards using the Munsell color notation system. J. Am. Soc. Hort. Sci. 121:548-553. Throop, J. A., D. J. Aneshansley, and B. L. Upchurch. 1993. Near-IR and color imaging for bruise detection on Golden Delicious apples. p. 33-44. In: J. A. DeShazer and G. K Meyer (eds.), Optics in agriculture and forestry. Soc. Photo-Optical Instru. Eng. 1836. Throop, J. A., D. J. Aneshansley, and B. L. Upchurch. 1994a. Camera system effects on detecting watercore in Red Delicious apples. Trans. Am. Soc. Agr. Eng. 37:873-877. Throop, J. A., D. J. Aneshansley, and B. L. Upchurch. 1994b. Neighborhood, window and bruise size for determining cooccurence matrix values for apple bruise detection. Am. Soc. Agr. Eng. Paper 94-6580. Throop, J. A., D. J. Aneshansley, and B. L. Upchurch. 1995a. Cooccurence texture feature variation for a moving window over apple images. p. 366-376. In: J. A. DeShazer and G. K Meyer (eds.), Optics in agriculture, forestry, and biological processing. Proc. Soc. Photo-Optical Instr. Eng. 2345 Throop, J. A., D. J. Aneshansley, and B. L. Upchurch. 1995b. An image processing algorithm to find new and old bruises. Applied Eng. Agr. 11:751-757. Throop, J. A., D. J. Aneshansley, and B. L. Upchurch. 1995c. Apple orientation on automatic sorting equipment. Am. Soc. Agr. Eng. Paper 95-6176. Throop, J. A., G. K Rehkugler, and B. L. Upchurch. 1989. Applications of computer vision for detecting watercore in apples. Trans. Am. Soc. Agr. Eng. 32:2087-2092. Tian, M. S., A. B. Woolf, J. H. Bowen, and 1. B. Ferguson. 1996. Changes in color and chlorophyll fluorescence of broccoli florets following hot water treatment. J. Am. Soc. Hort. Sci. 121:310-313. Timm, K J., G. K. Brown, P. R. Armstrong, and R. M. Beaudry. 1993. A portable instrument for measuring firmness of cherries and berries. Am. Soc. Agr. Eng. Paper 93-6539. Toivonen, P. M. A. 1992. Chlorophyll fluorescence as a nondestructive indicator of freshness in harvested broccoli. HortScience 27:1014-1015.

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Tollner, E. W. 1990. Magnetic resonance for non-contact measurement of weight and pit presence in tart cherries. p. 184-193. Proc. Food Processing Automation Conf. Publ., Am. Soc. Agr. Eng., St. Joseph, MI. Tollner, E. W., J. K. Brecht, and B. 1. Upchurch. 1993. Nondestructive evaluation: detection of external and internal attributes frequently associated with quality or damage. p. 225-255. In: S. E. Prussia and R. 1. Shewfelt (eds.), Postharvest handling: a systems approach. Academic Press, New York. Tollner, E. W., Y. C. Hung, B. W. Maw, D. R. Sumner, and R. D. Gitaitis. 1995. Nondestructive testing for identifying poor quality onions. p. 392-397. In: J. A. DeShazer and G. E. Meyer (eds.), Optics in agriculture, forestry, and biological processing. Proc. Soc. Photo-Optical Instru. Eng. 2345. Tollner, E. W., Y. C. Hung, B. 1. Upchurch, and S. E. Prussia. 1992. Relating X-ray absorption to density and water content in apples. Trans. Am. Soc. Agr. Eng. 35:1921-1928. Tran, V. N., S. S. Stuchly, and A. Kraszewski. 1984. Dielectric properties of selected vegetables and fruits at 0.1-10.0 GHz. J. Microwave Power 19:251-258. Tu, K., R. De Busscher, J. De Baerdemaeker, and E. Schrevens. 1995. Using laser beam as light source to study tomato and apple quality non-destructively. p.528-536. Proc. Food Processing Automation IV Conf., Am. Soc. Agr. Eng., St. Joseph, MI. Uozumi, J. L, S. Kawano, M. Iwamoto, and K. Nishinari. 1987. Spectrophotometric system for quality evaluation of unevenly colored food (in Japanese). Nippon Shokuhin Kogyo Gakkaishi (J. Japan. Soc. Food Sci. Technol.) 34:163-170. Upchurch, B. 1., H. A. Affeldt, D. J. Aneshansley, G. S. Birth, R. P. Cavalieri, P. Chen, W. M. Miller, Y. Sarig, Z. Schmilovitch, J. A. Throop, and B. W. Tollner. 1994a. Detection of internal disorders. p. 80-85. In: G. Brown and Y. Sarig (eds.), Nondestructive technologies for quality evaluation of fruits and vegetables. Proc. Int. Workshop, U.S.-Israel BARD Fund. Spokane, WA. June 15-19, 1993. Am. Soc. Agr. Eng., St. Joseph, MI. Upchurch, B. 1., H. A. Affeldt, W. R. Hruschka, K. H. Norris, andJ. A. Throop. 1990a. Spectrophotometric study of bruises on whole Red Delicious apples. Trans. Am. Soc. Agr. Eng. 33:585-589. Upchurch, B. 1., H. A. Affeldt, W. R. Hruschka, and J. A. Throop. 1991. Optical detetion of bruises and early frost damage on apples. Trans. Am. Soc. Agr. Eng. 34:1004-1009. Upchurch, B. 1., M. J. Delwiche, and D. 1. Peterson. 1990b. Evaluation of an optical measurement for peach maturity sorting. Applied Eng. Agr. 6:189-193. Upchurch, B. 1., E. S. Furgason, and G. E. Miles. 1985. Ultrasonic measurements for detecting damage on agricultural products. p. 990-993. Proc. Inst. Electrical & Electronics Eng. Ultrasonic Symposium. Upchurch, B. 1., G. E. Miles, R. 1. Stroshine, E. S. Furgason, and F. H. Emerson. 1987. Ultrasonic measurement for detecting apple bruises. Trans. Am. Soc. Agr. Eng. 30:803-809. Upchurch, B. 1., and J. A. Throop. 1994. Effects of storage duration on detecting watercore in apples using machine vision. Trans. Am. Soc. Agr. Eng. 37:483-486. Upchurch, B. 1., J. A. Throop, and D. J. Aneshansley. 1994b. Influence oftime, bruise-type, and severity on near-infrared reflectance from apple surfaces for automatic bruise detection. Trans. Am. Soc. Agr. Eng. 37:1571-1575. Upchurch, B. 1., J. A. Throop, and D. J. Aneshansley. 1995. Detection of internal browning in apples by light transmittance. p. 377-384. In: J. A. DeShazer and G. E. Meyer (eds.), Optics in agriculture, forestry, and biological processing. Proc. Soc. PhotoOptical Instru. Eng. 2345. USDA Agricultural Marketing Service. Grades and standards and inspection instructions for various commodities. U.S. Gov. Printing Office, Washington, DC. Vangdal, E. 1985. Quality criteria for fruit for fresh consumption. Acta Agr. Scand. 35:41-47.

1. TECHNOLOGIES FOR NONDESTRUCTIVE QUALITY EVALUATION

119

Van Woensel, G., A. Wouters, and J. De Baerdemaeker. 1987. Relation between mechanical properties of apple fruit and sensory quality. J. Food Proc. Eng. 9:173-189. Verner, L. 1931. Experiments with a new type of pressure tester on certain stone fruits. Proc. Am. Soc. Hart. Sci. 27:57-62. Virgin, H. 1. 1955. A new method for the determination of the turgor of plant tissues. Physiol. Plant. 8:954-962. Voisey, P. W. 1977. Examination of operational aspects of fruit pressure tests. J. Inst. Food Sci. Technol. 10:284-294. Wai, W. K., R L. Stroshine, and G. W. Krutz. 1995. A modified Hahn echo pulse sequence for proton magnetic resonance (lH-MR) measurements of percent soluble solids of fruits. Trans. Am. Soc. Agr. Eng. 38:849-855. Walker, D. 1990. The use of the oxygen electrode and fluorescence probes in simple measurements of photosynthesis. Robert Hill Inst., Univ. of Sheffield, Sheffield, U.K. p.17-98. Wang, C. Y., and P. C. Wang. 1989. Nondestructive detection of core breakdown in 'Bartlett' pears with nuclear magnetic resonance imaging. HortScience 24:106-109. Wang, C. Y., and P. C. Wang. 1992. Differences in nuclear magnetic resonance images between chilled and non-chilled zucchini squash. Environ. Expt. Bot. 32:213-219. Wang, S. Y., P. C. Wang, and M. Faust. 1988. Non-destructive detection of watercore in apple with nuclear magnetic resonance imaging. Scientia Hort. 35:227-234. Ward, G., and A. Nussinovitch. 1996. Gloss properties and surface morphology relationships of fruits. J. Food Sci. 61: 973-977. Watada, A. E., and J. A. Abbott. 1975. Objective method of estimating anthocyanin content for determining color grade of grapes. J. Food Sci. 40:1278-1279. Watada, A. E., K. H. Norris, J. T. Worthington, and D. R Massie. 1976. Estimation of chlorophyll and carotenoid contents of whole tomato by light absorbance technique. J. Food Sci. 41:329-332. Watkins, C. B., W. J. Bramlage, and B. A. Cregoe. 1995. Superficial scald of 'Granny Smith' apples is expressed as a typical chilling injury. J. Am. Soc. Hart. Sci. 120:88-94. Watts, K. c., and L. T. Russell. 1985. A review of techniques for detecting hollow heart in potatoes. Canadian Agr. Eng. 27:85-90. Wolfe, R R, M. L. Abrams, and W. F. Probasco. 1982. Controlling postharvest decay of blueberries using firmness separation and hot water treatments. Trans. Am. Soc. Agr. Eng. 25:312-315. Wolfe, R R, and M. Swaminathan. 1987. Determining orientation and shape of bell peppers by machine vision. Trans. Am. Soc. Agr. Eng. 30:1853-1856. Wolfe, R R, A. K. Singh, andP. A. Puthur. 1980. Roll-bounce firmness separation of blueberries. Trans. Am. Soc. Agr. Eng. 23:1330-1333, 1336. Wolfe, R R, H. S. Jalli, M. J. Ceponis, and R A. Cappellini. 1975. Evaluation of density separation for removing immature fruit from mechanically harvested blueberries. Trans. Am. Soc. AgT. Eng. 18:760-764, 769. Woolf, A. B., and W. A. Laing. 1996. Avocado fruit skin fluorescence following hot water treatments and pretreatments. J. Am. Soc. Hart. Sci. 121:147-151. Worthington, J. T. 1974. A light-transmittance technique for determining tomato ripening rate and quality. Acta Hort. 38:193-214. Worthington, J. T., D. R Massie, and K. H. Norris. 1976. Light transmission technique for predicting ripening time for intact green tomatoes. p. 46-49. In: J. J. Gaffney (ed.), Quality detection in foods. Am. Soc. Agr. Eng., St. Joseph, M1. Wu, N., M. J. Pitts, D. C. Davis, and R P. Cavalieri. 1994. Modeling mechanical behavior of apple with the finite element method. Am. Soc. Agr. Eng. Paper 94-3585. Wyszecki, G., and W. S. Stiles. 1967. Color Science: concepts and methods, quantitative data and formulas. Wiley, New York.

120

J.

ABBOTT, R LU, B. UPCHURCH, AND R STROSHINE

Yahia, K M. 1994. Apple flavor. Hort. Rev. 16:197-234. Yakushiji, H., S. Ono, H. Takao, and Y. Iba. 1995. Nondestructive measurement of fruit firmness with soft touch sensor in Japanese persimmon (in Japanese). Bul. Fruit Tree Res. Sta. 28:39-50. Yamamoto, H., and S. Haginuma. 1982. Vibrating reed method and nondestructive acoustic impulse response method for measuring textural quality of apple flesh. J. Japan. Soc. Hort. Sci. 51:210-218. Yamamoto, H., M. Iwamoto, and S. Haginuma. 1980. Acoustic impulse response method for measuring natural frequency of intact fruits and preliminary applications to internal quality evaluation of apples and watermelons. J. Texture Studies 11:117-136. Yamamoto, H., M. Iwamoto, and S. Haginuma. 1981. Nondestructive acoustic impulse response method for measuring internal quality of apples and watermelons. J. Japan. Soc. Hort. Sci. 50:247-261. Yang, Q. 1993a. Finding stalk and calyx of apples using structured lighting. Computers Electronics Agr. 8:31-42. Yang, Q. 1993b. Classification of apple surface features using machine vision and neural networks. Computers Electronics Agr. 9:1-12. Yang, Q. 1994. An approach to apple surface feature detection by machine vision. Computers Electronics Agr. 11:249-264. Yeatman, J. N., G. S. Birth, J. V. Ernest, R W. Bender, and A. P. Sidwell. 1961. Spectrophotometric evaluation of anthocyanin pigment development and scald damage in intact red tart cherries. Food Technol. 15:521-525. Yeatman, J. N., and K H. Norris. 1965. Evaluating internal quality of apples with new automatic fruit sorter. Food Technol. 19:123-125. Yong, Y. C., and W. K Bilanski. 1979. Modes of vibration of spheroids at the first and second resonant frequencies. Trans. Am. Soc. Agr. Eng. 22:1463-1466. Younce, F. 1., and D. C. Davis. 1995. A dynamic sensor for cherry firmness. Trans. Am. Soc. Agr. Eng. 38:1467-1476. Zaltzman, A., R Feller, A. Mizrach, and Z. Schmilovitch. 1983. Separating potatoes from clods and stones in a fluidized bed medium. Trans. Am. Soc. Agr. Eng. 26:987-990, 995. Zaltzman, A., Z. Schmilovitch, and A. Mizrach. 1985. Separating flower bulbs from clods and stones in a fluidized bed. Canadian Agr. Eng. 27:63-67. Zaltzman, A., B. P. Verma, and Z. Schmilovitch. 1987. Potential of quality sorting of fruits and vegetables using fluidized bed medium. Trans. Am. Soc. Agr. Eng. 30:823-831. Zapp, H. R, S. H. Ehlert, G. K Brown, P. R Armstrong, and S. S. Sober. 1990. Advanced instrumented sphere (IS) for impact measurements. Trans. Am. Soc. Agr. Eng. 33:955960. Zhang, X., and G. H. Brusewitz. 1991. Impact force model related to peach firmness. Trans. Am. Soc. Agr. Eng. 34:2094-2098. Zhang, X., M. 1. Stone, D. Chen, N. O. Maness, and G. H. Brusewitz. 1994. Peach firmness determination by puncture resistance, drop impact and sonic impulse. Trans. Am. Soc. Agr. Eng. 37:495-500. Zhao, K, R P. Cavalieri, G. M. Hyde, and M. J. Pitts. 1993. Apparent density of gas-liquid mixtures. Trans. Am. Soc. Agr. Eng. 35:103-111. Ziegler, G. K, and C. T. Morrow. 1970. Radiographic bruise detection. Am. Soc. Agr. Eng. Paper 70-553. Zion, B., P. Chen, and M. J. McCarthy. 1995. Nondestructive quality evaluation of fresh prunes by NMR spectroscopy. J. Sci. Food Agr. 67:423-429. Zion, B., M. J. McCarthy, and P. Chen. 1994. Real-time detection of pits in processed cherries by magnetic resonance projections. Lebensmittel-Wissenschaft und Technoloy (Food Sci. Technol.) 27:457-462.

2 Texture of Fresh Fruit F. Roger Harker, Robert J. Redgwell, Ian C. Hallett, and Shona H. Murray The Horticulture and Food Research Institute of New Zealand Mt. Albert Research Centre Private Bag 92 169 Auckland, New Zealand Gordon Carter Department of Restorative Dentistry University of Otago P.O. Box 56 Dunedin, New Zealand

I. Introduction II. What Is Fruit Texture? III. Cellular Basis of Texture A. Fruit Anatomy and Cellular Construction B. Mechanical and Physiological Properties of Cells C. Chemical and Biochemical Changes in the Cell Wall D. Turgor Pressure E. Membranes IV. Food-Mouth Interactions A. The Mouth as a Sensory Organ B. Sensitivity of the Mouth C. Mastication and Chewing D. The Rhythmical Nature of Chewing E. Measuring Masticatory Efficiency F. Saliva G. Implications for Texture Measurement V. Consumer Awareness and Attitudes VI. Why Measure Texture? A. Research Perspective B. Maturity C. Regulatory Perspective

Horticultural Reviews, Volume 20, Edited by Jules Janick ISBN 0-471-18906-5 © 1997 John Wiley & Sons, Inc. 121

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VII. Methods for Measuring Texture A. Puncture Tests B. Whole-Fruit Deformation C. Tactile Assessment D. Instrumental Tests on Excised Tissue E. Twist Test F. Strength of Aggregate Fruits G. Tissue Juiciness H. Auditory Recording of Chewing Sounds 1. Sensory Evaluations J. Electrical Impedance K. Nondestructive Measurement 1. Relationship Between Instrumental and Sensory Measurements of Texture VIII. Factors That Influence Texture A. Genetics B. Environment C. Light Irradiation D. Minerals (Other Than Calcium) E. Calcium F. Fruit Size G. Maturity and Ripening H. Temperature 1. Heat Treatments J. Controlled Atmospheres K. Prediction of Firmness IX. Texture Disorders A. Texture Associated with Chilling Injury B. Texture Associated with Overmaturity C. Texture-Modifying Substances X. Concluding Remarks Literature Cited

I. INTRODUCTION

Textural properties are key factors influencing the acceptability of fruit to the consumer. Terms such as hard, soft, crisp, juicy, melting, floury, and gritty are popularly used to describe individual texture characteristics (Table 2.1). Comparative and quantitative assessment of texture can involve instrumental and/or sensory measurements. It is often suggested that the relevance of instrumental measurements depends on how well they predict sensory attributes (Voisey 1971). However, instrumental texture measurements are also frequently used to indicate fruit attributes that are not directly associated with consumer acceptability, for example, whether fruit are sufficiently mature to harvest and whether fruit placed in a distribution network will survive transport.

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Fruit tissues are often consumed fresh, and texture attributes can therefore be related to the characteristics of living cells. These characteristics change as a result of physiological and biochemical processes which occur throughout storage and ripening. Along with high levels of biological variability (between individual fruits and between orchard lines), this makes the assessment of texture problematical. Generally, measurements of fruit texture require a large number of fruit and are only relevant to the particular time of evaluation. Thus, they cannot be used to predict future consumer responses. This differs considerably from many processed, dried, or frozen food products in which the texture remains relatively constant under optimum storage conditions. The ability to measure texture is of key importance in studies of fruit quality. The development of instrumental methods for measuring texture has underpinned our knowledge on the influence of environment, preharvest factors, breeding, and postharvest technologies on texture. Recent advances in the study of the chemistry and biochemistry of the fruit cell wall have provided insight into the mechanisms controlling fruit softening and texture change. Improvements in our understanding of the molecular and enzymatic basis of texture are expected over the next few years. Hopefully, this will lead to the release of genetically modified fruit with improved storage and texture characteristics. Transgenic fruit have become a commercial reality with the release of the transgenic tomato FlavrSavr. ™ There have been many reviews of fruit texture (Mohsenin 1977; Peleg 1979; Bourne 1979, 1980, 1983; Jackman and Stanley 1995a), fruit softening (Poovaiah et al. 1988), and cell wall composition and metabolism (Knee and Bartley 1981; Huber 1983a; Giovannoni et al. 1992). In the present review, we bring together research from the diverse disciplines of food rheology, food structure, sensory science, dental science, preharvest and postharvest fruit physiology, cell wall chemistry and biochemistry, and fruit breeding in order to provide an overview of the science of fruit texture. Given this broad specification, we have concentrated on research from the last decade. II. WHAT IS FRUIT TEXTURE? It is important to realize that the term texture covers a wide range of attributes which determine the feel of food within the mouth and the way these attributes can be measured using sensory and instrumental methods. A number of definitions have been suggested by food scientists (reviewed by Bourne 1982, and Szczesniak 1990). Key definitions which summa-

I--' N

*"

Table 2.1. Lexicon of sensory texture attributes and their associated reference standards as developed and used with fruit by trained sensory panels at The Horticulture and Food Research Institute of New Zealand. The starting point for this lexicon was descriptions provided by Jowitt (1974) and Meilgaard et al. (1991). See Section VII(I) for further discussion. Attribute

Description

Reference standard (Absent/Low)

Reference standard (Extreme/High)

Crispness

The amount and pitch of sound generated when the is first bitten with the front teeth.

Ripe banana

Fresh potato crisp

Crunchiness

The amount of noise generated when the sample is chewed at a fast rate with the back teeth.

Ripe banana

Raw celery

Ease of breakdown

The amount of chewing required to break down the sample so that it can be swallowed.

Apple puree

Raw swede

Fibrousness

The amount of readily separated filaments present.

Ripe banana

Celery

Flouriness

The amount of dry, fine, powdery particles that coat the mouth during chewing.

Raw carrot

Overcooked beans (chick peas)

Graininess

The presence of small firm particles detected during chewing.

Cream (liquid)

Semolina

Grittiness

The presence of small hard sharp particles detected during chewing.

Cream (liquid)

White sugar crystals Raw carrot

Hardness

The force required to compress the sample with the back teeth.

Ripe banana

Juiciness

The amount of free fluid released from the

Ripe banana

Watermelon

Mealiness

The amount of small, lumpy particles that become apparent during chewing.

Canned mango slices

Porridge (made with rolled oats)

Melting

The degree to which the sample disintegrates evenly in the mouth, often without chewing.

Raw swede

Canned mango slices

Pastiness

The amount of soft, smooth mass that doesn't release moisture during chewing.

Watermelon

Peanut butter

Pulpiness

The amount of wet, weblike material that develops during chewing.

Raw carrot

Watermelon

Starchiness

The amount of fine particles that coat the mouth during chewing.

Raw carrot

Raw potato

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rize the structural, sensory and mechanical properties associated with texture are: The textural properties of a food are that group of physical characteristics that arise from the structural elements of the food, are sensed by the feeling of touch, are related to the deformation, disintegration, and flow of food under force, and are measured objectively by functions of mass, time and distance (Bourne 1982). In its fullest sense the textural experience during chewing is a dynamic integration of mouth-feel, the prior tactile responses while handling the foodstuff, and a psychic anticipatory state arising from the visible perception of the food's overall geometry and surface features. This complexity in its nature does not prevent one from, however, defining texture very simply as the forces and feelings other than flavor sensed in the mouth while chewing a piece of food. Although true, the generality of definition does not make it particularly useful as a guide for studies directed towards its objective detection and measurement. It does, however, focus attention on where the action is-in the mouth and in the mind! [segment from a detailed definition by Corey (1970)]. The sensory manifestation of the structure of the food and the manner in which this structure reacts to applied forces, the specific senses involved being vision, kinesthesia, and hearing (Szczesniak 1990). [Note that kinesthesia is the sensation of presence, position, or movement resulting from stimulation of sensory nerve endings (or mechanoreceptors) in muscles, tendons, and joints.]

The definition proposed by Bourne (1982) provides a concise, holistic view of texture. Although the sensory aspects of food texture are inherent in Bourne's definition, we believe the definition proposed by Corey (1970) is useful as it focuses the reader's attention more acutely on the human sensory and psychological responses to food. The recent definition by Szczesniak (1990) emphasizes the importance of those human physiological and neuromuscular processes associated with sensing of food characteristics. All of the preceding definitions are reasonable, although each emphasizes different aspects of our knowledge of texture. Together they provide a robust and comprehensive base from which we can explore the diversity of issues associated with the study of fruit texture. Szczesniak (1963) separates textural attributes of food into three broad categories: mechanical characteristics, geometrical characteristics, and other characteristics (mainly moisture and fat content). This approach is very useful when considering fruit. For example, European pear exhibits all of these categories: its mechanical properties are associated

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with cell strength; it has an inherent grittiness or graininess associated with the geometric properties of stone cell clusters; and the attribute juiciness changes as the fruit ripen. The complexity of fruit texture is associated with the way that changes in any single characteristic (mechanical, geometric, and juiciness) will interfere with the perception of the others. For example, as a honeydew melon softens, the perception of fibers (vasculature) increases (Diehl and Hamann 1979). Research on fruit texture has tended to focus priInarily on the mechanical properties of the tissue (Section VII). However, there has been increasing emphasis on the assessment of juiciness over the last decade (Section VIIG). The few studies on the geometric properties of fruit tissue, such as grittiness in pear, have been undertaken mainly by fruit breeders (Bell and Janick 1990; White and Selby 1994) or as part of sensory studies (Diehl and Hamann 1979; Stec et al. 1989). The characterization of mechanical properties of fruit tissue has led to widespread usage of the terms firmness, flesh firmness, and fruit firmness. These terms have the potential to cause much confusion in the literature since the word firmness has different meanings to sensory scientists, food rheologists, and horticulturalists. The Oxford English dictionary (Compact Edition) provides a number of definitions for the word firm, of which the following is relevant to texture: "Having a close consistence, of solid or compact structure or texture; not readily yielding to pressure or impact." Firmness can be used as a sensory term, for example, "Force required to bring the teeth together" (Diehl and Hamann 1979; see also Paoletti et al. 1993); or as a rheological term relating to the modulus of elasticity (stress/strain) (Finney 1968). However, horticulturalists use firmness to describe the mechanical properties of the fruit tissue, particularly when measured as the force required to push a cylindrical probe to a predetermined depth into the fruit flesh (Mohsenin 1970; Abbott et al. 1992). Indeed, it is the policy of The American Society for Horticultural Science to describe the instruments which make this type of test as penetrometers or firmness testers (ASHS 1991). A number of other phrases are also used to describe these mechanical properties of fruit, including Magness-Taylor firmness (Abbott et al. 1992), strength, and bioyield strength (Mohsenin 1970). Further discussion on the meaning of firmness and hardness are presented by Szczesniak and Bourne (1969), Mohsenin (1970), Bourne (1980), and Peleg (1980). In this review, we use the term firmness, particularly in Section VII, according to its usage by horticulturalists. A decline in firmness occurs as fruit soften during the ripening process. Bourne (1979) divides fruit into two texture categories: those that soften greatly as they ripen (peach, strawberry) and those that soften moderately as they ripen (apple, cranberry). These categories are par-

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ticularly useful as fruits from the first category tend to have a short storage life, straightforward texture measurements, and a poor relationship between firmness at harvest and after storage. Fruits belonging to the second category tend to have a long storage life, problematic texture measurement, and a close relationship between firmness at harvest and after storage (Bourne 1979; Knee and Farman 1989; Hopkirk et al. 1992). Further categorization of fruit textures might be possible using combinations of texture attributes. For example, fruit hardness and juiciness combine as soft and dry (banana), hard and dry (most unripe fruit), hard and juicy (apple), and soft and juicy (ripe peach). Such categories are useful as they tend to separate fruit according to physiological, cell structure, and cell failure mechanisms (Harker et al. 1997). The complex nature of fruit texture is associated with the diversity of attributes required to fully describe textural properties (see Table 2.1) and the changes (often unexpected) in these attributes as fruit ripen. In attempting to address this complexity, it is important that we consider the depth of understanding of the genetic and molecular basis of fruit texture. From a biological perspective, texture at the point of consumption is determined by the genetic compliment and the physiological and biochemical processes that occur in living tissue during fruit development and ripening (Dilley et al. 1993). Most research focuses on the process of ripening itself. The texture of a fruit can often be predicted from biochemical events occurring prior to, or at the initiation, of ripening. These events can be detected by measurements of cell wall composition, the presence of key enzymes, and by measurements of cell wall condition such as electrical impedance. We speculate that, in the future, texture characteristics and softening behavior of fruit may be predicted from the presence of marker genes (i.e., quantitative trait loci analysis) before a tree has even started to bear fruit. Marker genes have already been mapped in tomato for fruit mass, soluble solids concentration, and fruit pH (Paterson et al. 1988). Such possibilities clearly represent powerful tools for traditional fruit breeding. For physiologists, texture is the outcome of a complex series of biological events/parameters including DNA compliment, transcription and translation of RNA, protein/enzyme synthesis, and production and degradation of complex carbohydrates (especially the cell wall). III. CELLULAR BASIS OF TEXTURE

Gross morphology is an important determinant of texture in some fruits, for example the influence of druplet separation on the texture of raspberry (Reeve 1970). However, in most cases it is at a cellular level that

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the structural basis of texture is best addressed. Thus, it is important to consider the relationships between anatomy and cellular construction of the edible tissues, the mechanical and physiological properties of the cells, the mechanisms of cell failure, and the biochemistry of the cell walls. Those cell characteristics which have a critical influence on texture are shown in Fig. 2.1. A. Fruit Anatomy and Cellular Construction Fruit anatomy has been described in a number of books and reviews (Esau 1965; Cutter 1971; Coombe 1976; Roth 1977; Ilker and Szczesniak 1990; Kays 1991; Salunkhe et al. 1991). The edible portions of fruit (the flesh) are derived from a wide range of flower structures (Coombe 1976). During the period from anthesis to maturity, there is a marked increase in fruit volume. In most edible fruit tissues, cell division ceases early in fruit development and the majority of fruit enlargement is due to cell expansion (Bollard 1970). A notable exception to this is the fruit skin which tends to retain its meristematic function throughout growth (Harker and Ferguson 1988; Tetley 1930). In avocado, where fruit cells remain relatively small, cell division continues well into fruit expansion, and fruit can be grafted together even when nearly mature (Schroeder et al. 1959). Cell expansion can be reflected within the tissue not only by changes in cell size, but also by changes in the packing and shape of the cells (Jackson and Coombe 1966). In all fruits, the flesh is primarily composed of parenchyma cells which have thin, nonlignified walls (normally between 0.4 )1 and 1 )1) and a large vacuole which may contain 90% of the water in the cell (Pitt 1982). The walls of neighboring cells are separated by a morphologically distinct region known as the middle lamella which is rich in pectin subunits (Huber 1983a). The gross texture of the fruit depends upon the characteristics of these cells (cell size, cell wall thickness and strength, and cell turgor pressure) and the manner in which they bind to form the ground tissue (cell-cell adhesion, cell shape and packing). In most fruits, texture is modified by the presence of nonparenchyma cells. These include collenchyma cells and phloem elements which have thickened primary walls; epidermal cells which also have a thickened wall and special wall layers (cuticle and wax) on their outer surface; and xylem tracheids and vessels, fibers, and sclereids which have thick lignified walls and no living contents. Though in some cases these nonparenchymatous cells are scattered through the flesh, generally they are contained in specific tissues and structures, particularly the skin, vasculature, and seeds.

Cell Wall Mechanical Properties Influenced by: Polysaccharide composition Calcium content Enzymic hydrolysis Turgor

Plasma membrane (PM)

Adhesion between cells Infuenced by: Hydrolysis of pectins Plasmodesmata Calcium Cell-to-cell contact area

Plasmodesmatal pit field

Primary Cell Wall

composed of: rhamnogalacturonans arabinogalactans xyloglucan cellulose protein

(a) f-'"

~

Fig. 2.1.

(b)

Generalized fruit parenchyma cell showing important cell features (a) and processes and structures that influence texture (b).

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F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

The fruit skin varies in its size, complexity, and mechanical properties. In simple skins, the surface is composed of a layer of tightly fitting epidermal cells coated with cuticular and wax layers. Below this are layers of slightly modified cells (hypodermis) which are normally smaller than the cells of the flesh and may have all or part of the cell wall thickened (parenchyma or collenchyma cells). Such skins are frequently consumed with the flesh (apple, pear, stone fruit) and impart distinctive textural characteristics. In other fruits (melon, avocado, citrus) the skin may be inedible due to its thickness and the specialized cell types present below the epidermis (collenchyma, sclerenchyma, tannin impregnated cells, cork). The surface may also be modified with thick layers of wax or by the presence of trichomes and lignified or tannin-impregnated cells. Thus, in kiwifruit, dead tannin-impregnated cells and hairs on the surface result in an unappetizing texture. Vascular tissue (xylem, phloem, fibers, and associated parenchyma and collenchyma) traverses through the flesh from the main conducting strands. The elongated and strengthened cells may impart a fibrous texture, depending on variations in size and distribution. In many fruits (apple, pear), vasculature-induced fibrousness appears to play little or no part in the textural characteristics. In others, such as peach and nectarine, its presence is noticed as "stringiness" in the soft fruit. In pineapple, the peduncle becomes the fruit axis and the many flowers and their subtending bracts fuse and form the collective fruit. Vascular tissue in the noncarpellary parts develops a sclerenchymatous sheath (Okimoto 1948). The complex vascular system associated with original flowers thus contributes to the rather fibrous texture of this fruit. Sclerenchyma tissue is found in many regions besides the vasculature and skin. Layers of sclerefied cells may be found in the endocarp of fruit such as apple and many berries (Roth 1977). Many fruit, including blueberry (Reeve 1970; Gough 1983), guava (Marcelin et al. 1993) and pear (Sterling 1954), contain individual or groups of sclerenchymatous stone cells either scattered below the skin or through the tissue. These cells have very thick, secondary thickened (lignified) cell walls that are not broken down by chewing and result in a characteristic gritty texture. In pears, increased firmness of the ripe fruit has been associated with cultivars containing greater numbers of stone cell groups which firmly bind to the adjacent parenchyma cells (Martin-Cabrejas et al. 1994). Seeds are usually found in the interior of the flesh, although in strawberry they are found on the surface. Tiny seeds in strawberry, blueberry, and kiwifruit (Reeve 1970; Stec et al. 1989) are consumed and can make a significant contribution to overall texture (grittiness). Larger seeds can also be a major textural component of the edible portion,

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either being consumed with it (passionfruit, pomegranate) or being discarded during consumption (watermelon). Frequently, seeds that are too large (stonefruit) or associated with core tissue (apple and pear) are avoided during eating. The core tissue of apple and pear is the region within the core line containing seeds, seed cavities, and its sclerified lining and vascular strands. The term core is often used as a general description of central tissue; thus, the core of kiwifruit (pith, collumela) is composed of parenchyma cells (Ferguson 1984) and is part of the edible portion of the fruit. Many fruit soften during ripening, a process primarily associated with changes in the cell walls of the parenchyma cells (see Section IIlC). This softening can modify the perceived texture of other tissues such as the vasculature and skin (Diehl and Hamann 1979; Stec et al. 1989). Pear tissue may have a grainy texture when firm and a gritty texture when soft. This relates to the mouth-feel associated with smooth, hard aggregates of stone cells and attached parenchyma cells in firm, grainy tissue, or the greater perception of the angular stone cells themselves in soft, gritty tissue. The internal content of cells can have an impact on overall texture of fruit. Juiciness and turgor pressure of parenchyma cells are considered later in this chapter. However, these cells may contain a variety ofinclusions of which starch is the most frequent. Starch is accumulated during the development of a number of fruit (apple, pear, kiwifruit, banana) and is normally hydrolyzed to simple sugars during ripening. The presence of unhydrolyzed starch can result in a floury mouth-feel, while partly hydrolyzed starch in banana may account for low apparent juiciness in this fruit by binding of free water (Szczesniak and Ilker 1988). It has also been suggested that increased concentrations of starch in the cell may increase overall tissue firmness (Wiley and Stembridge 1961; Ilker and Szczesniak 1990). Avocado presents an unusual situation in that the dominant material accumulating during development is oil, which is partly responsible for the perception of a creamy texture in the ripe fruit. Other inclusions of such compounds as tannins and calcium oxalate are considered in Section VIlIC . The physical characteristics of cell size, shape, packing and wall thickness influence texture in a number of ways. Cell size and packing patterns determine the volume of intercellular space and indirectly affect cell adhesion by determining the extent of cell-to-cell contact. In kiwifruit, virtually all wall surfaces are in contact and there is only 1 to 2% extracellular space (Hallett et al. 1992). Whereas in apple (Reeve 1953) and strawberry (Neal 1965), only very limited cell-to-cell contact occurs in the ripe fruit. In apple, extracellular space is as high as 20 to

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25% (Reeve 1953). Vincent (1989) estimated that no more than 40% of the cell wall areas of apple were in contact with each other. Cell size varies considerably between fruits (Bollard 1970). Our studies have found cell cross-sectional diameters as low as 40 f.1m in mature fruit of avocado, around 200 to 300 f.1m in the flesh of apples, and 500 to 700 f.1m in watermelon (Harker et al. 1997). Szczesniak and Ilker (1988) have listed mean cross-sectional areas ranging from 2.8 x 10 3 f.1m 2 in banana to 2.12 x 10 6 f.1m 2 in watermelon. The cells of the spermoderm of pomegranate can reach lengths of up to 2 mm (Winton and Winton 1935). Many fruit show zonal differences in cell size, although care must be taken not to confuse differences in size with those of cell shape sectioned in one plane (Jackson and Coorribe 1966; Khan and Vincent 1990). Some fruit, including gooseberry (Roth 1977) and kiwifruit (Hallett et al. 1992), are clearly composed of distinct populations of differently sized cells dispersed through the tissue. The edible outer pericarp of kiwifruit is composed of giant cells (500-800 f.1m) in a matrix of smaller cells (100-200 f.1m).

The total strength of a tissue is increased by a greater proportion of cell wall material per unit volume. Increased cell wall thickness, and especially decreased cell size, have this effect. Additionally, both cell size and wall thickness can also influence juiciness (Szczesniak and Ilker 1988) through their effect on packaging and compartmentalization ofliquids. In most cases, the generally good relationship between larger cells and increasing juiciness is more important (Szczesniak and Ilker 1988; Harker et al. 1997). The extreme case is presented by citrus where the juice sacks develop as large multicellular organs in which the inner cells become gorged with juice while a layer of cells at the surface remains small and acts as a surrounding wall (Burns et al. 1992). In 'Valencia' orange, these sacs are about 10 mm long and 2 mm wide (Bain 1958) and are analogous to a single parenchyma cell. The shape of fruit parenchyma cells is frequently represented by a simple isodiametric shape (Pitt 1982). However, in most fruit the cells show anisotropy to a greater or lesser extent and the size and shape of the cells may differ depending on the region of the fruit, for example, apricot (Jackson and Coombe 1966; Archibald and Melton 1987) or apple (Khan and Vincent 1990). In apple, research by Khan and Vincent (1990) showed that cells are small and radially flattened just beneath the skin. However, progressing toward the core, the cells become radially elongated and organized into distinct columns, with a maximum diameter of approximately 300 f.1m occurring about 5 to 10 mm below the skin. As a result of this orientation of apple cells, the elastic modulus is higher and the strain at failure is lower when tissue plugs are com-

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pressed in a radial rather than a tangential orientation (Khan and Vincent 1993). The tissue fails by cell layers crushing when compressed radially and by cells shearing at 45° angles to applied force when compressed tangentially. In comparison, mechanical properties of potato tuber tissue are similar when measured in both radial and tangential directions since the cells are oriented isotropically (Khan and Vincent 1993). The stiffness of apple fruit tissue is related to cell size and efficiency of cell packing. Vincent (1989) used a mathematical model to convert tissue density into degree of cell-to-cell contact and demonstrated that these cell attributes correlate well with tissue stiffness. The complexity of fruit tissue excludes the use of simple structural models (such as a regular polyhedral network within a homogenous matrix) to evaluate tissue breakdown during chewing. Any sensory or mechanical analysis of texture needs to be based on a good knowledge of the tissue structure.

B. Mechanical and Physiological Properties of Cells Fruit tissues are weak and fragile with relatively few cells adapted for strengthening, as compared to stem and root tissues. This is necessary given that edible fruits have evolved to be eaten by seed-dispersing animals and further bred by humans to improve their palatability. A good indication of the fragility of cells in fruit tissue is the ease with which they burst when incubated in water (Simon 1977). The physical factors determining the strength and texture of parenchymatous fruit tissue are the mechanical properties of cell walls, the internal pressure (turgor) of cells, and the strength of bonds between neighboring cells (Pitt 1982). Fruit tissue is a hydrostatic structure in which individual fluid-filled cells provide resistance to compressive forces. In the absence of turgor, the thin cell walls of parenchyma cells have relatively little load-bearing capacity. Detailed information on mechanical properties of the plant cell wall have been gathered in studies characterizing the mechanisms involved in cell extension during processes such as stem elongation. These studies generally separate cell wall extension into its elastic and plastic components. Methodologies and results associated with this area of research have been reviewed by Cosgrove (1993). An understanding of how cell water relations and the resulting turgor interact with the tensile properties of the cell wall is essential when considering the physiological basis of cell strength. General reviews of cell water relations include those of Dainty (1976) and Tomos (1988). The intracellular fluid is maintained at a slight positive pressure as a function of both osmotic gradients across semipermeable membranes and the

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F. HARKER, R. REDGWELL, I. HALLETT, S. MURRAY, AND G. CARTER

rigidity of the cell wall. When water potential inside the cell is lower than that outside the cell, water will flow into the cell by the process of osmosis. In plants, this net movement of water usually continues until it is balanced by the development of an internal pressure or turgor. At equilibrium, If/=P-JT

[Eq. 1]

where If/ is the water potential (or activity) of the tissue, P is the turgor pressure, and JT is the osmotic pressure of the cell contents (Tomos 1988). Note that in recent years the term osmotic pressure (-osmotic potential) has replaced osmotic potential, and thus Eq. 1 differs from earlier derivations such as those presented by Salisbury and Ross (1985). The interaction between water relations and the cell wall is described by the formula C = dV/dlf/ = V/(E - JT)

[Eq. 2]

where C is the cell water capacity, that is, the increment of volume (dVj per unit increment of water potential (dlf/), Vis the initial volume, E is the cell wall elastic modulus, and JT is the osmotic pressure of the intracellular fluid (Dainty 1976; Tomos 1988). The elastic modulus of the cell wall, E , is usually the dominating factor in determining cell water capacity (Dainty 1976). For higher plants, E varies between 0.1 and 27 MPa and is dependent on cell size and shape as well as the rheological properties of the cell wall (Tomos 1988). Despite problems with the osmotic instability of the apple fruit tissue, Steudle and Wieneke (1985) were able to use a pressure probe to determine changes in E of cells during growth and development. Its value increased with cell turgor, and ranged between 3 and 18 MPa for turgors higher than 0.5 MPa (Steudle and Wieneke 1985). The threefold increase in E that occurred during apple development was attributed to stress-hardening associated with cell wall tension in expanding cells (Steudle and Wieneke 1985). Using a pressure probe, Rygol and Luttge (1983,1984) found that E varied from 1.5 to 27 MPa in capsicum pericarp cells. Note that E should not be confused with tissue elastic modulus which is often determined in fruit studies. Mathematical models indicate that turgor tends to prestress the fruit cell wall, making it more brittle and more likely to rupture when external loads are applied (Pitt and Chen 1983). This was verified experimentally in studies on apple (Lin and Pitt 1986) and tomato (Jackman et al. 1992b). At high turgor (induced by incubation of disks in isotonic

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or hypotonic solutions) and low turgor (induced by incubation of disks in hypertonic solutions), fruit tissue fails by cell bursting and cell debonding, respectively (Lin and Pitt 1986; Jackman et al. 1992b). The way cells separate or break open and release their contents is the most critical factor influencing the perception of fruit texture (Diehl et al. 1979; Pitt 1982, Pitt and Chen 1983). This is determined by the strength of the cell wall relative to the strength of adhesion between neighboring cells, and both might be expected to decline as fruit ripen and soften. During deformation, cells break open and release their content when cell adhesion has a greater strength than the cell wall; conversely, neighboring cells separate from each other without breaking open when cell adhesion is the weaker component. Tissue may also collapse without the cell walls breaking or separating if fluids are forced out of the cell by compressive forces in a process known as cell relaxation (Peleg et al. 1976) or exosmosis (Jackman and Stanley 1995b). Cell adhesion is presumed to be a function of three factors: (1) the strength of the middle lamella; (2) the area of cell-to-cell contact; and (3) the extent of plasmodesmatal connections. The middle lamella is a pectin-rich layer between neighboring cell walls, which provides the bond between neighboring cells. Often, when cells are pulled apart strands of cell wall material (presumably pectin) are observed bridging two adjacent cell walls (Harker and Hallett 1992). The area of cell-to-cell contact is another determinant of the adhesion between cells. Generally, the extent of cell-to-cell contact depends on the type of fruit (Vincent 1989), although it will change during storage ripening (Harker and Hallett 1992). A third factor is the extent of plasmodesmatal connectivity between cells. Plasmodesmatal connectivity has been reviewed in detail by Lucas et al. (1993). The cytoplasm of nearly all plant cells is connected via plasmodesmata, which are normally concentrated into discrete regions of the cell wall (plasmodesmatal pit fields). Plasmodesmatal pit fields can significantly modify the structural behavior of walls and, thus, the adhesion between cells. During ripening of most fruit, plasmodesmatal connections are retained even though the walls swell, degrade, or split apart (Neal 1965; Ben-Arie et al. 1979; Hallett et al. 1992). In kiwifruit, apple, and pear, the wall regions around the plasmodesmatal pit fields show little change and retain the general morphology of cell walls of unsoftened fruit (Ben-Arie et al. 1979; Hallett et al. 1992; Martin-Cabrejas et al. 1994). These areas are likely to show the greatest resistance to cell-cell disengagement, and examination of cells that have pulled apart reveals that most cell wall disruption and distortion is in the region of the plasmodesmata (1. C. Hallett, unpublished observation in apple). Avocado differs from other fruit examined in that

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F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

the plasmodesmatal pit field undergoes similar changes to that of the rest of the cell wall and can be seen to physically split along the line of the middle lamella (Platt-Aloia et al. 1980; 1. C. Hallett unpublished observation) which, along with the high oil content of the fruit, may be mainly responsible for the distinctive creamy texture. The cellular pattern of tissue breakage has been examined in a number of studies that have used tensile tests to break blocks of fruit tissue and then examined the fracture surface using a low-temperature scanning electron microscope or other microscopic techniques (Glenn and Poovaiah 1990; Harker and Hallett 1992; Harker and Sutherland 1993; Harker and Hallett 1994; Harker et al. 1997). Lapsley et al. (1992) similarly observed apple tissue that had been broken by hand. The results from these studies are summarized in Fig 2.2. Generally, tissue from unripe fruit fractures due to individual cells breaking (Harker and Sutherland 1993; Harker and Hallett 1994). Following ripening, cells from fruit which tend to be crisp (such as apple and watermelon) continue to break or rupture while cells from fruit that are soft (such as banana, nectarine, and kiwifruit) tend to separate from neighboring cells (Harker et al. 1997). Following tensile testing of fruit from soft juicy tissues-as in nectarine (Harker and Sutherland 1993) and kiwifruit (Harker and Hallett 1994)-the fracture surfaces are covered in a layer of juice even though cell damage is not visible. In this case, we believe it is the characteristics of the cell surface which may determine textural characteristics. While the origin of the juice is unknown, it is most likely either naturally occurring extracellular fluid or intracellular fluid released during application of tension as a result of membrane damage or the process of exosmosis (cell relaxation) (Harker and Sutherland 1993). Observations of fracture surfaces obtained during tensile testing seem to provide an intuitive explanation of textural attributes as perceived during chewing. Confirmation of the relevance of tensile measurements requires examination of tissue fragments generated during biting and chewing. Preliminary studies have demonstrated that fracture surfaces observed during tensile testing are similar to the fractures that occur during biting and chewing (Fig. 2.3, F. R. Harker and 1. C. Hallett, unpublished data). The cell wall per se, and particularly its water binding properties, has an impact on texture. During ripening of kiwifruit, a three- to fourfold increase in cell wall thickness can be observed (Hallett et al. 1992). This cell wall hydration represents a major change in tissue structure as kiwifruit soften to eating ripeness and suggests that hydration of the wall and perhaps the presence of a layer of free juice over the surface of undamaged cells will be associated with juiciness of tissue (Harker and

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(a)

(b)

(c)

/

Apple Watermelon Asian Pear (Most Unripe fruit - inedible)

Apple

Mealy apple Woolly Nectarine Banana

Surface Fluid

(d)

Nectarine Kiwifruit Avocado Strawberry European Pear

Fig. 2.2. Patterns of cell failure during tensile testing of ripe fruit (unless otherwise indicated). Cells either break open (a and b), or separate from neighboring cells (c and d). Data summarized from Harker and Hallett (1992), Harker and Sutherland (1993), (Harker and Hallett 1994), and (Harker et al. 1997).

Hallett 1994). In stonefruit, the loss of juiciness is thought to occur when pectates bind water into a gel-like structure within the wall (BenArie and Lavee 1971). In this respect, the spatial distribution of cell wall material and free fluid may be important. The cell wall can be considered as a three-dimensional network containing many fluid-filled pores.

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F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

Tooth cleaves through tissue Tissue fractures ahead of teeth assisted by pulling motion of hand

Fig. 2.3. Surface of carrot and banana tissue after a bite has been taken. Surfaces were examined by low-temperature scanning electron microscope. Bar represents 0.1 mm.

These interconnect to form pathways for solutes through the walls. In nonfruit cells, trans-wall pores can have limiting diameters between 3.5 and 9.2 nm (Read and Badc 1996). Bulk movement of solutes as assessed by solute exclusion (Carpita et al. 1979) is limited by maximum diameters of up to 5.2 nm, although slow penetration of larger molecules

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assessed using tracers (Baron-Epel et al. 1988) suggests the presence of small numbers of larger-diameter pathways. In dicotyledonous plants and nongraminaceous monocotyledonous plants, the network of pectic polymers appears to have the finest mesh size and determines the porosity (Read and Bacic 1996). Mild treatments with pectinase enlarge the trans-wall channels in soybean root suspension cells (Baron-Epel et al. 1988). Thus, the hydrolysis of pectins within the cell wall during fruit ripening might also change the effective pore size. The rheological properties of parenchymatous tissue have been described using mechanical and statistical models (Pitt 1982). Tissue failure during compression at constant rates of strain (deformation as a proportion of the original size of the specimen) can be statistically modeled using a two- or three-parameter Weibull distributions, assuming that tissue failure is catastrophic in that a single cell or bond failure will initiate failure of the entire tissue (Pitt 1982; McLaughlin 1987). Under these conditions, tissue strength is related to the mean strength of individual cells or bonds; variability in yield strength (strength of tissue at point of catastrophic failure) is directly related to variability in cell or bond strength; and the variability in tissue yield strength is always higher than the variation in cell or bond strength. C. Chemical and Biochemical Changes in the Cell Wall

The cell walls of fleshy fruits are for the most part unlignified; their primary walls are separated by a morphologically distinct region known as the middle lamella which separates adjacent cells and is rich in pectic substances (Huber 1983a). The mechanical properties of the fruit primary wall are mostly determined by a unique mixture of matrix (pectic and hemicellulosic) and fibrous (cellulose) polysaccharides. These polysaccharides confer on the wall two important but seemingly incompatible properties. The first is its plasticity which enables it to expand as the cell enlarges during fruit development. The second is its rigidity which confers strength and determines cell shape. This versatility is a consequence of the unique chemistry of the integrated cell wall complex. Models of the primary cell wall have been proposed (Keegstra et al. 1973; Carpita and Gibeaut 1993). Unfortunately, there is so little data on specific intermolecular interactions between different cell wall polymers within the wall that the models must be regarded as largely speculatory. They therefore do not provide a reliable basis for predicting the chemical nature of cell wall change in ripening fruit. Most attempts to define the chemical changes have focused on specific classes of polysaccharide (pectin, hemicellulose) and not on the cell wall as a whole. These stud-

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ies primarily have used three approaches: (1) chemical analysis of isolated cell walls, (2) monitoring the activity and expression of cell wall associated enzymes, and (3) histochemical techniques using conventional and electron microscopy. The two chemical changes in cell wall composition which have been reported for nearly every ripening fruit examined are an increase in soluble pectin and a net loss of the noncellulosic neutral sugars, galactose and arabinose (Knee 1973; Pressey 1977; Wallner and Bloom 1977; Gross and Sams 1984). Most of the latter are lost from the side-chains of the pectic polymers. Modification of cell wall pectin involves two processes: solubilization and degradation or depolymerization. In kiwifruit and tomato, solubilization of the pectic polysaccharides can occur without a marked reduction in molecular weight (Huber 1992; Redgwell et al. 1992). In kiwifruit, most of the degradation of the pectic polymers (including galactose loss) occurs after solubilization (Redgwell et al. 1992). The cell walls of kiwifruit swelled markedly during softening (Hallett et al. 1992), and swelling correlated strongly with pectin solubilization but not galactose loss (Redgwell and Percy 1992). Recent work has confirmed the correlation between cell wall swelling and pectin solubilization during ripening for several fruit species (Redgwell et al. 1997b). This argued against the idea that removal of galactose from the pectic side-chains was necessary for pectin solubilization. Additional evidence to support this has been provided by a study with kiwifruit disks (Redgwell and Harker 1995). Galactose loss from cell walls was prevented by inhibiting the action of endogenous galactosidase, but this did not affect the rate of disk softening or the degree of pectin solubilization. Recent results have shown that most of the galactose lost from the cell wall during fruit ripening originates from highly branched polysaccharides intimately associated with the cellulose fibrils (Redgwell et al. 1997a). In tomato, fruit softening commenced before galactose loss was detectable (Carrington and Pressey 1996). Despite these findings, it would be most likely that removal ofgalactose from pectin side-chains did affect the physical or rheological properties of cell wall pectin. In vitro experiments with avocado have shown that f3-galactosidase can significantly increase the solubility and decrease the molecular size of chelator-soluble pectin by removing galactose residues which account for as little as 2% of the total carbohydrate (De Veau et al. 1993). Less has been published on changes to the hemicelluloses during fruit ripening. Nevertheless, hemicelluloses have shown a decrease in molecular weight distribution during the ripening of several fruit, including tomato (Huber 1983b), strawberry (Huber 1984), pepper (Gross et al. 1986), muskmelon (McCollum, et al. 1989), and kiwifruit (Redgwell et al.

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1991). In kiwifruit, xyloglucan showed a decrease in the molecular weight distribution, but two other hemicelluloses-a 4-0-methylglucuronoxylan and a galactoglucomannan-showed no change in either the molecular weight or primary structure (Redgwell et al. 1991). Studies of the enzymology associated with the chemical changes just described have accelerated because of the advance in molecular biology. Cell wall hydrolyzing enzymes have featured prominently in publications on the molecular biology of fruit ripening. A significant advance was made by Giovannoni et al. (1989) when they introduced the endopolygalacturonase (endo-PG) gene into a nonripening mutant tomato. Expression of this gene resulted in the accumulation of active endo-PG and the consequent degradation of pectin at a time corresponding to ripening in wild-type fruit. However, the rate of softening did not increase markedly. It was concluded that endo-PG activity, although necessary for pectin degradation, was not the primary determinant of softening in tomato, a result echoed by the findings of Smith et al. (1990), who investigated the role of endo-PG using mutant tomato lines in which the expression of the PG gene was suppressed by antisense RNA. Since these initial findings, some controversy has arisen over the conclusions of the work based on criticisms of the methods used to assess softening (Jackman and Stanley 1995a). While doubt continues to exist over the role of endo-PG early in ripening, it does appear to affect tomato texture in the latter stages of softening. Kramer et al. (1992) showed that processing and fresh market tomato genotypes transformed with an antisense PG construct had a significant increase in the serum viscosity of processed juice and paste and a significant decrease in softening during storage compared to nontransgenic controls. Endo-PG is not, of course, the only enzyme capable of disrupting the structure of pectic polysaccharides. Enzymes that attack the neutral polymer side-chains could destabilize the pectic polysaccharide matrix and contribute to fruit softening. Studies on the activities of glycosidases during fruit ripening have been reported for many fruit (Bartley 1974; Yamaki and Matsuda 1977; Pressey 1983). Recent attention has focused on f3-galactosidase because the net loss of galactose residues during ripening is characteristic of so many fruit species. f3-Galactosidase has been purified from kiwifruit (Ross et al. 1993), apple (Ross et al. 1994), and tomato (Carey et al. 1995) and has been shown to remove galactose residues from polysaccharide native to the cell wall of each fruit. In apple and tomato, a f3-galactosidase related cDNA clone has been characterized, but as yet there have been no published reports that an antisense construct for f3-galactosidase has been used to transform plants in order to test down regulation of the enzyme on fruit softening.

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Enzymes that can specifically degrade hemicelluloses have not, until recently, been identified in fruit. However, it is now known that xyloglucan endotransglycosylase (XET) occurs in kiwifruit and that its activity increases ten-fold in the outer pericarp of the fruit during ripening (Redgwell and Fry 1993). In contrast, XET activity in apple decreased during ripening (Percy et al. 1996). The most likely role for XET is as a cell wall loosening enzyme during cell expansion (Fry et al. 1992). How this relates to cell wall breakdown during kiwifruit softening is not known, but the contrasting activity of XET in ripening kiwifruit and apple implies that XET may be multifunctional and is involved in processes unconnected with cell wall loosening during cell expansion. Cellulase is widespread in fruit, but is thought to playa minor role in the softening of tomato, peach, and pear (Hobson 1968). In avocado, Cx-cellulase activity increases dramatically during ripening (Awad and Young 1979); and Pesis et al. (1978) found a correlation between C[cellulase activity and softening. Cellulase is capable of degrading the backbone ofxyloglucan, af3-1-4-glucan. However, in avocado it has been demonstrated that the ripening related depolymerization of xyloglucan does not involve Cx-cellulase (O'Donoghue and Huber 1992). The enzymes responsible for changes to xyloglucan during fruit ripening have still to be identified. Histological work has confirmed that ripening-related fruit softening involves extensive cell wall disruption (Ben-Arie et al. 1979; Hobson 1981). There is a correlation in kiwifruit (Hallett et al. 1992), avocado (Platt-Aloia et al. 1980), pear (Ben-Arie et al. 1979), and tomatoes (Crookes and Grierson 1983) between softening and a loss of electron density in the middle lamellar region of cell walls. The attractive forces between the walls of adjacent cells is as important as the integrity of the primary wall of individual cells in determining the overall physical properties of the fruit. The mealiness of apple is probably caused by a failure of the middle lamella to maintain intercellular integrity during chewing (Harker and Hallett 1992), but the chemical basis for the phenomenon has still to be defined. It is almost certain that cell wall breakdown has a marked effect on textural changes, particularly softening, which accompany fruit ripening. However, to date, physiologists and biochemists have been frustrated by their inability to establish a cause-and-effect relationship between the two events. The advent of molecular techniques means that we are close to identifying genes for several cell wall degrading enzymes. It might appear that we stand on the edge of rapid progress in understanding their role in cell wall modification, and yet, recent results with transgenics have been unable to identify anyone enzyme as a key player in fruit soft-

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ening. This might simply mean that cell wall breakdown is the result of the coordinated action of several enzymes and that "switching off" a single enzyme is by itself insufficient to impair softening. Alternatively, it may mean that processes such as pectin solubilization are caused by unknown enzymes which catalyze chemical changes that have not yet been revealed. D. Turgor Pressure

The excess internal pressure, or turgor, of cells provides the hydrostatic component of cell and tissue strength and also influences the brittleness of the cell wall, as described in Section HIB. It is important to consider what pressures occur, how they change, and what physiological processes influence turgor in fruit. There are, however, relatively few studies that have measured turgor of fruit cells. This is probably a reflection of the problems associated with measurement of plant water status (Bennett 1990). Such problems become paramount when attempting to measure water status of bulky, mature, and ripe fruit. A number of methods involve the removal of tissue disks from the fruit before using such instruments as vapor pressure osmometers (Harker and Hallett 1994) and pressure probes (Shackel et al. 1991). In both cases, the cutting of disks might be expected to release some of the pressure associated with the cells being compressed within the fruit structure and skin. A vapor pressure osmometer can be used to measure ljf and rc, and turgor is calculated using Eq. 1. However, contamination by the leakage of the contents from damaged cells onto the surface of disks may result in water potentials which are closer to the osmotic potential of the tissue than occur in vivo (Bennett 1990), and thus subsequent calculations may underestimate turgor. Another method involves use of a pressure chamber to assess water status of plants, including intact fruit. For fruit, this involves placing fruit in a chamber such that the stalk projects through a sealed port in the chamber wall. As the pressure inside the chamber increases, water is forced out of cells, into the xylem, eventually being collected as droplets from the stalk. Characteristics of curves of l/pressure against volume of expressed sap are used to indicate ljf and rc (Salisbury and Ross 1985), and P is determined by using Eq. 1. However, this method is often unsuccessful when examining mature ripe fruit, since vascular continuity between pedicel/stalk and fruit often fails during late stages of fruit development (Lang and Ryan 1994). Turgor can be measured directly using a pressure probe that is impaled into an individual cell. In tomato, studies using a pressure probe indicated that pressures were less than 0.2 MPa (Shackel et al. 1991). Indi-

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rect measurements using vapor pressure osmometers suggested that turgor of nectarine and kiwifruit cells were less than 0.32 and 0.51 MPa, respectively (Harker and Sutherland 1993; Harker and Hallett 1994). To put these values into perspective, a pressure of 0.5 MPa equals a force of 49 N over an area of 49 mm 2 , e.g., the cross-sectional area of a 7.9-mm diameter Effegi or Magness-Taylor probe. In tomato, cell turgor decreases during ripening (Shackel et al. 1991). Presumably, this is due to a combination of events that lead to the dilution of the cell content and/or the direct loss of water from the fruit. These events may include: (1) redistribution of solutes, particularly solute leakage from the cell; (2) the increase in cell volume that may occur when the cell wall elastic modulus changes as a result of turnover and breakdown of cell wall structure; and (3) transpiration. There has been speculation that an elevation in apoplastic solute concentrations may occur as a result of increasing membrane leakiness and/or breakdown of complex cell wall polysaccharides into smaller soluble components (Shackel et al. 1991; Jackman and Stanley 1995b). While the increase in membrane leakiness has been characterized (see Section HIE), the possible influence of solutes derived from cell wall degradative processes on the osmotic potential of the extracellular fluid, or apoplast, has not been characterized. Many fruit are harvested while still containing substantial amounts of starch. Turgor might be expected to increase when cell osmotic pressure increases as a result of starch hydrolysis during fruit maturation and ripening. However, in some plant tissues, such as beetroot, sucrose accumulation leads to an increase in osmotic pressure without a corresponding change in turgor (Tomos et al. 1992). In this example, it is postulated that solutes accumulate in both the apoplast and symplast such that constant turgor is maintained. In tomato, it has been suggested that the presence of solutes in the apoplast is responsible for the turgor being lower than might be expected from the osmotic potential (Shackel et al. 1991). Thus, starch hydrolysis might not necessarily be associated with changes in turgor. Clearly, cell turgor has a major function in determining tissue strength, and changes in turgor are an integral part of fruit softening. However, from a practical perspective, there are a number of situations where water loss can have a profound influence on texture. The turgor of cells in freshly harvested apples may contribute to the softening of fruit associated with the increasing separation of individual cells during storage (Hatfield and Knee 1988, Harker and Hallett 1992). During storage, apple volume and internal air space increase while tissue strength decreases with increasing separation of individual cells. Apples stored in conditions that promote water loss tend to maintain texture to a greater extent than in apples

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stored at higher relative humidity (Hatfield and Knee 1988). However, when water loss is extreme, many fruit tend to develop a "rubbery" texture and the maximum force during puncture testing is higher than might be expected. This commonly observed phenomenon can be explained by the processes described by Pitt and Chen (1983). When cells are fully turgid, the cell wall is prestressed and thus is more brittle than in a flacid cell. Premature loss of turgor may also influence the softening process, as is thought to occur during the anomalous softening of chilling injured tomato (Jackman et al. 1992b, Jackman and Stanley 1995b, Marangoni et al. 1995). E. Membranes

Most research into control of fruit softening has concentrated on the chemistry and biochemistry of the cell wall. However, membrane integrity is a key element that is often underappreciated. The plasma membrane is the barrier between intracellular and extracellular compartments of plant tissues. The transport of cell wall materials and enzymes across the plasma membrane are critical for both assembly and degradation of the cell wall. Furthermore, the plasma membrane is expected to influence texture through its role in osmoregulation and the associated regulation of turgor (see Section IIID) and through the regulation of the ionic composition of the extracellular solution. For example, through transmembrane proton efflux, the extracellular pH may decrease. Acidification of extracellular fluid during development of peach and apricot has been observed by Ugalde et al. (1988). Such a change could remove calcium from binding sites within the wall and thus weaken the cell (see Section VIIE), since Ca 2+ and H+ have similar affinities for nondiffusible anionic charges in the cell wall (Demarty et al. 1984). There are two issues that need to be resolved. First, what impact does membrane deterioration have on texture; and second, are these changes in the membranes controlling influences on fruit softening or a consequence of ripening and senescence? Generally, in ripening fruit membrane conductivity (permeability) increases although membrane structure remains intact (Brady 1987; Harker and Maindonald 1994). Associated with these changes, there is a tendency for solutes to leak into the extracellular fluid, for the cell wall to hydrate, and for intercellular spaces to become water-soaked. This might be expected to have a profound influence on the perception of juiciness of fruit tissues which, during chewing, break into smaller pieces by the process of cell-to-cell separation rather than cell rupture (Section III-B). At the extreme stages of senescence, membranes lose their integrity and tissues become exces-

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sively soft and are often invaded by microorganisms; at this stage, the fruit is usually unpalatable. An exception to this are astringent varieties of persimmon, which lose their astringency and thus become palatable at this late stage of ripening (Section VIlle). The structure of membranes and their role in the deterioration of quality have been reviewed by Stanley (1991). A number of studies have considered the changes in membrane permeability in relation to other physiological changes associated with fruit ripening. An increase in membrane permeability has been reported in ripening wild-type tomato fruit, but not in the ripening-inhibited (Tin) mutant (Poovaiah et al. 1975). Membrane conductance changes were not primary initiators of ripening in tomato (Poovaiah et al. 1975) or banana (Brady et al. 1970). However, membrane damage was thought to mediate the development of mealy texture in chill-injured tomato (Jackman et al. 1992b, Jackman and Stanley 1995b, Marangoni et al. 1995). In avocado, major changes in plasma membrane ultrastructure, including increases in the density ofintramembrane particles (Platt-Aloia and Thomson 1981), membrane density, and polypeptide banding patterns (Dallman et aI. 1988), occur during ripening. The relative timing of cell wall and membrane permeability changes has been investigated in watermelon by Elkashif and Huber (1988), who found that the appearance of polygalacturonase activity preceded membrane permeability changes during ripening of ethylene-treated watermelon. At present, there are not enough published studies to determine whether or not membrane changes are a controlling influence or a consequence of texture change. During ripening of fruit, both membrane and cell wall changes may occur simultaneously, and thus it is difficult to distinguish their separate influences on fruit texture using observational data. However, if membranes become dysfunctional, or membrane changes occur out of phase with the cell wall changes, the development of an anomalous texture might be an expected outcome. IV. FOOD-MOUTH INTERACTIONS

Knowledge of the structure and physiology of the mouth and of the processes involved in breakdown of food during chewing provide the links between food structure and how its texture is perceived in sensory terms. Such knowledge is therefore critical to our understanding of fruit texture and its measurement. While there have been a number of reviews on texture from a dental perspective (Boyar and Kilcast 1986; Heath and Lucas 1988; Heath 1991), such information is relatively inaccessible to horticulturalists.

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The lips, tongue, teeth, and jaws are well adapted to detect the shape, size, surface texture, orientation, and mechanical properties of food placed in the mouth. This is in part due to the high density of nerves associated with these structures and to the strong representation of this region in the brain (Penfield and Rasmussen 1950). When food is placed in the mouth, the teeth reduce the food to a size and shape that optimizes flow through the gut and produces sites suitable for endogenous or bacterial enzymatic hydrolysis (Sanson 1989). In addition, the saliva that is mixed with the food initiates digestion and alters the overall texture of the food to facilitate swallowing (Jenkins 1978). A. The Mouth as a Sensory Organ Mechanoreceptors are cells which detect mechanical deformation and thus are sensitive to touching and stretching. Within the mouth, mechanoreceptors are present in the oral mucosa (lining of the oral cavity), the periodontal ligament (which supports the teeth), the temporomandibular joint capsule (point of attachment between the skull and lower jaw), the periosteum (membrane covering the jaw bones), and in bony sutures of the jaw (Fig. 2.4). Collectively they provide textural information about objects placed in the mouth and information about the position of the jaw. These mechanoreceptors are assisted in these tasks by auditory cues (see Section VIIH; Meier-Ewart et al. 1974; Vickers 1981) and spindles in the jaw-closing muscles, which provide information about the tension of the muscles. Attempts have been made to determine the relative contribution of each group of mechanoreceptors to the complex behavior of chewing. For example, when input from the muscle spindles of the jaw-closing muscles of monkeys was eliminated, the effects on overall masticatory behavior were short lived, and after a week the masticatory movements appeared to be normal (Goodwin and Luschei 1974). Thus, information from muscle spindles either contributed little to the pattern of masticatory movement, or the other mechanoreceptors could easily compensate. Selective local anesthesia has also been employed to determine which groups of mechanoreceptors are responsible for sensitivity in the mouth. Tests of sensitivity include: (1) "interdental size threshold detection," where variously sized objects are placed between upper and lower teeth and the subject determines the smallest size that can be detected; and (2) "interdental size discrimination," where the subject determines the smallest increment in size that can be detected. SiiriHi and Laine (1963) found that interdental size threshold detection was only slightly reduced following the anesthetization of upper and lower teeth. The same authors (SiiriHi and Laine 1969) showed that the threshold of detection

MEDIAN LINE

peridontal ligament gums (gingiva) Oral mucosa ----\--... sulcus - - - - I - f . / bone - - - - i - - - - H .

(a)

(c)

(b)

(d)

Fig. 2.4. Line drawings of the teeth and associated structures: (0) terms used to describe the surfaces of the teeth. In each jaw, the anterior teeth include four incisor teeth and two canine teeth; the posterior or postcanine teeth include two premolar teeth and three molar teeth on each side; (b) the roots of the teeth are attached to bone by the peridontalligament. The upper jaw, the maxilla, and the lower jaw, the mandible, are covered with a thick layer of bone forming fibrous tissue, the periosteum, which is not shown; (c) the temporomandibular joint, (TMJ) and the muscles of mastication; temporalis, A, masseter, B, and median pterygoid, not shown, are jaw-closing muscles. Digastric, C, is a jaw-opening muscle; (d) the lateral pterygoid, D, is associated with jaw opening and jaw protrusion. 148

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for denture wearers, who have no periodontal ligament, was six times higher than that of individuals with their natural teeth. This suggests that periodontal mechanoreceptors may have a role in threshold detection. However, unlike natural teeth, dentures are not fixed to the jaw. Thus, differences observed by SiiriHi and Laine (1969) may also relate to the attachment of teeth to the jaw. Caffesse et al. (1973) found that interdental size threshold detection was reduced following injection of local anesthetic into both temporomandibular joints, suggesting a role for temporomandibular joint mechanoreceptors. In a similar experiment which tested interdental size discrimination in people with and without teeth, Siirila and Laine (1972) found that interdental size discrimination was not altered by bilateral anesthetization of the temporomandibular joints. Manley et al. (1952) found no difference in interdental size discrimination between dentate and edentulous subjects. The results of these experiments suggest a small role for mechanoreceptors from the periodontal ligament and temporomandibular joint in interdental size threshold detection and little evidence for a primary role in interdental size discrimination. Despite the apparent simplicity of the tests for interdental size detection and discrimination, the results of the tests are dependent on the methods used and vary with the method of presentation of the objects and the degree of mouth opening (Morimoto 1990). Skin and mucosal mechanoreceptors, muscle spindles, and tendon organs remain as possible sources for determining interdental size. There are, however, few reports of Golgi tendon organs (mechanoreceptors in tendons) associated with the masticatory muscles (Dubner et al. 1978). While the influence of cutaneous receptors remains to be determined for the jaw, it is known that anesthetization of the skin over a limb joint decreases the threshold for detection of movement of the joint (Meyer 1921). Muscle spindles have been implicated in kinesthetic sensibility in particular muscle tension and senses of movement and position in joints (McCloskey 1978). In the jaw, the role of muscle spindles in determining jaw position has been supported by the work of Christensen and Troest (1975) who found that local anesthetic injected into the lateral ptyergoid muscle (Fig. 2.4d) impaired mandibular position sense. Indirect support for the role of muscle spindles in interdental size discrimination is provided by Morimoto and Kawamura (1976) who found that interdental size discrimination was disrupted by vibration. Muscle spindles are sensitive to vibration of 100 to 200 Hz, which produces an involuntary stiffening of the muscle (tonic vibration reflex). In addition, the interdental size discrimination is reduced in patients with Duchenne muscular dystrophy,

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a disorder that is thought to influence the output of muscle spindles (Morimoto et al. 1981). B. Sensitivity of the Mouth Sensitivity of the mouth may be measured by psychophysical tests which include oral stereognosis, two-point discrimination, mandibular position, tactile thresholds and discrimination, and interdental size threshold and discrimination. Two-point discrimination, which is the distance between two tactile stimuli at which each can be discerned as separate, is a measure of spatial discrimination and a reflection of the density of touch receptors. Values as low as 2 mm have been recorded for the tongue (Langley and Cherasin 1956; Grossman 1964). This compares with values of approximately 15 mm for the back of the hand, which itself is a relatively sensitive surface when compared with the back of the neck (Langley and Cherasin 1956). Oral stereognosis (Grossman 1964), which is a test of the ability to recognize the shape of an object placed in the mouth, probably involves sensory memory derived from visual and tactile experiences as well as direct oral sensory examination (Berry and Mahood 1966). Oral stereognostic ability is reduced in denture wearers and decreases with age (Litvak et al. 1971). Interdental size detection and discrimination were described in the preceding section. Values for detection of an object as small as 8 J.1m (SiiriHi and Laine 1963) and with an increment size of 18 J.1m (Manley et al. 1952) have been recorded. There appears to be no correlation between individuals with acute interdental size discrimination and those with high scores in tests of oral stereognosis (Williams and La Pointe 1972), suggesting that different mechanisms or methods of processing are involved. In addition, the interdental size threshold determination measured during chewing, on average (0.91 mm), is about 60 times larger than during biting (0.015 mm) (Owall and M0ller 1974). Information which may be considered as "noise" may be filtered out to allow rhythmical mastication to proceed (Dubner et al. 1978). Mandibular position estimation is a test of the ability of a subject to duplicate a jaw opening. Thilander (1961) recorded a range of 3.2 mm for 10 subjects. This figure was calculated by measuring the difference between the extremes of 10 attempted replications of the original jaw position. Local anesthesia injected into the temporomandibular joints increased the range, but anesthetization of the teeth and gums did not significantly alter the range. As noted earlier, Christensen and Troest (1975) found that local anesthetic injected into the lateral ptyergoid

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muscle impaired mandibular position sense. These experiments suggest a role for mechanoreceptors in the joint capsule and muscle spindles in determining mandibular position. Humans can detect loads less than 0.01 N applied to the incisor teeth and loads of approximately 0.1 N applied to the molar teeth (Manley et al. 1952). Localization of a force is also best in the incisor teeth (Nishiyama et al. 1967). Load detection in incisor teeth appears to be sensitive to direction, as the threshold for labially directed forces is less than that of axially directed forces (Manley et al. 1952). Neurophysiological studies in animals have also demonstrated that mechanoreceptors in the periodontal ligament respond maximally to a force applied to the tooth from a particular direction (Hannam 1970) and that the receptors respond to tension in the periodontal ligament (Cash and Linden 1982). Force discrimination is best between 0.5 to 5 N (Bowman and Nakfoor 1968) but may not be different for labially or axially applied forces (Bonaguro et al. 1969; Bowman and Nakfoor 1968). Tooth displacement of approximately 2 to 10 f.1m has been recorded at the threshold for detection (Yamada and Kumano 1969). There is little evidence to suggest that receptors in the tooth pulp have a role to play in tactile sensa"' tion (Stewart 1927; Loewenstein and Rathkamp 1955; Linden 1975; Dong et al. 1985; Matthews 1986). Information from craniofacial mechanoreceptors enables an individual to determine the form and resistance to loading of an object placed in the mouth as well as the position of the jaw. Information from these receptors may initiate jaw muscle reflexes (Lund 1990) and be useful in learning new tasks such as mastication when the teeth erupt or learning to eat with artificial dentures for the first time. C. Mastication and Chewing Humans are not born with the ability to chew. We learn to chew as we develop an increased capacity for coordinated muscle movements, as our diet changes to solid foodstuffs, and as the teeth erupt into the mouth. By four to five years of age (Ahlgren 1976), a regular chewing pattern may be observed. But even in an individual, consecutive cycles are different. The masticatory cycle has an opening, closing, and intercuspal phase during which teeth contact. In a frontal view, the movement of the mandible (lower jaw) describes a "teardrop shape." As the mandible opens, it moves to the chewing side; during the later part of closure and during the contact "glide" as the teeth finally close to the position of "best fit," the mandible returns to a central position. The average duration of a cycle is 0.7 s but there is wide variation which may depend on the consistency

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of the test food, the occlusion (Ahlgren 1966; Ahlgren 1976), and external constraints (Rugh 1972). The mandible accelerates at the commencement of opening and closing and slows at maximum opening and as the teeth approach. Speeds greater than 75 mmls may be achieved during opening and closing. These speeds are faster than those commonly used in machine testing of food. The usual vertical distance traveled by the mandible is approximately 20 mm, which is about half of the usual jaw opening potential (Posselt 1956; Ahlgren 1976). Movement during chewing is, of course, more complex than the preceding description. The mandible moves in three dimensions and, as well as opening and lateral movement, there is protrusion (movement forward) and retrusion (movement backward) of the lower jaw. In addition, the tongue, assisted by the muscles of the inner surface of the cheek and the lips, is active in all stages, shaping and placing the bolus (food mass in the mouth). Chewing is usually performed on one side although the bolus may be swapped from side to side (Hildebrand 1931; Beyron 1964). The preferred side is the side with the greatest occlusal contact (Le., area of greatest tooth contact between upper and lower jaw; Yurkstas and Manley 1949; Yurkstas 1965). Where each side presents a similar occlusal area, the preferred side may be the same as the preferred hand (Yurkstas 1965). The area of occlusal contact of molar and premolar teeth (the posterior teeth in Fig 2.4a) is on average 48.4 mm 2 , which is about one-fifth of the surface area of these teeth (Yurkstas 1965). In the later part of the closing phase, food is penetrated and crushed by the posterior teeth. However, tough andlor fibrous food may be broken by applied tensile forces (Sanson 1989). During grinding, the shape of the masticatory cycle in the frontal plane is wider (Beyron 1964). Applied forces increase as the bolus is penetrated and are greatest at maximum intercuspation (Ahlgren 1976). The forces measured during chewing vary with the resistance of the bolus (De Boevar et al. 1978) and are much less than potential forces recorded in maximum biting tasks. In a study by Gibbs et al. (1986), the total occlusal force measured during chewing averaged 262 N, while the maximum bite force recorded in an individual is 4,344 N. Most studies have reported maximum biting forces which average 726 N and range between 245 to 1,245 N (Gibbs et al. 1981). For denture wearers, smaller forces averaging 157 N and ranging between 98 to 206 N have been recorded (Colaizzi et al. 1984). It is very difficult to measure the force and the direction of the force between the teeth during rhythmical chewing without introducing an experimental factor which may confound the interpretation of the data. For example, is the experimenter examining normal chewing if the bite

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is raised in order to insert a measuring device (Anderson and Picton 1958)? A few workers have used telemetry devices inserted into dental bridgework (Anderson and Picton 1958; Graf 1975; De Boevar et al. 1978). The number of subjects in these experiments was necessarily small, and the recordings represented the force on individual molar teeth. Data from these unobtrusive recorders have suggested bite forces between 20 to 78 N (Graf 1975) and 78 to 137 N (Anderson and Picton 1958) and mean forces of approximately 9.8 N, 9.0 N, and 19.0 N (three individuals) between the molar teeth during chewing (De Boevar et al. 1978). During rhythmical chewing, as the bolus is penetrated additional muscle activity may be recorded in the jaw-closing muscles. The additional muscle activity varies with the texture of the food (Plesh et al. 1986; Horio and Kawamura 1989). The additional muscle activity may have two components. The first is an anticipated activity level gathered from information derived from previous cycles, which may be under the control of a central pattern generator (Dellow and Lund 1971; Lund 1991) and influenced by the cortical masticatory area of the brain (Enemoto et al. 1987). The second is information gathered from the periphery, that is, the mechanoreceptors or muscle spindles during the actual cycle. Ottenhoff (1992) was able to alter the resistance to simulated chewing by attaching a coil to a subject's mandible and placing the coil in a magnetic field. By altering the current through the coil, the known force acting on the mandible could be varied. Surface EMG was recorded from the masseter, temporalis, and suprahyoid muscles. In "disappear" experiments in the first cycle, following many repetitive cycles when an anticipated food-simulating force of 24 or 19 N was withdrawn, the contribution of an anticipating mechanism was 21 and 27% of the additional muscle activity, respectively. The additional muscle force was not fixed but adapted to changes suggesting that the role of anticipation may be to reduce the disturbance at food contact by providing additional muscle force and stiffness. D. The Rhythmical Nature of Chewing The rhythmical nature of automatic chewing is controlled by a pattern generator situated in the brainstem (Dellow and Lund 1971; Lund 1991). The pattern generator controls the basic opening and closing movements, the muscle sequence, and some tongue movement. Superimposed on this basic control is information gained from peripheral receptors; for example, the additional muscle activity applied during chewing may be modified by the changes in resistance of an artificial foodstuff (Ottenhoff 1992). The additional muscle activity may con-

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tribute to the activity of the central pattern generator in an attempt to maintain constant rhythm (Olthoff 1986). But this is not always successful, as chewing harder food may result in a longer chewing cycle (Plesh et al. 1986). In addition, jaw muscle reflexes, particularly if they are caused by noxious stimuli such as electrical shocks, may influence the cycle (Bosman et al. 1985; Hannam and Lund 1981). It is a common experience that chewing may be under conscious control or it may be automatic after the first bite. However, the complete masticatory act seen in automatic chewing may require information from higher centers. Enemoto et al. (1987) found that destruction of the cortical masticatory area of the brain disrupts the regular chewing cycle in rabbits. After one week, the chewing was restored but there were residual difficulties in manipulating, reducing, and swallowing food (Enemoto et al. 1987). The anticipatory component of the additional muscle activity demonstrated by Ottenhoff suggests involvement of centers other than the brainstem (Ottenhoff 1992). In addition, directions given to individuals on how to eat their food will in the short term influence masticatory behavior and efficiency (Rugh 1972). Although the pattern generator provides the basic substrate for mastication, information from the periphery and sites other than the brainstem is required to produce the complete masticatory act and to modify aspects of chewing. E. Measuring Masticatory Efficiency Masticatory efficiency has been measured by determining the proportion of food particles that will pass a sieve or series of sieves (Edlund and Lamm 1980; Helkimo et al. 1978; Jiffry 1981,1983,1987; Lucas and Luke 1984; Manley and Braley 1950; Olthoff et al. 1984; Yurkstas et al. 1951) after a stated number of chewing cycles or until the food reaches a swallowable composition (Jiffry 1987). The results have also been expressed by fitting the data to a particle size distribution function (Olthoff et al. 1984) and have been measured optically by particle size analysis (Mowlana et al. 1994). Masticatory efficiency varies with the type of food; for example, mashed potatoes generally need no chewing (Yurkstas 1965). The initial form and particle size determines the size distribution achieved during mastication (van der Bilt et al. 1992). There appears to be an increase in particle size reduction rate and the size of particles at swallowing when the mouthful is less than the size of a freely chosen mouthful (Yurkstas 1965; Jiffry 1983; Lucas and Luke 1984). There may be an optimum moisture level for masticatory efficiency (Yurkstas 1965; Jiffry 1983; Lucas and Luke 1984). Masticatory efficiency decreases as the

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number of teeth present decreases (Manley and Braley 1950; Yurkstas 1954; Yurkstas 1965), as the area of the occlusal contact decreases (Yurkstas and Manley 1949; Yurkstas 1965), and as natural teeth are replaced with dentures (Abel and Manley 1953; Helkimo et al. 1978; Heath 1982; Wayler and Chauncey 1983; Wayler et al. 1984). Masticatory efficiency increases with an increased number of chewing cycles (Olthoff et al. 1984) and varies with the number of sieves used to measure the efficiency (Lucas et al. 1986). The reduced potential masticatory force available to denture wearers may reduce their masticatory efficiency (Heath 1982). Because all of these variable may not have been considered, it may be difficult to compare the results of different authors. Although most common foods are soft (Dahlberg 1942), experimenters have chosen to use hard foods such as carrots or artificial foods that readily fracture into separate particles. Hard, brittle foods are probably broken by a process of crack initiation and propagation. The separate particles will pass sieves and can be counted, thus producing data which is simple to analyze. Foods which melt and or go into solution do not make an ideal test material and a different process of breakage may be involved. For this reason, the data gained from most published experiments may not be applicable to the breakdown of soft fruits. As food is chewed, it is broken down by a process of breakage and selection (Lucas and Luke 1983). Selection is related to the chance that a particle is placed between the teeth and either broken or damaged. Breakage is related to fragmentation of the particle into a smaller size, effectively the size distribution of broken pieces (Lucas and Luke 1983). Manley and Braley (1950) suggested that comminution of peanuts was selective because particles of larger size were reduced more than those of a small size. Lucas and Luke (1983) found that intraoral selection depended mainly on particle size and that selection was responsible for the decline in the rate of breakdown with increasing numbers of chews. They suggested that the breakage of carrot and similar hard foods may be best imitated by a mechanical device with cusplike structures (Sherman and Deghaidy 1978; Lucas and Luke 1983). Olthoff (1986) constructed a mechanical test probe based on the average cusp form of the posterior teeth with a 90° buccolingual angle and 120° mesiodistal angle (Du BruI1988). For a variety of foods, he found that the forces required to exhibit yielding reduced considerably compared to those required with flat plates. The particle breakdown by onetime mechanical compression of an artificial food did not compare with in vivo findings: In a human bite, the artificial food fragmented into smaller particles (Olthoff 1986). Better methods of simulating the human bite may be required for mechanical devices that test the hardness of food.

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F. Saliva

Although part of the final product of mastication is broken down food, it seems that the final particle size may not be critical in determining the swallowing threshold. As teeth are lost from the dentition, the individual does not chew for longer to compensate (Dahlberg 1946; Yurkstas et al. 1951; Yurkstas 1965); instead, he or she chooses to swallow particles of larger size. Particles of larger size are also swallowed when the size of the mouthful is increased (Yurkstas 1965; Lucas and Luke 1984). Lucas and Luke suggested smaller mouthfuls may be subject to a saliva criterion for swallowing and larger mouthfuls to a size criterion (Yurkstas 1965; Lucas and Luke 1984). On the other hand, Dahlberg (1942) suggested that each individual had a habitual number of chews before choosing to swallow food. The absence of moisture affects swallowing: It is impossible to swallow dry food particles without additional moisture. Yurkstas (1965) found that subjects given artificially dry mouths with atropine sulfate swallowed smaller peanut particles than normal, indicating that moisture facilitates swallowing and thereby leads to the swallowing of larger particles of food. As food is chewed, saliva is added to form a bolus for swallowing. Saliva is produced by reflex in response to chewing and even to unilateral biting on tasteless substances (Lashly 1916; Kerr 1961; Hector and Linden 1987). Taste, particularly acid, is also a powerful salivary stimulant (Funakoshi and Kawamura 1967). In an experiment designed to calculate the relative quantities of saliva produced by chewing and by taste alone, Wantabe and Dawes (1988) found that taste elicited flow rates that ranged from 73 to 87% of those produced when the food was chewed. Hence, the contribution made by the masticatory-salivary reflex during normal chewing is relatively small. G. Implications for Texture Measurement

Clearly, the mouth is extremely sensitive to textural information, especially when biting and chewing is conscious and tentative. However, during automatic rhythmical chewing, sensitivity is much reduced. For example, in a study by Owall and M0ller (1974) people only detected the presence of a ball bearing in a peanut when the diameter of the ball bearing was about 0.9 mm. From a horticultural perspective, this is the size of small seeds such as occur in strawberry and kiwifruit. The differences in sensitivity between conscious biting and automatic chewing are clearly important when interpreting results obtained using an analytical sensory panel (in which panelists focus on providing detailed

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information on sensory attributes) in relation to consumer responses (where chewing is more automatic in its nature). Differences between analytical and consumer panels are acknowledged in the sensory science literature (Meilgaard et al. 1991). The speeds measured during chewing are considerably faster than those used during instrumental testing of food. Masticatory speeds could cause shear thinning which would not be replicated in the lower instrument speeds. For this reason alone textural measurements made using instruments may not represent the events occurring in the mouth. As discussed previously (Section IVC), the forces used during chewing are relatively low. Fruit such as kiwifruit are generally considered to be eating-ripe at a firmness around 9.8 N (Stec et al. 1989). This firmness is similar to the force applied during chewing, perhaps inviting speculation that "hardness" or "softness" of a fruit relates to whether firmness is higher or lower than the forces generally used during chewing. Research into the breakdown of food during chewing concentrates on hard tissues that easily fracture (e.g., carrot, nuts, artificial plastic structures). While such data may be applicable to firm fruit such as apple, which only soften moderately during ripening, it should be used with extreme caution when considering soft fruit such as banana or peach. More studies are required to determine the influence of moisture in food on the cessation of chewing and the initiation of swallowing. The results of such studies may be particularly pertinent to understanding the mechanisms of breakdown of soft fruit during chewing. When is a fruit a liquid to be swallowed and when is it a solid to be chewed? V. CONSUMER AWARENESS AND ATTITUDES

Many factors affect the perception of texture by the consumer. A key study by Szczesniak and Kahn (1971) examined critical issues relating to consumer awareness and attitudes to food texture. These include socially and culturally learned expectations of appropriate texture associated with specific foods, socioeconomic status, gender, eating occasions (social context in which the food is eaten), and flavor intensity. They found that at the conscious level flavor is of greater importance to the consumer than texture. However, this does not mean that texture is ignored. Indeed, many consumers cannot identify foods based on flavor alone. A study in which blindfolded subjects were given pureed foods found that, depending on age and obesity, only 55 to 81 % could identify apple, 33 to 78% could identify strawberry, and 24 to 69% could identify banana (Schiffman 1973). Furthermore, in foods lacking distinct

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flavor, texture does become an important quality attribute (Szczesniak and Kleyn 1963). However, for most food, the texture is largely ignored unless it does not meet expectation. Unexpected texture usually signals that the food is of poor quality (Szczesniak and Kahn 1971). For example, with lettuce the texture, particularly crispness, was identified by some consumers as being an indicator of whether the food was fit to eat (Szczesniak and Kahn 1971). The implication for fresh fruit is that flavor is the predominant characteristic which determines acceptability as long as the textural properties are within acceptable limits. If texture is unacceptable, flavor is irrelevant. However, in some fruits, consumer preferences can be based on both texture and flavor attributes. For example, using preference mapping techniques to examine 12 varieties of apple, Daillant-Spinnler et al. (1996) demonstrated that consumers in the United Kingdom tended to prefer either a sweet, hard apple or a juicy, acidic apple. Furthermore, within a single commodity, it is likely that texture preferences, particularly firmness at which fruit are eaten, will vary between individuals (Stec et al. 1989). Attitudes to fruit texture might be expected to change during a year. This will be influenced by availability of the range of fruit and whether it is freshly harvested or stored. There has been little published on this issue. Sensory studies suggest that criteria that determine acceptability may change during storage (S. H. Murray et al. unpublished). In a study profiling aroma, flavor, and texture of 'Royal Gala' apple, there was a general decline in acceptability of apple associated with decreases in flavor attributes. However, an increase in acceptability, juiciness, and crispness was observed between 16 and 20 weeks in storage (S. H. Murray et al. unpublished). This suggests that flavor was the main criterion for apples at harvest and during early periods of cool storage. After longer periods in storage, the importance of flavor decreased while texture attributes, especially crispness and juiciness, became critical. These issues are of critical importance to marketers of fruit, and there is a clear need for more research in this area. Increasing trade in fruit between hemispheres along with improvements in storage technology have made traditionally seasonal fruit available all year. The impact of this on consumer attitudes to texture and associated purchasing criteria are unknown. Furthermore, it is important to know the preferences and limits of acceptability of target consumers in different countries. Generally, this issue is explored in sensory research conducted in the country of interest and/or using a sensory panel of specified ethnic origin recruited from the local community. In our research, we have found that consumer expectations of fruits can markedly differ according to ethnic origin (Stec and Charles 1993; Phelps and Murray 1995).

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VI. WHY MEASURE TEXTURE?

A. Research Perspective Measurement of fruit texture is important in many areas of fruit research, as well as in the classification and/or grading of "fruit quality" in commerce. In this section, we focus on the way instruments and methodologies of texture measurement are used in wider scientific and commercial communities. Biochemical and physiological studies use a number of indices to assess the stages of fruit ripening and senescence. Changes in fruit firmness that occur during ripening can often be measured using simple equipment that is available in most fruit physiology laboratories. Thus, fruit firmness, changes in fruit skin color, and detection of the climacteric (increase in respiration rate and ethylene production) often provide convenient methods for following fruit ripening. Applied studies often focus on characteristics which relate to storage potential of fruit, (e.g., its ability to survive transport through the distribution network) as well as those attributes which influence consumer perception of fruit quality. Texture measurements are widely used for these purposes; for example, fruit breeders often wish to select for preferred textural attributes, while preharvest management of fruit crops (rootstocks, tree spacing, pruning, fruit position) and postharvest storage techniques can all influence texture. An initial indication of texture can be obtained using simple, low-cost instruments such as hand penetrometers. These devices measure the hardness/softness of fruit, an attribute often described in terms of fruit or flesh firmness. More detailed evaluation of texture requires physiological/biochemical assessment of internal condition and/or sensory evaluation of texture/flavor characteristics. B. Maturity Within the period of fruit maturation, it is important that growers are able to select the appropriate time to harvest fruit. Fruit should be harvested when they retain a high storage potential and have matured to the stage that they have the capacity to ripen and develop a full flavor profile (Lau 1985). A number of fruit attributes are used by growers to help decide when to harvest. Cornmon maturity indices include measuring changes in fruit color, assessing the state of starch hydrolysis, and detection of the climacteric (Lau 1985; Beever and Hopkirk 1990). Flesh firmness, as measured using a puncture test, is often also used as .an indicator of maturity in stonefruit (Lill et al. 1989) and pome fruits (Lau 1985; Kingston 1992). However, most researchers recommend extreme care

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when using firmness as an indicator of maturity since environmental and seasonal variations in firmness are great (Lau 1985; see also Section VIII).

C. Regulatory Perspective Regulations based on texture measurements are often used by fruit industries to manage and market their crops. In some cases, the regulations are imposed by the marketplace, for example, Organisation for Economic Co-operation and Development (OECD) grade standards for apple, pear (OECD 1983) and kiwifruit (OECD 1992) and USDA standards for grades of apple (U.S. Dept. Agr. 1976). Grade standards such as those imposed by the OECD give general descriptions of expected texture. For apple and pear, the degree of ripeness is categorized by the OECD (1983) as hard, firm, firm-ripe, turning (pear only), and ripe, rather than by instrumental values of firmness. Similarly, USDA standards for the degree of apple ripeness give the following categories: immature, hard, firm, firm-ripe, ripe, and overripe (U.S. Dept. Agr. 1976). Abbott et al. (1992) described the parameters used by eight experienced inspectors from the Agricultural Marketing Service to grade apples according to the U.S. standard. Assessments were based on integrated information obtained from visual, manual, oral, and auditory sensations, but firmness was the paramount characteristic. Imposition of grades based on instrumental measurements is more frequently applied by wholesale/retail organizations, by fruit producers, or by their marketing agents. For example, supermarket chains in the U.K. often impose minimum firmness on apple suppliers, while both the New Zealand Kiwifruit Marketing Board and the Washington State apple industry require fruit to be above a minimum firmness before they will allow the fruit to be exported. In each of these examples, the regulations are based on puncture tests using Magness-Taylor or Effegi probe. The purposes of such regulations is to ensure that: • The fruit is of a uniform high quality when it reaches the consumer. This is particularly important when developing consumer loyalty to a particular brand name or producer. • Fruit placed into storage facilities will derive maximum benefit from the storage treatment. Postharvest treatments such as controlled atmosphere storage are expensive to apply and may not provide a financial return unless fruit can be sold at a premium price. Such prices are only possible if the best quality fruit is placed into the facility.

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• Fruit placed in the distribution network will survive the postharvest handling and transport and provide a reasonable shelf life to the retailer (Polderdijk et al., 1993). An example is the New Zealand kiwifruit industry, which does not export fruit if the flesh firmness is less than 10 N, as measured by a puncture test with a 7.9-mm diameter probe. A number of concerns with regulatory control of texture must be expressed. Most regulations are based on the puncture test which is empirical and, in many situations, poorly related to both sensory perception of texture and the physiological condition of fruit. Furthermore, different devices and operators can provide different values for firmness (see Section VIlA). VII. METHODS FOR MEASURING TEXTURE

A wide range of fundamental, empirical, and imitative methods are available for measuring fruit texture (Voisey 1971; Bourne 1982; Vincent 1994). The diversity of methods and instruments can be overwhelming. The science of food rheology has underpinned the development of many of these methods. Rheology is the study of deformation and flow in a material. The principles of rheology and definitions of rheological terms have been reviewed by Mohsenin (1970). Fruit tissues are viscoelastic in that they exhibit both elastic and viscous properties. As a result of this, the mechanical behavior of the tissue is time dependent. For example, if tissue is compressed and held at a constant deformation, the force required to maintain that deformation will gradually decrease. This viscoelastic behavior of fruit has many implications for texture measurement, including the influence that the rate of deformation has on tissue strength (Mohsenin 1963,1977). For a more detailed discussion of viscoelasticity in biological materials, see Vincent (1990). In our review, we provide a summary of many of the methods that have been specifically used to objectively measure fruit texture. A useful place to start is with the American Society of Agricultural Engineers Standard (ASAE S368.1) compression test of food materials of convex shape, which describes conventions for measuring some mechanical properties of food such as fruit (ASAE Standard 1984). While some fruit studies do not fulfill these standards, it would seem sensible that fruit physiologists follow them as closely as is feasible. Limitations placed on fruit physiologists include availability of equipment as well as time and logistical constraints associated with running large experiments. The action of compressing or puncturing a whole fruit

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results in an increase in force with increasing deformation. This is often described by a force-distance curve such as shown in Fig. 2.5. The ASAE Standard (1984) defines the following points on the curve: Bioyield point: A point ... where an increase in deformation results in a decrease or no change in force. Point of inflection: A typical force-deformation curve is first concave up and then concave down. The point at which the change of slope (second derivative) of the curve becomes zero is called the point of inflection. This point, designated as Pi' can be found by using a straight edge to follow the change of slope of the curve and to determine the point at which the slope begins to decrease. Rupture point: The point on the force-deformation curve at which the loaded specimen shows visible or invisible failure in the form of breaks or cracks. This point is detected as a continuous decrease of the load in the force-deformation diagram.

A number of criteria need to be considered when selecting a method for measuring texture. These include (1) identifying the most relevant textural attribute or attributes to measure; (2) considering whether the

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Deformation Fig. 2.5. Force-deformation curve for materials with and without a bioyield point. Pi = point of deflection, Dpi deformation at point of inflection. Redrawn from Mohsenin and Mittal (1977).

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method might reasonably be expected to measure the required texture attributes; (3) the sensitivity of the device to changes/differences in texture; (4) variations in measurements associated with different operators; (5) whether destructive or nondestructive measurements can be used; (6) the availability of the equipment; and (7) the speed of measurement. A. Puncture Tests Puncture tests involve pushing a cylindrical probe with a convex tip into the flesh of a fruit. A range of handheld devices are available for making these measurements, including Magness-Taylor, Effegi, Ballauf, and Chatillon (Bourne 1982). The early history of puncture tests has been reviewed by Haller (1941) and Bourne (1966). The first publication describing puncture testing of fruit using a device that is recognizably still in use today was that of Magness and Taylor (1925). Over the last 70 years, the puncture test has become the most widely used instrumental method for measuring fruit texture. The terms pressure test, puncture test, deformation test, and penetration test are often confused and used interchangeably. We are using the definitions given by Bourne (1966), which describe the puncture test as: "Measur(ing) the force required to push some kind of a punch into a food product. The test is characterized by (a) using a force-measuring instrument; (b) penetration of the punch into the food; and (c) a distance usually held constant." The instrument used to make the test was originally called a pressure tester, but more recently a penetrometer or fruit firmness tester. The term pressure tester is incorrect although it was used for many years. While pressure is force/area, these devices measure only force regardless of the probe size. The measurements of force obtained with these instruments are often described as firmness, fruit firmness, or flesh firmness. Further discussion on the meaning of the term firmness was presented in Section II. A number of recommendations have been made on how a puncture test should be carried out (Bourne 1974; Blanpied et al. 1978; Watkins and Harman 1981; Smith 1985). A small slice of skin is usually removed from the part of the fruit that is being puncture tested. The presence of intact skin can result in a yield point which is not representative of the flesh (Bourne 1965; Blanpied et al. 1978). A working group, sponsored by the Commission of the European Communities, recommended that a press-pull beam instrument (e.g., Instron) should be used to drive an Il-mm diameter probe with a convex tip into each side of the apple fruit to a depth of 8 mm at speeds between 50 and 250 mm/min (Smith 1985).

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They recommended the use of a handheld puncture tester as a supplementary method when an Instron-type instrument was not available. Earlier reports (Bourne 1974; Blanpied et al. 1978; Watkins and Harman 1981) described techniques used to obtain relatively consistent measurements with a handheld puncture tester. Recommendations for the Magness-Taylor tester are that the fruit should be held firmly against a vertical surface and the probe pushed horizontally into the flesh with a steady motion (Bourne 1974). For the Effegi puncture tester, the fruit should be held on a flat surface and a steady force should be used to push the probe into the fruit (Blanpied et al. 1978, Watkins and Harman 1981). It is often recommended that hand-puncture testers should be mounted in a drill press to allow more control over penetration (Blanpied et al. 1978). However, even with experienced operators, differences in technique result in variation between measurements (Voisey 1977a; Blanpied et al. 1978; Harker et al. 1996). When considering the cause of these operator differences it is important to examine the shape of the force-distance curves. Three types of force-distance curve can be obtained when testing fruit: (a) the force increases steadily up to the bioyield point and then continues to increase at a slower rate until maximum penetration; (b) the force increases steadily until bioyield and rupture occur in close succession and then remains approximately constant until maximum penetration; and (c) the force increases to a point where bioyield and rupture occur simultaneously and then decreases until maximum penetration (Bourne 1965). In type (a) curves, force measurements are sensitive to the depth of penetration, which might be expected to vary when different operators use hand-puncture testers. However, in our experience most fruit exhibit type (b) or (c) curves, and thus variations in the depth of penetration are unlikely to be the cause of operator differences. In a recent study, Harker et al. (1996) suggested that differences in the way the probe accelerates into the fruit may be the cause of operator differences. Issues relating to the replication and sample size required for measuring fruit texture were discussed by Worthington and Yeatman (1968), Blanpied et al (1978) and Saltveit (1978). In recent years, a number of new instruments for making puncture . tests have been developed. The diversity of instruments available for puncture testing fruit has potential to cause problems for industries using puncture tests to regulate fruit quality. Often, different instruments provide quite different values for flesh firmness (Abbott et al. 1976; Bongers 1992; Harker et al. 1996) and also differ in the precision with which they detect changes/differences in strength (Harker et al. 1996). Experimental procedures for comparing the precision with which

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instruments differentiate between hard and soft fruit are described by Harker et al. (1996). The process of driving a probe into fruit tissue produces a combination of shear and compression stresses (Bourne 1966; Yang and Mohsenin 1974; Bourne 1975). The shear force is associated with the perimeter of the probe, while the compression force is associated with the cross-sectional area of the probe contact surface. This relationship is described by the formula, F = Ka A + Kp P + C

[Eq.3]

where F is puncture force, K a is an area-dependent coefficient, K p is a perimeter-dependent coefficient, A is probe area, P is probe perimeter, and C is a constant (Bourne 1966, 1975). This relationship was originally described using flat-tipped rectangular probes of constant area and varying perimeter and probes of constant perimeter and varying area (Bourne 1966). In more recent studies, methods for determining K a and Kp using flat-tipped cylindrical probes of varying diameter are described (Bourne 1975). For cylindrical probes, deviation from this relationship only occurs at small diameters «2 mm; Bourne 1975). Jackman and Stanley (1992a) proposed an even simpler method of determining K a and K p which involved compressing tissue (using parallel flat plates) in conjunction with puncture tests. Using this method, they described how the contributions of perimeter-dependent and areadependent forces varied relative to each other as tomato fruit softened (Jackman and Stanley 1992a). Yang and Mohsenin (1974) analyzed the mechanics of puncturing fruit using cylindrical probes with slightly convex tips (the usual tip shape). Penetration of the probe was divided into two processes: loading up to the point where the convex tip was embedded but had not punctured into the flesh (modeled using theory of inelasticity) and piercing of the flesh (modeled according to the piercing problem). Their analysis indicates that probe size, shape, radius of curvature, and the mechanical properties of the fruit all influence the overall puncture force. The research encompassed in the just mentioned studies suggests that attempts to convert measurements obtained using different sized probes to a common scale should be undertaken with care. The need to make such a conversion might occur after a mistake has been made when selecting an appropriate diameter probe (devices such as the Effegi Fruit Pressure Tester are usually supplied with two probes, 7.9-mm and 11.1-mm diameter; kiwifruit and pear are usually tested using the smaller probe, while apple is usually tested with the larger probe). Pub-

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lished multiplication factors for converting from small to large probes vary between 1.97 (Bourne 1965) and 1.85 (Yang and Mohsenin 1974) for 'Rome' apple. However, observations that conversion factors vary with apple cultivar (Bourne 1965), and that perimeter-dependent and area-dependent forces vary relative to each other during tomato ripening (Jackman and Stanley 1992a) suggest that conversion factors, if required, should be determined for each specified fruit sample. There have been few attempts to determine what happens to the cell/tissue structure during penetrometer measurements. Maximum forces are usually generated within the first few mm of penetration, even though the test does not finish until the probe has traveled at least 8 mm into the flesh. Microscopy studies of the hemispherical zone of squashed cells in front of the tip of the probe in apple indicate that it extends to a depth approximately half the diameter of the probe (Roudot et al. 1990). The changes in the mechanisms of cell failure (cell rupture to cell-to-cell debonding) that occur during fruit ripening were discussed in Section HIC. When small-diameter probes (1 mm) and/or star-shaped probes are driven deep into the flesh, the force-distance curves may reflect the firmness of different tissue zones. Ourecky and Bourne (1968) used this approach to determine firmness of the skin, flesh, and core tissues in strawberry. Similarly, Holt (1970) used this approach to determine the firmness of the exocarp, mesocarp, endocarp, and locular juice in whole tomato fruit. When longitudinal and equatorial slices are cut from fruit, puncture tests using small-diameter probes can be applied in the different tissue zones (Thompson et al. 1982,1992). However, probes with diameters less than 2 mm are known to deviate from expected areadependent and perimeter-dependent characteristics of larger probes (Bourne 1975). B. Whole-Fruit Deformation

Fruit deformation usually involves compressing an intact fruit between two parallel flat plates, although in some instances a probe with a spherical tip is used to indent the fruit (ASAE Standard 1984). When parallel plates are used, the measurement may involve application of a constant force with measurement of the incurred fruit deformation (Hamson 1952; Ahmed and Fluck 1972; Swarts 1981; Polderdijk et al. 1993), deforming the fruit by a fixed distance and measuring the force required to achieve this deformation (Fils-Lycaon and Buret 1990), and increasing the force up to the point that the fruit ruptures (Sundstrom and Carter 1983; AI-Kahtani 1992). Fruit are viscoelastic, so the rate of

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compression must be specified because it significantly affects the measurement of strength (see Section VII). Generally, when whole-fruit compression tests are used, the maximum force or maximum distance are presented. However, it is also possible to calculate the modulus of elasticity, modulus of deformability, and stress index for fruit (ASAE Standard S368.1 1984). Experiments using whole-fruit deformation have been used to characterize the viscoelastic properties of orange (Sarig and Orlovsky 1974) and peach (Ahmed and Fluck 1972). The use of whole-fruit deformation tests by horticulturalists is second only to puncture tests. Thus, an important question is, Do the two tests respond to changes in properties of the fruit in a similar fashion? The answer is, not always! It appears that whole-fruit compression is a relatively insensitive method when compared with whole-fruit puncture or tissue compression (Ahrens and Huber 1990; Jackman et al. 1990). This insensitivity of whole fruit compression is attributed to a variety of confounding influences, including variation in fruit morphology, size, shape, turgor, viscosity, and content of locular fluid (Ahrens and Huber 1990; Jackman et al. 1992a). The rate of deformation can also influence tissue strength (Mohsenin 1963,1977). C. Tactile Assessment Sight, touch, and smell are the key senses that consumers use when selecting fruit. Squeezing fruit by hand is an important method of evaluating textural quality. Furthermore, hand squeezing is often used in research when sufficiently sensitive instrumental methods are not available. Such a situation occurred in a study on atemoya (Batten 1990). In experiments where quality is evaluated when the fruit is ripe and individual fruit ripen at different rates, researchers may hand sort the fruit at regular intervals and only evaluate fruit that are soft as in custard apple (Brown et al. 1988) or avocado (Hopkirk et al. 1994b). Fruit industries such as the New Zealand Kiwifruit Industry continue to hand sort for soft fruit. Kiwifruit industry regulations are based on puncture tests with a 7. 9-mm diameter probe and prohibit export of trays of fruit if any individual fruit within the tray is less than 1 kgf (10 N) or if the average puncture strength of fruit within the tray is less than 1.2 kgf (12 N) (McGlone and Schaare 1993). People employed to grade fruit are able to detect these soft fruit by hand squeezing. Toward the end of the onshore storage period, kiwifruit are sorted and repacked to exclude soft fruit. This process requires each fruit to be lightly squeezed (McGlone and Schaare 1993). Nondestructive devices designed to automatically grade for soft fruit may be equal to, but certainly no better than, commercial

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hand sorting (McGlone and Schaare 1993). These observations suggest that the human tactile sense is as sensitive as most objective measurements. Voisey and Crete (1973) describe a method for determining force and rate of force application during squeezing of fruit by consumers. They found that variability in technique was high, although generally force increased nonlinearly as the fingers made contact with the produce before increasing at an approximately linear rate. The rates and maximum forces depend on firmness of product. Using a range of compliant materials, mainly sponge and rubber, Harper and Stevens (1964) examined how human perception of hardness as assessed by squeezing between fingers was related to the elastic modulus (force/deformation). They found a logarithmic decrease in the ability to discriminate hardness using tactile assessment as the materials became harder. Formally, this was described as the subjective perception of hardness increasing in proportion to physical hardness raised to the power of 0.8. The implications of these results to fruit is obvious. When fruit are relatively soft, people can easily discriminate between fruit of different firmness. However, as fruit firmness increases beyond a threshold, people become unable to discriminate between fruit. Thus, while industries such as the New Zealand Kiwifruit industry are able exclude "soft fruit" by hand sorting, this approach would not be possible with hard fruit such as apple. D. Instrumental Tests on Excised Tissue 1. Shear and Extrusion. Shear measurements are commonly used to

assess texture of processed or cooked foods such as sausage, cooked rice, bread, canned peas (Szcznesniak et al. 1970), but a number of studies have included fresh fruit. In its simplest form, a shear test can be conducted by punching out a plug of tissue from a thin slice that is held between two rigid plates (Mohsenin 1977). The Kramer shear cell is the most frequently used method for measuring the shear or extrusion properties of fruit tissue. The Kramer shear cell is a box with a grate at the bottom. A fixed amount of fruit tissue is loaded into the box and a series of 10 shear blades are driven through the tissue and eventually through the slots in the bottom grate. The influence of different sizes of test cells and different numbers of blades have been characterized (Voisey and Kloek, 1981; Timbers et al. 1985). Results are expressed as maximum force or as areas under the force-distance curve. However, the relationship between maximum force and sample weight is generally nonlinear, and thus it is recommended that data should not be expressed as

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force/sample weight (Szczesniak et al. 1970). The forces that develop during the test are a mixture of shear, extrusion, and compression (Szczesniak et al. 1970). Voisey (1977b) indicated that direct observation of the mode of action of test cells on food, through a clear plastic sidewall, was a valuable aid to interpreting results. For fresh apple slices, he concluded that the peak force was an indication of rupturing, cohesive and adhesive effects, and the shear strength was indicated by a change in the slope on the force-distance curve (Voisey 1977b). In contrast to fresh apple tissue, no shearing in the planes of the blades was observed in wilted apple (Voisey 1977b). Bin and McLellan (1988) found that maximum shear force increased with sample weight and that random or crosswise orientation of apple slices was preferred. In a recent study, a horizontal thin wire was held taught between rigid supports and driven through cylindrical plugs of peach fruit flesh using an Instron (Murillo et al. 1994). Shear tests undertaken in this fashion have a number of advantages: The tissue-blade friction associated with the use of solid blades is excluded and fast Fourier transformation can be used to quantify the jaggedness of force-distance curves (Murillo et al. 1994). Further research is required to determine whether this approach can provide information which is relevant to the sensory description of textural attributes of fruit. Back extrusion is often used to assess fresh peas and processed products, but rarely to measure texture of fresh fruit. A loose-fitting plunger is driven into a sample chamber containing the food until the food flows up between the side of the chamber and the plunger (Bourne 1982). A study on texture of fresh and processed peaches found a high degree of correlation between back extrusion and other methods of measuring texture, including the Kramer shear cell and shear compression tests (Schweingruber et al. 1981). 2. Compression Tests. Compression tests are generally applied to samples of tissue excised from whole fruit. Blocks of tissue can be compressed until the tissue fails (Peleg and Gomez Brito 1977; Diehl et al. 1979), subjected to a series of compression cycles (Bourne 1978; Abbott et al. 1984), or subjected to creep tests (Mittal et al. 1987). Dimensions of the excised tissue are important. Peleg et al. (1976) found that apparent tissue strength based on sample cross-section area (N/cm 2 ) was greater with 13.2-mm than 8.6-mm diameter samples of tissue. Under cyclic compression, initial compression can indicate combined plastic and elastic deformation and the return cycle will indicate the extent of plastic deformation. Following compression, it is possible to see the different patterns of tissue failure. For example, plugs of unripe tissue

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from plantain fruit break along a single inclined failure plane, while irregular failure planes occur in ripe fruit (Peleg and Gomez Brito 1977). Failure patterns during compression have also been studied for mango, melon, papaya, pineapple, and watermelon (Peleg et al. 1976). Examination of fractures that occurred as a result of uniaxial compression of apple, melon, and raw potato tuber tissue using a scanning electron microscope indicated that cell walls had failed (Diehl et al. 1979). For most fruit, the shape of the force-distance curve changed as ripening progressed such that there was a smaller deformation associated with tissue failure and a distinct phase associated with liquid expression (Peleg et al. 1976). It has been shown that combinations of texture profile variables obtained during compression tests (single cycle of compression and relaxation) are more closely related to sensory attributes such as crispness, hardness, and toughness than single variables or puncture measurements of apple (Abbott et al. 1984). A compression test involving two compression cycles is often called texture profile analysis (not to be confused with the sensory technique with the same name). A bite-sized block of tissue is compressed through two cycles, both to about 25% of its original height (Friedman et al. 1963; Brennan et al. 1970; Bourne 1978). The resulting force-distance curve is then used to characterize a number offood attributes, including hardness (peak force during the first cycle), cohesiveness (ratio of positive area during first and second cycle, A z/ AI)' adhesiveness (negative area as the plunger returns to its start position), springiness (height of the food at the completion of the first compression cycle), gumminess (hardness x cohesiveness), and chewiness (gumminess x springiness). Although the General Foods Texturometer was designed to make these measurements, the Instron also can be used (Bourne 1978). 3. Beam Tests. Although beam tests are often used to measure stiffness of biological materials (Vincent 1990), there are only a few studies which have used this test on fruit (Bolin et al. 1964; Lapsley 1989). Generally, a cylindrical or rectangular tissue sample is supported at both ends by pivots (often metal blocks). A blunt blade, located between the pivots, then drives down at a constant speed so that the tissue sample bends then breaks. This is known as three-point bending and can be easily carried out using materials testing machines. Alternatively, four-point bending or a cantilever bending tests can be carried out (Vincent 1990). When using a beam test it is important that the surface of the specimen is sufficiently hard not to deform (Vincent 1990). In many cases, excised samples of fruit tissue do not fulfill this requirement, and herein

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lies the reason that the beam test is so infrequently used. Indeed, the beam test has mainly been used on apple (Bolin et al. 1964; Lapsley 1989), which is a relatively hard tissue. Beam tests can be used in an empirical fashion to determine rupture force (Lapsley 1989) or to determine shear and/or elastic modulus (Vincent 1990). In most biological studies, the aim is to measure the elastic modulus. Thus, the span (5, distance between pivots) to sample depth (D) ratio has to be sufficient to avoid shear. This can be checked by plotting the elastic modulus against the SID ratio (Jackson et al. 1988; Vincent 1990). The safest SID ratios occur within the region where plots indicate that a plateau has been reached. 4. Wedge Tests. The wedge test has been used to assess texture of apple (Vincent et al. 1991). A sharp wedge is driven into a block of tissue: A

number of events can be detected on the resulting force-distance curves. The surface of the specimen is slightly compressed before the wedge starts to cut into the tissue; as the wedge enters further, the two halves are forced apart storing strain energy; eventually a free-running crack is propagated ahead of the wedge; following initiation of the crack, the force declines to a constant level (Vincent et al. 1991). 5. Tensile Tests. The ability to undertake tensile measurements is depen-

dent on securely fixing both ends of a tissue sample into the instrument used to apply the test. This has been achieved using clamps (Schoor! and Holt 1983), cutting tissue into a shape which slots between sets of claws (Stow 1989; Harker and Hallett 1992), or by gluing (Harker and Hallett 1994). Recommended adhesives are fast-setting cyanoacrylates such as Loctite 401 (Loctite Corp. Newington, CT). Holt and Schoorl (1985) applied cyclic loading to 75% of expected load failure, allowing them to identify extent of hysteresis in the loading and unloading portion of the force-deformation curve. One of the main advantages of tensile measurements is the ability to examine fracture surfaces using a scanning electron microscope and to determine whether tissue failure occurred by cell-to-cell debonding or cell rupture (Glen and Poovaiah 1990; Harker and Hallett 1992; Lapsley et al. 1992; Harker and Hallett 1994). Use of low-temperature scanning electron microscopy (Harker and Hallett 1992,1994; Harker and Sutherland 1993) has additional advantages in that the specimen does not need to be dehydrated, thus avoiding artifacts and allowing direct observation of surface fluids. 6. Torsion Tests. Fruit texture has been measured by applying torsion tests to the point of tissue failure (Diehl and Hamann 1979; Diehl et al.

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1979). Diehl et al. (1979) compared shear stresses that occurred during torsional and uniaxial compression testing of fresh fruit and vegetables. For both compression and torsion tests, the strain rate was approximately 0.26 S-l. In melon and apple, shear stress at failure was generally higher when tested in torsion than in uniaxial compression (Diehl et al. 1979). This was explained by observations that tissue failed by shear during torsion and by cell rupture in uniaxial compression. From these results, it was suggested that the buildup in turgor that occurred during uniaxial compression promoted tissue failure. As in uniaxial compression and tension tests, fracture surfaces can be examined using scanning electron microscopy (Diehl et al. 1979). 7. Dynamic Tests. Dynamic tests deform tissue by applying a sinusoidal stress or strain (Mohsenin 1970; Rao 1984). The frequencies with which the stress or strain are applied are generally between 0.1 and 500 Hz. Petrell et al. (1980) compared results obtained using a dynamic test in which sinusoidal stress was applied to plugs of apple tissue at frequencies between 150 and 275 Hz with results obtained using a puncture test. While puncture measurements were better at detecting ripening, sinusoidal stress was better able to distinguish between cultivars (Petrell et al. 1980). These different sensitivities of the dynamic and puncture tests were probably due to the tests being based on different physical principles and measuring different physical properties of the apple tissue. Vincent (1989) found a good relationship between tissue density and shear stiffness of apple plugs by applying a torque of about 5 x 10-5 N mm at frequencies of 0.1 Hz to apple cortical tissue. Jackman and Stanley (1992b) demonstrated considerable changes in storage modulus (representing the elastic component), loss modulus (representing the viscous component), and loss tangent (representing relative energies from viscous and elastic components) of tissue during chilling and ripening of tomato. Dynamic tests are often used in the nondestructive testing of whole fruit (Abbott et al. 1997).

8. Stress-Relaxation Measurement Using Conical Probes. An empirical form of stress-relaxation measurement can be obtained using a conical probe. The development of this test has been described in a series of papers with tomato (Kojima et al. 1991; Sakurai and Nevins 1992, 1993) and banana (Kojima et al. 1994). A conical probe is located on an arm that is lowered until the probe penetrates into a tissue slice (7 to 8 mm thick) to a depth of 0.6 mm. A strain gauge on the arm then senses deflection, which is calibrated against load. The subsequent decay in load is measured at 1 s intervals for 1 min and four stress-relaxation parameters calculated according to a modified Maxwell-viscoelastic model.

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The parameters relate to texture attributes as follows: High initial load reflects higher elastic properties (and corresponds to measurement by conventional compression methods); higher Tm (time when decay stops) reflects a higher tissue viscosity; and To (time when decay starts) can be used as an index of wall loosening and is correlated with appearance of polygalacturonase in tomato (Sakurai and Nevins 1992). The advantage of this method is that it allows fruit to be characterized according to both firmness and tissue viscosity (both can vary independently). E. Twist Test The twist test was developed by Studman and Yuwana (1992). In its simplest form it consists of a rectangular blade fixed radially at its axis to a sharpened spindle. The fruit is impaled onto the spindle until the blade completely enters the flesh. The fruit is then twisted by hand (Studman and Yuwana 1992) or by using an automated system (Studman 1993) until the blade crushes the tissue. More recent papers use the bioyield point rather than the maximum force to indicate tissue strength. An advantage of this method is that tissue strength can be measured in different tissue zones by altering the length of the spindle, which is adjustable. Furthermore, there is no need to remove the skin as in penetrometer measurements. In a study comparing different firmness testers (mainly puncture tests), the twist test was found to be among the most precise for measuring kiwifruit firmness (Harker et al. 1996). However, results from a study by Hopkirk et al. (1994a) suggest that twist tests and puncture tests may measure different aspects of the mechanical strength of fruit. They examined kiwifruit from two orchards and found that while fruit from orchard A were firmer than fruit from orchard B according to puncture tests, the twist test indicated the reverse. F. Strength of Aggregate Fruits Aggregate or drupaceous fruit consist of a collection of drupes that are loosely held together and include raspberry, blackberry, boysenberry and dewberry (Bourne 1983). As a result of the unique morphology of these fruit, a number of specialized instrumental measurements have been used to measure firmness. Raspberry is harvested by pulling the aggregate of drupes from the receptacle, thus leaving an opening at the base of the fruit. Firmness can be measured as the force required to compress the fruit until this opening closes (Barritt et al. 1980). Juice will exude from the fruit when the fruit is further compressed, thus providing an additional indicator of the resistance of the fruit to the applied force. Robbins and Sjulin (1986) inserted the fruit opening over two split

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cylindrical rods and measured the force required to tear the druplets apart. Fruit strength, as measured by compression, was correlated with morphological characteristics including weight, druplet number, receptacle cavity depth, and individual pit weight (Robbins and Moore 1990, 1991). Taking a different approach, O'Donoghue and Martin (1988) characterized berry physicomechanical integrity by measuring juice loss from fruit samples after centrifugation. G. Tissue Juiciness

The importance of juiciness, particularly in relation to fruit, has been demonstrated by a number of studies on consumer awareness of texture attributes (Szczesniak and Kleyn 1963; Szczesniak and Kahn 1971; Szczesniak 1972; Szczesniak and Kahn 1984). Despite this, there has been relatively little development of instrumental methods and experimental procedures for measuring juiciness. Intuitively, one might expect that water content of a tissue would largely determine its juiciness. While the data presented by Szczesniak and Ilker (1988) confirm this relationship, for many fruit the inability of cells to release free juice has a greater impact. For example, the water content of juicy and chillinginjured peaches is similar, yet the chill-injured fruit have a dryish mouth-feel (see Sections lIIB and IX). A number of different methods have been used to measure juiciness. Generally, juiciness is characterized as weight or percentage of juice released from a fixed weight of tissue. Juice can be extracted from fruit tissue by homogenizing in a Waring blender, then centrifuging to separate juice from solids (stonefruit-von Mollendorff et al. 1992), or using domestic juice extractors which operate on similar principles (citrus-EI-Zeftawi 1973, pears-Chen et al. 1981; Chen and Borgic 1985). Alternatively, tissue may be subjected to mild homogenization by forcing tissue through a syringe, the homogenate being collected in microfuge tubes which are spun in a micro-centrifuge so that juice is separated from compacted cellular material (Lill and van der Mespel 1988; Harker et al. 1997). A number of studies have determined the amount of juice released during compression testing of excised tissue (Peleg et al. 1976; Szczesniak and Ilker 1988, Paoletti et al. 1993). This has been done in two ways. First, juice release can be calculated as difference in tissue weight before and after compression of cylinders of tissue, taking care to allow all extracted liquid to drain (Peleg et al. 1976). Alternatively, dry filter paper (presoaked in 5% CuS0 4 ) can be placed above and below the tissue sample, and the extent of juice release during compression can be

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determined from the area of wetted paper which is visible as a blue outline (Szczesniak and Ilker 1988; Paoletti et al. 1993). A juice press (succulometer cell attached to food testing machines) has been used to measure juiciness of apple (Mackey et al. 1973; Hard et al. 1977). Apple slices were placed in a succulometer cell and a piston driven downward to a predetermined endpoint. Juice-press values correlated with sensory assessments of juiciness, crispness, and tenderness of the flesh (Hard et al. 1977). Measurement of juice release from a freshly cut surface can simply involve measuring weight of juice absorbed into tissue or filter paper (Harker et al. 1997). H. Auditory Recording of Chewing Sounds The relationship between the sound generated during chewing and the crispness of a food is well known (Drake 1963; Vickers 1988). Correlations between these measurements are often as high as 0.99 (Vickers 1988). Furthermore, the relationship is apparent in both wet crisp foods such as fruit and vegetables and dry crisp processed foods such as chips and biscuits (Edmister and Vickers 1985). Sound generated during biting and chewing can be recorded using a microphone pressed firmly against an ear and analyzed from amplitudetime plots for the number of peaks, mean peak height, and duration of sound (Edmister and Vickers 1985). Most studies have focused on characterizing the fundamental relationship between food-crushing sound and sensory assessment of crispness rather than on using the technique in routine assessment of fruit quality. Early studies led to speculation that food-crushing sounds were the primary determinant of crispness (Vickers and Bourne 1976). However, sensory panelists could determine "crispness" even when a loud masking noise was used to block biting and chewing sounds (Christensen and Vickers 1981). Thus, it is now suggested that vibratory stimuli remain the basis for crispness determination and that vibrations produced during biting and chewing produce both auditory and within-mouth tactile sensations (Vickers 1988). Food-crushing sounds continue to be important in sensory evaluation. Indeed, the sensory attribute "crispness" is often defined by the amount and pitch of sound generated during biting and chewing (Guinard and Mazzucchelli 1996; see also Table 2.1). I. Sensory Evaluations

Sensory assessment can be broadly classified under two main headings: hedonic and analytical. Hedonic testing is undertaken by untrained

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consumers who give their opinion of how much they like or dislike a product. Consumer panels indicate preference or acceptability of a product by current or potential users of that product. Analytical testing is undertaken by individuals who have been trained to identify, describe, and measure the sensory attributes or characteristics of a product. Analytical panels provide an objective sensory assessment of a product's sensory attributes. Methods used by analytical sensory panels can be either difference tests (overall or attribute difference) or descriptive analysis techniques. Difference tests, such as paired comparison, triangular, and ranking, determine if a statistically significant difference exists between products. Difference tests do not provide information about the direction or magnitude of the difference. Sensory panelists can only be asked to detect differences for one sensory attribute at a time. The use of difference tests, therefore, requires large amounts of product and numerous testing sessions. Paired comparison tests were used by Richardson (1986) and Paoletti et al. (1993) on apple and by O'Mahony et al. (1985) on cherry. Paired comparison tests for seven sensory attributes, including juiciness, firmness, sliminess, and grittiness were also used by Geeson et al. (1991) to detect differences between modified-atmosphere- and air-stored pears. Descriptive analysis techniques involve the detection and description of both the qualitative (sensory attributes) and quantitative (intensity) sensory properties of a product (Meilgaard et al. 1991). The most complete sensory technique for assessment of texture is the General Foods Texture Profile initially developed by Brandt et al. (1963) and Szczesniak (1963). This approach recognizes that texture reflects the physical structure of a product and that sensory perception of texture is a dynamic process. The method classifies texture into three textural characteristics (Szcesniak 1963): mechanical (how the food reacts to stress), geometrical (the arrangement of the constituents of a food), and other (which refers mainly to moisture and fat content). Specific sensory attributes are assessed following an order of appearance: initial (first few bites), during chewing, and residual (changes made during mastication) (Brandt et al. 1963). Sensory panels consisting of six or more individuals undergo training in the precise use of the sensory terms and of the scale used to measure the attributes (Szczesniak 1963). Szczesniak et al. (1963) developed standard rating scales for specific attributes so that "the degree of intensity of a given textural parameter in an unknown product could be illustrated in terms of a known product." Details for training a texture profile panel were published by Civille and Szczesniak (1973), followed by modifications and examples of applications (Civille and Liska 1975). Although time is required to train the panel to an ade-

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quate level of agreement and reproducibility, descriptive profiling techniques require less product and several measurements can be made on the same sample. Most researchers use a modified form of the texture profile technique. Paoletti et al. (1993) used defined sensory attributes of firmness, fracturability, cohesiveness, juiciness, and mealiness to assess the texture of apple cultivars and examined the relationships between the sensory properties and the mechanical properties of fruit. Hardness, fracturability, and juiciness were included as part of a full sensory descriptive analysis (aroma, flavor, and texture) examining maturity, ripening, and storage effects on peach (Lyon et al. 1993). Abbott et al. (1984) examined apple texture using data from modifi~d Instron texture profile analysis and the sensory attributes crisp, hard, tough, mealy, spongy, and juicy. Watada et al. (1980) developed a profile to describe and measure the intensities of flavor and texture attributes in several varieties of apple. The intensities were plotted on a circular graph and the patterns of the plots were found to differ among cultivars and with successive harvests and storage. Texture profile techniques were used by Diehl and Hamann (1979) to assess the texture of raw potato, melon, and apple in order to examine the relationships between sensory texture and the mechanical properties measured by uniaxial compression and torsion. These authors also presented the sensory definitions for each product that were used by the panel. The definition of sensory texture attributes and an indication of foods used as reference standards during panel training is critical to the interpretation of sensory data. Many studies fail to provide information on how the panelists use a particular word or term. In these cases, the specific meaning of the terms as used by that panel would be unclear to the reader, and therefore the results would only relate to a specific panel and possibly the community from which the panelists were recruited. Linguistic studies on the etiology of words clearly show that the meaning of a word varies between communities and cultures and evolves across time (Barnhart 1988). There is an additional problem in the translation of the meaning of words between languages, for example the Japanese language has seven words describing different aspects of crispness (Bourne 1982). Thus, it is difficult to provide universal definitions of sensory terms, particularly since panelists need to feel comfortable when using the term or word and be familiar with its meaning. In an alternative approach, we might attempt to define textural properties according to our understanding of the physiological and neuromuscular mechanisms associated with the breakdown of food during chewing. However, our knowledge of these processes (see Section IV) is not suf-

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ficient to allow definition at this level. For example, although crispness of a food is associated with the sound generated during biting, other unknown factors are also important (Section VUH). Nevertheless, it is important to be able to describe or define the sensory term as it is used within a specific study. For this reason, many studies use working definitions developed by the panel itself, such as those described by Abbott et al. (1984). This allows other researchers to understand how the term is being used. A number of very general lexicons, or dictionaries, for sensory terms have been described (Meilgaard et al. 1991; Jowitt 1974). Only a few have focused on fruit (Williams and Carter 1977; Abbott et al. 1984; Paoletti et al. 1993; Daillant-Spinnler et al. 1996). In our laboratory, we use a lexicon for assessing fruit texture as presented in Table 2.1. This lexicon was originally based on definitions such as those provided by Jowitt (1974) and early editions of Meilgaard et al. (1991). However, over the last 15 years they have been further modified for use with fruit by the trained panelists themselves with guidance from sensory scientists. The terms are used to describe specific sensations but sometimes do not match the meaning as used by other sensory panels and/or postharvest fruit physiologists. Specific examples where our working definitions differ from other published definitions include crispness, crunchiness, hardness, and mealiness. Crispness of a food is sensed within the mouth via neuromuscular mechanisms associated with the jaw closure (Section IVA) and by auditory cues (Section VIIH). Jowitt (1974) defined crispness as "possessing the textural property manifested by a tendency when subjected to an external force to yield suddenly with a characteristic sound." He defined crunchiness as having the characteristics of brittleness and crumbliness; that is, "possessing the textural property manifested by a tendency to crack, fracture or shatter without substantial prior deformation on the application of force," and "possessing the textural property manifested by a tendency to break down easily into small, irregular particles," respectively. However, we find that the most convenient definitions for training panelists to identify crispness and crunchiness are based on the amount of noise generated during the first bite and during chewing with molars, respectively (Table 2.1). Other authors differentiate between crisp and crunchy on the basis that crunchy foods have lower frequencies of sound than crisp foods, or on the degree of jaggedness of forcedeformation curves (Guinard and Mazzucchelli 1996). Hard and firm are often used interchangeably. Jowitt (1974) prefers to use firm, which he defines as "possessing the textural property manifested by a high resistance to deformation of an applied force." Generally, sensory definitions of firmness/hardness specifically focus on the

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forces used during biting or chewing; for example, "force required to compress a substance between molar teeth (in the case of solids)" (Civille and Szczesniak 1973) and "force required to bring the teeth together" (Diehl and Hamman 1979). The term mealy is frequently used to describe texture of fruit (see Section IX). Jowitt (1974) defines mealy as "possessing the textural property manifested by the presence of components of different degrees of firmness or toughness." However, most of the research in this area has been on potatoes and the term flouriness is often used (Lapsley 1989). Our working definition of mealy (Table 2.1) was modified from that proposed by Jowitt (1974) so that it provided a more detailed description of particle size. Postharvest physiologists often use the terms mealy or woolly to describe the texture that occurs as a result of chilling injury in stonefruit and overmaturity in apple (see Section IX). In these studies, however, the lack of juiciness is often emphasized to a greater extent than the size and shape of the food particles (Lill and van der MespeI1988). In mealy fruits, cell-to-cell adhesion is low (Harker and Hallett 1992; Harker and Sutherland 1993), suggesting that the dryish mouth-feel during chewing may be associated with the dry surfaces of intact cells (see Section lIIB). Thus, the extreme symptoms of these disorders are probably more closely described by the term flo uriness than the term mealiness. Indeed, in a study by Lill (1985), sensory panelists described the texture that developed in chill-injured nectarines as being dry and floury. The working definition for flouriness presented in Table 2.1 is based on the Jowitt's (1974) definition of powdery. Members of our sensory panels tend use the word floury rather than powdery to describe these textural properties. Jowitt (1974) concisely indicated the problems associated with the definitions of sensory texture attributes by quoting a sentence from Lewis Carroll's novel Alice Through the Looking Glass: " 'When I use a word,' Humpty Dumpty said in a rather scornful tone, 'it means just what I choose it to mean-neither more nor less.' " Sensory research by necessity is based on a language relevant and familiar to consumers, and thus definitions are subject to changes in word usage. There is continuing need to develop, in collaboration with both sensory scientists and linguists, sensory terms that specifically describe fruit texture.

J.

Electrical Impedance

We include electrical impedance as a method of measuring texture since it can detect changes in the cell wall as fruit soften. More generally, electrical impedance spectroscopy is used to determine the resistance

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(inverse of conductivity) of various intracellular and extracellular compartments within plant tissues (Zhang and Willison 1991). At low frequencies of alternating current (e.g., 50 Hz), the resistance of the extracellular pathway is measured (Cole 1972). Changes in tissue resistance at 50 Hz are thus related to physiological changes associated with texture changes, including degradation and hydration of the cell wall and increases in the concentration of mobile ions in the apoplast. In nectarines, resistance at 50 Hz was closely related to changes in flesh firmness (puncture test) and apparent juice content (Harker and Maindonald 1994). Measurements of impedance or conductance at low frequency have been used in studies of woolliness in stonefruit (Furmanski and Buescher 1979; von Mollendorff et al. 1992; Harker and Dunlop 1994; Harker and Maindonald 1994). K. Nondestructive Measurement

There are considerable advantages in having a nondestructive measurement of fruit firmness both for industry and research. An alluring possibility for industry is that soft fruit or potentially soft fruit can be excluded by nondestructive testing as part of the grading and packing process. Enthusiasm for such possibilities should be moderated by the observation that for many fruit, firmness at harvest is not related to firmness after storage (Hopkirk et al. 1992). However, segregation of fruit according to firmness has great possibilities for fruit that undergo minimal softening during storage and ripening (e.g., apple) and for firmness grading during packing or repacking ripe, soft fruit. Thus, it is not surprising that a number of research programs are involved with evaluation and comparison of these nondestructive devices (Pitts et al. 1993, Hopkirk et al. 1994a). As Jackman and Stanley (1995a) point out, however, any test that involves a measurable compression, tension, or shear cannot be regarded as totally nondestructive. Yet, from an industry perspective, the requirement for absolutely no damage is becoming increasingly inappropriate. A more realistic aim is to avoid any detectable damage to fruit of salable quality, but to accept damage to those grades of fruit that are unsalable (Sanger 1994). Generally, nondestructive devices for measuring fruit texture can be divided into categories based on properties they measure. These categories are elastic modulus, energy transfer, and nuclear magnetic resonance (Pitts et al. 1994). Given that the technologies for nondestructive quality evaluation of fruit and vegetables are reviewed in detail in this volume (see Chapter 1), we will only briefly summarize the types of devices in each of these categories.

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1. Elastic Modulus. This property relates to the elastic properties of a material and is the ratio of stress to strain (Mohsenin 1970). For example, a high value would occur in a material such as steel and a low value would occur in a material such as rubber. In fruit, the elastic modulus can be measured nondestructively by deformation under a compressive load, modal response (both frequency and amplitude), and the velocity and attenuation of sonic waves (Pitts et al. 1994). A number instruments have been developed which use small forces or small deformations to nondestructively measure fruit firmness (reviewed by Abbott et al. 1997). Among these, an exciting advance is the development of a noncontact firmness detector which uses a short puff of air to deform a localized area of the fruit surface and measures the deformation using a laser (Fan et al. 1994). When discussing the relevance of small deformations on predicting firmness, it is important to realize that these instruments may primarily assess mechanical properties of the skin and a thin layer of underlying cells. Cells in these outer layers of the fruit are smaller, more regular in shape, and more closely packed than cells located deeper in the fruit flesh (see Section IlIA). There has been little consideration of how the mechanical properties of cells located in these two tissue zones differ. However, when peeling a soft peach or kiwifruit, the skin and underlying cells easily separate from the fruit, an observation that emphasizes the morphological differences between skin and flesh tissues. Methods that might be expected to measure the elastic properties of the whole fruit, rather than the outer (skin) tissues, generally involve measurement of resonance or vibrational properties. These methods are discussed by Abbott et al. (1997) in the section on sonic vibrations. The studies of Abbott and coworkers provide a good example of the methodology (Abbott et al. 1992; Abbott 1994). They use an electromagnetic vibrator to impart vibrational energy to one side of the fruit at frequencies between 0 and 2000 Hz, and an accelerometer is used to detect vibrations at the other side of the fruit. The output data can be presented in plots of amplitude against frequency which generally indicate a number of resonant modes that occur at different frequencies and that exhibit different amplitudes (Abbott et al. 1992; Abbott 1994). Stiffness coefficients can be calculated as f2 m or f2 m 2/3, where fis the frequency of the second or third resonance peak and m is fruit mass. Another method assesses the natural frequency of vibration of fruit (Armstrong et al. 1990). Vibrations within a fruit are induced by lightly tapping the surface with a hammer, and are detected with a microphone on the opposite side of the fruit, placed approximately 10 mm away from the fruit surface (Armstrong et al. 1990). The natural frequencies of the fruit are

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determined via fast Fourier transformation of the output signal (Armstrong et al. 1990). 2. Energy Transfer. Another method of nondestructively sensing the firmness of fruit is to determine the way in which energy is transferred between a fruit and another object. For example, if a fruit is dropped onto a table, the height to which it bounces depends on how energy is transferred between the fruit and the table. A hard fruit will bounce, while a soft fruit will absorb the energy and not bounce. It is, however, important to ensure that the drop is less than the height at which bruising occurs. The theory of firmness measurement based on impact forces is discussed by Delwiche (1987). Based on these principles, a number of instruments have been developed and tested (reviewed by Abbott et al. 1997).

3. Magnetic Resonance Imaging (MRI). Magnetic resonance imaging is a noninvasive, nondestructive technology which examines the distribution and mobility of protons in water molecules and other concentrated metabolites in biological tissues (Callaghan 1991; Abbott et al. 1997; Faust et al. 1997). MRI has been used as a research tool to study a number physiological disorders including watercore in apple (Wang et al. 1988) and chilling injury in persimmon (Clark and Forbes 1994). In addition, single sensors can be used to measure magnetic resonance parameters of individual fruit or areas of fruit, that is, without providing three-dimensional information (Abbott et al. 1997). This approach has been used in attempts to develop nondestructive techniques capable of being used on fruit-grading machinery. Detailed information on MRI and fruit quality are covered in other reviews in this volume (Abbott et al. 1997; Faust et al. 1997). From a texture perspective, MRI can provide a powerful tool for examining the physiological basis of texture disorders that result in a dry mouth-feel such as those discussed in Section IX. For example, Sonego and coworkers (1995) used MRI to examine woolly breakdown in nectarines and concluded that the dryish texture associated with woolliness occurred without any noticeable modification of water status. It is occasionally suggested that single magnetic resonance sensors can be used as an aid to sensing of fruit firmness (Pitts et al. 1994). A number of examples of studies showing correlations between firmness and magnetic resonance parameters are discussed by Pitts et al. (1994) and Abbott et al. (1997). In many of these examples, it is likely that magnetic resonance is detecting fruit ripening, perhaps by characterizing changes in proton mobility associated with partitioning of water into intracellu-

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lar and extracellular compartments and!or chemical changes within the fruit. For example, MRI is likely to detect the increase in soluble solids concentration that occurs as a result of starch hydrolysis and concurrent with fruit softening. In these situations, the magnetic resonance parameters are not directly measuring firmness; instead, they are providing a measurement that may correlate with changes in firmness. 1. Relationship Between Instrumental and Sensory

Measurements of Texture It is often suggested that the validity of instrumental texture measure-

ments should be based on how well they predict sensory texture attributes (Voisey 1971). Szczesniak (1987) lists four reasons for seeking correlations between instrumental and sensory measurements: (1) the need for quality control instruments; (2) the desire to predict consumer response; (3) the desire to understand what is being perceived in sensory assessment; (4) the need to develop improved!optimized instrumental test methods and, ultimately, to construct a texture-testing apparatus that will duplicate the sensory evaluation. These objectives drive much of the research on fruit texture. A number of issues should be considered when correlating instrumental and sensory data: the appropriateness of the comparison (Le., the instruments and sensory assessments should measure attributes that might sensibly be expected to be related), the range and diversity oftexture encompassed within the study, and selection of sensory terms and scales (Szczesniak 1987). There is an understandable urge to assess the usefulness of an instrumental method based on correlations presented in studies such as those described here. However, it is important to consider both the range of textures examined within the study as well as the level of correlation. On this issue, it is interesting to note that most studies examining texture of apple have focused on commercial cultivars which mostly produce fruit of acceptable texture. The high level of inbreeding in commercial apple cultivars (Noiton and Shelbourne 1992) is likely to have reduced the range of possible textures. Another consideration is the expected theoretical relationship between an instrumental and a sensory measurement. Psychophysical relationships between stimuli and the resulting sensory sensation are generally described by logarithmic and!or power relationships according to Fechner's and Stevens' laws (Meilgaard et al. 1991). Peleg (1980) provided a theoretical analysis of the relationship between mechanical hardness and its sensory assessment. In a study examining a wide range of soft to firm textures in fruit, Harker et al. (1997) found that the rela-

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tionship between instrumental and sensory measurements was logarithmic or power, rather than linear. According to Fechner's and Stevens' laws, one might expect that sensory responses are more sensitive than instrumental measurements when examining soft fruit, but less sensitive than instrumental measurements when examining hard fruit (Harker et al. 1997). Given that the majority of fruit are eaten when soft, developers of instrumental texture measurements are facing a difficult task in attempting to produce a test which duplicates sensory responses. As a general principle, instrumental tests that subject tissue to small strains provide relatively poor correlations with mechanical properties associated with tissue failure or with sensory evaluation (Mohsenin and Mittal 1977). A number of studies have examined the relationship between instrumental and sensory measurements of fruit texture, with most concentrating on apples (Finney 1971; Bowman et al. 1972; Blanpied and Blak 1977; Hard et al. 1977; Diehl and Hamann 1979; Abbott et al. 1984; Richardson 1986, Paoletti et al. 1993). Other fruit that have been subjected to sensory and instrumental comparison include melons (Dinus and Mackey 1974; Hard et al. 1977; Diehl and Hamann 1979) and kiwifruit (Stec et al. 1989). For apple, most studies have demonstrated reasonable relationships between sensory texture attributes, such as firmness and crispness, and instrumental texture measurements including puncture tests (Finney 1971; Hard et al. 1977; Abbott et al. 1984), texture profile analysis (Brennan et al. 1970), Kramer shear (Brennan et al. 1970; Bowman et al. 1972; Hard et al. 1977), disk compression (Abbott et al. 1984), vibration (Finney 1971), and tension (Holt and Schoorl 1985). However, some studies have shown poor relationships such as those using puncture tests (Blanpied and Blak 1977) and disk compression (Paoletti et al. 1993). In a detailed study, Abbott et al. (1984) examined the relationship between penetrometer, disk compression, and sensory assessments of texture in five apple cultivars harvested at different dates and assessed after different storage and ripening periods. Principle component analysis indicated that the "structural strength" of tissue accounted for most of the variability in the data (both mechanical and sensory attributes). The second component varied between cultivar and year but was generally related to deformation (e.g., deformation at breakpoint, deformation at failure). The relationship between sensory and instrumental assessments was greatly influenced by the cultivar. For example, R2 values obtained during stepwise multiple regressions of the sensory attribute "crispness" on penetrometer measurements were 0.86 for 'Rome' apple and 0.33 for 'York Imperial'. The force-distance curves obtained during disk compression experiments were characterized by a

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number of different but distinct force, slope, and work measurements. Disk compression provided values that were more closely related to sensory attributes, although which force-distance attribute was used varied between cultivars. When stepwise multiple regression was used, the R2 values often improved further. The highest R 2 value was 0.94. On the basis of these studies, it was recommended that multivariable mechanical measurements be used instead of penetrometers to measure texture (Abbott et al. 1984). Paoletti et al. (1993) compared mechanical and sensory properties of six selected apple cultivars purchased from a local supermarket using a similar experimental design to that of Abbott and coworkers (1984). Their results agreed with those of Abbott et al. (1984) in that principle component analysis accounted for 85% of the total variance and identified tissue strength and tissue deformation as the two main components. However, the sensory panels experienced difficulties in discriminating between cultivars. In most cases, sensory differences were detected by pairwise ranking and not by texture profile analysis. While significant correlations were found between most sensory and mechanical assessments of texture, the nonhomogeneous distribution of values caused some problems in relating sensory and mechanical assessments. While studies such as that of Abbott et al. (1984) can demonstrate agreement between instrumental and sensory measurements, variation between seasons and over the storage period can cause problems when firmness regulations are considered for classifying fruit as ripe or overripe according to regulatory standards (Blanpied and Blak 1977). A number of recent studies have compared USDA-AMS grade standards as interpreted by experienced inspectors and instrumental measurements of fruit firmness (Abbott et al. 1992; Abbott 1994). The studies of Abbott and coworkers (Abbott et al. 1992; Abbott 1994) are of critical importance to fruit industries since they demonstrate that instrumental measurements can be used to grade fruit. However, it should be pointed out that grade standards do not strictly fulfill the requirements for a sensory scale because they use a composite scale where undefined terms change along a single scale (e.g., immature, hard, overripe) rather than intensity scales describing a single attribute (e.g., firmness: absent to extreme), judgments are based on integrated information from multiple attributes (visual, flavor, texture), and the weight placed on an individual attribute often varies from person to person (Sidel et al. 1983). Abbott (1994) found relatively high correlations between apple grade and puncture tests (r> 0.84), apple grade and disk compression (r> 0.77), and apple grade and sonic transmission properties (r> 0.47). However, correlations were lower when calculated on individual fruit rather than cal-

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culated for groups of fruit. For instance, correlations of grade against maximum force during puncture tests gave r = 0.84 on an individual fruit basis, but r 0.99 forgroups of fruit (Abbott 1994). From a practical perspective, these results indicate that while instrumental tests are poorer at predicting the grade of an individual fruit (as would be required for nondestructive testing during fruit packing), they may be useful when a subsample of fruit is used to determine if a consignment of fruit fulfills a grade standard. It is, however, important to note that instrumental measurements are for the most part unable to differentiate between mealy and nonmealy apples (Harker and Hallett 1992). This insensitivity to detection of mealiness might be expected to reduce the usefulness of instrumental methods in commercial grading of fruit and prediction of consumer responses. Stec et al. (1989) examined the sensory qualities of ripe kiwifruit from three firmness categories (0.4 to 0.6 N, 0.6 to 0.8 N, and 0.8 to 1.0 N). This study is one of the few which consider the relationships between firmness (measured by puncture) and sensory attributes of a fruit that softens markedly during ripening. They demonstrated a number of important issues relating to texture measurements: Preferred firmness at eating-ripeness can vary among assessors; small changes in fruit firmness can have a considerable impact on sensory texture attributes (sensory descriptors soft/mushy were associated with soft fruit, while crunchy, rubbery, and stringy were associated with firm fruit); fruit softening is often associated with other ripening-related changes in sensory attributes (flavor, acidity, sweetness) (Stec et al. 1989). In honeydew melons, shear stress at failure in torsion and compression were positively correlated with sensory assessment of both firmness and denseness and negatively correlated with moisture release and fibers (Diehl and Hamann 1979). The sensory perception of fibers became more pronounced as the strength of parenchyma tissue became weaker relative to vascular bundles. In muskmelon, the possible influence of starch and cell wall polysaccharides on the sensory texture attributes has been examined (Dinus and Mackey 1974; Hard et al. 1977). These studies indicate that protopectin, starch, and, to a smaller extent, cellulose are the main constituents that correlate with a range of sensory texture attributes. Juiciness is an important texture attribute. Although there have been no formal attempts to determine correlations, a relationship between instrumental and sensory assessments of juiciness has been described (Szczesniak and Ilker 1988; Harker et al. 1997). Plots of cell size, fluid release during mechanical chewing, apparent juice content, and absorbed juice against "sensory juiciness score" were found to be closely related (Szczesniak and Ilker 1988; Harker et al. 1997).

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VIII. FACTORS TIIAT INFLUENCE TEXTURE A. Genetics

The textural characteristics and softening behavior of fruits are ultimately genetically controlled. Although environmental factors often modify the extent that these textural characteristics are expressed, this environmental influence is relatively minor when viewed in relation to the genetic basis of texture. Natural selection plays an important role in interactions between herbivores and plants. The effect of these interactions on plant texture was reviewed by Vincent (1991). Fruit are naturally adapted to be eaten or to become susceptible to decay as part of the seed dispersal mechanism. Breeders have continued to select for improved fruit quality based on human perceptions (Janick 1991). They have been relatively successful in programs aiming to remove fibrous elements from edible tissues, as in green beans and celery (Reeve 1970) and grittiness from pear (Bell and Janick 1990). Different selections and cultivars from the same species often vary considerably in absolute firmness, rate of softening, and overall texture. Examples include peach and cherry (Kader et al. 1982; Brown and Bourne 1988), apple (Smith and Stow 1985), banana (Smith et al. 1989), papaya (Zhang and Paull 1990), and kiwifruit (Cotter et al. 1991). However, the level of interbreeding in some fruit, for example, apple (Noiton and Shelbourne 1992), might be expected to reduce the diversity occurring for any particular quality attribute. Many texture attributes are under tight genetic control. In pear, grit content has a relatively high heritability (Bell and Janick 1990) and is thought to be determined by a minimUID of four gene pairs with additive action (Thompson et al. 1974); juiciness is a dominant characteristic over dryness (Zielinski et al. 1965); and overall tissue texture is moderately heritable, also being considerably affected by environmental factors (Bell and Janick 1990). A great range of textures occur in progeny from crosses between European pears (Pyrus communis), which have a soft melting texture when ripe, and Asian pears (Pyrus pyrifolia), which have a crisp texture (White and Selby 1994). Peach exhibits two distinct texture categories: the melting flesh associated with freestone types and the firm "rubbery" flesh associated with clingstone types (Janick 1991). Freestone peaches are important to the fresh fruit industry, while clingstone peaches tend to be used in canning and processing. The gene for melting flesh is dominant over nonmelting, and the gene for freestone is dominant over clingstone (Beckman and Sherman 1996). The genes controlling texture and pit-to-flesh adhesion were thought to be linked (Baily and French 1932). However, recent

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crosses by Beckman and Sherman (1996) have produced nonmelting semifreestone peaches. Although more detailed segregation data is required to confirm the genetic basis of the nonmelting semifreestone characteristic, the presence of these progeny suggest that the proposed linkage between nonmelting and clingstone traits may have been broken (Beckman and Sherman 1996). A nonripening "hard stony" peach has been identified that does not markedly soften unless exposed to ethylene (Yoshida 1976). In strawberry, fruit firmness is controlled by additive genetic effects (Shaw et al. 1987), and firmness of offspring can be predicted from average parent firmness. In crosses between highbush and lowbush blueberry, Finn and Luby (1992) found that parents with large amounts of lowbush ancestry tended to produce offspring with softer fruit than parents with larger amounts of highbush ancestry. However, parental phenotype did not reliably predict progeny texture. The existence of mutant fruit lines such as "ripening inhibited" (rin) and "nonripening" (nor) tomato has provided a powerful tool in the study of fruit softening. Their use in breeding programs has provided lines of fruit which maintain their firmness and have a longer shelf life than wild-type and other commercial tomato cultivars (see Richardson and Hobson 1987). The development of transgenic lines of fruit has allowed the genetic basis of texture and texture change to be examined. The results with tomato (Giovannoni et al. 1989; Smith et al. 1990) challenged the early dogma that polygalacturonase activity was responsible for fruit softening. This was based on the findings that tomatoes containing the antisense polygalacturase gene softened at the same rate as untransformed plants according to measurements using whole-fruit compression (Schuch et al. 1991). However, the storage life of the transgenic fruit and their ability to be transported without damage was improved since there was a reduction in the proportion of fruit that cracked and/or became infected with rot. B. Environment

Environmental factors including climate, tree and orchard management, and nutrition can have a profound influence on fruit quality (Sharples 1973). Many studies have described how fruit firmness can vary between seasons, between orchards, and between regions in pear (Luton and Holland 1986) and in apple (Knee and Smith 1989; Watkins et al. 1993). The extent that photosynthates (the products of photosynthesis) are transported into fruit will markedly influence fruit-quality attributes such as tissue strength and overall texture. Positive correlations between

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dry matter content, which largely reflects the import of photosynthates, and fruit firmness are common for apples (Johnson et al. 1980). The initial stages of establishing fruit on the tree can influence texture greatly. Johnson (1992) found that removal of all but the axillary flowers or fruitlets within 37 days after full bloom increased firmness of 'Cox's Orange Pippin' apple in two out of three years. This effect of thinning was apparent even though the fruit were larger, less dense, and lower in calcium (i.e., factors that might have been expected to result in reduced firmness). In a more recent paper (Johnson 1994), fruit were thinned at five-day intervals. This allowed the critical period in which thinning influenced texture to be identified as being between 5 and 15 days after full bloom. These results suggest that when the rate of photosynthate import into fruitlets is enhanced during early stages of development, the influence is maintained throughout fruit growth, often resulting in firmer fruit at harvest. Apples harvested from inner-shaded zones within the tree canopy are generally softer than apples harvested from outer zones (Blanpied et al. 1978). Similarly, cherries grown under artificial shading were softer than cherries grown in natural light (Patten and Proebsting 1986). Rootstock can also influence texture properties of fruit. Apples harvested from trees with 'M9' rootstock were markedly softer than those from trees with 'MM106' rootstock (Smith and Stow 1985). While the influence of rootstock on firmness may reflect an expectation that fruit from trees on 'MM106' generally mature later in the season, Smith and Stow (1985) speculate that this may be too simple an explanation given the rootstock x clone interactions they observed. Autio (1991) examined the effect of rootstock on 'Delicious' apples. He concluded that the main effects of rootstock on storability were related to its effects on maturity and calcium levels. There was a consistent effect of rootstock on fruit size, and fruit size was covaried with flesh firmness and calcium. Clearly, there are many possible mechanisms through which rootstock can influence firmness, including maturity, mineral content, crop load, and fruit size. The growth habit of trees can also influence fruit quality. Meheriuk and Lane (1983) found that apple trees with different spurbearing habits (standard 'McIntosh' and spur types 'Dewar,' 'MacSpur,' 'Morspur,' and 'Starkspur') produced fruit which differed in firmness at harvest and rate of softening during controlled-atmosphere storage. Compounds that alter partitioning of photosynthates into different cell compartments/structures during fruit development might be expected to influence texture. One such example is the increase in firmness of cherry following preharvest foliar application of gibberellic acid (Facteau 1982). The effect of gibberellic acid was attributed to increases in the alcohol insoluble solids (approximates cell wall fraction of tissue), higher or no

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change in pectinase-soluble pectins, and decreases in water-soluble pectins (Facteau 1982). Postbloom foliar applications of paclobutrazol (thought to reduce endogenous gibberellins) have increased flesh firmness of apples at harvest, although the effect was not apparent in fruit evaluated after storage (Greene 1986). While it is sometimes suggested that there has been a deterioration in textural properties of fruit associated with development of more intensive growing systems within the orchard, it is difficult to find evidence for this. Horscroft (1989), however, found evidence for this when examining firmness data collected on 'Cox's Orange Pippin' apples grown commercially in England. Over a period between 1966 and 1984, the firmness of apples after storage had declined by more than 10 N. Interpretation of data, such as that examined by Horscroft, is problematic since fruit size, methods for measuring firmness, storage technologies, and the length of the storage period can also change as a fruit industry develops and becomes more sophisticated. Changes in these postharvest handling factors can have as much of an impact on firmness as the change to more intensive production systems. However, in the data presented by Horscroft, the decline in firmness occurred despite considerable improvements in the controlled-atmosphere regimes used for storing apples.

C. Light Irradiation Exposure of fruit to intense sunlight (electromagnetic radiation) often results in damage known as sunscald or sunburn (Lurie et al. 1991). It is often difficult to separate this damage from that associated with elevated temperature. For example, at 30°C air temperatures, apple flesh 5 to 10 mm below the skin can reach temperatures of up to 43°C (1. B. Ferguson, pers. comm.). The combination of exposure to intense sunlight and elevated flesh temperatures is expected to influence fruit texture. Sunscald is associated with loss of fruit quality in 'Braeburn' apples (Watkins et al. 1993). Adegoroye et al. (1989) artificially induced sunscald in tomato using electromagnetic radiation. They found that regions of the fruit which had been sunscalded exhibited a shallower forcedeformation curve and had a higher tissue compliance (deformation/ bioyield force) than undamaged regions.

D. Minerals (Other than Calcium) Most studies examining the relationship between fruit mineral content and texture have focused on apple. Application of high nitrogen fertil-

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izer treatments, especially in combination with irrigation and orchard floor management regimes which reduce competition from grass, can markedly reduce firmness of apple as assessed by puncture tests (Blanpied et al. 1978; Hipps and Perring 1989; Neilsen et al. 1984) and sensory evaluation (Richardson 1986). This is possibly explained by the soft apples having fewer but larger cells (Letham 1961) and a correspondingly lower alcohol insoluble solids content (approximates cell wall and starch content; Richardson 1986). In the studies just described, the effect of nitrogen is emphasized when the trees receive an adequate supply of water. The importance of tree water status on apple texture was further demonstrated in studies by Lidster and Webster (1983). Preharvest dipping of leaves in antitranspirants and spraying fruit with surfactants improved fruit firmness both at harvest and during storage. Improved firmness was presumed to be due to either decreased fruit mass (antitranspirant) or smaller fruit mass and increased dry matter content. The relationship between N and texture has been studied in peach (Reeve 1970) and kiwifruit (Prasad et al. 1988; Prasad and Spiers 1991). Peaches grown under high N conditions had smaller-sized cells and firmer texture than those grown under low N conditions (Reeve 1970). Kiwifruit with high N soften more rapidly than fruit with low N during storage at 0-1°C (Prasad et al. 1988; Prasad and Spiers 1991). However, Smith et al. (1988) suggest that nutritional disorders have a relatively minor effect on the postharvest storage characteristics of kiwifruit. Positive correlations between P and flesh strength have been often found in apple (Sharples 1980; Johnson et al. 1987). However, Johnson et al. (1987) found that regressions of fruit P on firmness of fruit stored in controlled atmospheres (1.25% and 2% O 2 ) varied between seasons, precluding the establishment ofP thresholds. Sharples (1980) suggests low P increases the vulnerability of low-calcium apples to mealy breakdown. The occurrence of fruit which are unexpectedly soft or have poor overall texture can be a problem for fruit industries. When such problems occur, the possibility that mineral imbalances or deficiencies are involved should be considered. However, the importance of soil type, cultivar, tree growth habit, and other environmental factors as modulators of nutrient-texture interactions should not be forgotten. For example, while N has been found to have a significant influence on firmness of 'Cox's Orange Pippin' apples grown in the United Kingdom (Richardson 1986; Hipps and Perring 1989), similar studies on 'Fuji' apples grown in New Zealand did not find an effect of N on firmness (R. K. Volz, pers comm.).

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E. Calcium

Calcium has a well-established role in strengthening the cell wall (Demarty et al. 1984). The influence of calcium on diverse physiological and biochemical changes during fruit softening has been reviewed (Poovaiah et al. 1988). Postharvest treatments involving dipping or infiltrating with calcium are known to maintain firmness during storage of a wide range of fruit, including apple (Scott and Wills 1977; Sams et al. 1993) and kiwifruit (Hopkirk et al. 1990). In apple, the force-distance curves obtained during puncture testing of calcium-infiltrated apples had a steeper initial slope than control fruit, suggesting that tissue rigidity was greater following infiltration with calcium (Sams et al. 1993). Abbott et al. (1989) examined texture of apples infiltrated with calcium using uniaxial compression (single cycle of compression and relaxation) of plugs of apple tissue and puncture tests. They found that there was often a sigmoidal relationship between concentration of calcium in the infiltration solution and increase in tissue strength (as assessed by a number of characteristics of force-distance curves); that the effect of storage on texture was relatively small compared to the effect of calcium infiltration; and that the patterns of texture change during storage of apples infiltrated with calcium were complex and differed markedly to texture changes observed in control apples (infiltrated with water). Stow (1989) found that vacuum infiltrating Ca 2 + into tissue plugs from airstored apples increased tensile strength from 28 to 85% of that of controlled-atmosphere stored fruit. The specificity of this effect was examined by infiltration with cations of similar size. Conway and Sams (1987) found that Ca 2 + was more effective than M g2+ or Sr 2 +. However, Stow (1989) found that Sr 2 + and Ba 2 + were as effective as Ca 2 +, while M g2+, Sm 3 +, La 3 +, and Ce 3 + were less effective (Stow 1989). Examination of fracture surfaces following tensile testing of apple cortex indicated that tissue failure from calcium-treated and control apples was due to cell rupture and cell debonding, respectively (Glenn and Poovaiah 1990). This difference in the mechanism of tissue failure can be attributed to inhibited solubilization of polyuronide and arabinose moieties and reduced galactose loss during storage (Glenn and Poovaiah 1990). While most evidence suggests that calcium influences texture through its interactions with cell wall polysaccharides, the possibility that it may affect texture through interactions with membranes (Legge et al. 1982) cannot be discounted. Indeed, Glenn et al. (1988) found that postharvest infiltration of calcium into apples resulted in distinct and specific changes in polypeptide and phosphoprotein patterns and reduced membrane permeability, as well as maintenance of cell wall structure.

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F. Fruit Size Large fruit are generally softer than small fruit. This is the case in strawberry (Ourecky and Bourne 1968), blueberry (Ballinger et al. 1973), raspberry (Robbins and Moore 1990), kiwifruit (Hopkirk et al. 1990), and apple (Blanpied et al. 1978). However, as Johnson (1992) pointed out, focusing on fruit size alone may be an oversimplification since fruit size is related to both cell number and cell size. The relationship between fruit size and firmness might be expected to be strongest among fruit sampled from a single tree or a population of trees of consistent genotype grown in similar environment (within an orchard). In this situation, a large fruit will generally be composed of a population of larger cells, and the number of cells per unit volume will be lower than in a small fruit. Thus, the amount of cell wall and the number of cell-to-cell contacts within a fixed volume of tissue will be lower. This would markedly reduced the strength of large fruit relative to small fruit.

G. Maturity and Ripening Generally, fruits soften with advancing harvest date as a result of increasing maturity and ripeness. Examples of this include peach (Kader et al. 1982), pear (Stow 1988), apple (Lau 1985; Knee and Smith 1989), and kiwifruit (MacRae et al. 1989). In apple, much of the variation in firmness between fruit harvested on different harvest dates may be reduced during storage. However, correlations between firmness at harvest and firmness after storage have been established for some fruit, such as apple (Lau 1985; Knee and Smith 1989), but not others, such as pear (Stow 1988), tomato (Ahrens and Huber 1990), and kiwifruit (Hopkirk et al. 1992). Existence of relationships between firmness at harvest and after storage depend on whether fruit soften only slightly (apple) or soften markedly (kiwifruit, tomato, and mango) during ripening. Maturity at harvest can often also influence subsequent softening behavior of fruit such as kiwifruit (MacRae et al. 1989) and mango (Seymour et al. 1990). Fruit tend to soften in a distinct pattern. Firmness of peach declines longitudinally from the stem to the blossom end and laterally from the suture to the cheeks (Maness et al. 1992). In many fruit, different tissues within the flesh exhibit different firmness and may soften at different rates, for example, strawberry (Ourecky and Bourne 1968), tomato (Holt 1970), and kiwifruit (MacRae et al. 1989). When firmness of adjacent tissues is markedly different, consumers may find the texture unacceptable. In some selections from crosses between Actinidia (kiwifruit) spp., the core did not soften to the same extent as the outer and inner peri-

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carp (A. Hassell pers. comm.). This difference in tissue softening resulted in an unacceptable overall texture. Softening is not usually a linear process. For example, softening of ethylene-treated kiwifruit can be separated into two phases: a rapid phase lasting three to four days during which firmness decreased from about 90 N to about 20 N, and a slow phase during which firmness decreased to an eating ripeness of 7 N after 6 to 10 days (Lallu et al. 1989). A similar pattern of kiwifruit softening was observed during storage at O°C, although the process takes place over a period of up to 25 weeks (McDonald 1990). The rapid phase is associated with solubilization of pectic polymers (Redgwell et al. 1992) and a decline in cell-to-cell adhesion (Harker and Hallett 1994). Similar biphasic softening curves occur in many fruits such as pear (Bourne 1968) and nectarine (King et al. 1989). Preharvest sprays with compounds such as Alar (N-dimethyl amino succinamic acid) and aminoethoxyvinylglycine (AVG) can be used to control the rate of fruit maturation and ripening in apple. A number of studies have reported that fruit from trees sprayed with Alar or AVG are firmer than those from unsprayed trees (Blanpied et al. 1967; Sharples 1973; Bramlage et al. 1980; Williams 1980; Child et al. 1984). During prolonged storage, the beneficial effect of preharvest treatment by Alar (Blanpied et al. 1967) and AVG (Bramlage et al. 1980) is often lost when both control and treated fruit soften. However, other studies have shown that while AVG-treated and control apples were the same firmness at harvest, the AVG-treated apples softened at a slower rate during storage (Williams 1980; Child et al. 1984). H. Temperature There are three ways in which low temperature can influence fruit texture: a rapid and reversible physical effect of temperature on tissue firmness; a general inhibition of metabolic processes which often minimize texture change over long periods of storage; and a deterioration in texture when fruit are stored at temperatures that induce chilling injury. Many studies have demonstrated that fruit of standardized ripeness are softer at warmer temperatures. This is the case in pear (Hartman 1924), cherry (Hartman and Bullis 1929), strawberry (Rose et al. 1934; Ourecky and Bourne 1968), blueberry (Ballinger et al. 1973), peach (Werner and Frenkel 1978), and apple (Blanpied et al. 1978). A few studies have found no effect of temperature on firmness in apples (Haller 1941; Saltveit 1984) and tomato (Polderdijk et al. 1993). Saltveit (1984) speculated that failure to observe an effect of temperature on firmness in his study on apple may reflect the use of stored instead of freshly harvested fruit. Recommendations on the use

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of puncture tests usually indicate that apples should be the same temperature (Blanpied et al. 1978), often suggesting 20°C as an appropriate temperature (Smith 1985). The physical effect of temperature on fruit firmness can occur within minutes (Werner and Frenkel 1978). The change in firmness, particularly during rehardening of ripe peach, occurs with little or no change in pectic substances (Werner et al. 1978). Furthermore, solutions containing peach pectin increase in viscosity at low temperatures (Werner and Frenkel 1978). Based on these observations, Werner and Frenkel (1978) suggested that the influence of temperature may, in part, reflect the gelling behavior of pectin fractions. Low-temperature storage is the most widely used technology for preserving fruit quality. Fruit should be stored at the lowest temperature that does not cause damage. This minimum temperature varies between commodities. Recommendations for minimum temperatures and storage periods are available for a wide range of products (Hardenberg et al. 1986). Low-temperature storage inhibits a wide range of metabolic processes including those associated with fruit softening and the deterioration of various texture attributes. In some cases, however, lowtemperature storage can have a deleterious effect on texture. The effect of chilling injury on fruit texture is discussed in Section IX. I. Heat Treatments

Recent research has demonstrated that high-temperature treatment of apple can suppress softening and other ripening-related changes (Liu 1978; Porritt and Lidster 1978; Klein et al. 1990; Lurie et al. 1995). Details of force-distance curves obtained during puncture testing of heat-treated apples that had been stored for six months were presented by Sams et al. (1993). Although the slopes of force-distance curves were similar for heat-treated and non-heat-treated apples, the maximum force was higher in heat-treated fruit (Sams et al. 1993). The mechanism involved in preserving firmness is unknown. However, after 10 days of storage at 17°C, the insoluble pectin content was higher in heat-treated apples, while soluble pectin and cell wall arabinose and xylose content were lower (Klein et al. 1990).

J.

Controlled Atmospheres

Cold storage in controlled atmospheres (CA) will inhibit ripening and texture change to a greater extent than is possible in air alone. Thus, for many fruits, CA storage can be used to extend the storage period beyond that possible in air (Fidler 1973; Smock 1979; Kader et al. 1989; Kader

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1994). Considerable research effort continues to focus on optimizing CA treatments for fruit grown in specified regions in order to maximize the benefits of CA and minimize deleterious effects on high CO z and low Oz induced disorders (Northeast Regional Agr. Eng. Service 1993). The mechanisms by which elevated CO z and low Oz atmospheres influence the regulation of respiratory metabolism is still poorly understood but is the subject of increasing attention in the literature (Mathooko 1996). There are three ways in which CA can influence fruit texture: a rapid effect of CO z on tissue strength; a general inhibition of metabolic processes which often minimize texture change over long periods of storage; and a deterioration in quality and texture associated with use of injurious atmospheres. A rapid and direct influence of CO z on tissue strength has, to date, only been observed in strawberry (Plocharski 1982; Smith 1992; Smith and Skog 1992, Larsen and Watkins 1995). Similar affects of CO z have not been published for other fruits. This may indicate that strawberry has a unique interaction with CO z. Alternatively, in other fruits, the effect of CO z on firmness may have been too small to detect with the devices used. In strawberry, firmness was enhanced by storage in elevated concentrations ofCO z (Plocharski 1982; Smith 1992; Smith and Skog 1992; Larsen and Watkins 1995). The maximum firmness enhancement was generally at 15 to 20% CO z when storage temperature was DoC. It has been suggested that CO z was responsible for the induction of changes in pectic substances (Plocharski 1982) but this was not verified by Smith (1992). Studies using tensile tests in conjunction with low-temperature SEM indicate differences are associated with cell debonding (F. R. Harker and C. B. Watkins unpublished data). The more general influence of CA on fruit quality attributes and the symptoms of injury associated with inappropriate CA conditions are reviewed by Fidler (1973) and Smock (1979). We will not cover these issues in this review. However, it is important to note that storage atmospheres can have a subsequent influence on the rate of fruit softening. For example, Stow (1984) found that rate of softening of pear following removal from storage was slower after CA storage in 0.5% Oz than CA storage in 1 or 2 % Oz.

K. Prediction of Firmness

The ability to predict texture both at harvest and after removal from storage can provide a powerful tool to marketers of fruit. Although there have been a few studies aimed at predicting texture, they have all focused on firmness of apple. Fallahi et al. (1985) developed stepwise multiple regression equations to predict a range of quality parameters.

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They found that predictions of firmness were generally weak, that fruit size was negatively associated with firmness, and that fruit calcium was positively associated with firmness (see also Sections VllIF and VIllE, respectively). Knee and Farman (1989) reviewed their studies over a seven-year period on the relationships between poststorage quality, maturity, and harvest date of 'Cox's Orange Pippin.' Regressions based on harvest firmness and internal ethylene concentration (IEC) predicted poststorage firmness with rvalues up to 0.94 (15 degrees of freedom) for single years. This compared with an r value of 0.92 when regressions were based on harvest firmness and harvest date. However, when the data over a five-year period was combined, prediction of poststorage firmness was better when based on harvest firmness and harvest date than when based on harvest firmness and IEC (r = 0.64 and r = 0.60, respectively; 31 degrees of freedom). Inclusion of variables including fruit weight, diffusive resistance, and respiration did not improve prediction of poststorage firmness. Ingle and Morris (1989) examined the relationship between firmness at harvest, softening at 20°C, and softening at O°C. Using regression analysis, they found that both firmness at harvest and a combination of firmness at harvest and softening at 20°C were poor predictors of softening during storage at O°C (r 2 0.15, P < 0.04 and r 2 = 0.15, P < 0.12, respectively). However, for individual years, correlations between harvest firmness and firmness change during storage at O°C were relatively high, varying between r = 0.80 and r = 0.95. Ingle and Morris (1989) suggested that at least prediction may indicate the likelihood of major texture problems. Prediction of texture attributes remains elusive. The influences of maturity, orchard, and season act independently and tend to confound 'attempts to generate robust predictions (Knee and Farman 1989). However, in our opinion, multiple regression analysis of historical data, particularly those analyses which include climatic conditions, maturity and physiological condition at harvest, mineral content, firmness at harvest; and texture after storage will eventually lead to the development of robust models for predicting textural properties of fruit. The question will be whether the models are universal or relate to fruit grown in specific regions.

IX. TEXTURE DISORDERS A. Texture Associated with Chilling Injury 1. Woolliness in Stonefruit. Chilling injury in stonefruit has been reviewed by Ben-Arie et al. (1989) and Lill et al. (1989). Injury occurs if fruit are stored at low temperatures (0 to 8°C). During the subsequent

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ripening at ambient temperatures, the injury becomes manifest as a dryish mouth-feel even though there has been no excessive loss of water during storage (Ben-Arie et al. 1989) and no detectable difference in water status of the tissue (Sonego et al. 1995). The dry texture or mouthfeel is often described as woolliness or mealiness. In some cases, fruit may recover juiciness if allowed to ripen further (von Mollendorf et al. 1992). Postharvest treatments, including storage in high CO 2 atmospheres and intermittent warming during storage, have reduced the incidence of woolliness in some studies (Wade 1981; Lill et al. 1989; Retamales et al. 1992). Ultrastructural and cytochemical changes associated with the development of chilling injury in peach have been studied by Luza et al. (1992). They described two symptoms of chilling injury: mealiness, which they characterized as being associated with separation of mesocarp parenchyma cells and accumulation of pectic substances within intercellular spaces; and leatheriness, which they differentiated from mealiness in that mesocarp parenchyma cells also collapsed and the hemicellulose-staining fraction of the cell wall thickened. Examination of fracture surfaces following tensile testing of ripe nectarine indicated that tissue failure occurred when apparently undamaged neighboring cells separated at the middle lamella in both woolly and nonwoolly tissue (Harker and Sutherland 1993). The only difference was that a layer of juice covering the fracture surface of nonwoolly tissue was not observed in woolly tissue. Biochemical studies have suggested that an imbalance between the cell wall degrading enzymes pectinesterase and polygalacturonase occurs in chilling-injured peach, resulting in a build up of de-esterified pectate (Ben-Arie and Sonego 1980, Ben-Arie et al. 1989). A later chemical study by Dawson et al. (1992) provided evidence that this process is also involved in the development of chilling injury in nectarine. The de-esterified pectates are thought to form a gel-like structure in the cell wall and interfere with the release of juice into the mouth during chew~ ing (Ben-Arie and Lavee 1971). The presence of such a gel is supported by high electrical resistance (or low conductance) of extracellular compartments of woolly tissues, as measured at low frequencies of alternating current (Furmanski and Buescher 1979; Harker and Maindonald 1994), and by the higher calcium binding and retention properties and reduced ion leakage properties of the woolly tissue (Furmanski and Buescher 1979, Dawson et al. 1993). Harker and Maindonald (1994) identified a resistive component of electrical impedance (possibly associated with membranes) which distinguished between nonwoolly and woolly fruit prior to the development of symptoms during ripening.

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2. Tomato. Chilling injury in tomato is characterized by a range of symptoms including an increased rate of ripening, extensive green patches on otherwise red fruit, an uneven surface due to collapse of cells, and the production of excessively soft fruit (Hobson 1987). More important, from a texture perspective, is the observation that tomatoes with slight chilling injury had developed a mealy texture (Jackman et al. 1992a). Mealiness was characterized by absence of expressed fluid during compression of disks of pericarp tissue and may be associated with elevated activity of the cell-waIl-degrading enzyme fJ-glucosidase (Jackman et al. 1992a) and higher levels of pectinmethylesterase (Marangoni et al. 1995). Thus, the mechanisms associated with development of chilling injury in tomatoes are consistent with those suggested for stonefruit (Section IXA1). Suppression of polygalacturonase mRNA has been noted in chillinjured tomato (Watkins et al. 1990). Thus, exposure to chilling temperatures may interfere with the production of cell-waIl-degrading enzymes during both transcription and translation. Similar texture disorders might be expected in a wide range of chilling-sensitive fruit. Studies undertaken by the HortResearch Postharvest Group have indicated that chilling injury occurs during storage of kiwifruit (N. Lallu, pers. comm.) and other species of Actinidia. In selections from crosses between A. chinensis, chill-injured fruit developed a mealy texture which was characterized by a dry mouth-feel and a lack of juice on the surface of fractures obtained during tensile tests (A. White, pers. comm.). In noninjured fruit, the fracture surface was covered in juice as is normal for ripe kiwifruit (Harker and Hallett 1994).

B. Texture Associated with Overmaturity 1. Mealiness in Apple. Storage of overmature apples tends to result in fruit that have a dryish mouth-feel (Fisher 1943, Harker and Hallett 1992). This texture is often described in the literature as mealiness. Symptoms of mealiness are associated with tissues in which neighboring cells are only loosely bonded together. Application of tensile tests to mealy tissue results in fracture surfaces composed of intact, undamaged cells (Harker and Hallett 1992, Lapsley et al. 1992). Similar tests on juicy tissue result in fractured cells which have released their cell contents. Apple flesh contains a high volume of air space, which often increases during storage (Hatfield and Knee 1988, Harker and Hallett 1992). Relationships between flesh density, area of cell-to-cell contact, and tissue strength have been established (Vincent 1989). It seems likely that turgor-driven expansion of cells associated with changes in condition of

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the middle lamella eventually lead to development of mealy texture. When apples are stored in moist conditions and high temperatures, fruit volume increases, the tissue becomes mealy, and skin eventually splits (Wilkinson 1965). Under such conditions, turgor is likely to be maintained throughout ripening. Alternatively, when apples were stored in the presence of desiccants, turgor was reduced, air space was maintained at lower levels, and fruit were firmer than fruit stored without desiccants (Hatfield and Knee 1988). 2. Loss of Juice in Citrus Fruits. In citrus fruits, the juice vesicle is comprised of epidermal and subepidermal cell layers, an elongate cell layer, and juice cells (Shomer et al. 1989). Disorders associated with the lignification of the juice cells and dehydration of the juice vesicle are found in many late-harvested citrus (Burns and Achor 1989; Shomer et al. 1989). The disorder can occur in fruit that remain on the tree late in the season, but becomes more severe during storage of these late-harvested fruit. Clearly, lignification must occur as a result of cell wall synthesis. Respiration was higher in juice vesicles isolated from fruit showing a disorder than in vesicles from control fruit, suggesting that metabolic activity could be supporting cell wall synthesis (Burns 1990). Affected juice vesicles contained twice as much structural polysaccharide (pectins, hemicellulose and cellulose) than normal vesicles (Hwang et al. 1990).

C. Texture-Modifying Substances The sensation of astringency, common in many unripe fruits, is associated with the presence of soluble tannins (Goldstein and Swain 1963; Matsuo and Itoo 1982; Ozawa et al. 1987). These tannins are responsible for the perception of dryness during chewing due to their interaction with salivary proteins and glycoproteins (Guinard et al. 1986). Astringency is usually lost during fruit ripening as a result of tannin polymerization and insolubilization (Goldstein and Swain 1963; Matsuo and Itoo 1982; Ozawa et al. 1987). Jowitt (1974) classifies astringency as a "mouth-feel sensation" to indicate that it is not a physical textural property. Good examples of astringent fruits include astringent varieties of persimmon which contain high concentrations of tannins located within vacuoles of idioblast cells (Taira et al. 1989; Gottreich and Blumenfeld 1991). In persimmon, the fruit remain astringent until overripe, and thus must be eaten in this state or have the astringency removed using postharvest treatments involving exposure to alcohol vapor, high CO 2 , or hot water (Taira et al. 1989).

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Calcium oxalate crystals are found in a wide range of plants and plant structures (Franceschi and Horner 1980). The crystals frequently take the form of raphides, needle-shaped crystals sometimes exceeding 0.2 mm in length. These have been found in a number of fruits and vegetables (Perera 1994) including pineapple, kiwifruit, and ceriman (Monstera deliciosa). Raphides are often associated with irritation to the mouth or throat during eating (Sakai et al. 1984). Such irritation may be caused by toxins, as in taro, where the raphides contain a poison-filled groove. Release of the toxin when the raphides physically interact with the mucous membranes aggravates the irritation and can lead to severe illness (Tang and Sakai 1983). Purely physical irritation has been associated with raphide length and shape-presence of grooves and barbs (Sakai et al. 1984). The perception of irritation may change during ripening or be enhanced by processing. Consumption of fresh kiwifruit does not normally cause irritation of the mouth or throat. However, consumption of processed kiwifruit products results in an irritation known as "catch in the throat" (Perera et al. 1990; Perera and Hallett 1991). This irritation of the throat is thought to be due to mechanical action of the raphides on the mucous membranes, and can be demonstrated by simulating "catch" symptoms in apple puree by adding small quantities of kiwifruit raphides (Perera et al. 1990). Fruit of ceriman are exceptionally sweet but contain raphides in tricoscleroid cells that cause irritation to the mouth and throat, particularly in green fruit. The tricoscleroid cells themselves, which are lignified and have pointed ends, may also playa part in the irritation (Davies et al. 1994). X. CONCLUDING REMARKS

An understanding of texture requires knowledge of how food interacts with the mouth to provide a sensory response and how differences in the structure and mechanical properties of foods are responsible for different food-mouth interactions. The textural properties of fruit are determined primarily by the genetic makeup of the plant, although they are modified by environmental influences. Much applied fruit research tends to focus on the optimization of quality of commercially established fruits through manipulation of the tree and orchard environment and development of appropriate postharvest treatments and storage conditions. In these studies, the measurement of texture is an integral part of routine fruit quality evaluation. Indeed, in some cases, measurements of texture provide the clearest evidence that a treatment has influenced fruit-eating quality. It is therefore a concern that many studies continue

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to use puncture tests or whole-fruit compression in preference to other instrumental and sensory methods. While puncture and whole-fruit compression tests are easy to use, convenient, and accessible in most laboratories, there is clear evidence that they can provide misleading results. For example, puncture tests are often unable to detect mealiness in apple (Harker and Hallett 1992), and there is concern that whole-fruit compression can be an insensitive measure of tomato softening (Ahrens and Huber 1990; Jackman et al. 1990; Jackman and Stanley 1995a). It is important, when designing an experiment, to decide how important the assessment of textural properties is to the study. Based on this, one can decide on the appropriate methodologies. When changes and/or differences in texture are expected to represent a major component of the study, it may be appropriate to consider some of the alternative methods, or combinations of methods, for measuring textural attributes that have been described in this review. However, it is important to keep in mind that the majority of these methods are empirical in that the validity of their use is based on observational data rather than on fundamental scientific principles. Based on our reading of the literature, the most promising new instrumental methods are those which provide two or more textural parameters, or allow mechanism of cell failure to be visually evaluated, as well as providing an assessment of mechanical strength. Recent approaches using dynamic testing of excised tissue (Section VIID6) and stress relaxation using conical probes (Section VIID8) show considerable potential but require validification across a wider range of fruits. However, the ultimate assessment of texture continues to be provided by sensory panels. The establishment of a precise relationship between instrumental texture measurements and factors governing consumer reactions, an issue raised by Voisey (1971), continues to provide a challenge to both research and fruit industries.

LITERATURE CITED Abbott, J. A. 1994. Firmness measurement of freshly harvested 'Delicious' apples by sensory methods, sonic transmission, Magness-Taylor, and compression. J. Am. Soc. Hort. Sci. 119:510-515. Abbott, J. A., H. A. Affeldt, and 1. A. Liljedahl. 1992. Firmness measurement of stored 'Delicious' apples by sensory methods, Magness-Taylor, and sonic transmission. J. Am. Soc. Hort. Sci. 117:590-595. Abbott, J. A., W. S. Conway, and C. E. Sams. 1989. Postharvest calcium chloride infiltration affects textural attributes of apples. J. Am. Soc. Hort. Sci. 114:932-936. Abbott, J. A., R. Lu, B. L. Upchurch, and R. L. Stroshine. 1997. Nondestructive quality evaluation of fruits and vegetables. Hort. Rev. 20:1-120.

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Abbott, J. A., A. K Watada, and D. R. Massie. 1976. Effe-gi, Magnus-Taylor, and Instron fruit pressure testing devices for apples, peaches, and nectarines. J. Am. Soc. Hort. Sci. 101:698-700. Abbott, J. A., A. K Watada, and D. R. Massie. 1984. Sensory and instrument measurement of apple texture. J. Am. Soc. Hort. Sci. 109:221-228. Abel, L. F., and R. S. Manley. 1953. Masticatory function of partial denture patients among navy personnel. J. Prosthet. Dent. 3: 382-392. Adegoroye, A. S., P. A. Jolliffe, and M. A. Tung. 1989. Textural characteristics of tomato fruits (Lycopersicon esculentum) affected by sunscald. J. Sci. Food Agr. 49:95-102. Ahlgren, J. 1966. Mechanisms of mastication. Acta. Odontol. Scand. 24:1-109. Ahlgren, J. 1976. Masticatory movements in man. p. 119-130. In: D.J. Anderson and B. Matthews (eds.), Mastication. Wright, Bristol, England. Ahmed, K M., and R. C. Fluck. 1972. Textural properties of stored and irradiated peaches II. Rheological properties. J. Text. Stud. 3:319-328. Ahrens, M. J., and D. J. Huber. 1990. Physiology and firmness determination of ripening tomato fruit. Physiol. Plant. 78:8-14. AI-Kahtani, H. A. 1992. Intercultivar differences in quality and postharvest life of pomegranates influenced by partial drying. J. Am. Soc. Hort. Sci. 117:100-104. Archibald, R. D., and L. D. Melton. 1987. The anatomy of the fleshy pericarp of maturing Moorpark apricots, Prunus armeniaca. New Zealand J. Bot. 25: 181-184. Armstrong, P., H. R. Zapp, and G. K. Brown. 1990. Impulsive excitation of acoustic vibrations in apples for firmness determination. Trans. Am. Soc. Agr. Eng. 33:1353-1359. Anderson, D. J., and D. C. A. Picton. 1958. Masticatory stress in normal and modified ocelusion. J. Dent. Res. 37:312. ASAE Standards, 31st ed. 1984. S368.1. p. 99-102. Compression test offood materials of convex shape. ASAE, St. Joseph, MI. ASHS Publications Manual. 1991. ASHS, Alexandria, VA. Autio, W. R. 1991. Rootstocks affect ripening and other qualities of 'Delicious' apples. J. Am. Soc. Hort. Sci. 116:378-382. A~ad, M., and R. KYoung. 1979. Postharvest variation in cellulase, polygalacturonase, and pectinmethylesterase in avocado (Persea americana Mill, cv. Fuerte) fruits in relation to respiration and ethylene production. Plant Physiol. 64:306-308. Bailey, J. S., and A. P. French. 1932. The inheritance of certain characters in the peach. Proc. Am. Soc. Hort. Sci. 29:127-130. Bain, J. M. 1958. Morphological, anatomical, and physiological changes in the developing fruit of the Valencia orange, Citrus sinesis (L.) Osbeck. Austral. J. Bot. 6:1-24. Ballinger, W. K, L. J. Kushman, and D. D. Hamann. 1973. Factors affecting the firmness of highbush blueberries. J. Am. Soc. Hort. Sci. 98:583-587. Barnhart, R. K. (ed.) 1988. The Barnhart dictionary of etymology. Wilson, New York. Baron-Epel, 0., P. K. Gharyal, and M. Schindler. 1988. Pectins as mediators of wall porosity in soybean cells. Planta 175:389-395. Barritt, B. H., L. C. Torre, H. S. Pepin, and H. A. Daubeny. 1980. Fruit firmness measurements in red raspberry. HortScience 15:38-39. Bartley, I. M. 1974. ,8-galactosidase activity in ripening apples. Phytochemistry 13:21072111. Batten, D. J. 1990. Effect of temperature on ripening and postharvest life of fruit of atemoya (Annona cherimola Mill. x A. squamosa L.) cv. 'African Pride'. Scientia Hart. 45:129136. Beckman, T. G., and W. B. Sherman. 1996. The non-melting semi-freestone peach. Fruit Var. J. 50:189-193.

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F. HARKER, R REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

Beever, D. J., and G. Hopkirk. 1990. Fruit development and fruit physiology, p.97-126. In: 1. J. Warrington and G. C. Weston (eds.). Kiwifruit: science and management. New Zealand Soc. Hort. Sci., Auckland. Bell, R 1., and J. Janick. 1990. Quantitative genetic analysis of fruit quality in pear. J. Am. Soc. Hort. Sci. 115:829-834. Ben-Arie, R, N. Kislev, and C. Frenkel. 1979. Ultrastructural changes in the cell walls of ripening apple and pear fruit. Plant Physiol. 64: 197-202. Ben-Arie, R., and S. Lavee. 1971. Pectic changes occurring in Elberta peaches suffering from woolly breakdown. Phytochemistry 10:531-538. Ben-Arie, R, and 1. Sonego. 1980. Pectolytic enzyme activity involved in woolly breakdown of stored peaches. Phytochemistry 19:2553-2555. Ben-Arie, R, 1. Sonego, M. Zeidman, and S. Lurie. 1989. Cell wall changes in ripening peaches, p. 253-262. In: D.J. Osbourne and M.B. Jackson (eds.). Cell separation in plants: physiology, biochemistry, and molecular biology. Springer-Verlag, Berlin. Bennett, J. M. 1990. Problems associated with measuring plant water status. HortScience 25:1551-1554. Berry, D. c., and M. Mahood. 1966. Oral stereognosis and oral ability in relation to prosthetic treatment. Brit. Dent. J. 179-185. Beyron, H. 1964. Occlusal relations and mastication in Australian aborigines. Acta. Odontol. Scand. 22: 597-678. Bin, F., and M. R McLellan. 1988. Effect of sample weight and orientation on the texture press force of apple slices. J. Text. Stud. 19:153-160. Blanpied, G. D., and V. A. Blak. 1977. A comparison of pressure tests, acid levels, and sensory evaluations of overripeness in apples. HortScience 12:73-74. Blanpied, G. D., W. J. Bramlage, D. H. Dewey, R 1. LaBelle, 1. M. Massey Jr., G. E. Mattus, W. C. Stiles, and A. E. Watada. 1978. A standardized method for collecting apple pressure test data. Bul. 74 Cornell Expt. Sta. Blanpied, G. D., R. M. Smock, and D. A. Kollas. 1967. The effect of Alar on optimum harvest dates and keeping quality of apples. Proc. Am. Soc. Hort. Sci. 90:467-474. Bolin, H. R, F. S. Nury, and B. J. Finkle. 1964. An improved process for preservation of fresh peeled apples. Bakers Digest 38:46-48. Bollard, E. G. 1970. The physiology and nutrition of developing fruits, p. 387-425. In: A. C. Hulme (ed.), The biochemistry of fruits and their products, vol. 1. Academic Press, London. Bonaguro, J. G., G. R Dusza, and D. C. Bowman. 1969. Ability of human subjects to discriminate forces applied to certain teeth. J. Dent. Res. 48: 236-241. Bongers A. J. 1992. Comparison of three penetrometers used to evaluate apple firmness. Washington State Univ. Tree Fruit Postharvest J. 3:7-9. Bosman, F., A. van der Bilt, A. van Rijn, and C. J. Erkelens. 1985. Reflex control of human jaw movement. J. Oral Rehabil. 12:550. Bourne, M. C. 1965. Studies on punch testing of apples. Food Technol. 19:413-415. Bourne, M. C. 1966. Measure of shear and compression components of puncture tests. J. Food Sci. 31:282-291. Bourne, M. C. 1968. Texture profile of ripening pears. J. Food Sci. 33:223-226. Bourne, M. C. 1974. Comparison of results from the use of the Magness-Talyor pressure tip in hand- and machine-operation. J. Text. Stud. 5:105-108. Bourne, M. C. 1975. Method for obtaining compression and shear coefficients of foods using cylindrical punches. J. Text. Stud. 5:459-469. Bourne, M. C. 1978. Texture profile analysis. Food Technol. 32:62-66,72. Bourne, M. C. 1979. Texture of temperate fruits. J. Text. Stud. 10:25-44.

2. TEXTURE OF FRESH FRUIT

205

Bourne, M. C. 1980. Texture evaluation of horticultural crops. HortScience 15:51-57. Bourne, M. C. 1982. Food texture and viscosity: Concept and measurement. Academic Press, San Diego, California. Bourne, M. C. 1983. Physical properties and structure of horticultural crops, p. 207-228. In: M. Peleg and E. B. Bagley (eds.), Physical properties of foods. AVI, Wesport, CN. Bowman, F., A. M. Kylen, and S. F. Adam. 1972. Relationship between certain physicalchemical measurements and sensory appraisals of apple texture. J. Text. Stud. 3:478-491. Bowman, D. c., and P. M. Nakfoor. 1968. Evaluation of the human subjects' ability to differentiate intensity of forces applied to the maxillary central incisor. J. Dent. Res. 47:252-259. Boyar, M. M., and D. Kilcast. 1986. Food texture and dental science. J. Text. Stud. 17:221-252. Brady, C. J. 1987. Fruit ripening. Annu. Rev. Plant Physiol. 38:155-178. Brady, C. J., P. B. H. O'Connell, J. Smydzuk, and N. L. Wade. 1970. Permeability, sugar accumulation, and respiration rate in ripening banana fruits. Austral. J. BioI. Sci. 23:1143-1152. Bramlage, W. J., D. W. Greene, W. R. Autio, and J. M. McLaughlin. 1980. Effects of aminoethoxyvinylglycine on internal ethylene concentrations and storage of apples. J. Am. Soc. Hort. Sci. 105:847-851. Brandt, M. A., E. Z. Skinner, and J. A. Coleman. 1963. Texture profile method. J. Food Sci. 28:404-409. Brennan, J. G., R. Jowitt, and O. A. Mughsi. 1970. Some experiences with the general foods texturometer. J. Text. Stud. 1:167-184. Brown, B. 1., L. S. Wong, A. P. George, and R. J. Nissen. 1988. Comparitive studies on the postharvest physiology of fruit from different species of Annona (custard apple). J. Hort. Sci. 63:521-528. Brown, S. K, and M. C. Bourne. 1988. Assessment of components of fruit firmness in selected sweet cherry genotypes. HortScience 23:902-904. Burns, J. K 1990. Respiratory rates and glycosidase activities of juice vesicles associated with section-drying in citrus. HortScience 25:544-546. Burns, J. K., and D. S. Achor. 1989. Cell wall changes in juice vesicles associated with "section drying" in stored late-harvested grapefruit. J. Am. Soc. Hort. Sci. 114:283-287. Burns, J. K, D. S. Achor, and E. Echeverria. 1992. Ultrastructural studies on the ontogeny of grapefruit juice vesicles (Citrus paradisi Mac£. cv Star Ruby). Int. J. Plant Sci. 153:14-25. Caffesse, P. G., J. J. Carraro, and E. A. Albano. 1973. Influence oftemporomandibular joint receptors on tactile occlusal perception. J. Periodont. Res. 8: 400-403. Callaghan, P. T. 1991. Principles of nuclear magnetic resonance microscopy. Oxford Univ. Press, Oxford. Carey, A. T., K. Holt, S. Picard, R. Wilde, G. A. Tucker, C. R. Bird, W. Schuch, and G. B. Seymour. 1995. Tomato Exo-(1~4)-~-D-Galactanase:isolation, changes during ripening in normal and mutant tomato fruit, and characterization of a related cDNA clone. Plant Physiol. 108:1099-1107. Carpita, N. C, and D. M. Gibeaut. 1993. Structural models of primary cell walls in flowering plants: consistency of molecular structure with the physical properties of the walls during growth. Plant J. 3: 1-30. Carpita, N., D. Sabularse, D. Montezinos, and D. P. Delmer. 1979. Determination of the pore size of cell walls of living plant cells. Science 205:1144-1147. Carrington, C. M. S., and R. Pressey. 1996. j3-galactosidase II activity in relation to changes

206

F. HARKER, R REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

in cell wall galactosyl composition during tomato ripening. J. Am. Soc. Hort. Sci. 121:132-136. Cash, R. M., and R W. A. Linden. 1982. The distribution ofmechanoreceptors in the periodontalligament of the mandibular canine tooth of the cat. J. Physiol. (London). 330: 439-447. Chen, P. M., and D. M. Borgic. 1985. Changes in water soluble polyuronides in the pulp tissue of ripening 'Bosc' pears following cold storage in air or in 1 % oxygen. J. Am. Soc. Hort. Sci. 110:667-671. Chen, P. M., R. A. Spotts, and W. M. Mellenthin. 1981. Stem-end decay and quality oflow oxygen stored 'd'Anjou' pears. J. Am. Soc. Hort. Sci. 106:695-698. Child, RD., A. A. Williams, G. V. Hoad, and C. R Baines. 1984. The effects of aminoethyoxyvinylglycine on maturity and post-harvest changes in Cox's Orange Pippin apples. J. Sci. Food Agr. 35:773-781. Christensen, C. M., and Z. M. Vickers. 1981. Relationships of chewing sounds to judgments offood crispness. J. Food Sci. 46:574-578. Christensen, 1. V., and T. Troest. 1975. Clinical kinesthetic experiments on the lateral pterygoid muscle and temporomandibular joint in man. Scand. J. Dent. Res. 83: 238-244. Civille, G. V., and 1. II. Liska. 1975. Modifications and applications to foods of the general foods sensory texture profile technique. J. Text. Stud. 6:19-31. Civille, G. V., and A. S. Szczesniak. 1973. Guidelines to training a texture profile panel. J. Text. Stud. 4:204-223. Clark, C. J., and S. K. Forbes. 1994. Nuclear magnetic resonance imaging of the development of chilling injury in 'Fuyu' persimmon (Diospyros kaki). New Zealand J. Crop Hort. Sci. 22:209-215. Colaizzi, F. A., N. S. Javid, c. G. Michael, and C. H. Gibbs. 1984. Biting force EMG and jaw movements in denture wearers. J. Dent. Res. 63:329. Cole, K. S. 1972. Membranes, ions and impulses: a chapter of classical biophysics. Univ. California Press, Berkeley and Los Angeles. Conway, W. S., and C. E. Sams. 1987. The effects of postharvest infiltration of calcium, magnesium, or strontium on decay, firmness, respiration, and ethylene production in apples. J. Am. Soc. Hort. Sci. 112:300-303. Coombe, B. G. 1976. The development of fleshy fruits. Annu. Rev. Plant Physiol. 27:207-228. Corey, H. 1970. Texture in foodstuffs. CRC Crit. Rev. Food Technol. 1:161-198. Cosgrove, D. J. 1993. Wall extensibility: its nature, measurement and relationship to plant cell growth. New Phytol. 124:1-23. Cotter, R 1., E. A. MacRae, A. R Ferguson, K. 1. McMath, and C. J. Brennan. 1991. A comparison of the ripening, storage and sensory qualities of seven cultivars of kiwifruit. J. Hort. Sci. 66:291-300. Crookes, P. R, and D. Grierson.1983. Ultrastructure of tomato fruit ripening and the role of polygalacturonase isoenzymes in cell wall degradation. Plant Physiol. 72:1088-1093. Cutter, E. G. 1971. Plant anatomy: experiment and interpretation. Part II: Organs. Edward Arnold Ltd., London. Dahlberg, B. 1942. The masticatory effect. Acta Med. Scand. Suppl. 139:1-155. Dahlberg, B. 1946. The masticatory habits. An analysis of the number of chews when consuming food. J. Dent. Res. 25:67-72. Daillant-Spinnler, B., H. J. H. MacFie, P. K. Beyts, and D. Hedderley. 1996. Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Quality Preference 7:113-126.

2. TEXTURE OF FRESH FRUIT

207

Dainty, J. 1976. Water relations of plant cells. Encycl. Plant Physiol. 2A:12-35. Dallman, T. F., 1. 1. Eaks, W. W. Thomson, and E. A. Nothnagel. 1988. Posthavest density changes in membranes from ripening avocado fruits. J. Am. Soc. Hort. Sci. 113:729733. Davies, R, B. McGlasson, V. Sarafis, and B. Firth. 1994. The ripening physiology of Monstem deliciosa. p. 415-418. Proc. Australasian Postharvest Conference, Sept 1993. Univ. Queensland Gatton College, Lawes, Australia. Dawson, D. M., 1. D. Melton, and C. B. Watkins. 1992. Cell wall changes in nectarines (Prunus persica): Solubilization and depolymerization of pectic and neutral polymers during ripening and in mealy fruit. Plant Physiol. 100:1203-1210. Dawson, D. M., C. B. Watkins, and L. D. Melton. 1993. Calcium uptake and efflux, ion leakage, internal air space and cation exchange capacity in relation to mealiness in nectarine tissue. Postharvest BioI. Technol. 3:131-141. De Boevar, J. A., W. D. McCall, S. Holden, and M. M. Ash. 1978. Functional occlusal forces: an investigation by telemetry. J. Prosthet. Dent. 40:326-333. Dellow, P. G., and J. P. Lund. 1971. Evidence for central timing ofrhythmical mastication. J. Physiol. (London). 215:1-13. Delwiche, M. J. 1987. Theory of fruit firmness sorting by impact forces. Trans. Am. Soc. Agr. Eng. 30:1160-1166, 1171. Demarty, M., C. Morvan, and M. Thellier. 1984. Calcium and the cell wall. Plant Cell Environ. 7:441-448. De Veau, E. J. 1., K C. Gross, D. J. Huber, and A. E. Watada.1993. Degradation and solubilization of pectin by j3-galactosidases purified from avocado mesocarp. Physiol. Plant 87:279-285. Diehl, K C., and D. D. Hamann. 1979. Relationships between sensory profile parameters and fundamental mechanical parameters for raw potatoes, melons and apples. J. Text. Stud. 10:401-420. Diehl, K c., D. D. Hamann, and J. K Whitfield. 1979. Structural failure in selected raw fruits and vegetables. J. Text. Stud. 10:371-400. Dilley, D. R, I. D. Wilson, J. Kuai, 1. Poneleit, Y. Zhu, and Y. Pekker. 1993. Molecular biology of pome fruit ripening and senescence: concepts, status, perspectives and future prospects. Acta Hort. 326:325-334. Dinus, 1. A., and A. C. Mackey. 1974. Chemical and physical attributes of muskmelon related to texture. J. Text. Stud. 5:41-50. Dong, W. K, E. H. Chudler, and R F. Martin. 1985. Physiological properties of intradental mechanoreceptors. Brain Res. 334: 389-395. Drake, B. K 1963. Food crushing sounds: an introductory study. J. Food Sci. 28:233-241. Dubner, R, B. J. Sessle, and A. T. Storey. 1978. The neural basis of oral and facial function. Plenum Press, New York. Du Brul, E. 1. 1988. Sicher and Du Brul's oral anatomy. Mosby, St Louis. Edlund, J., and C. J. Lamm. 1980. Masticatory efficiency. J. Oral Rehabil. 7: 123-130. Edmister, J. A., and Z. M. Vickers. 1985. Instrumental acoustical measures of crispness in foods. J. Text. Stud. 16:153-167. Elkashif, M. E., and D. J. Huber. 1988. Electrolyte leakage, firmness, and scanning electron microscopic studies of watermelon fruit treated with ethylene. J. Am. Soc. Hort. Sci. 113:378-381. El-Zeftawi, B. M. 1973. Granulation of Valencia oranges. Food Technol. Australia 25:103-105. Enemoto, S., G. Schwartz, and J. P. Lund. 1987. The effects of cortical ablation on mastication in the rabbit. Neurosci. Lett. 82:162-166.

208

F. HARKER, R REDGWELL, I. HALLETT, S. MURRAY, AND G. CARTER

Esau, K. 1965. Plant anatomy (2nd ed.). Wiley, New York. Facteau, T. J. 1982. Levels of pectic substances and calcium in Gibberellic Acid-treated sweet cherry fruit. J. Am. Soc. Hort. Sci. 107:148-151. Fallahi, E., T. L. Righetti, and D. G. Richardson. 1985. Predictions of quality by preharvest fruit and leaf mineral analyses in 'Starkspur Golden Delicious' apple. J. Am. Soc. Hort. Sci. 110:524-527. Fan, S., S. E. Prussia, and Y. C. Hung. 1994. Evaluating the UGA laser-pufffood firmness detector. Am. Soc. Agr. Eng. Paper 94-6540. Faust, M., P. C. Wang, and J. Maas. 1997. The use of magnetic resonance imaging in plant science. Hort. Rev. 20:225-266. Ferguson, A. R 1984. Kiwifruit: a botanical review. Hort. Rev. 6:1-64. Fidler, J. C. 1973. Conditions of storage, p. 1-61. In: J. C. Fidler, B. G. Wilkinson, K. L. Edney, and R O. Sharples (eds.), The biology of apple and pear storage. Commonwealth Agr. Bureau, Slough, U.K. Fils-Lycaon, B., and M. Buret. 1990. Loss of firmness and changes in pectic fractions during ripening and overripening of sweet cherry. HortScience 25:777-778. Finn, C. E., and J. J. Luby. 1992. Inheritance of fruit quality traits in blueberry. J. Am. Soc. Hort. Sci. 117:617-621. Finney, E. E., Jr. 1968. Defining and measuring attributes oftexture in fruits and vegetables. Paper 68-385, 61st Ann. Meeting Am. Soc. Agr. Eng. Finney, E. E., Jr. 1971. Dynamic elastic properties and sensory quality of apple fruit. J. Text. Stud. 2:62-74. Fisher, D. V. 1943. Mealiness and quality of Delicious apples as affected by growing conditions, maturity and storage techniques. Scientific Agr. 23:569-588. Franceschi, V. R, and H. T. Horner, Jr. 1980. Calcium oxalate crystals in plants. Bot. Rev. 46:361-427. Friedman, H. H., J. E. Whitney, and A. S. Szczesniak. 1963. The texturometer-a new instrument for objective texture measurement. J. Food Sci. 28:391-396. Fry, S. c., R C. Smith, K. F. Renwick, D. J. Martin, S. K. Hodge, and K. J. Matthews. 1992. Xyloglucan endotransglycosylase, a new wall-loosening enzyme activity from plants. Biochem. J. 282:821-828. Funakoshi, M., and Y. Kawamura. 1967. Relations between taste qualities and parotid gland secretion rate. p. 281-287. In: T. Hayashi (ed.), Olfaction and taste II. Pergamon Press, Oxford. Furmanski, R J., and R W. Buescher. 1979. Influence of chilling on electrolyte leakage and internal conductivity of peach fruits. HortScience 14:167-168. Geeson, J. D., P. M. Genge, S. M. Smith, and R O. Sharples. 1991. The response of unripe Conference pears to modified atmosphere retail packaging. Int. J. Food Sci. Technol. 26:215-223. Gibbs, C. H., P. E. Mahan, H. C. Lundeen, K. Brehnan, E. K. Walsh, and W. B. Holbrook. 1981. Occlusal forces during chewing and swallowing as measured by sound transmission. J. Prosthet. Dent. 46:443-449. Gibbs, C. H., P. E. Mahan, A. Mauderli, H. C. Lundeen, and E. K. Walsh. 1986. Limits of human bite strength. J. Prosthet. Dent. 56:226-229. Giovannoni, J. J., D. DellaPenna, A. B. Bennett, and R L. Fischer. 1989. Expression of a chimeric polygalacturonase gene in transgenic rin (ripening inhibitor) tomato fruit results in polyuronide degradation but not fruit softening. Plant Cell 1:53-63. Giovannoni, J. J., D. DellaPenna, A. B. Bennett, and R L. Fischer. 1992. Polygalacturonase and tomato fruit ripening. Hort. Rev. 13:67-103. Glenn, G. M., and B. W. Poovaiah. 1990. Calcium-mediated postharvest changes in tex-

2. TEXTURE OF FRESH FRUIT

209

ture and cell wall structure and composition in 'Golden Delicious' apples. J. Am. Soc. Hort. Sci. 115:962-968. Glenn, G. M., A. S. N. Reddy, and B. W. Poovaiah. 1988. Effect of calcium on cell wall structure, protein phosphorylation and protein profile in senescing apples. Plant Cell Physiol. 29:565-572. Goldstein J. L., and T. Swain. 1963. Changes in tannins in ripening fruits. Phytochemistry 2:371-383.

Goodwin, G. M., and K S. Luschei. 1974. Effects of destroying spindle afferents from jaw muscles on mastication in monkeys. J. Neurophysiol. 37: 967-981. Gottreich, M., and A. Blumenfeld. 1991. Light microscopic observations of tannin cell walls in persimmon fruit. J. Hort. Sci. 66:731-736. Gough, R K 1983. The occurrence of mesocarpic stone cells in the fruit of cultivated highbush blueberry. J. Am. Soc. Hort. Sci. 108:1064-1067. Graf, H. 1975. Occlusal forces during function. p. 90-111. In: N. H. Rowe (ed.), Research in form and function. Univ. Michigan, Ann Arbor. Greene, D. W. 1986. Effect ofpaclobutrazol and analogs on growth, yield, fruit quality, and storage potential of 'Delicious' apples. J. Am. Soc. Hort. Sci. 111:328-332. Gross, K. c., and C. K Sams. 1984. Changes in cell wall neutral sugar composition during fruit ripening: a species survey. Phytochemistry 23:2457-2461. Gross, K. c., A. K Watada, M. S. Kang, S. D. Kim, K. S. Kim, and S. W. Lee. 1986. Biochemical changes associated with the ripening of hot pepper fruit. Physiol. Plant. 66:31-36. Grossman, R C. 1964. Methods for evaluating surface sensation. J. Dent. Res. 43:301. Guinard, J.-X., and R Mazzucchelli. 1996. The sensory perception of texture and mouthfeel. Trends Food Sci. Technol. 7:213-219. Guinard, J.-X., R M. Pangborn, and M. J. Lewis. 1986. Preliminary studies on acidityastringency interactions in model solutions and wines. J. Sci. Food Agr. 37:811-817. Haller, M. H. 1941. Fruit pressure testers and their practical applications. USDA Circ. 627. Hallett, 1. C., K A. MacRae, and T. F. Wegrzyn. 1992. Changes in kiwifruit cell wall ultrastructure and cell packing during postharvest ripening. Int. J. Plant Sci. 153:49-60. Hamson, A. R 1952. Measuring firmness of tomatoes in a breeding program. Proc. Am. Soc. Hort. Sci. 60:425-433. Hannam, A. G. 1970. Receptor fields of periodontal mechanosensitive units in the dog. Arch. Oral BioI. 15: 951-978. Hannam, A. G., and J. P. Lund. 1981. The effect of intra-oral stimulation on the human masticatory cycle. Arch. Oral BioI. 26: 865-870. Hard, M. M., M. V. Zaebringer, F. Bowman, and A. C. Mackey. 1977. Predicting texture of fresh fruits and vegetables by chemical and physical methods. College of Agr., Washington State Univ. Bul. 836. Hardenburg, R K, A. K Watada, and C. Y. Wang. 1986. The commercial storage of fruits, vegetables, and florist and nursery stocks. USDA Agr. Handb. U.S. Govt. Printing Office, Washington, DC. Harker, F. R, and J. Dunlop. 1994. Electrical impedance studies of nectarines during coolstorage and fruit ripening. Postharvest BioI. Technol. 4:125-134. Harker, F. R, and 1. B. Ferguson. 1988. Transport of calcium across cuticles isolated from apple fruit. Scientia Hort. 36:205-217. Harker, F. R, and 1. C. Hallett. 1992. Physiological changes associated with development of mealiness of apple fruit during cool storage. HortScience 27:1291-1294. Harker, F. R, and 1. C. Hallett. 1994. Physiological and mechanical properties of kiwifruit tissue associated with texture change during cool storage. J. Am. Soc. Hort. Sci. 119: 987-993.

210

F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

Harker, F. R., andJ. H. Maindonald. 1994. Ripening of nectarine fruit: Changes in the cell wall, vacuole, and membranes detected using electrical impedance measurements. Plant Physioi. 106:165-171. Harker, F. R., J. H. Maindonald, and P. J. Jackson. 1996. Measurement of apple and kiwifruit texture: operator and instrument differences. J. Am. Soc. Hort. Sci. 121:927-936. Harker, F. R., M. G. H. Stec, 1. C. Hallett, and C. 1. Bennett. 1997. Texture of parenchymatous plant tissue: a comparison between tensile and other instrumental and sensory measurements of tissue strength and juiciness. Postharvest BioI. Technoi. (in press). Harker, F. R., and P. W. Sutherland. 1993. Physiological changes associated with fruit ripening and the development of mealy texture during storage of nectarines. Postharvest BioI. Technoi. 2:269-277. Harper, R., and S. S. Stevens. 1964. Subjective hardness of compliant materials. Quart. J. Exp. Psychoi. 16:204-215. Hartman, H. 1924. Studies relating to the harvesting and storage of apples and pears. Oregon Agr. College Expt. Sta. Bul. 206. Hartman, H., and D. E. Bullis. 1929. Investigations relating to the handling of sweet cherries with special reference to chemical and physiological activities during ripening. Oregon State Agr. College Bui. 247. Hatfield, S. G. S., and M. Knee. 1988. Effects of water loss on apples in storage. Int. J. Food Sci. Technoi. 23:575-583. Heath, M. R. 1982. The effect of maximum biting force and bone loss upon masticatory function and dietary selection of the elderly. Int. Dent. J. 32:345-356. Heath, M. R. 1991. The basic mechanics of mastication: man's adaptive success, p. 143-166. In: J. F. V. Vincent and P. J. Lillford (eds.), Feeding and the texture of food. Cambridge Univ. Press, Cambridge. Heath, M. R., and P. W. Lucas. 1988. Oral perception of texture, p. 465-481. In: J. M. V. Blanchard and J. R. Mitchell (eds.), Food structure, its creation and evaluation. Butterworths, Somerset. Hector, M. P., and R. W. A. Linden. 1987. The possible role of periodontal mechanoreceptors in the control of parotid secretion in man. Quart. J. Expi. Physioi. 72: 285-301. Helkimo, E., G. E. Carlsson, and M. Helkimo. 1978. Chewing efficiency and state of dentition. A methodological study. Acta. Odontol. Scand. 36:33-41. Hildebrand, G. Y. 1931. Studies in the masticatory movements of the human lower jaw. Scand. Arch. Physiol. Suppi. 61:1-190. Hipps, N. A., and M. A. PeTTing. 1989. Effects of soil management systems and nitrogen fertiliser on the firmness and mean fruit weight of Cox's Orange Pippin apples at harvest. J. Sci. Food Agr. 48:507-510. Hobson, G. E. 1968. Cellulase activity during the maturation and ripening of tomato fruit. J. Food Sci. 33:588-592. Hobson, G. E. 1981. Enzymes and texture changes during ripening, p. 123-132. In: J. Friend and M.J.C. Rhodes (eds.). Recent advances in the biochemistry of fruit and vegetables. Academic Press, London. Hobson, G. E. 1987. Low-temperature injury and the storage of ripening tomatoes. J. Hort. Sci. 62:55-62. Holt, C. B. 1970. Measurement oftomato firmness with a universal testing machine. J. Text. Stud. 1:491-501. Holt, J. E., and D. Schoorl. 1985. Mechanical properties and texture of stored apples. J. Text. Stud. 15:377-394. Hopkirk, G., F. R. Harker, and J. E. Harman. 1990. Calcium and firmness of kiwifruit. New Zealand J. Crop Hort. Sci. 18:215-219.

2. TEXTURE OF FRESH FRUIT

211

Hopkirk, G., A. White, M. S. Arnold, and J. H. Maindonald. 1994a. Four new devices for measuring kiwifruit firmness, p.335-339. In: Proc. Australasian Postharvest Conference, Gatton, Queensland, Sept. 20-24, 1993. Univ. of Queensland, Gatton College, Lawes. Hopkirk, G., A. White, D. J. Beever, and S. K. Forbes. 1994b. Influence of postharvest temperatures and the rate of fruit ripening on internal postharvest rots and disorders of New Zealand 'Hass' avocado fruit. New Zealand J. Crop Hart. Sci. 22:305-311. Hopkirk, G., A. White, and B. Mills. 1992. Measuring fruit firmness without destroying the fruit. New Zealand Kiwifruit 87:14-15. Horio, T., and Y. Kawamura. 1989. Effects of texture of food on chewing patterns in human subjects. J. Oral Rehabil. 16:177-183. Horscroft, J. 1989. Production factors influencing the textural qualities of apples. Ph.D. Thesis, Wye College, Univ. London. Huber, D. J. 1983a. The role of cell wall hydrolases in fruit softening. Hort. Rev. 5:169-219. Huber, D. J. 1983b. Polyuronide degradation and hemicellulose modifications in ripening tomato fruit. J. Am. Soc. Hort. Sci. 108:405-409. Huber, D. J. 1984. Strawberry fruit softening: The potential roles ofpolyuronides and hemicelluloses. J. Food Sci. 49:1310-1315. Huber D. J. 1992. The inactivation of pectin depolymerase associated with isolated tomato fruit cell wall: implications for the analysis of pectin solubility and molecular weight. Physiol. Plant. 86:25-32. Hwang, Y.-S., D. J. Huber, and L. G. Albrigo. 1990. Comparison of cell wall components in normal and disordered juice vesicles of grapefruit. J. Am. Soc. Hort. Sci. 115:281-287. Ilker, R, and A. S. Szczesniak. 1990. Structural and chemical basis for texture of plant foodstuffs. J. Text. Stud. 21:1-36. Ingle, M., and J. C. Morris. 1989. Predicting firmness changes of 'Rome' apples in refrigerated storage. J. Am. Soc. Hort. Sci. 114:90-94. Jackman, R L., H. J. Gibson, and D. W. Stanley. 1992a. Effects of chilling on tomato fruit texture. Physiol. Plant. 86: 600-608. Jackman, R L., A. G. Marangoni, and D. W. Stanley. 1990. Measurement of tomato fruit firmness. HortScience 25:781-783. Jackman, R L., A. G. Marangoni, and D. W. Stanley. 1992b. The effects ofturgor pressure on puncture and viscoelastic properties of tomato tissue. J. Text. Stud. 23:491-505. Jackman, R L., and D. W. Stanley. 1992a. Area- and perimeter-dependent properties and failure of mature-green and red-ripe tomato pericarp tissue. J. Text. Stud. 23:461-474. Jackman, R L., and D. W. Stanley. 1992b. Failure mechanisms of tomato pericarp tissue suggested by large and small deformation tests. J. Text. Stud. 23:475-489. Jackman, R L., and D. W. Stanley. 1995a. Perspectives in the textural evaluation of plant foods. Trends Food Sci. Technol. 6:187-194. Jackman, R L., and D. W. Stanley. 1995b. Creep behaviour oftomato pericarp tissue as influenced by ambient temperature ripening and chilled storage. J. Text. Stud. 26:537-552. Jackson A. P., J. F. V. Vincent, R M. Turner. 1988. The mechanical design of nacre. Proc. Royal Soc. B 234:415-440. Jackson, D. 1., and B. G. Coombe. 1966. The growth of apricot fruit. 1. Morphological changes during development and the effects of various tree factors. Austral. J. Agr. Res. 17:465-477. Janick, J. 1991. New fruits from old genes. Acta Hart. 297:25-42. Jenkins, G. N. 1978. The physiology and biochemistry of the mouth. Blackwell, Oxford. Jiffry, M. T. M. 1981. Analysis of particles produced at the end of mastication in subjects with normal dentition. J. Oral Rehabil. 8:113-119.

212

F. HARKER, R. REDGWELL, I. HALLETT, S. MURRAY, AND G. CARTER

Jiffry, M. T. M. 1983. Variation in the particles produced at the end of mastication in subjects with different types of dentition. J. Oral Rehabil. 10:357-362. Jiffry, M. T. M. 1987. The coefficient of swallowable composition of masticated hard-baked soya beans. J. Oral Rehabil. 14:51-54. Johnson, D. S. 1992. The effect of flower and fruit thinillng on the firmness of 'Cox's Orange Pippin' apples at harvest and after storage. J. Hort. Sci. 67:95-101. Johnson, D. S. 1994. Influence oftime of flower and fruit thinning on the firmness of 'Cox's Orange Pippin' apples at harvest and after storage. J. Hort. Sci. 69:197-203. Johnson, D. S., M. J. Marks, and K. Pearson. 1987. Storage quality of Cox's Orange Pippin apples in relation to fruit mineral composition during development. J. Hort. Sci. 62:17-25. Jowitt, R. 1974. The terminology offood texture. J. Text. Stud. 5:351-358. Kader, A. A. 1994. Modified and controlled atmosphere storage of tropical fruits, p. 239-249. In: B. R. Champ, E. Highly, and G. I. Johnson (eds.). Postharvest handling of tropical fruits. Australian Centre for Int. Agr. Res., Canberra. Kader, A. A., C. M. Heintz, and A. Chordas. 1982. Postharvest quality of fresh and canned clingstone peaches as influenced by genotypes and maturity at harvest. J. Am. Soc. Hort. Sci. 107:947-951. Kader, A. A., D. Zagory, and E. 1. Kerbel. 1989. Modified atmosphere packaging of fruits and vegetables. Crit. Rev. Food Sci. Nutr. 28:1-30. Kays, S. J. 1991. Nature and structure of harvested products, p. 23-73. Postharvest physiology of perishable plant products. AVI, Westport, CT. Keegstra, K., K. W. Talmadge, W. D. Bauer, andP. Albersheim. 1973. The structure of plant cell walls III. A model of the walls of suspension-cultured sycamore cells based on the interconnections of macromolecular components. Plant Physiol. 51:188-197. Kerr, A. C. 1961. The physiological regulation of salivary secretion in man. Pergamon Press, Oxford. Khan, A. A., and J. F. V. Vincent. 1990. Anisotropy of apple parenchyma. J. Sci. Food Agr. 52:455-466. Khan, A. A., and J. F. V. Vincent. 1993. Compressive stiffness and fracture properties of apple and potato parenchyma. J. Text. Stud. 24:423-435. King, G. A., K. G. Henderson, and R. E. Lill. 1989. Ultrastructural changes in the nectarine cell wall accompanying ripening and storage in a chilling-resistant and chillingsensitive cultivar. New Zealand J. Crop Hort. Sci. 17:337-344. Kingston, C. M. 1992. Maturity indices for apple and pear. Hort. Rev. 13:407-432. Klein, J. D., S. Lurie, and R. Ben-Arie. 1990. Quality and cell wall components of 'Anna' and 'Granny Smith' apples treated with heat, calcium, and ethylene. J. Am. Soc. Hort. Sci. 115:954-958. Knee, M. 1973. Polysaccharide changes in cell walls ofripening apples. Phytochemistry 12:1543-1549. Knee, M., and I. M. Bartley. 1981. Composition and metabolism of cell wall polysaccharides in ripening fruits, p. 133-148. In: J. Friend and M. J. C. Rhodes (eds.), Recent advances in the biochemistry of fruits and vegetables. Academic Press, London. Knee, M., and D. Farman. 1989. Sources of variation in the quality of CA stored apples. p. 255-261. Proc. 5th Int. Controlled Atmosphere Research Conference, Wenatchee, Washington, USA. Knee, M., and S. M. Smith. 1989. Variation in quality of apple fruits stored after harvest on different dates. J. Hort. Sci. 64:413-419. Kojima, K., N. Sakurai, and S. Kuraishi. 1994. Fruit softening in banana: correlation among stress-relaxation parameters, cell wall components and starch during ripening. Physiol. Plant. 90:772-778.

2. TEXTURE OF FRESH FRUIT

213

Kojima, K, N. Sakurai, S. Kuraishi, R Yamamoto, and D. J. Nevins. 1991. Novel technique for measuring tissue firmness within tomato (Lycopersicon esculentum Mill.) fruit. Plant Physiol. 96:545-550. Kramer, M., R Sanders, H. Bolkan, C. Waters, R K Sheehy, and W. R Hiatt. 1992. Postharvest evaluation of transgenic tomatoes with reduced levels of polygalacturonase: processing, firmness and disease resistance. Postharvest BioI. Technol. 1:241-255. Lallu, N., A. N. Searle, and K A. MacRae. 1989. An investigation of ripening and handling strategies for early season kiwifruit (Actinidia deliciosa cv Hayward). J. Sci. Food Agr. 47:387-400. Lang, A., and KG. Ryan. 1994. Vascular development and sap flow in apple pedicels. Ann. Bot. 74:381-388. Langley, L., and K Cherasin. 1956. The physiological foundation of Dental Practice. Mosby, St Louis. Lapsley, K G. 1989. Texture of fresh apples-evaluation and relationship to structure. Ph.D. Dissert., Swiss Federal Inst. Techno!., Zurich. Lapsley, KG., F. K Escher, and K Hoehn. 1992. The cellular structure of selected apple varieties. Food Struct. 11:339-349. Larsen, M., and C. B. Watkins. 1995. Firmness and concentrations of acetaldehyde, ethyl acetate and ethanol in strawberries stored in controlled and modified atmospheres. Postharvest BioI. Technol. 5:39-50. Lashly, K. S. 1916. Reflex secretion of the human parotid gland. J. Expt. Psychol. 1:461-493. Lau, O. L. 1985. Harvest indices for B.C. apples. British Columbia Orchardist 7:1A-20A. Legge, R L., J. K Thompson, J. K Baker, and M. Lieberman. 1982. The effect of calcium on the fluidity and phase properties of microsomal membranes isolated from postclimacteric Golden Delicious apples. Plant Cell Physiol. 23:161-169. Letham, D. S. 1961. Influence of fertilizer treatment on apple fruit composition and physiology: 1. Influence on cell size and cell number. Austral. J. Agr. Res. 12:600-611. Lidster, P. D., and D. H. Webster. 1983. Effects of modified fruit and leaf weight loss on apple mineral content and firmness loss in storage. HortScience 18:924-926. Lill, R K 1985. Alleviation of internal breakdown of nectarines during cold storage by intermittent warming. Scientia Hort. 25:241-246. LilI, R K, K M. O'Donoghue, and G. A. King. 1989. Postharvest physiology of peaches and nectarines. Hort. Rev. 11:413-452. Lill, R K, and G. J. van der Mespel. 1988. A method for measuring the juice content of mealy nectarines. Scientia Hort. 36:267-271. Lin, T.-T., and R K Pitt. 1986. Rheology of apple and potato tissue as affected by cell turgor pressure. J. Text. Stud. 17:291-313. Linden, R W. A. 1975. Touch thresholds of vital and non vital human teeth. Expt. Neurol. 48:387-390. Litvak, H., S. 1. Silverman, and L. Garfinkel. 1971. Oral stereognosis in dentulous and edentulous subjects. J. Prosthet. Dent. 25: 139-151. Liu, F. W. 1978. Modification of apple quality by high temperature. J. Am. Soc. Hart. Sci. 103:730-732. Loewenstein, W. R, and R Rathkamp. 1955. A study on the pressoreceptive sensibility of the tooth. J. Dent. Res. 34: 287-294. Lucas, P. W., and D. A. Luke. 1983. Methods far analysing the breakdown offood in human mastication. Arch. Oral BioI. 28:813-819. Lucas, P. W., and D. A. Luke. 1984. Optimum mouthful for food comminution in human mastication. Arch. Oral BioI. 29:205-210. Lucas, P. W., D. A. Luke, F. C. T. Voon, C. L. Chew, and ROw. 1986. Food breakdown

214

F. HARKER, R REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

patterns produced by human subjects possessing artificial and natural teeth. J. Oral Rehabil. 13:205-214. Lucas, W. J., B. Ding, and C. van der Schoot. 1993. Plasmodesmata and the supracellular nature of plants. New Phytol. 125:435-476 Lund, J. P. 1990. Specialization of the reflexes ofthe jaws. p. 142-161. In: Taylor, A. (ed.), Neurophysiology of the jaws and teeth. Macmillan, London. Lund, J. P. 1991. Mastication and its control by the brain stem. Crit. Rev. Oral BioI. Med. 2:33-64.

Lurie, S., S. Othman, and A. Borochov. 1995. Effects of heat treatment on plasma membrane of apple fruit. Postharvest BioI. Technol. 5:29-38. Lurie, S., E. Pesis, and R Ben-Arie. 1991. Darkening of sunscald on apples in storage is a non-enzymatic and non-oxidative process. Postharvest BioI. Technol. 1:119-125. Luton, M. T., and D. A. Holland. 1986. The effects of preharvest factors on the quality of stored Conference pears 1. Effects of orchard factors. J. Hart. Sci. 61:23-32. Luza, J. G., R van Gorsel, V. S. Polito, and A. A. Kader. 1992. Chilling injury in peaches: a cytochemical and ultrastructural cell wall study. J. Am. Soc. Hort. Sci. 117:114-118. Lyon, B. G., J. A. Robertson, and F. 1. Meredith. 1993. Sensory descriptive analysis of cv. Cresthaven peaches-maturity, ripening and storage effects. J. Food Sci. 58:177-181. Mackey, A. c., M. M. Hard, and M. V. Zaehringer. 1973. Measuring textural characteristics of fresh fruits and vegetables-apples, carrots, cantaloupe. A manual of selected procedures. Oregon State Agri. Expt. Sta. Tech. Bul. 123. MacRae, E. A., N. Lallu, A. N. Searle, and J. H. Bowen. 1989. Changes in the softening and composition of kiwifruit (Actinidia deliciosa) affected by maturity at harvest and postharvest treatments. J. Sci. Food Agr. 49:413-430. Magness, J. R, and G. F. Taylor. 1925. An improved type of pressure tester for the determination of fruit maturity. USDA Dept. Circ. 350. Maness N. 0., G. H. Brusewitz, and T. G. McCollum. 1992. Internal variation in peach fruit firmness. HortScience 27:903-905. Manley, R S., and 1. C. Braley. 1950. Masticatory performance and efficiency. J. Dent. Res. 29:448-462.

Manley, R S., C. Pfaffmann, D. D. Lathrop, and J. Keyser. 1952. Oral sensory thresholds of persons with natural and artificial dentitions. J. Dent. Res. 31:305-312. Marangoni, A. G., R 1. Jackman, and D. W. Stanley. 1995. Chilling-associated softening of tomato fruit is related to increased pectinmethylesterase activity. J. Food Sci. 60:1277-1281.

Marcelin, 0., P. Williams, and J.-M. Brillouet. 1993. Isolation and characterisation of the two main cell-wall types from guava (Psidium guajava 1.) pulp. Carbohyd. Res. 240:233-234.

Martin-Cabrejas, M. A., K. W. Waldron, R R Selvendran, M. 1. Parker, and G. K. Moates. 1994. Ripening-related changes in the cell walls of Spanish pear (Pyrus communis). Physiol. Plant. 91:671-679 Mathooko, F. M. 1996. Regulation ofrespiratory metabolism in fruits and vegetables by carbon dioxide. Postharvest BioI. Technol. 9:247-264. Matsuo, T., and S. Itoo. 1982. A model experiment for de-astringency of persimmon fruit with high carbon dioxide treatment: In vivo gelation of kaki-tannin by reacting with acetaldehyde. Agr. BioI. Chem. 46:683-689. Matthews, B. 1986. Responses of intradental nerves to mechanical stimulation of teeth in the cat. J. Dent. Res. 65:506. McCloskey, D. 1. 1978. Kinesthetic sensibility. Physiol. Rev. 58:763-820. McCollum, T. G., D. J. Huber, and D. J. Cantliffe. 1989. Modification ofpolyuronides and hemicelluloses during muskmelon fruit softening. Physiol. Plant. 76:303-308.

2. TEXTURE OF FRESH FRUIT

215

McDonald, B. 1990. Precooling, storage, and transport of kiwifruit, p. 429-459. In: 1. J. Warrington and G. C. Weston (eds.), Kiwifruit: science and management. New Zealand Soc. Hort. Sci., Auckland. McGlone, V. A., and P. N. Schaare. 1993. The application of impact response analysis in the New Zealand fruit industry. Am. Soc. Agr. Eng. Paper 93-6537. McLaughlin, N. B. 1987. Statistical models for failure of apple tissue under constantstrain-rate loading. J. Text. Stud. 18:173-186. Meheriuk, M., and W. D. Lane. 1983. A comparison of fruit from spur and standard 'McIntosh' at harvest and after controlled-atmosphere storage. HortScience 18:220-221. Meier-Ewart, K, K Gleitsmann, and F. Reiter. 1974. Acoustic jaw reflex in man: its relationship to other brain-stem and microreflexes. Electroencephal. Clin. Neurophysiol. 36:629-637. Meilgaard, M., G. V. Civille, and B. T. Carr. 1991. Sensory evaluation techniques, 2nd edition. CRC Press, Boca Raton, Florida. Meyer, O. B. 1921. Zur Frage der Gelensensibilitat. Dtsch. Z. Nervenheilk. 70:60-61 (as reported by Dubner et al. 1978, p. 168). Mittal, J. P., N. N. Mohsenin, and M. G. Sharma. 1987. Rheological characterization of apple cortex. J. Text. Stud. 18:65-93. Mohsenin, N. N. 1963. A testing machine for determination of mechanical and rheological properties of agricultural products. Bul. 701, Penn. Agr. Expt. Sta. Mohsenin, N. N. 1970. Physical properties of plant and animal materials: structure, physical characteristics and mechanical properties. Gordan and Breach Science Publ., New York. Mohsenin, N. N. 1977. Characterization and failure in solid foods with particular reference to fruits and vegetables. J. Text. Stud. 8:169-193. Mohsenin, N. N., and J. P. Mittal. 1977. Use ofrheological terms and correlation of compatible measurements in food texture research. J. Text. Stud. 8:395-408. Morimoto, T. 1990. Perception of intraoral stimulation. p. 369-390. In: A. Taylor (ed.), Neurophysiology of the jaws and teeth. Macmillan, London. Morimoto, T., and Y. Kawamura. 1976. Conditioning-effect of vibratory stimulation on dimensional discrimination of objects held between human tooth arches. Arch. Oral BioI. 21:219-220. Morimoto, T., H. Takebe, T. Hamada, and Y. Kawamura. 1981. Oral kinaesthesia in patients with Duchenne muscular dystrophy. J. Neurol. Sci. 49:285-291. Mowlana, F., M. R. Heath, A. van der Bilt., and H. W. van der Glas. 1994. Assessment of chewing efficiency: a comparison of particle size distribution determined using optical scanning and sieving of almonds. J. Oral Rehabil. 21:545-551. Murillo,J. A., G. H. Brusewitz, M. L. Stone, andN. O. Maness. 1994. Texture measurement of mealy peaches. Presented at June 19-22, Am. Soc. Agr. Eng. lnt. Summer Meeting, Paper 946002, Am. Soc. Agr. Eng., St. Joseph, MI. Neal, G. E. 1965. Changes occurring in the cell walls of strawberries during ripening. J. Sci. Food Agr. 16:604-611. Neilsen, G. H., M. Meheriuk, and E. J. Hogue. 1984. The effect of orchard floor management and nitrogen fertilization on nutrient uptake and fruit quality of 'Golden Delicious' apple trees. HortScience 19:547-550. Nishiyama, T., M. Funaskoshi, and Y. Kawamura. 1967. A study of sensitivity of the human tooth. J. Dent. Res. 46:136. Noiton, D., and C. J. A. Shelbourne. 1992. Quantitative genetics in an apple breeding strategy. Euphytica 60:213-219. Northeast Regional Agricultural Engineering Service. 1993. CA 93. Proc. 6th Int. Controlled Atmosphere Res. Conference. Northeast Regional Agr. Eng. Service, Ithaca, New York.

216

F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

O'Donoghue, K M., and D. J. Huber. 1992. Modification of matrix polysaccharides during avocado (Persea americana) fruit ripening: an assessment of the role of Cx-cellulase. Physiol. Plant. 86:33-42. O'Donoghue, K M. and W. Martin. 1988. Juice loss as a simple method for measuring integrity changes in berry fruit. J. Hort. Sci. 63:217-220. Okimoto, M. C. 1948. Anatomy and histology of the pineapple inflorescence and fruit. Bot. Gaz.ll0:217-231. Olthoff, L. W. 1986. Comminution and neuromuscular mechanisms in human mastication. Thesis, Univ. Utrecht, The Netherlands. Olthoff, L. W., A. van der Bilt, F. Bosman, and H. H. Kleizen. 1984. Distribution of particle sizes in food comminuted by human mastication. Arch. Oral BioI. 29:899-903. O'Mahony, M., S-Y. Wong, and N. Odbert. 1985. Initial sensory evaluation of Bing cherries treated with low doses of gamma-radiation. J. Food Sci. 50:1048-1050. Organisation for Economic Co-operation and Development. 1983. Apples and Pears. Int. standardisation of fruit and vegetables. OECD, Paris. Organisation for Economic Co-operation and Development. 1992. Kiwifruit. International standardisation of fruit and vegetables. OECD, Paris. Ottenhoff, F. A. M. 1992. Neuro-muscular control of mandibular movements during chewing in man. Thesis, Univ. Utrecht, The Netherlands. Ourecky, D. K., and M. C. Bourne. 1968. Measurement of strawberry texture with an Instron Machine. Proc. Am. Soc. Hort. Sci. 93:317-325. Owall, B., and K M0ller. 1974. Oral tactile sensibility during biting and chewing. Odont Revy 25: 327-346. Ozawa, T., T. H. Lilley, and K Haslam. 1987. Polyphenol interactions: Astringency in ripening fruit. Phytochemistry 26:2937-2942. Paoletti, F., E. Moneta, A. Bertone, and F. Sinesio. 1993. Mechanical properties and sensory evaluation of selected apple cultivars. Lebensm.-Wiss.Technol. 26:264-270. Paterson, A. H., K S. Lander, J. D. Hewitt, S. Peterson, S. K Lincoln, and S. D. Tanksley. 1988. Resolution of quantitative traits into mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 335:721-726. Patten, K. D., and K L. Proebsting. 1986. Effect of different artificial shading times and natural light intensities on the fruit quality of 'Bing' sweet cherry. J. Am. Soc. Hort. Sci. 111:360-363. Peleg, M. 1979. Evaluation by instrumental methods of the textural properties of some tropical fruits. J. Text. Stud. 10:45-65. Peleg, M. 1980. Theoretical analysis of the relationship between mechanical hardness and its sensory assessment. J. Food Sci. 45:1156-1160. Peleg, M., and L. Gomez Brito. 1977. Textural changes in ripening plantains. J. Text. Stud. 7:457-463. Peleg, M., L. Gomez Brito, and Y. Malevski. 1976. Compressive failure patterns of some juicy fruits. J. Food Sci. 41:1320-1324. Penfield, W., and T. Rasmussen. 1950. The cerebral cortex of man. Macmillan, New York. Percy, A. K, 1. K W. O'Brien, P. K Jameson, L. D. Melton, K A. MacRae, and R. J. Redgwell. 1996. Xyloglucan endotransglycosylase activity during fruit development and ripening of apple and kiwifruit. Physiol. Plant. 96:43-50. Perera, C. O. 1994. Calcium oxalate crystals: the irritant factor in kiwifruit and other plant foods. Australian Chemistry Resource Book 13:26-34. Perera, C. 0., and 1. C. Hallett. 1991. Characteristics of the irritant (catch) factor in processed kiwifruit. Acta Hort. 297:675-680. Perera, C. 0., 1. C. Hallett, T. T. Nguyen, and J. C. Charles. 1990. Calcium oxalate crystals: the irritant factor in kiwifruit. J. Food Sci. 55:1066-1069, 1080.

2. TEXTURE OF FRESH FRUIT

217

Pesis, E., Y. Fuchs, and G. Zauberman. 1978. Cellulase activity and fruit softening in avocado. Plant Physiol. 61:416-419. Petrell, R J., N. N. Mohsenin, and S. Wallner. 1980. Dynamic mechanical properties ofthe apple cortex in relation to sample location and ripening. J. Text. Stud. 10:217-229. Phelps, T., and S. Murray. 1995. Ethnic preferences for blueberries. New Zealand Commercial Grower 50(10):40-41. Pitt, R E. 1982. Models for the rheology and statistical strength of uniformly stressed vegetative tissue. Trans. Am. Soc. Agr. Eng. 25:1776-1784. Pitt, R E., and H. L. Chen. 1983. Time-dependent aspects of the strength and rheology of vegetative tissue. Trans. Am. Soc. Agr. Eng. 26:1275-1280. Pitts, M. J., J. A Abbott, P. R Armstrong, G. K. Brown, G. H. Brusewitz, D. C. Davis, M. J. Delwiche, N. Galili, S. Gan-Mor, C. G. Haugh, D. R Massie, A Mizrach, D. Nahir, K. Peleg, R P. Rohrbach, Y. Sarig. P. N. Schaare, Z. Schmilovitch, 1. Shmulevich, M. L. Stone, R L. Stroshine, and F. L. Younce. 1994. Sensing fruit and vegetable firmness, p.31-43. In: Nondestructive technologies for quality evaluation of fruits and vegetables. Proc. Int. workshop, BARD. Am. Soc. Agr. Eng., St. Joseph, M1. Pitts, M., R Cavalieri, and S. Drake. 1993. Apple firmness sensor update, p. 75-78. Proceedings of the 9th Annual Washington Tree Fruit Postharvest Conference. Washington State Hort. Assoc., Wenatchee. Platt-Aloia, K. A., and W. W. Thomson. 1981. Ultrastructure of the mesocarp of mature avocado fruit and changes associated with ripening. Ann. Bot. 48:451-465. Platt-Aloia, K. A, W. W. Thomson, and R E. Young. 1980. Ultrastructural changes in the walls of ripening avocados: Transmission, scanning, and freeze fracture microscopy. Bot. Gaz. 141:366-373. Plesh, 0., B. Bishop, and W. D. McCall. 1986. Effect of gum hardness on chewing pattern. Expt. Neurol. 92:502-512. Plocharski, W. 1982. Strawberries-quality of fruits, their storage life and suitability for processing. Fruit Sci. Rep. 9:111-122. Polderdijk, J. J., L. M. M. Tijskens, J. E. Robbers, and H. C. P. M. van der Valko 1993. Predictive model of keeping quality of tomatoes. Postharvest BioI. Technol. 2:179-185. Poovaiah, B. W., G. M. Glenn, and AS. N. Reddy. 1988. Calcium and fruit softening: physiology and biochemistry. Hort. Rev. 10:107-152. Poovaiah, B. W., Y. Mizrahi, H. C. Dostal, J. H. Cherry, and A. C. Leopold. 1975. Water permeability during tomato fruit development in normal and rin non-ripening mutant. Plant Physiol. 56:813-815. Porritt, S. W., and P. D. Lidster. 1978. The effect of pre-storage heating on ripening and senescence of apples during cold storage. J. Am. Soc. Hort. Sci. 103:584-587. Posselt, U. 1956. Range of movement of the mandible. J. Am. Dent. Assoc. 56:227-242. Prasad, M., G. King, and M. Spiers. 1988. Nutrient influence on storage quality. p. 22-24. Proc. NZKA Nat. Res. Conf., Rotorua, July 1988. New Zealand Kiwifruit Special Publ. 2. Prasad, M., and T. M. Spiers. 1991. The effect of nutrition on the storage quality of kiwifruit (a review). Acta Hort. 297 (II):579-585. Pressey, R, 1977. Enzymes involved in fruit softening, p. 172-191. In: RL. Ory and AJ. St Angelo (eds.), Enzymes in food and beverage processing, ACS Symp. Series 47, Am. Chem. Soc., Washington DC. Pressey, R 1983. f3-galactosidases in ripening tomatoes. Plant Physiol. 71:132-135. Rao, V. N. M. 1984. Dynamic force-deformation properties of foods. Food Technol. 38:103-109. Read, S. M., and A Bacic. 1996. Cell wall porosity and its determination. In H. F. Linskens and J. F. Jackson (eds.), Plant cell wall analysis, Modern methods of plant analysis, vol. 17. Springer Verlag, Berlin.

218

F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

Redgwell, R. J., A Fischer, E. Kendall, and E. A. MacRae. 1997a. Galactose loss and fruit ripening: high molecular weight arabinogalactans in the pectic polysaccharides of fruit cell walls. Planta (in press). Redgwell, R. J., and S. C. Fry. 1993. Xyloglucan endotransglycosylase activity increases during kiwifruit (Actinidia deliciosa) ripening. Plant Physiol. 103:1399-1406. Redgwell, R. J., and R. Harker. 1995. Softening of kiwifruit discs: effect of inhibition of galactose loss from cell walls. Phytochemistry 39:1319-1323. Redgwell, R. J., E. MacRae, 1. Hallett, M. Fischer, J. Perry, and R. Harker. 1997b. In-vivo and in-vitro swelling of cell walls during fruit ripening. Planta (in press). Redgwell, R. J., 1. D. Melton, and D. J. Brasch. 1991. Cell-wall polysaccharides of kiwifruit (Actinidia deliciosa): effect of ripening on the structural features of cell-wall materials. Carbohydrate Res. 209:191-202. Redgwell, R. J., 1. D. Melton, and D. J. Brasch. 1992. Cell wall dissolution in ripening kiwifruit (Actinidia deliciosa): Solubilization of the pectic polymers. Plant Physiol. 98:71-81. Redgwell, R. J., and A E. Percy. 1992. Cell wall changes during on-vine softening of kiwifruit. New Zealand J. Crop Hort. Sci. 20:453-456. Reeve, R. M. 1953. Histological investigations of texture in apples II: Structure and intercellular spaces. Food Res. 18:604-617. Reeve, R. M. 1970. Relationships of histological structure to texture of fresh and processed fruits and vegetables. J. Text. Stud. 1:247-284. Retamales, J., T. Cooper, J. Streif, and J. c. Kania. 1992. Preventing cold storage disorders in nectarines. J. Hort. Sci. 67:619-626. Richardson, A 1986. The effects of herbicide soil management systems and nitrogen fertilizer on the eating quality of Cox's Orange Pippin apples. J. Hort. Sci. 61:447-456. Richardson, C., and G. E. Hobson. 1987. Compositional changes in normal and mutant tomato fruit during ripening and storage. J. Sci. Food Agr. 40:245-252. Robbins, J., and P. P. Moore. 1990. Relationship of fruit morphology and weight to fruit strength in 'Meeker' red raspberry. HortScience 25:679-681. Robbins, J., and P. P. Moore. 1991. Fruit morphology and fruit strength in a seedling population of red raspberry. HortScience 26:294-295. Robbins, J., and T. Sjulin. 1986. A comparison of two methods for measurement of fruit strength in red raspberry. HortScience 21:1054-1055. Rose, D. H., M. H. HaIler, and P. 1. Harding. 1934. Relation oftemperature of fruit to firmness in strawberries. Proc. Am. Soc. Hort. Sci. 32:429-430. Ross, G. S., R. J. Redgwell, and E. A MacRae. 1993. Kiwifruit f3-galactosidase: isolation and activity against specific fruit cell-wall polysaccharides. Planta 189:499-506. Ross, G. S., T. Wegrzyn, E. A. MacRae, and R. J. Redgwell. 1994. Apple f3-galactosidase: Activity against cell wall polysaccharides and characterization of a related eDNA clone. Plant Physiol. 106:521-528. Roth, 1. 1977. Fruits of angiosperms. Gebruder Borntraeger, Berlin. Roudot, A-C., F. Duprat, and E. Pietri. 1990. Simulation of a penetrometric test on apples using Voronoi-Delaunay tessellation. Food Struct. 9:215-222. Rugh, J. D. 1972. Variation in human masticatory behaviour under temporal constraints. J. Compo Physiol. Psychol. 80:169-174. Rygol, J., and U. Luttge. 1983. Water-relation parameters of giant and normal cells of Capsicum annuum pericarp. Plant Cell Environ. 6:545-553. Rygol, J., and U. Luttge. 1984. Effects of various benzene derivatives, dodecylbenzenesulfonate and HgC1 2 on water-relation parameters at the cellular level. Physiol. Veg. 22:783-792.

2. TEXTURE OF FRESH FRUIT

219

Sakai, W. S., S. S. Shiroma, and M. A. Nagao. 1984. A study of raphide microstructure in relation to irritation. Scanning Electron Microscopy II:979-986. Sakurai, N., and D. J. Nevins. 1992. Evaluation of stress-relaxation in fruit tissue. Hort Tech. 2:398-402. Sakurai, N., and D. J. Nevins. 1993. Changes in physical properties and cell wall polysaccharides of tomato (Lycopersicon esculentum) pericarp tissues. Physiol. Plant. 89:681-686. Salisbury, F. B., and C. W. Ross. 1985. Plant physiology, 3rd ed. Wadsworth, Belmont, California. Saltveit, M. K, Jr. 1978. Selecting an experimental design for pressure testing apples. J. Am. Soc. Hort. Sci. 103:105-109. Saltveit, M. K, Jr. 1984. Effects of temperature on firmness and bruising of 'Sarkrimson Delicious' and 'Golden Delicious' apples. HortScience 19:550-551. Salunkhe, D. K., H. R. Bolin, and N. R. Reddy. 1991. Storage, processing, and nutritional quality of fruits and vegetables (2nd ed.): Vol 1. Fresh fruits and vegetables. CRC Press, Boca Raton, Florida. Sams, C. K, W. S. Conway, J. A Abbott, R. J. Lewis, and N. Ben-Shalom. 1993. Firmness and decay of apples following postharvest pressure infiltration of calcium and heat treatment. J. Am. Soc. Hort. Sci. 118:623-627. Sanger, S. 1. 1994. Pressure tester search has changed. Good Fruit Grower 45:7. Sanson, G. D. 1989. Morphological adaptations ofteeth to diets and feeding in the Macropodoidea. p. 151-168. In: G. Grigg, P. Jarman and 1. Hume (eds.), Kangaroos wallabies and rat-kangaroos. Surrey Beatty & Sons, New South Wales, Australia. Sarig, Y., and S. Orlovsky. 1974. Viscoelastic properties of Shamouti oranges. J. Text. Stud. 5:339-349. Schiffman, S. S. 1973. The dietary rehabilitation clinic and a multi-aspect, dietary, and behavioral approach to the treatment of obesity. (b) Taste and smell of foods. Meet. Assoc. Adv. Behav. Ther., Miami Beach, Fla. (cited in M. C. Bourne. 1982. Food texture and viscosity: concept and measurement, p. 2. Academic Press, San Diego.) Schood, D., and J. K Holt. 1983. A practical method for tensile testing of apple tissue. J. Text. Stud. 14:155-164. Schroeder, C. A, J. Briggs, and K Kay. 1959. Fruit graft in avocado. Calif. Avocado Soc. Yearb.43:108-109. Schuch, W., J. Kanczler, D. Roberston, G. Hobson, G. Tucker, D. Grierson, S. Bright, and C. Bird. 1991. Fruit quality characteristics of transgenic tomato fruit with altered polygalacturonase activity. HortScience 26:1517-1520. Schweingruber, P. J., P. A Carroad, S. J. Leonard, J. R. Heil, T. K. Wolcott, M. O'Mahony, and A Wilson. 1981. Evaluation of instrumental methods for firmness measurement of fresh and canned clingstone peaches. J. Text. Stud. 12:389-399. Scott, K. J, and R. B. H. Wills. 1977. Vacuum infilration of calcium chloride: A method for reducing bitter pit and senescence of apples during storage at ambient temperatures. HortScience 12: 71-72. Seymour, G. B., M. N'Diaye, H. Wainwright, and G. A Tucker. 1990. Effects of cultivar and harvest maturity on ripening of mangoes during storage. J. Hort. Sci. 65:479-483. Shackel, K. A., C. Greve, J. M. Labavitch, and H. Ahmadi. 1991. Cell turgor changes associated with ripening in tomato pericarp tissue. Plant Physiol. 97:814-816. Sharples, R. 0., 1973. Orchard and climatic factors, p. 173-226. In: J. C. Fidler, B. G. Wilkinson, K. L. Edney, and R. O. Sharples (eds.), The biology of apple and pear storage. Commonwealth Agricultural Bureau, Slough, U.K. Sharples, R. O. 1980. The influence of orchard nutrition on the storage quality of apples

220

F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

and pears grown in the United Kingdom, p.17-28. In: D. Atkinson, J. K Jackson, R. O. Sharples, and W. M. Waller (eds.), Mineral nutrition of fruit trees. Butterworths, London. Shaw, D. V., R. S. Bringhurst, and V. Voth. 1987. Genetic variation for quality traits in an advanced-cycle breeding population of strawberries. J. Am. Soc. Hort. Sci. 112:699-702. Sherman, P., and F. S. Deghaidy. 1978. Force-deformation conditions associated with the evaluation of brittleness and crispness in selected foods. J. Text. Stud. 9: 437-459. Shomer, 1., K Chalutz, R. Vasiliver, K Lomaniec, and M. Berman. 1989. Sclerification of juice sacs in pummelo (Citrus grandis) fruit. Can. J. Bot. 67:625-632. Sidel, J. 1., H. Stone, and J. Bloomquist. 1983. Industrial approaches to defining quality, pA8-57. In: A. A. Williams and R. K. Atkin (eds.), Sensory quality in foods and beverages: definition, measurement and control. Ellis Horwood Ltd., Chichester, u.K. SiiriHi, H. S., and P. Laine. 1963. The tactile sensibility ofthe periodontium to slight axial loadings of the teeth. Acta. Odontol. Scand. 21:415-426. SiiriHi., H. S., and P. Laine. 1969. Occlusal tactile threshold in denture wearers. Acta Odontol. Scand. 27:193-197. Siirilii, H. S., and P. Laine. 1972. Sensory thresholds in discriminating differences in thickness between the teeth by different degrees of mouth opening. Proc. Finn. Dent. Soc. 68:134-139. Simon, K W. 1977. Leakage from fruit cells in water. J. Expt. Bot. 28:1147-1152. Smith, C. J. S., C. F. Watson, P. C. Morris, C. R. Bird, G. B. Seymour, J. K Gray, C. Arnold, G. A. Tucker, W. Schuch., S. Harding, and D. Grierson. 1990. Inheritance and effect on ripening of antisense polygalacturonase genes in transgenic tomatoes. Plant Mol. BioI. 14:369-379. Smith, G. S., J. G. Buwalda, and C. J. Clark. 1988. Nutrient dynamics of a kiwifruit ecosystem. Scientia Hort. 37:87-109. Smith, N. J. S., G. A. Tucker, and M. J. Jeger. 1989. Softening and cell wall changes in bananas and plantains. Aspects Appl. BioI. 20:57-65. Smith, R. B. 1992. Controlled atmosphere storage of 'Redcoat' strawberry fruit. J. Am. Soc. Hort. Sci. 117:260-264. Smith, R. B., and 1. J. Skog. 1992. Postharvest carbon dioxide treatment enhances firmness of several cultivars of strawberry. HortScience 27:420-421. Smith, S. M. 1985. Measurement of the quality of apples: Recommendations of an EEC working group. Commission of the European Communities, Brussels. Smith, S. M., andJ. R. Stow. 1985. Variation in storage and eating quality of clones of Cox's Orange Pippin apples. J. Hort. Sci. 60:297-303. Smock, R. M. 1979. Controlled atmosphere storage of fruits. Hort. Rev. 1:301-336. Sonego, 1., R. Ben-Arie, J. Raynal, and J. C. Pech. 1995. Biochemical and physical evaluation of textural characteristics of nectarines exhibiting woolly breakdown: NMR imaging, x-ray computed tomography and pectin composition. Postharvest BioI. Technol. 5:187-198. Stanley, D. W. 1991. Biological membrane deterioration and associated quality losses in food tissues. Crit. Rev. Food Sci. Nutr. 30: 487-553. Stec, M. G. H., andJ. Charles. 1993. Japanese market access for New Zealand apples: Japanese tastes in apples. Orchardist of New Zealand 66(10):48-49. Stec, M. G. H., J. A. Hodgson, K A. MacRae, and C. M. Triggs. 1989. Role of fruit firmness in the sensory evaluation of kiwifruit (Actinidia deliciosa cv Hayward). J. Sci. Food Agr. 47:417-433. Sterling, C. 1954. Sclereid development and the texture of Bartlett pears. Food Res. 19:433-443. Steudle, K, and J. Wieneke. 1985. Changes in water relations and elastic properties of apple fruit cells during growth and development. J. Am. Soc. Hort. Sci. 110:824-829.

2. TEXTURE OF FRESH FRUIT

221

Stewart, D. 1927. Some aspects of the innervation of the teeth. Proc. Royal. Soc. Med. 20: 1675-1686. Stow, J. R. 1984. The controlled atmosphere storage of Conference pears. Hort. Sci. 59:507-513. Stow, J. R. 1988. The effect of cooling rate and harvest date on the storage behaviour of 'Conference' pears. J. Hort. Sci. 63:59-67. Stow, J. 1989. The involvement of calcium ions in maintenance of apple fruit tissue structure. J. Expt. Bot. 40:1053-1057. Studman, C. J. 1993. Fruit firmness measurement techniques~anew approach. Int. Conference Physical Properties Agricultural Materials, Sept. 6-8 1993, Bonn, Germany. Paper 93-1189. Studman, C. J., and Yuwana. 1992. Twist test for measuring fruit firmness. J. Text. Stud. 23:215-227. Sundstrom, F. J., and S. J. Carter. 1983. Influence ofK and Ca on quality and yield of watermelon. J. Am. Soc. Hort. Sci. 108:879-881. Swarts, D. H. 1981. Fermometer-ondersoeke by avokado's. South African Avocado Growers Assoc. Yearb. 4:42-46. Szczesniak, A. S. 1963. Classification of textural characteristics. J. Food Sci. 28:385-389. Szczesniak, A. S. 1972. Consumer awareness of and attitudes to food texture II. Children and teenagers. J. Text. Stud. 3:206-217. Szczesniak, A. S. 1987. Correlating sensory with instrumental texture measurements~an overview of recent developments. J. Text. Stud. 18:1-15. Szczesniak, A. S. 1990. Psychorheology and texture as factors controlling the consumer acceptance offood. Cereal Foods World 351:1201-1205. Szczesniak, A. S., and M. C. Bourne. 1969. Sensory evaluation of food firmness. J. Text. Stud. 1:52-64. Szczesniak, A. S., M. A. Brandt, and H. H. Friedman. 1963. Development of standard rating scales for mechanical parameters of texture and correlation between the objective and the sensory methods of texture evaluation. J. Food Sci. 28:397-403. Szczesniak, A. S., P. R. Humbaugh, and H. W. Block. 1970. Behaviour of different foods in the standard shear compression cell of the shear press and the effect of sample weight on peak area and maximum force. J. Text. Stud. 1:356-378. Szczesniak, A. S., and R. Ilker. 1988. The meaning of textural characteristics~juiciness in plant foodstuffs. J. Text. Stud. 19:61-78. Szczesniak, A. S., and E. L. Kahn. 1971. Consumer awareness of and attitudes to food texture. I. Adults. J. Text. Stud. 2:280-295. Szczesniak, A. S., and E. L. Kahn. 1984. Texture contrasts and combinations: A valued consumer attribute. J. Text. Stud. 15:285-301. Szczesniak, A. S., and D. H. Kleyn. 1963. Consumer awareness of texture and other food attributes. Food Technol. 74:74-77. Taira, S., H. Itamura, K. Abe, and S. Watanabe. 1989. Comparison of the characteristics of removal of astringency in two Japanese persimmon cultivars, 'Denkuro' and 'Hiratanenashi'. J. Japan Soc. Hort. Sci. 58:319-325. Tang, c.-S., and W. S. Sakai. 1983. Acridity oftaro and related plants, p.148-163. In: J.-K. Wang (ed.), Taro-a review of Colocasia esculanta and its potentials. Univ. Hawaii Press, Honolulu. Tetley, U. 1930. A study of the anatomical development of the apple and some observations on the "pectic constituents" of the cell-walls. J. Pomol. Hort. Sci. 8:153-172. Thilander, B. 1961. Innervation of the temporo-mandibular joint capsule in man: An anatomic investigation and a neurophysiological study of the perception of mandibular position. Trans. Royal Sch. Dent. Stockholm Umea 7:9-67.

r.

222

F. HARKER, R. REDGWELL, 1. HALLETT, S. MURRAY, AND G. CARTER

Thompson, J. M., T. van der Zwet, and W. A. Oitto. 1974. Inheritance of grit content in fruits of Pyrus communis L. J. Am. Soc. Hart. Sci. 99:141-143. Thompson, R. L., H. P. Fleming, and D. D. Hamann. 1992. Delineation of puncture forces for exocarp and mesocarp tissues in cucumber fruit. J. Text. Stud. 23:169-184. Thompson, R. L., H. P. Fleming, D. D. Hamann, and J. R. Monroe. 1982. Method for determination of firmness in cucumber slices. J. Text. Stud. 13:311-324. Timbers, G. E., P. W. Voisey, and M. Kloek. 1985. Influence of number and thickness of blades on the performance of the Kramer type shear-compression cell. J. Text. Stud. 16:303-311. Tomas, A. D. 1988. Cellular water relations in plants. Water Sci. Rev. 3:186-277. Tomas, A D., R. A Leigh, J. A Palta, and J. H. H. Williams. 1992. Sucrose and cell water relations, p.71-89. In: C. J. Pollock, J. F. Farrar, A. J. Gordon (eds.), Carbon partitioning within and between organisms. BIOS Scientific Publ., Oxford. Ugalde, T. D., P. H. Jerie, and D. J. Chalmers. 1988. Intercellular pH of peach and apricot mesocarp. Austral. J. Plant Physiol. 15:505-517. U.S. Dept. of Agriculture. 1976. United States standards for grades of apples. Agr. Marketing Service, Washington, DC. van der Bilt, A, H. W. van der Glas, and F. Bosman. 1992. A computer simulation of the influence of selection and breakage. J. Dent. Res. 71: 458-465. Vickers, Z. M. 1981. Relationships of chewing sounds to judgments of crispness, crunchiness and hardness. J. Food Sci. 47:121-124. Vickers, Z. M. 1988. Evaluation of crispness, p. 433-448. In: J. M. V. Blanshard and J. R. Mitchell (eds.), Food Structure-its creation and evaluation. Butterworths, Somerset. Vickers, Z., and M. C. Bourne. 1976. A psychoacoustical theory of crispness. J. Food Sci. 41:1158-1164. Vincent, J. F. V. 1989. Relationship between density and stiffness of apple flesh. J. Sci. Food Agr. 47:443-462. Vincent, J. F. V. 1990. Structural biomaterials, revised edition. Princeton Univ. Press, Princeton. Vincent, J. F. V. 1991. Texture of plants and fruits, p.19-33. In: J. F. V. Vincent and P. J. Lillford (eds.), Feeding and the texture of food. Cambridge Univ. Press, Cambridge. Vincent, J. F. V. 1994. Texture of plants. Modern Methods Plant Anal. 16:57-72. Vincent, J. F. V., G. Jeronimidis, A A Khan, and H. Luyten. 1991. The wedge fracture test: a new method for measurement of food texture. J. Text. Stud. 22:45-57. Voisey, P. W. 1971. Modernization of texture instrumentation. J. Text. Stud. 2:129-195. Voisey, P. W. 1977a. Examination of operational aspects of fruit pressure tests. Can. Inst. Food Sci. Technol. 10:284-294. Voisey, P. W. 1977b. Interpretation of force-deformation curves from the shear-compression cell. J. Text. Stud. 8:19-37. Voisey, P. W., and R. Crete. 1973. A technique for establishing instrumental conditions for measuring food firmness to simulate consumer evaluations. J. Text. Stud. 4:371377. Voisey, P. W., and M. Kloek. 1981. Effect of cell size on the performance of the shear-compression texture test cell. J. Text. Stud. 12:133-139. von Mollendorff, L. J., G. Jacobs, and O. T. de Villiers. 1992. Cold storage influences internal characteristics of nectarines during ripening. HortScience 27:1295-1297. Wade, N. L. 1981. Effects of storage atmosphere, temperature and calcium on lowtemperature injury of peach fruit. Scientia Hort. 15:145-154. Wallner, S. J., and H. L. Bloom. 1977. Characteristics of tomato cell wall degradation in vitro: implications for the study of fruit-softening enzymes. Plant Physiol. 60:207210.

2. TEXTURE OF FRESH FRUIT

223

Wang, S. Y., P. C. Wang, and M. Faust. 1988. Non-destructive detection of watercore in apple with nuclear magnetic resonance imaging. Scientia Hort. 35:227-234. Wantabe, S., and C. Dawes. 1988. A comparison of the effects of tasting and chewing foods on the flow rate of whole saliva in man. Arch. Oral BioI. 33:761-764. Watada, A. E., J. A Abbott, and R. E. Hardenburg. 1980. Sensory charateristics of apple fruit. J. Am. Soc. Hort. Sci. 105:371-375. Watkins, c., R. Harker, P. Brookfield, and S. Tustin. 1993. Maturity of Royal Gala, Braeburn and Fuji-the New Zealand experience. p. 16-19. Proc. 9th Annu. Washington Treefruit Postharvest Conf. Wenatchee. Watkins, C., and J. Harman. 1981. Use of penetrometer to measure flesh firmness of fruit. Orchardist of New Zealand 54:14, 16. Watkins, C. B., S. Picton, and D. Grierson. 1990. Stimulation and inhibition of expression of ripening-related mRNAs in tomatoes as influenced by chilling temperatures. J. Plant Physiol. 136:318-323. Wayler, A. H., and H. H. Chauncey. 1983. Impact of complete dentures and impaired natural dentition on masticatory performance on food choice in healthy aging men. J. Prosthet. Dent. 49:427-433. Wayler, A H., M. E. Muench, K. K. Kapur, and H. H. Chauncey. 1984. Masticatory performance and food acceptability in persons with removable partial dentures full dentures and intact dentition. J. Gerontol. 39:284-289. Werner, R. A, and C. Frenkel. 1978. Rapid changes in the firmness of peaches as influenced by temperature. HortScience 13:470-471. Werner, R. A, 1. F. Hough, and C. Frenkel. 1978. Rehardening of peach fruit in cold storage. J. Am. Soc. Hort. Sci. 103:90-91. White, A G., and H. 1. Selby. 1994. Segregation for fruit characters in some crosses of European and Asian pears, p.235-238. In: H. Schmidt and M. Kellerhals (eds.), Progress in temperate fruit breeding. Kluwer Academic Publ., The Netherlands. Wiley, R. c., and G. E. Stembridge. 1961. Factors influencing apple texture. Proc. Am. Soc. Hort. Sci. 77:60-72. Wilkinson, B. G. 1965. Some effects of storage under different conditions of humidity on the physical properties of apples. J. Hort. Sci. 40:58-65. Williams, A A., and C. S. Carter. 1977. A language and procedure for the sensory assessment of Cox's Orange Pippin apples. J. Sci. Food Agr. 28:1090-1104. Williams, M. W. 1980. Retention offruit firmness and increase in vegetative growth and fruit set of apples with aminoethoxyvinylglycine. HortScience 15:76-77. Williams, W. N., and 1. 1. La Pointe. 1972. Relationship among oral form recognition, interdental thickness discrimination and interdental weight discrimination. Percept. Mot. Skills 35:191-194. Winton, A 1., and K. B. Winton. 1935. The structure and composition offoods. Vol II: Vegetables, legumes, fruits. Wiley, New York. Worthington, J. T., and J. N. Yeatman. 1968. A statistical evaluation of objective measurement of apple firmness. Proc. Am. Soc. Hort. Sci. 92:739-747. Yamada, M., and T. Kumano. 1969. Mobility and assessment of the tactile sensation of teeth. Int. Dent. J. 19: 295-296. Yamaki, S., and K. Matsuda. 1977. Changes in the activities of some cell wall-degrading enzymes during development and ripening of Japanese pear fruit (Pyrus seratina Rehder var. culta Rehder). Plant Cell Physiol. 18:81-93. Yang, Y. M., and N. Mohsenin. 1974. Analysis of the mechanics of the fruit pressure tester. J. Text. Stud. 5:213-238. Yoshida, M. 1976. Genetical studies on the fruit quality of peach varieties: III. Texture and keeping quality. Bul. Fruit Tree Res. Sta. A Hiratsuka 3:1-16.

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Yurkstas, A 1954. The effect of missing teeth on masticatory performance and efficiency. J. Prosthet. Dent. 4:120-123. Yurkstas, A A 1965. The masticatory act. J. Prosthet. Dent. 15: 248-260. Yurkstas, A A, H. H. Fridley, and R. S. Manley. 1951. A functional evaluation of fixed and removable bridgework. J. Prosthet. Dent. 1:570-577. Yurkstas, A. A, and R. S. Manley. 1949. Measurement of occlusal contact area effective in mastication. Am. J. Othod. 35:185-195. Zhang, L-X, and R. E. Paull. 1990. Ripening behavior of papaya genotypes. HortScience 25:454-455.

Zhang, M. 1. N., and J. H. M. Willison. 1991. Electrical impedance analysis in plant tissues: A double shell model. J. Exp. Bot. 42:1465-1475. Zielinski, Q. B., F. C. Reimer, and V. L. Quackenbush. 1965. Breeding behavior of fruit characteristics in pears, Pyrus communis L. Proc. Am. Soc. Hort. Sci. 86:81-87.

3 The Use of Magnetic Resonance Imaging in Plant Science Miklos Faust Fruit Laboratory Beltsville Agricultural Research Center Agricultural Research Service Beltsville, Maryland 20705 Paul C. Wang Department of Radiology Howard University Hospital Washington, D.C. 20060 John Moos Fruit Laboratory Beltsville Agricultural Research Center Agricultural Research Service Beltsville, Maryland 20705

I. Introduction II. Theory of MR Imaging A. 2DFT Spin-Echo Imaging B. Three-Dimensional FT Imaging C. Techniques for Rapid Scan D. Image Contrast III. MRI A. Seeds B. Buds C. Fungal Colonization D. Detecting Physiological Disorders and Maturity Changes E. Roots in Soil F. Water Flow in Plants

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IV. Artifacts V. Conclusions Literature Cited

I. INTRODUCTION

There is a long-standing desire of plant scientists to study various physiological processes in plants in situ in a nondestructive manner. In addition, there is a need to visualize the tissue where the process is measured. Few techniques are available that are capable of providing both physiological and anatomical information and most are destructive in nature. Nuclear magnetic resonance (MR) imaging, more recently called magnetic resonance imaging (MRI) or magnetic resonance microscopy (MRM) , is one such technique. MR has been extensively used as an analytic tool in chemistry and biochemistry to study molecular structure. Since the late 1970s and early 19S0s, MRI has been widely used in medical and agricultural applications (Pfeffer and Gerasimowicz 19S9; Stark and Bradley 1992). First reports on in vivo MR spectroscopy in plants appeared in the mid-1970s. Schaeffer et al. (1975) used 13C MR spectroscopy to follow the incorporation of CO 2 into sugars and lipids in soybean, and Llinas et al. (1975) demonstrated the possibility of in vivo 15N MR spectroscopy after isotopic enrichment. Linescan images of spring onions (Hinshaw 1976) and okra (Mansfield and Pykett 1975) soon followed. Lauterbur (1977) devised his "zeugmatogram" of green pepper; Hinshaw et al. (1979) demonstrated the resolving power of MRI in apple, plum, and 'Satsuma' orange; Lai and Lauterbur (19S1) created a full three-dimensional image reconstruction of coconut; and Mansfield and Morris (19S2) used echo-polar imaging technique on red pepper. Intense work with MRI started about a decade after the original appearance of in vivo MRI when Rogers et al. (19S5) and Omassa et al. (19S5) used it successfully to visualize root systems in soil in situ. The scope of research using MRI was greatly expanded in the following decade. As research in this area grew, botanists, agronomists, and horticulturists became involved in MRI, increasing the need for a practical review on the subject. MRI technology has been thoroughly described in several references (Callaghan 1991; Kuhn 1990). This review summarizes the uses and applications of the imaging technique and calls to the attention of user-investigators the variety of possible image interpretations and the scope of understanding needed to formulate meaningful physiological interpretations of the data generated by MRI. MRI can produce two- or three-dimensional images that are very use-

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ful in visualizing inner structures of plants. It is also helpful in understanding transport processes through vascular systems. A unique feature of MRI is that it may record intensities of certain processes by visualizing proton relaxation times, which can be extremely useful for the physiologist. Images created byMR enable us to visualize tissues affected by pathogens injury, or physiological problems which may give a better understanding of the nature of disease development, tissue reaction to injury, and development of techniques to avoid these problems. Numerous applications for which MRI is well suited are being developed in the plant sciences. Our purpose in this review is to summarize MRI techniques useful for the horticultural sciences. Several reviews on various aspects of MRI of plants have been published, including those by Gassner (1989), Kramer et al. (1990), and Johnson et al. (1992). Clark et al. (1996) reviewed the postharvest application of MRI of fruits and vegetables. Morris et al. (1990b) and Kuhn (1990) reviewed MR microscopy of plants, and Sarafis et al. (1992) listed the areas of plant science and agriculture as being amenable to MR microscopy without discussing any of them in detail. Pope (1992) discussed the application of chemical shift microscopy to noninvasive histochemistry of plant materials, and Walter et al. (1992) reviewed studies of plant systems by in vivo MR spectroscopy. The use ofMRI in food science (Schmidt and Lai 1991; McCarthy 1994; Clark et al. 1996) is not covered here. II. THEORY OF MR IMAGING

The MR signal originates from the interaction of nuclear spins with a radio frequency (RF) pulse. A nucleus with an odd number of protons and/or neutrons has a magnetic field associated with its charge distribution and nuclear spin. The magnetic moment describes the strength and orientation of this magnetic field. In the presence of an external magnetic field (B o ) provided by a strong magnet, the nuclear spins will precess around the Bo field. The precessing frequency is called the Lamar frequency, OJ, and it is proportional to the strength of the magnetic field, B o •

where yis the gyromagnetic ratio of the nucleus. For different nuclei, y values are different, thus their Lamor frequencies are different in the same magnetic field. The gyromagnetic ratios of H, 13C, and 31p are 42.576,10.705, and 17.236 MHz/tesla, respectively. After an initial dis-

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turbance from its equilibrium by a RF pulse, the precession of nuclear spins will induce an oscillating signal in a receiver coil placed perpendicular to the B o field. The nuclei will absorb a specific RF wave which is characterized by its energy, hm/Zn (h, Planck constant), and move away from the direction of the external magnetic field, B o . After the RF pulse stops, the nuclear spins will release energy to their environment and return to their equilibrium orientation realigned with the direction of the external magnetic field. Resonance is energy coupling created by the energy that individual nuclei selectively absorb and release at a later time. Nuclei return to the equilibrium state through two mechanisms: a spin-lattice and a spin-spin interaction. Each mechanism is characterized by a time constant called relaxation time. The spin-lattice relaxation time (TI ) describes the process of realignment of the magnetic moment with the external magnetic field. The spin-spin relaxation time (Tz) describes the time-dependent decay of MR signal due to the dephasing process of the individual spins with respect to each other. The presence of electrons and their orbital motions will generate a small magnetic field which shields the nucleus from the external magnetic field, B o • Strictly speaking, the magnetic field experienced by the nucleus includes not only the external applied field (B o ) but also the effect of electron shielding. The shielding variations of nuclei in different chemical environments cause resonance frequency shifts. This is the origin of the phenomenon known as chemical shift. The appearance of a spectrum for a given compound is governed by intramolecular chemical shift differences. Based on the chemical shifts and the peak intensities in a spectrum, the chemical structure of an unknown compound can be determined. The chemical shift difference increases proportionally when B o increases. By convention, the chemical shift is presented in parts per million (ppm). The chemical shift, measured in Hz, is divided by the operating frequency of the spectrometer. MRI relies on the ability to spatially encode MR signals of the sample and reconstruct an image from these signals. The spatial encoding is achieved by applying a magnetic gradient field in addition to the B o field. For the convenience of the discussion, we use a Cartesian coordinate system to describe the static magnetic field and the gradient. The direction is defined as the direction of the static magnetic field. The x and y directions are two octagonal axes lying in a plane perpendicular to the z direction. The term gradient indicates that the magnetic field is altered along a selected direction. For example, when the magnetic field varies linearly along the x-axis in a Cartesian coordinate system (dB/ dx = Gx = constant), the resonance frequencies become dependent upon the x-axis

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locations of the volume elements of interest. The frequency variations associated with the positions may be written

where X is the position of the nuclear spins. This principle can be illustrated with a simple example consisting of two water-filled tubes aligned with the z-axis (axis of the static field) but the cross-sections of the tubes are at two different locations in X (Fig. 3.1). In the absence of the gradient Ex (G x = 0), the resonance frequencies are the same and the samples are indistinguishable in the spectrum. However, when the gradients are turned on, the two samples will no longer experience the same magnetic field strength, and the resulting MR signals appear as a projection of the sample tube cross-section in the frequency domain (in the x-axis direction). Both the x and y gradients can be turned on at the same time to create gradients with different magnitudes and different directions on

B(X) I I I I

I

------------~-------------r-------I I

Tube 1

I

I I

Tube 2

I I I

X, w(X)

Fig. 3.1. Water in two tubes positioned at different magnetic fields, B(Xj, resonate at different Larmor frequencies, W(Xj. The magnetic field is composed of a static magnetic field, B o' and a gradient field, G. The height of the NMR signal represents the total amount of water. Without gradient field, Gx ' the signals from both tubes will be at (00 are not distinguishable.

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x-y plane. One way to create a two-dimensional image is to apply a

series of angular, equally spaced gradients to the sample. This will generate a series of projections along the direction of each gradient. Using a filtered back-projection technique, well known from X-raycomputed tomography, an image can be reconstructed from the projections of the sample. This method is called the projection-reconstruction (PR) technique, and it was the first method used in MRI. However, this method has been largely abandoned in favor of two-dimensional Fourier transform (2DFT) methods. The PR technique is sensitive to the effect of magnetic field inhomogeneity, and it takes a long time to obtain an image because the data acquisition is inefficient. In agricultural applications, some of these restrictions are not important and the PR technique sometimes is preferable because it can detect short T z signals and has a better signal-to-noise ratio. A. 2DFT Spin-Echo Imaging

One of the most commonly used FT imaging techniques is the spin-echo (SE) technique. The timing diagram for a 2DFT SE technique describing a series of events occurring in a time sequence is shown in Fig. 3.2. The

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231

sequence includes two radio frequency (RF) pulses with sufficient magnitude and duration to cause the magnetization to rotate by 90 or 180°. In general, the 90° pulse is a selective pulse, that is, it has a well-defined frequency range. When the 90° pulse is on, a magnetic field gradient is also on. It serves as a slice-selection gradient, Gs (for example, in the z direction). The combination of the selective 90° pulse and Gs determines the location of imaging slices. The bandwidth of the RF pulse and the amplitude of the slice-selection gradient determine the slice thickness (Fig. 3.3). The slice thickness can be reduced by either increasing the gradient strength or decreasing the RF bandwidth. This also holds true for the in-plane resolution. Resolution within the selected slice is achieved by applying magnetic gradients along in-plane directions (in this example, the x and y directions) before and during signal measurement. When the MR machine is pushed to its limits of high spatial resolution with small pixel size and thin slices, the image will exhibit "noise" interference. Besides the limitation of machine hardware and the intrinsic weak signal from the sample, the ultimate spatial resolution is also limited by other physical properties of the sample such as nuclear

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M. FAUST, P. WANG, AND J. MAAS

232

self-diffusion. The second pulse, which produces a 180 0 rotation, is applied to refocus the dephasing process of the nuclear spins due to the magnetic field inhomogeneity. The MR signals are generated and detected by a RF coil. They occur at the echo time (TE), which is twice as long as the time between 90 and 180 pulses. During the signal measurement, a frequency-encoding or readout gradient (Gr) is applied in the x direction. The signals from the plane spread into a range of slightly different frequencies corresponding to different locations in x. In order to resolve the signal from a position in the x-y plane, another field phaseencoding gradient (Gp ) in the y direction is required. The phase-encoding gradient is applied between the 90 and 180 pulses. The imaging sequence thus includes a 90 0 pulse followed by a phase-encoding gradient, a 180 0 pulse, and data acquisition. The entire sample excitation and detection process is repeated with a new phase-encoding gradient strength. There are many phase-encoding steps in each complete sequence. The number of phase-encoding steps determines the spatial resolution in that direction. The time required for each phase-encoding step is called the repetition time (TR). The total imaging time is TR times the number of phase-encoding steps. If there is more than one average, Le., if the whole phase-encoding cycle is repeated more than once, then the total imaging time increases accordingly. Generally, multiple contiguous images are required to study a sample. During the repetition time, the magnetic moment will realign with the external magnetic field through the T 1 relaxation process. TR cannot be shortened without adverse effects on contrast. In a typical spin-echo sequence, the actual data acquisition uses only a fraction of the repetition time. It is a common practice to utilize the time following the sampling to apply a series of sequential excitations to several other slices; this permits multislice imaging without increasing total imaging time. 0

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3. THE USE OF MAGNETIC RESONANCE IMAGING IN PLANT SCIENCE

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the subsequent application of the readout gradient, Gx • Each independent increment of the phase-encoding gradient, Gyand Gx ' requires one excitation. The total imaging time is the repetition time multiplied by the number of phase-encoding steps in both y and z directions. Because of the additional phase-encoding in the third dimension, the total imaging time usually is long. If the increments for the phase-encoding gradients, Gyand Gz ' are equal, the resolution among all three spatial directions can be made the same. This is called isotropic 3D imaging. In an anisotropic 3D imaging technique, on the other hand, one of the voxel dimensions is not equal to the other two. For example, if one of the phase-encoding gradients, Gz ' has fewer increment steps, the spacing between contiguous x-y planes will be larger than the pixel size in the x-yplane. The main advantage of isotropic 3D data acquisition is the ability to display images in any plane without significant loss of resolution. Another incentive to use 3D rather

234

M. FAUST, P. WANG, AND J. MAAS

than 2D multislice techniques is the ability of the 3D technique to provide thin contiguous slices with minimum interslice interference. C. Techniques for Rapid Scan

There are numerous motivations to acquire MRI data quickly in a clinical setting. The principal objectives are to improve efficiency and to minimize patient discomfort. Although these may not be important in many plant applications, shortening scan time does open new avenues for dynamic studies, such as imaging of water transport in plants. For a typical2DFT spin-echo technique, the total imaging time is determined by TR, the number of phase-encoding steps, and the number of averages. The number of averages is dictated by the required signal-to-noise ratio. The number of phase-encoding steps can be reduced at a given field-ofview size; however, it results in increased pixel size and a loss of spatial resolution. It is possible to maintain spatial resolution by acquiring only half the number of phase-encoding steps and to retrospectively make use of the inherent symmetry properties of the raw data to fill in the missing half of the information. In this half-Fourier imaging approach, time is traded for signal-to-noise ratio. The repetition time can also be shortened. However, the limitation of TR is dictated by the rate of the T 1 relaxation process. As TR is shortened, the signal-to-noise ratio decreases as well. For a sample with a long T 1 relaxation time, a long TR must be used. For fast MRI techniques, the imaging times are of the order of a few seconds to a few tenths of milliseconds. Some of the fast imaging methods include gradient-echo (GE), steady-state-free-precession (SSFP), multiple spin-echo, and echo-planar imaging (EPI) techniques. For a detailed review of fast imaging techniques, see Frahm et al. (1992). The gradient-echo technique uses a low RF flip angle (a rotation less than 90°, such as 30°) for excitation and short TR to acquire of a gradient echo. The contrast capabilities of the low flip angle, gradient-echo sequence include T 1 and spin density-weighted images. In this technique, images commonly exhibit a strong flow enhancement due to the inflow of unsaturated spins from outside the imaging volume. In a SSFP sequence, the repetition time is very short as compared with T z. A steady state for both the longitudinal and transverse magnetizations can be established. The signals of a SSFP sequence are obtained from either free-induction-decay (FID) or echo when the magnetic field gradients are applied. The overall phase of the transverse magnetization must be made constant from one repetition cycle to the next. For the SSFP-FID technique, the image contrast behavior various from T 1

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235

weighting for medium to high flip angles to spin-density contrasts at low flip angles. The SSFP-echo technique provides a high degree of T z weighting. For the multiple spin-echo imaging technique, a train of differently phase-encoded spin echos are generated by a series of 180° refocusing pulses following an initial 90° RF pulse. In this technique, only samples with very long T z relaxation times provide a sufficient signal. Therefore, the technique yields strong T z contrasts. For echo-planar imaging (EPI), an oscillating frequency-encoding readout gradient is used in combination with small blips of short phaseencoding gradient pulses. Among all of the fast imaging techniques, the EPI technique provides the shortest total imaging time, which can be in the order of milliseconds. D.

Image Contrast

The primary goal of MRI is to discern the target object from the background, that is, to enhance the contrast between the two. The contrast of an MR image is influenced by both the inherent MR properties of the sample and the parameters of the imaging technique used. The inherent properties of the sample include spin density (p), T I and Tz relaxation times, and the mobility of the spins. The mobility of spins includes the effects of spin motion caused by diffusion and flow. The imaging parameters depend on the technique used. For a 2DFT spin-echo technique, the imaging parameters include the repetition time, TR, the echo time, TE, and the number of phase-encoding steps. Assuming all the spins are stationary, the signal intensity, 5, in this technique, is proportional to p exp (-Tz/TE) exp (1-TR/TI ). For a given TE and TR, the signal intensity varies according to the spin density and relaxation times. In imaging fruits and vegetables using a 2DFT spin-echo sequence, different structures can be discerned as a result of the differences between these intrinsic parameters. However, the contrast may also vary if different TE or TR is applied. In general, for a median TR (TR = T I ) as the TE increases, the image is more T z weighted. If the TR is long and similar to T I and the TE spin-echo sequence is short, the image contrast predominantly reflects the spin-density differences. For a medium TR and a short TE, the image contrast is T I weighted. Different imaging techniques enhance or weight the image contrast using various properties of the sample such as variation in elf-diffusion coefficient of water. In an inversion-recovery (IR) technique, a 180° pulse is used to first invert the spin followed by a spin-echo technique. Because the spin is fully inverted in an IR technique, it will have greater T I weighting than a spin-

236

M. FAUST, P. WANG, AND J. MAAS

echo sequence. As a second, in a diffusion-weighted sequence (Stejskal and Tanner 1965), two diffusion gradients can be added before and after the 180 0 pulse. The image obtained will be diffusion weighted. Other techniques can be employed to obtain flow-sensitive images or chemicalspecific images (Le Behan 1995; Metzler et al. 1995; Alger 1993). III. MRI

Many investigators have used MRI solely to determine the anatomical features of plants. Others have used MRI to nondestructively view the inner structure of plant organs. Table 3.1 summarizes reports in this area. McDougall et al. (1992) compared images of flax produced by MR with highly magnified photomicrographs of sectioned tissues. Comparison of the images with the photomicrographs showed the high accuracy and utility of MRI. Although IH MR micro-imaging illustrates many features of structures, there are some inherent limits to the accuracy of the technique. Bowtell et al. (1992) found that when averaging 12 data sets, in-plane resolution as small as 6.8 f.1m could be achieved using a slice thickness of 100 f.1m, and a 256 x 256 image. The procedure took 50 min per sample. More commonly, however, images produced have a 40 f.1m resolution and slice thickness of 200 to 400 f.1m. This may result in some loss in cellular details but allows the investigator to acquire the picture more rapidly. The MRI represents a composition superimposition of the mobile IH density of a 200 to 400 f.1m horizontal "slice" of tissue. In most plant tissues, this provides few problems as the change in tissue composition over 400 f.1m is slight and the superimposition of tissues with similar vertical mobile IH produces a clearly defined image. However, if the vertical arrangement of cells in tissues changes greatly within the 400 f.1m slice, the image obtained is blurred and indistinct (McDougall et al. 1992). The image created by MR greatly depends on the technique employed and the plant material under study (Color Plate 1). So far, apparently, one cannot generalize as to which techniques create the best image. A few examples given here will illustrate this point. Use of three-dimensional acquisition protocols and thin slices (0.215 mm) resulted in especially sharp images of fig fruit (MacFall and Johnson 1994). Short TR and TE were necessary to obtain clear bright images of vascular traces in okra (MacFall and Johnson 1994) and cucumber stems (Veres et al. 1991a). Changes in the vascular system in a leaf during abscission also are clearly demonstrable. Millard and Chudek (1993) were able to show the devel-

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Plate 1 Upper picture shows inversion recovery spin-echo image of terminal buds of apple at various TE times. Lower picture shows T z image, proton-density image, and error-signal distribution for T z image. Pixels with high variance values are indicated on the image. In both pictures, the upper row is a local selection 'USDA B3,' the lower row is the cultivar 'Gala.'

Plate 2 Density, T 1 and error images (left to right) of strawberries of various ages. Top row 5, center row 15, and bottom row 25 days after anthesis (DAA). Bottom row is considered fully ripe fruit. Achenes (seeds) are highly visible on the surface of the 5 DAA on the T 1 images, whereas in older fruit this feature is missing. Proton-density scale is 8 equal sections. Tz values area as follows: dark red, <10; red, 11-15; yellow, 16-20; green, 26-30; maroon, 31-35; pink, 36-50; and purple, 51-100 ms.

Plate 3 Proton-density images (left) and T z maps (right) of cross-sections of apple buds and adjoining stems (the larger circle is the stem, the smaller circle is the bud). Lower (b, d) images illustrating buds at the end of the winter. Upper images illustrate buds 48 h after they were triggered to grow with cytokinin analog (thidiazuron) treatments. Color scales and error definition are the same as in Plate 2.

Plate 4 (top plate) Inversion recovery spin-echo images of strawberry fruit with multiple problems. Infected with Botrytis conerea. Colletotrichum acutatum and bruised at two spots. Top row: inversion recovery times (left to right) 1.5, 1.0, 0.5, and 0.1 s. Bottom row: Density, T1 , and error images (left to right). Location of problems marked on the inset, lower left. Color scale is the same as in Plate 2. Plate 5 (bottom plate) Inversion recovery spinecho MR images of strawberry fruit infected with Phytophtora cactorum. Top row: inversion recovery times (left to right) 1.5, 1.0, 0.5, and 0.1 s. Bottom row: Density T 1 , and error images (left to right). Color scale and error definition are the same as in Plate 2.

3. THE USE OF MAGNETIC RESONANCE IMAGING IN PLANT SCIENCE

Table 3.1.

237

The use of MR imaging for the purpose of determining plant structures. Plant

Acetabularia mediterranea

Organ and Tissue Reproductive caps of giant singlecelled alga

Borkh.)

Harrison et al. 1988

Eccles and Callaghan 1986

Alyssum tenium Apple (Malus x domestica

Reference

Fruit

MacFall and Johnson 1994

Stem (graft union)

Warmund et al. 1990, 1993

Aspen (Populus tremuloides Mich:x.)

Wood

Hall et al. 1986

Aster tripolium

Roots

Zimmerman et al. 1992

Blechnum fern (Blechnum unilaterale)

Veres et al. 1991a

Cherry (Prunus avium 1.)

Wood

Wang and Chang 1986

Cherry (Prunus avium 1.)

Petiole

Millard and Chudek 1993

Fig (Ficus carica 1.)

Fruit

MacFall and Johnson 1994

Flax (Linum usitatissimum 1.)

Stem

McDougall et al. 1992

Geranium (Pelargonium sp.)

Petiole

Bowtell et al. 1992

Gooseberry (Ribes glossularia 1.)

Fruit

Williamson et al. 1993

Kiwifruit (Actinidia deliciosa Planch)

Fruit

MacFall and Johnson 1994

Maize (Zea mays 1.)

Roots

Connelly et al. 1987;

Seedling

Kutchenbrod et al. 1995

Melon (Cucumis melo 1.)

Pope 1992

Mesembryanthem um crystallinum L.

Walter et al. 1989 Pods

Bowtell et al. 1992; MacFall and Johnson 1994

Persimmon (Diospyros khaki 1.)

Fruit

MacFall and Johnson 1994

Potato (Solanum tuberosum 1.)

Tuber

MacFall and Johnson 1994

Red raspberry (Rubus ideus 1.)

Fruit

Goodman et al. 1992b; Williamson et al. 1992b

Squash (Cucurbita sp.)

Stem

Duce et al. 1992a,b

Strawberry (Fragaria x ananassa 1.)

Fruit

Maas et al. 1992; Maas and Millard 1993; Maas and Line 1995a,b; Goodman et al. 1996

Sweet fennel (Foeniculum vulgare Hill)

Mericarp

Sarafis et al. 1990

Okra (Hibicus esculentum 1.)

Veres et al. 1991b

238

M. FAUST, P. WANG, AND J. MAAS

opment of the abscission zone in cherry petioles and the discontinuity in vascular strands caused by the development of the abscission layer. Most tissues of a squash stem can be distinguished with T 1 and N(H) maps (relative spin density) better than with Tz-weighted images (Veres et al. 1991b). Sclerenchyma and vessel elements are more easily seen with N(H)-weighted images because of the relatively low water content of sclerenchyma and high water content of squash stem vessel elements (Veres et al. 1991b). Alteration of the image-acquisition protocols resulted in very different contrast patterns in potato (MacFall and Johnson 1994). In kiwifruit, Tz-weighted images showed a dark vasculature, whereas T1 -weighted images produced a bright vasculature. In three-dimensional images, the maturity of persimmon fruit also made a difference in the signal intensity of developing seeds (MacFall and Johnson 194). In red raspberry fruit, vascular bundles have shown short relaxation times, creating a short T 1 effect (Goodman et al. 1992b). Independent determinations showed significant amounts of manganese ions in the receptacle tissue which in all probability caused the short T 1 relaxation times (Goodman et al. 1992b). When fruit was viewed by both spin-echo and gradient-echo methods with a 3 h image accumulation time in each case, the resulting images were very different. With the spin-echo method, seeds are visible as black elliptical structures and the boundaries of druplets as white lines. In contrast, the gradient-echo method did not show boundaries between druplets and showed crescent-shaped striations (Williamson et al. 1992b). Appreciable image intensities from parenchyma and vascular tissues of strawberry fruit were obtained only with a spin-echo imaging sequence and short values ofTE «5 ms), indicating predominantly short Tz values for these tissues (Goodman et al. 1996). These differences were most apparent in immature receptacles (Maas and Line 1995b). In contrast, in mature gooseberry fruit, the gradient-echo method clearly delineated the ovary wall from the parenchyma tissues which surround seeds in the locular cavity (Williamson et al. 1993). At very short TE (3.8 ms), MR images showed radial regions of high signal intensity extending out from the stele to the periphery of Aster tripolium roots. These areas were separated by regions of low signal intensity which, because of the low TE, can be attributed to air-filled regions. This assumption was confirmed by infiltrating the roots with nutrient solutions which produced very narrow regions of low-signal intensity (Zimmerman et al. 1992). Williamson et al. (1994) used a computer technique coupled with MR microscopy that highlights surfaces of specific tissue types. This combination of techniques was especially useful in delineation of the vas-

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239

cular tissue in the raspberry receptacle. The inner xylem and outer phloem tissues each appeared as a pair of fused columns in the surfacerendered images. The separation of vascular tissues from the cortical parenchyma was clear, but compromises had to be made in the range of gray tones to best view parts of interest in the vascular system. Leaves are relatively difficult to image with magnetic resonance (Veres et al. 1993). Expanded leaves contain much intercellular space and are often thin in one plane. These characteristics limit the number of water protons available to provide signals for imaging. Veres et al. (1993) found that the most difficult barrier to overcome in imaging leaves with high resolution was obtaining good signal-to-noise ratios (SNR). They used three steps to assure a good SNR: (1) a Helmholz-style coil and support apparatus specifically designed for leaf imaging; (2) imaging at high magnetic field strength, 7 or 9AT; and (3) short echo-times (TE), usually 3 to 4 ms. They needed from 17 min to 4.5 h to acquire the image and used the entire thickness of the leaf as the slice. A. Seeds One of the first studies examined developing seeds of snap beans during dehydration and germinating seeds of Vida faba L. as they were undergoing hydration (Gassner 1989). Maturing seeds of higher plants dehydrate and their water content decreases to about 10% of the total weight (designated as dry seed) compared with water content of more than 70% in developing plant tissues. The low moisture allows the seed to survive for long periods of time. Rehydration of dry seeds often results in germination. During germination, seeds take up water rapidly for 24 h and water uptake may continue for 4 to 5 d. Much is known about the biochemical event taking place during maturation and germination but much less is known about the role of water in the process. MRI can be used to simultaneously monitor the state of water (free or bound) and determine what fraction of water protons are bound to macromolecules and how tightly they are bound by the anatomically distinct tissues of the seed. Because MRI is nondestructive, it is possible to follow the imbibition process on the same seed over several days. Kano et al. (1990) followed free water distribution in barley and soybean seeds during maturation. In barley, free water content (Tz), as detected by imaging, paralleled respiratory activity, and the amount and the mobility of free water were considered to be indications of biological activity. T z decreased from 25 to 5 ms as water content decreased from 30 to 8%, and Oz uptake declined from 20 to 1,umol.g-1 dw.min- 1 . Disappearance of the free-water signal and the accumulation of polysacharides

M. FAUST, P. WANG, AND J. MAAS

240

and proteins proceeded simultaneously in the endosperm of barley seed. Images of barley ears on the 14th day after heading revealed that a large amount of water in the seeds was along the sieve tubes in the vascular bundles. Vascular bundles were thin in ripening seeds, and free water was undetectable in the vascular bundles at 39 days after heading. Water in glume, seed coat, pericarp, and aleurone layers could not be resolved by the apparatus used in this investigation (JEOL GSX-270). In maturing soybean seeds, large amounts of water were detected in both the pod and seed before the stage of maximum seed size (Kano et al. 1990). At the stage of maximum seed size when the water content of the seed was approximately 60%, the water signal from the seed became weak even though water was observed in the pod. At this stage, most of the water in the seed is considered to be bound by stored materials. As maturation progressed, the T 1 of seeds shortened while the T 1 of pods lengthened. T 1 values were as short as 0.6 to 0.7 s. Usually, T 1 in physiologically active cells exceeds 1.0 s. Although a proton signal was detected in dry seeds, Kano et al. (1990) did not attribute it to water but to fatty acids which were stored as triglycerides in the spheromones. The major seed characteristics and relaxation values in soybean during maturation are listed in Table 3.2. Eccles et al. (1988) attempted to elucidate the dependence of water diffusion on local structural features. They used an amalgamation of the pulsed-gradient, spin-echo, and steady-gradient MR imaging techniques in which they observed localized self-diffusion of water in sliced sections of wheat grain at a traverse resolution of -150 /lm. They identified large differences in localized self-diffusion of water. For example, self-diffusion was 5 x 10-10m 2 s-1 in the dorsal endosperm but it was 9 x 10-10m 2 s-1 in the combined aleurone, testa, and pericarp layers. Song et al. (1992) applied MRI to nondestructive measurements of the transient moisture transfer in individual maize kernels during the dry-

Table 3.2. Changes in water content and relaxation time (Ti ) during maturation of soybean. Data from Kano et al. (1990). Seed Growth Stage and Maturation Seed set Early growth Mid-growth Maximum size Seed drying stage

Fresh Weight of Seeds (mg)

Water Content of Seeds (%)

T i , Seed (s)

T i , Pod (s)

3 54 383 683 295

85.0 85.0 71.0 62.4 17.7

1.15 0.64 0.72 0.56 0.37

0.47 0.58 1.00 1.20

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241

ing process. They created three-dimensional images and T 1 , T z, and proton density maps of the kernel. They used the shortest possible echo time (TE = 8 ms) and the longest repetition time (TR = 73 ms). The readout gradient, Gy , was set to 7.5 x 10-4T.cm-1 • The phase-encoding gradients, Gx and Gz ' were set to 10-6 T.cm-1 /step with 32 increments and 5.7 x 10-6T.cm-1 /step with 128 increments. The gradient settings resulted in a voxel size of 93 x 156 x 312 J.1m 3 • The images revealed that the moisture content was highest in the floury endosperm and lowest in the vitreous endosperm. The moisture content of the pericarp was also high. During drying for 1 hr at 49°C, T 1 decreased from 560 to 535 ms and decreased further to 491 ms following 1 hr cooling at 20°C. Tz was generally low in the kernel (4-5 ms) and did not change considerably during drying. Song et al. concluded that moisture was distributed nonuniformly in the maize kernel, and that during drying the moisture loss was faster from the floury endosperm than from the vitreous endosperm. In the germ, the embryo lost moisture first. In contrast to maturation when water content decreases, Morris et al. (1990a) followed the imbibition process in castor bean (Ricinus communis) as water content increased. Castor bean seeds contain about 18% protein (dry weight) and the endosperms contain 60% lipids (dry weight). Lipid T 1 values ranged from 440 to 510 ms for the full range of water uptake times (0-100 h) with a very slight tendency toward longer relaxation times at higher water contents. The lipid Tz values showed a slightly more pronounced variation with water content, increasing from an initial value of 17 ms to 21 ms after 100 h of water uptake. Water T 1 values decreased as water uptake and fresh weight of the seeds increased. Water Tz measurements revealed a multiexponential decay. Typically, Tz values were initially 3 ms and increased to only 9 ms after 100 h of water uptake. This short relaxation time explained the poor quality of the images obtained in the preliminary studies. Water T1 measurements suggested the presence of two primary pools of water in the imbibed castor bean seeds: a pool of free water (T1 = 1.37 s) and a pool of more restricted water (T1 = 0.34 s), both of which increased with imbibition time. T 1 values also showed an anatomical differentiation. The T 1 values in the embryo were long (typically 1.7 s) compared with the endosperm (typically 0.3 s). They also noted a short T 1 component within the vascular structure of the endosperm. In oilseeds, lipids contain a major portion of mobile protons. Halloin et al. (1993) imaged lipid in pecan (Carya illioensis) embryos. Chemicalshift imaging allowed imaging of air dried embryos containing a single major peak for lipids. Imbibed seeds contained separate peaks for water and lipids. Values for T 1 and Tz in pecan seeds are shown in Table 3.3.

M. FAUST, P. WANG, AND J. MAAS

242

Table 3.3. T 1 and T z values for pecan seeds. Data from Halloin et al. (1993).

Pecan sample Air dried Imbibed Oil peak Water peak

Moisture content (% ofF.W).

T 1 (ms)

Tz(ms)

3.4

283

22

439 261

32 14

Images of embryos damaged by fungal infection or insects were less intense, indicating the lower oil content in the embryo. Sarafis et al. (1990) combined MRI at the microscopic scale with chemical-shift selection to demonstrate the application of MRI to plant histochemistry. By separating peaks in the spectrum of fennel seeds, they were able to detect lipid components as giving a chemical shift of 1.3 ppm and transanethole (flavor compound of fennel) at 6.7 ppm. Pope (1992) also used chemical-shift selective images of the fruit of coffee plant to show water and oil distribution in separate images. He used 1 mm-slice thickness, TE = 24 ms, TR = 1 sec, and 60 x 60 pixels. With this technique, it is possible to map the distribution of aromatics and carbohydrates with a resolution of a few tens of microns (Sarafis et al. 1990; Pope et al. 1991). Even though imaging of seeds seems to be a straightforward procedure, seeds in red raspberry fruit appeared as featureless black regions (Goodman et al. 1992b; Williamson et al. 1992b). Neither adjusting delay times from 10 s to 2 s, 0.4 s, or 0.08 s, nor adjusting parameters to give T1 - or Tz-weighted images produced any visible internal features in the seeds. Short T z times have been cited (Connelly et al. 1987; Morris et al. 1990a) to explain the low intensities often found in images of seeds. However, Goodman et al. (1992b) thought that the blackness of the seeds within isolated druplets was the result of a combination of small size and comparatively low mobile proton density compared with that in the surrounding mesocarp. The size of the seed is apparently not a factor in producing the dark areas because similar dark areas were observed when mango fruit, which has a large seed (Joyce et al. 1993), is imaged. Age of the seeds makes a difference in the image obtained. Unfertilized ovules and immature achenes of strawbery fruit imaged intensely bright in receptacles from 3 days before to 4 days after anthesis and gave at least a partial image 10 days after anthesis. Fifteen to 25 days after anthesis, achenes did not appear in the images (Color Plate 2) (Maas and Line

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1995b). Because strawberry achene is a fruit and not a seed, a direct comparison with true seeds may not be entirely appropriate. MRI spectroscopy may also be used for other types of studies. Rollins et al. (1989) used combined MRI and spectroscopy to determine 19F uptake by tomato plants. They used fluorine because many xenobiotics (especially herbicides and pesticides) contain fluorine, while the background level of fluorine in plants is negligible. 19F has a similar gyromagnetic ratio to IH. Thus, the nucleus has 83% of the sensitivity IH to the MR experiment, and the same experiment can be used for both nuclei. B. Buds Buds of temperate-zone trees are resting structures somewhat analogous to seeds. The water content of a resting bud is about 50% of the total weight and buds require exposure to cold temperatures between 4 and 9°C before they are able to resume growth. Liu et al. (1990) imaged buds of apple cultivars having low chilling requirement and noticed that Tz times were very short in the buds until their chilling requirement was satisfied. Faust et al. (1991) correlated the ability of buds to resume growth with satisfaction their chilling requirement and conversion of water from "bound" to "free" (Color Plate 3). Later, Faust et al. (1994) determined that the conversion of water from bound to free is an incremental process and proceeds in proportion to the chilling time received. Buds also enlarge during the dormant period. Buban and Faust (1994a,b) determined that bud enlargement commenced in early February and occurred only after at least 30% of the water was converted to free water. Tz times in apple buds were considered indicative of bound water when the value was between 5 and 10 ms and indicative of free water when it was >20 ms. Buds may also become dormant during the summer. This dormancy is not as deep as the winter dormancy. Nevertheless, the water is bound in the buds as indicated by short Tz times (Liu et al. 1993). The apical bud imposes dormancy which gives the water in all the buds short relaxation times. Removing the apical bud lengthens relaxation (Tz) times and initiates growth. Replacing the terminal bud with 20 nmol of IAA (indole-3-acetic acid) kept the buds dormant and the water bound (Wang et al. 1994). Thidiazuron (TDZ), a cytokinin-type growth regulator, induced bud growth even with the terminal bud in place and freed the water (Liu et al. 1993). Decapitation, TDZ, and IAA treatments in various combinations gave the results expected from their individual effects on both bud break and water binding. When bud growth commenced, about 50% of the water had Tzlarger than 25 ms. Decapitation and TDZ

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treatment produced an additive effect, whereas IAA could reverse to a high extent decapitation or decapitation plus TDZ effects (Wang et al. 1994). Blueberry (Vaccinium corymbosum L.) flower buds behaved very similarly to apple leaf buds (Rowland et al. 1992). However, both grape (Vitis riparia Michx.) buds and peach [Prunus persica (L.) Batsch] buds had very low Tz values and the difference between Tz values obtained in the unchilled, dormant state (Tz = 5-7 ms) and those obtained when buds were expanding (Tz = 8-11 ms) was very small (A. Fennell and A. Erez pers. comm.). Water states in peach buds determined by MR spectroscopy also indicated very short T 1 times, 0.3 to 0.4 s, until bud expansion started in March, and even then T 1 times increased only to 0.7 s (Sugiura et al. 1995). It is notable that images of buds revealed a mosaictype pattern of relaxation time values even within the same anatomical structure (Liu et al. 1993). By assigning various colors to ranges of Tz values, the pattern is easily discernable. MR spectroscopy also revealed more than one range of relaxation times in various plant tissues (Gusta et al. 1979; Bacic and Ratkovic 1984; Bala et al. 1985; eolire et al. 1988; and Sugiura et al. 1995). However, only MRI can relate various proton relaxation values to a given anatomical structure or to a given group of cells. The reason why the water in a certain cell group is in a different state than in other cell groups of the same tissue is not apparent at the present time. Maas and Line (1995b) imaged developed strawberry flower buds three days and one day before anthesis. The central pith and ovules of flower buds imaged intensely with T1 -weighted inversion echo times between 0.1 and 0.5 s. C. Fungal Colonization Nondestructive viewing of fungal colonization is important. It enables the investigator to nondestructively determine the affected area and to estimate the extent of the infection in advance of visible symptoms. Botrytis cinerea infection was investigated in raspberry and black current (Ribes nigrum L.) fruits by Williamson et aI. (1992a) who used the gradientecho pulse-sequence technique to view the extent of fungal infection. Radial striations were observed in the healthy and artificially wounded tissues, but such striations completely disappeared from the infected druplets. Bowtell et al. (1990) stated that such striation may be caused by the large differences in magnetic susceptibility that exist at the junctions between the water-filled cells and the gas-filled intercellular spaces. Goodman et al. (1992a) confirmed that the boundaries between striated and nonstriated components in the MR images corresponded to the limits of progression of the internal fungal mycelia. The disappearance of the

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striations from the image could mean that the fungal infection affected the cells in a way that destroyed their compartmentalization and allowed intercellular spaces to fill with liquid. The spread of infection produced a distinct boundary in raspberry that was visible even after four days of inoculation. In blackberry, the boundary also persisted for at least four days but the boundary was less defined than in raspberries. Strawberries inoculated with different pathogens, such as Botrytis cinerea Pers. Colletotrichum acutatum (Mont.) De Bary (Color Plate 4) and Phytophthora cactorum (Lebet and Cohn) Schrot. (Color Plate 5) (Maas and Line 1995a), or potatoes with P. infestans and Fusarium coeruleum (Lib. Sacc.) Booth (Snijder et al. 1995) produced MR images which were different for different pathogens. This indicates that the mechanism of action of the infection greatly depended on the pathogen, and that the pathogen could be detected by nondestructive means. In strawberry fruit, one to three zones of infected tissues with different T1 characteristics were obtained depending on the pathogen involved (Maas and Line 1995a). Spaine et al. (1994) and MacFall et al. (1994) used MR microscopy to determine water-transport processes in fusiform rust galls of pine caused by Cronartium quercuum f. sp. fusiforme (Hedge and N. Hunt) Burdsall and G. Snow. Infection changed the anatomy of stems. MRI clearly revealed that in the transition zone between the healthy stem and the gall, the cambium and phloem became wider, with proliferation of cortical parenchyma and secondary xylem. In later stages of infection, ray parenchyma and secondary phloem were formed which were unique in the infected plants. Images from the galled slash pine showed low signal intensity in the center of the gall, suggesting comparatively low water content. They also used a gadolinium (Gd)-containing preparation (Magnavist), a magnetic resonance contrast agent, introduced with the transpirational stream that allowed a definitive tracking of the watertransport path through the stem. They used rapid repetition times (TR = 200 ms). If water content and Tz are equal when images are acquired, samples with a long T1 will give comparatively less signal than those with a short T 1 . Preliminary measurements have shown this to be the case. The gall had longer T1 (1539 ms) than did water within the healthy stem (1040 ms). Addition of Magnavist to the water taken up by the transpiration stream effectively decreased T 1 in both the gall and healthy stems, making T 1 of water within the tissue more dependent on the contrast agent than on the interactions with plant tissue. The addition of the contrast agent effectively equalized the T 1 of the galled and healthy stems when normalized signal intensity was calculated based on a CuSOiDzO reference.

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Pearce et al. (1994) investigated fungal colonization of sapwood of Acer pseudoplatanus L. with the pathogens Chondrostereum purpureum (Pers. ex Fr.) and Ustulina deusta (Fr.). MRI was closely comparable to that seen in sectional material. U. deusta has only a limited capacity to invade living sapwood, whereas C. purpureum was a fast-growing pathogenic invader of living wood. U. deusta elicited a large reaction zone where proton density images indicated that the water content in the reaction zone was elevated by a factor of 2.5 to 3.0. No such accumulation of water was observed at the margin of C. purpureum lesions. D. Detecting Physiological Disorders and Maturity Changes Development of physiological disorders is always a problem in fruit, especially when the fruit is stored. When the disorder starts, it cannot be detected easily, and by the time it is visible it is too late to take corrective action. The greatest advantage of using MRI as a monitoring technique in detecting physiological disorders is its nondestructiveness. One can follow the development of a disorder in the same fruit by imaging the fruit at various intervals. The earliest MRI study of physiological disorders of fruit detected watercore in apples (Wang et al. 1988). The intercellular spaces of apples affected by watercore are filled with sorbitol and water. The watercoreaffected areas in the images were brighter than the nonaffected areas as a result of a stronger MR signal generated in the affected area. Severity of watercore was estimated from MR cross-sectional images at the center of the apple. The affected area was outlined by sliding a touch pen along the edge of the affected area of the image that appeared on the console. The numbers of pixels within the affected area and within the entire cross-section of apple were determined by the computer and the percentage of area affected could be calculated. The highest proportion of watercore tissue was found slightly toward the stem end from the center of the fruit. Watercore was much less severe toward the calix end at the same distance from the center (Wang et al. 1988). MRI was also used for detecting core breakdown in pears (Wang and Wang 1989). Pears were imaged at TR = 1 sand TE = 41 ms. A distinct degeneration of the tissue in the periphery of the core area of the pear was observed after three months of storage at DOC in air and four or more days of ripening at 20°C. The collapse of the tissue increased with time. The increasing amount of free water in the affected core breakdown region produced higher signal intensities and bright areas in the MR images. Exposure to above-freezing temperatures between 2.5° and 12.5°C for

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three days causes chilling injury of many fruit of tropical or subtropical origin. The damage is caused by cellular breakdown and loss of membrane integrity. Measurements of T1 and T z relaxation times from the cortex area of zucchini squash revealed that the chilled squash had a shorter T1 and a longer T z relaxation time than the nonchilled squash. T 1 was 1.4 to 0.9 s for chilled and nonchilled squash, while Tz was 31 to 46 ms, respectively, for chilled and nonchilled tissue. Chilling injury was also detected with MRI in persimon (Clark and Forbes 1994). Development of chilling injury was much easier to detect in transverse images than in longitudinal sections after six weeks of exposure to chilling temperatures. Following removal from chilling temperatures, relaxation properties were considerably changed after two days at room temperature, indicating the severe development of injury. However, incipient stages of injury could not be identified either by visual inspection or variation in relaxation properties (Clark and MacFall 1996). Split pit, a typical problem of early-ripening peaches, could be easily detected by imaging the center of the fruit with MR (Heil et aI. 1991). Heil and coworkers also found that internal breakdown in nectarines greatly increased the value of T z• Detecting bruised fruits is a major concern of people who are involved in sorting fruit and producing highquality produce. Bruises could be detected as a result of large increases in Tz times in the bruised areas of peach (Chen et aI. 1989; Heil et aI., 1991), apple (Chen et aI. 1989; McCarthy et aI. 1995; Zion et aI. 1995), Asian pear (Chen et aI. 1989), onion (Chen et aI. 1989), and strawberry (Maas et aI. 1992; Maas and Millard 1993). Woolly breakdown in nectarines is another physiological disorder that prevents cold storage of this fruit. Images obtained after 30 days of cold storage and six days at 25°C clearly indicated tissue damage (Sonego et aI. 1995). Development of dry juice sacks in orange, also considered a metabolic disorder, can be readily detected by MRI (Chen et aI. 1989). Heat damage caused by thermal injury in papaya can be detected by MRI. Proton density and Tz values increased with degree of heat injury, whereas T 1 values decreased in all three tissues: endocarp, mesocarp, and exocarp (Suzuki et aI. 1994). Heat disinfestation, a possible alternative to chemical treatment in mango, impairs the conversion of starch to sugars. However, it also produces cavities in the fruit. Such damage was clearly visible in MR images as dark areas in the mesocarp tissue (Joyce et al. 1993). 1 H-MR micro-imaging was used to detect differences in spruce needles from healthy and declining forest sites (Masuch et al. 1991). Damaged needles of Picea abies had a higher proton signal intensity and a different distribution of free water compared with healthy tissue. Nonde-

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structive MRI together with scanning electron microscopy studies were used for an integrated interpretation of symptoms of trees from declining forests. Freezing can cause severe metabolic disorders in plants, although acclimation at low temperatures often allows plants to survive freezing temperatures. In wheat, during acclimation for 70 days at 2.SoC, Tz values decreased in crown tissues from 39 to 16 ms. There was no difference in Tz values between cultivars which were cold-tolerant or cold-sensitive (Millard et al. 1995). Freezing usually releases water from cellular compartments, which increases proton density in the affected area and changes in MR characteristics. Brennan et al. (1993) observed this in flowers of black currant after freezing events. Freezing increased Tz relaxation times in blueberry (Gamble 1994), but the increases were localized, being most obvious in the placental tissues and locules where seeds are located. Tz changes caused by freezing are similar to those caused by bruising (Duce et al. 1992b; Gamble 1994; McCarthy et al. 1995). Fruit ripening is a physiological change that involves complex biochemical changes. There are relatively few reports available investigating this change with MRI (Fig. 3.S). Significant correlations were found between Tz times and firmness (softening) in peach, between T1 and firmness in nectarine, and between both T1 and Tz and firmness in plum (Heil et al. 1991). Results were apparently affected by the type of fruit and not by cultivar differences. Callaghan et al. (1994) determined T 1 and Tz-CPMG relaxation times during ripening in kiwifruit. Changes in ripening fruit as measured by MRI are not uniform. Ripening in strawberry was characterized by relatively small changes in T1 relaxation times in the parenchyma and vascular tissues and only minor differences in Tz relaxation times (Goodman et al. 1996), whereas in kiwifruit T1 decreased and Tz increased (Callaghan et al. 1994). During ripening of tomato, locular areas liquefy, increasing the mobility of water and, therefore, the brightness of the image (Pech et al. 1990). Spin-echo images of tomato fruit were created by using TR = 600 ms and TE = 34 ms. Relaxation times (T1 ) were calculated from four images taken at S, 2S, 2S0, and SOO ms. Water content and water mobility in tomato were also imaged by Ishida et al. (1989 and 1994). MRI could distinguish the physiological variation among different types of tissues and physiological changes occurring during maturation in tomato fruit. Saltveit (1991) created T1 maps of tomato indicative of such changes. Hills and Duce (1990) determined the effect of cell morphology on proton transverse relaxation (Tz) behavior in squash, onion, and apple parenchyma tissues. The mean cell radius of squash (~27.S pm) is much smaller than that of onion (~13S pm) or apple (~100 pm). In squash, there

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Fig. 3.5. Inversion recovery spin-echo MR images of strawberry fruit with various maturity. Top row 5, middle row 15, and bottom row 25 days after anthesis. Images from left to right with inversion recovery times of 1.5,1.0,0.5, and 0.1 s.

is complete diffusive averaging and essentially single exponential relaxation at the shortest 90 to 180 0 pulse spacing, but incomplete diffusive averaging and bioexponential relaxation in onion and apple. When determining degree of ripeness in fruits for comparative purposes, cell size of the tissue should be taken into account. Evaluating maturity of fruit for determining harvest time depends on the type of fruit involved. Chen et al. (1993) utilized MRI to evaluate avocado (Persea gratissima Gaertn.) maturity. Avocado has an oily fruit, and oil content is the determining factor in judging maturity. As the fruit matures, water content of the fruit decreases while oil content increases. This changes the mobility of hydrogen nuclei and causes changes in T1

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and of water in the fruit and thereby affecting the MR measurements. T 1 of water in avocado fruit decreased from 1.0 to 0.4 s as the percentage of dry weight of fruit increased from 15 to 35% during maturation. In contrast, T 1 of oil remained relatively unchanged at about 0.24 s during the same period of maturation. The trend in T z was the same. T z of water decreased from 90 to 50 ms while T z of oil remained around 90 ms as the dry weight increased to the levels indicated above. The percentage change in T 1 of water per unit is greater than that of T z. Therefore, for the purpose of enhancing the difference between the signals from mature and immature fruits, a T1 -weighted technique is more effective than a Tz-weighted one (Chen et al. 1993). E. Roots in Soil

The first application of MRI to plant roots demonstrated its ability to monitor the distribution of roots in soil without disturbance (Omassa et al. 1985; Bottomley et al. 1986). This continues to be an important area (Brown et al. 1991). Subsequently, much MRI has been aimed at increasing the spatial resolution of the technique for observing the distribution of roots in situ (Ratcliffe and Roberts 1990). A wide range of natural and artificial soils are amenable to MRI, although a significant number of soils cause interference (Rogers and Bottomley 1987). Among the natural soils, Eustic fine sandy loam (sandy, siliceous, thermic Psammentic Paleudults) and Kingston loamy sand (fine loamy, siliceous, acid, thermic Typic Fluvaquents) were found to be the best for MRI (Rogers and Bottomley 1987). These natural soils are transparent to conventional MRI and are good substrates for in situ imaging. They do not significantly distort or interfere with root in images of in situ plants. Even when these soils are near saturation, water is rendered invisible in MR images created with standard spin-echo imaging sequences (Rogers and Bottomley 1987; Bottomley et al. 1993). Peat is a very common medium for seedling growth but it produces large background signals in MR images. Brown et al. (1991) have shown that an artificial mixture of 5 sand:3 peat:2 kaolinite (v/v/v) gives good images of roots without much interference from the soil. Southon et al. (1992) visualized cold damage to roots of larch seedlings in situ in peat media by using a gradient-echo technique. The contrast between the signal from root and peat was greater in the gradient-echo acquisitions than in the spin-echo acquisitions on every occasion when the two techniques were compared. It is common to use a reference material on the soil surface or in the soil substrate in the viewed area. Several authors (Bottomley et al. 1986;

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Brown et al. 1986a; Rogers and Bottomley 1987; Johnson et al. 1987; Connelly et al. 1987; Tamiya et al. 1988; Jenner et al. 1988; MacFall et al. 1992; and Bottomley et al. 1993), were able to show that the image intensity relative to a reference material is proportional to the mobile water in the plant system. Brown et al. (1986b) detected changes in water content in areas as small as 0.15 x 0.15 mm on MR images. In many experiments, a sealed vial of CuS0 4 doped water, or DzO, or a combination of these (MacFall et al. 1991) served as a reference for MR signal intensity. Spin-lattice relaxation times (TI ) of reference materials are presented in Table 3.4. MacFall et al. (1990) determined the effect of water content on relaxation signal intensity in acid-washed quartz sand in tubes with 5,10,15, 20, and 25% water (wt/wt). T I relaxation time increased from 500 to about 1250 ms as water content increased from 5 to 25%. In contrast, T z times were very low and did not change. They argued that there is not necessarily a linear relationship between image brightness and water content. Factors such as machine parameter setting during image acquisition (TE and TR) and inherent properties of the subject (TI and T z) determine image brightness. This was clear in their experiment when the value of T I changed with water content of the sand, whereas values remained the same. TR had a major influence on relative signal intensity when imaging wet sand. As sand water content increased from 5 to 25%, signal intensity remained low when TR was 50 ms. In contrast, when TR was 4000 ms, the signal intensity was 10 times higher at 5% water content and then increased 5 times as water content increased to 25%. When other TR values (200, 600, and 1000 ms) were used, signal intensities were proportionally between these two extremes. Values of spin-lattice (TI ) relaxation times of sand with water content ranging from 0 to 25% (wt/wt) ranged from 472 to 1265 ms and increased with water content. Spin-spin (Tz) relaxation times ranged from 54 to 76 ms and did not change in a discernible pattern as water content increased. Using T I determinations, MacFall et al. were able to identify water depletion zones around roots of loblolly pines. Table 3.4.

Spin-lattice relaxation times (TI ) of reference material (MacFall et al. 1991). Reference

3.18 x 1O-3 M CuS0 4 + DzO (25 + 75%) 6 x 1O-3 M CuS0 4 + DzO (25 + 75%) 7 x 10-4M CuS0 4 + DzO (25 + 75%) HzO

Spin-Lattice Relaxation Time (ms) 1738±158 889±80 3290 3076±300

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The gradient-echo technique has been used for imaging roots. This technique optimizes the signal-to-noise ratio with a minimum repetition time of 30 ms. The spin-echo sequence was most effective when repetition times of 200 to 500 ms were used. Southon et al. (1992) used relatively dry peat. The typical water content of the media was 2 H 2 0.(g dry peat)-l. Acquisition times for both gradient-echo and spin-echo data collection were 17 min. In general, the signal from water in roots lasts longer than the signal from water in soil. In an imaging experiment, the MR signal is typically detected at least 10 ms after the sample has been excited. This allows for a substantial part of the soil signal to decay before acquisition and so makes the smaller but longer-lived signal from roots detectable. Southon and Jones (1992) remarked that there is a clear difference in signal intensities detected by the spin-echo and gradientecho techniques. The gradient-echo detects less signal from the soil, and in the case of drier soil, there is almost no detectable signal. The reason for the difference is due to the different way spin-echo and gradient-echo techniques create a detectable MR signal. Most of the decay of the MR signal from soil is caused by magnetic inhomogeneities. While the spinecho method is able to reverse much of the signal loss due to these field inhomogeneities, the gradient-echo method is unable to do so. The result is that the gradient-echo method produces smaller signals from soil (Southon and Jones 1992). Smaller soil signals do not necessarily lead to better images. The root signal must be stronger than the soil signal in order for the roots to be observable in the image, and the gradient-echo method will produce weaker root signals as well as weaker soil signals. The use of an extremely short TE of 3.8 ms or less reduces the signal dependence on T 2 effects. This is a prerequisite to determining the water content as well as the local distribution of mobile water molecules in plant tissues (Walter et al. 1989). Bottomley et al. (1986), Rogers and Bottomley (1987), and Bottomley et al. (1993) used an imaging sequence with a 1 s repetition period, a short (20 ms) spin-echo, and 8.5 min acquisition time to provide shadow graphs of the integrated water distribution in the soilroot system. Brown et al. (1986a) created images of water in Pelargonium hortorum roots by using long TE values of >20 ms. Such images do not show the distribution of water but, rather, the distribution of regions with high water content which have long T 2 values. MacFall et al. (1992) acquired spin-echo images of nodulated soybean roots at TR = 3,200 ms and TE 10 ms. Specimens were perfused for 0.5 to 1.5 h before initiation of the first T 1 image. Perfusion was a two-step process, with O 2 or air used in the first step and N 2 or O 2 in the second. When N 2 was used for the second step, T 1 was longer than when O 2 was

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used. The O 2-0 2 sequence produced no change in relaxation times, whereas the change was the same for both 02-N2 and air-N 2 perfusions. Change in calculated T 1 times was always higher in the cortex than in the attached inner nodules. MacFall et al. ruled out leghemoglobin reactions as cause of the T 1 extension. However, they stated that the observed change in T1 could have been caused by any of the following: paramagnetic ions such as molybdenum associated with the nitrogenase enzyme complex as cofactors; direct paramagnetic effects of molecular O 2 that reduce the T 1 of nearby protons; and/or repartitioning of water between intra- and intercellular compartments, particularly within the cortex. Matyac et al. (1989) imaged the development of nematode (Meloidogyme incognita)-induced gall in tomato roots in situ. Galls were clearly visible two weeks after inoculation and their development could be followed for six weeks after inoculation. F. Water Flow in Plants

Flow of water in plant tissue is of interest to many plant physiologists. MRI can be used for determining the spatial distribution of static water in tissues as well as the velocity and direction of flow of moving water (Callaghan and Eccles 1988; Jenner et al. 1988; Callaghan and Xia 1991; Xia and Callaghan 1992). Investigators have drawn conclusions on water flow from a variety of MRI measurements. Veres et al. (1991a) investigated the rate and extent of plant tissue dehydration and rehydration. They measured water binding and relative water content in Blechmum unilaterale stem regions as water stress first developed and then was relieved. T 1 relaxation times and stem-water potential correlated well. During dehydration, relative spin density [N(H)] decreased and increased after rewatering. The magnitude of T1 depended on the type of tissue in which relaxation times were measured. Even though pith parenchyma cells contain large liquid-filled vacuoles, little free water was associated with these cells. However, vascular bundles of Bleachnum unilaterale were areas of high relative N(H). Brown et al. (1986a) observed changes in signal intensity in roots of geranium (Pelargonium hortorum) which they interpreted as changes in water content during 8 h of transpiration. For Pelargonium stem regions, T 1 times were shortened as N(H) decreased during rapid transpiration (Johnson et al. 1987). MacFall et al. (1991) measured water extracted by loblolly pine (Pinus taeda 1.) seedlings from the soil surrounding the root and postulated that the extracted water can be used in estimating water transport by the plant. Tamiya et al. (1988) visualized distribution of water in the pulvinus

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of Mimosa. After stimulation of a Mimosa plant, water in the lower half of the main pulvinus disappeared. The water previously contained in this area seemingly was transferred to the upper half of the main pulvinus. Thus, water movement could be visualized sequentially with this noninvasive procedure. McCain and Markley (1992) presented contour plots that show how water in the sample is distributed across two dimensions. The technique allowed them to distinguish differences in water content between upper epidermal, mesophyll, and lower epidermal tissues in a variety of species including Acer platanoides L., Syringa vulgaris L., Salix alba L., Liriodendron tulipifera L., and Populus deltoides Bartr. Ex Marsh. Van As and Schaafsma (1984) measured water flow through the stem of intact cucumber plants by using pulsed MR. Zimmerman et al. (1992) measured active transport of water in roots. They used a relatively long repetition time (3 s) and a short TE (minimum of 3.8 ms) to eliminate the influence of both T I and Tz relaxation times. Local differences in the brightness of the image were due to differences in water content. This is in contrast to studies employing long TE values of >20 ms where images do not show the distribution of water but, rather, the distribution of regions having high water content and long Tz values. Regions of high water content having short Tz values exhibit low signal intensity when long TEs are used. This is the case when proteins and membrane structures are present. The use of an extremely short TE of 3.8 ms or less reduces the signal dependence on T z . This approach is used to identify the regions with high water content and to determine the local distribution of mobile water molecules in plant tissues (Walter et al. 1989). Zimmerman et al. (1993), investigating discrepancies between pressurebomb and xylem-probe estimation of tension in plants undergoing drought stress, used MRI to investigate water movement. They detected water throughout the leaves of Anacardium excelsum during the rainy season but, by contrast, MR-microscopy of Argyrodendron peralatum exposed to prolonged drought revealed large amounts of air present in the leaves. Xia and Callaghan (1992) and Xia et al. (1993) used a pulsed-gradient spin-echo (PGSE) method to obtain the velocity spectrum of molecules in each pixel of the image. This approach gives a direct measure of the local average velocity and is the basis of a sensitive PGSE technique for flow imaging which can be applied to elucidate the water flow paths and velocities in plants. They estimated the mean flow rate in Stachys sylvatica to be about 45 pm.s-I and in Equisetum hyemale to be about 40 pm.s- I . Because of the low water velocities involved, it was necessary

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to use long echo times during which there was considerable loss of signal due to relaxation. The low signal-to-noise ratio limits the usefulness of this method (Xia et al. 1993). IV. ARTIFACTS Distortion of images may be caused by the presence of intercellular spaces. Bowtell et al. (1990) noticed that high-resolution images of scented geranium (Pelargonium graveolens Thunb.) sterns obtained at high magnetic field strengths were distorted. The major effect in their images was the loss of signal resulting in dark areas which did not correspond to any real structure. The signal loss was caused by diffusion of water molecules in the large magnetic gradient which can occur close to the interfaces between regions of different magnetic susceptibility (Case et al. 1987). In the geranium stern, the main source of these gradients are the small intercellular air spaces which permeate the tissues of the stern. Water infiltration eliminated the artifact effect by filling the intercellular spaces and eliminating the susceptibility differences. Theoretical considerations for the distortion caused by the intercellular spaces are given by Bowtell et al. (1992). Distortions caused by intercellular spaces can explain the lack of appearance of some structures in MR micrographs of plant tissues. In tissues such as the cortex, the small cell size and the high density of intercellular spaces mean that there are large gradients within all cells. The diffusion-dependent dephasing of the magnetization therefore destroys all the MR signals from the cortical cells. Consequently, they do not appear on the image. There is a lower intercellular density in parenchyma tissues. Parenchymatous cells appear in the image, but diffusion in regions close to the air gaps makes the cells appear to be smaller than their actual size. Tissues which do not contain air spaces, such as the vascular bundles, appear bright in the images (Bowtell et al. 1992). Sterns are not the only type of tissues susceptible to air-space distortion. Connelly et al. (1987) noticed similar effects in maize roots where the central region appeared dark in the image because it contained a high proportion of intercellular spaces. Soaking in water changes the image. However, pure water is not suitable since the MR signal it produces obscures all detail in the images. Bowtell et al. (1992) recommended the use of Gd-DTPA, which lowers the T 1 relaxation time of the matching fluid. According to them, Gd-DTPA is unable to cross cell membranes and therefore does not effect the relaxation time of cell contents. How-

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ever, Ratcliffe, in a discussion following the presentation of Bowtell et al. (1990), warned that caution is needed in working with this agent because it can penetrate tissues of plants. Millard et al. (1993) noticed that Tz times in woody plants having low water content «48%) had unrealistic values. They used a TE of 11.21 ms for obtaining clear images in apple buds. Nonlinear least-squares fitting of the eight echo points gave a good curve fit when proton density signals were sufficiently high (high water content) and a poor fit when proton density signals were weak (low water content). The variance for values was nearly 50 times greater for the poor fit. The variance was considered too large if it exceeded>1% of the intensity. Tz values that exceeded the variance limit were eliminated from the image. Those signals that remained corresponded to areas where the water concentration was higher as indicated by the proton-density image. Correction resulted in a realistic image. The epidermal region of imaged plant parts appears as a very narrow band that often projects a bright, intense image. Wang and Wang (1992) observed this in squash and attributed it to higher water mobility and diffusion in skin tissues and adjacent areas which created a higher N(H) intensity in the outermost cell areas. Others have observed this phenomenon but without comment: Suzuki et al. (1994) observed it in papaya; McFall and Johnson (1994) in fig, kiwifruit, and potato; Joyce et al. (1993) in mango fruit; Duce et al. (1992b) in squash; Liu et al. (1990) in apple stem; and Brown et al. (1988) in geranium stem. This bright line at the outer surface of the specimens, according to McDougall et al. (1992), is due to water vapor gathering on the surface of the specimen. The occurrence of a high concentration of paramagnetic ions in plant tissues may also cause distortion of images. Plants, especially those grown on low pH soils, take up high levels of manganese (Mn) and other paramagnetic ions. Tissues, and especially vascular elements of such plants, are high in Mn. The presence ofMn ions in the vascular system may shorten the relaxation time and decrease brightness of the structures of the images. The presence of paramagnetic materials shortened the relaxation time in flax stems (McDougall et al. (1992), in maize shoots (Koizumi et al. 1992), and in the vascular system of raspberry fruit (Goodman et al. 1992b). Manganese has been shown to shorten relaxation times in MR spectroscopic studies of ivy bark (Stout and Steponkus 1978) and in Elodea (Stout et al. 1977). Sucrose may affect images obtained by MRI. Increased sucrose concentration was demonstrated to decrease values of T 1 and Tz in proportion to the concentration level (MacFall and Johnson 1994). This indicates that, given equal water content, regions with high sugar con-

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tent should have shorter T1 and T z values than regions with comparatively less sugar. The techniques used to image sugar beet tap-roots affect the appearance of the vasculature in the images (MacFall and Johnson 1994). In images acquired with short repetition and echo times (T1-weighed image), vasculature appeared darker than the surrounding tissues. In images acquired with long repetition and echo times (Tz-weighed image), vasculature appeared much brighter than the surrounding parenchyma. Sugar beet roots store considerable amounts of sucrose next to the vascular bundles. The darkest regions immediately surrounding the vascular traces are sink cells with the highest concentration of sucrose. It is likely that they give a lower signal level because they have short T z relaxation times. Medium-gray regions centered between vascular traces may have been regions with sucrose transport gradients, and thus T z relaxation time gradients, extending from the source cells, to the phloem, and the into sink cell regions (MacFall and Johnson 1994). Using spectroscopic techniques, other researchers have also described a concentration dependence of relaxation times for sugar solutions (Lai and Smith 1991). Cho et al. (1993) found good correlations between soluble solids and T z values in intact 'Red Flame' grapes and sweet cherries. With increasing soluble solids concentration, the decrease in T z values was less in grapes (12.5 ms/% sol. sol.) than in cherries (18.1 ms/% sol. soL). An increase in brightness in localized spectra, which indicated an increase in sugars, accompanied ripening of tomato (Pech et al. 1990) and grape (Goodman et al. 1993). Pope et al. (1991) observed elevated levels of sugars close to the seed surface in developing grape berries. They observed an enhancement of the intensity of the signal centered on 8 = 3.6 ppm in the region of the seed surface and interpreted this as a signal produced by sugar. Goodman et al. (1993) observed a pronounced sugar concentration gradient radiating from the seed in "overripe" grape berries, whereas "unripe" or "fully ripe" berries did not show such a gradient. In subsequent articles, Pope et al. (1992, 1993) suggested that in their 1991 study the image they thought was caused by elevated sugars was in fact an artifact produced by the presence of air pockets in some of the seed traces. V. CONCLUSIONS Investigations during the last decade have clearly indicated that MRI is a very powerful tool for nondestructively investigating physiological phenomena and internal structures in vivo without prior preparation to

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the tissues. This allows investigators to make measurements under conditions in which the life processes of the tissue are close to normal. The variety of information that can be obtained by using MRI is almost limitless. The major advantage of MRI is that the information is tissuespecific and allows for the visual identification of tissues where the process is occurring. Methods that give the best MR images greatly depend on the species and type of tissue examined. Although certain principles apply to all examinations, for the best images investigators must explore a number of settings for each component and expect long time requirements per sample. The influence of various parameters on the signal intensity can be observed by the acquisition of a series of different spin-echo images, each obtained using different experimental values of TR and TE. The most common parameters used to create the various maps are listed in Table 3.5. There are values used outside of those indicated in Table 3.5. Walter et al. (1989) used 4.4 to 18 ms TE and 1 s TR times resulting in a measuring time of about 20 min. Veres et al. (1991a) used six singleslice images each with a different TR (267; 467; 867; 1,667; 3,267; and 6,467 ms) with a fixed TE (18 ms). The length of each scan was approximately 2 h. T z values are often used to describe the biological state of tissues. Differences in T z values of biological tissues are interpreted in terms of differences of the ratio of "free water" to "bound water." This ratio is dependent on the protein concentrations in most highly proteinous biological systems (Le., animals). In plants, other macromolecules may also bind water. Perhaps, because of many possibilities to bind water

Table 3.5.

Common parameters used in MR with plant tissues.

Image

TR (Repetition Time)

TE (Echo Time)

T1 -weighed image (shorter T 1 times indicate more bound water); values range from 80-1,700 ms

200 ms

8-gms

Tz-weighed image (shorter Tz times indicate more bound water); values range from 5-60 ms

1,000-1,500 ms

40-80 ms

Tz-weighed image; fast spin echo

1,500-2,000 ms

10-12 ms

1,500 ms

gms

Spin density (N[H])-weighed image (gives information on the concentration of mobile hydrogen atoms); values are indicated by the intensity of the image.

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in plants, T z values obtained in plants are much shorter than those obtained in animals, and the difference between high level of bound water when the plant generally does not grow versus when free water is present, when the plant is able to grow, is relatively small, often being only a few ms. Unfortunately, MRI also has some limitations. Time required to obtain a good image of a given sample is long. This can limit the ability of investigators to analyze data statistically, which is in most cases a prerequisite to obtaining agricultural or horticultural studies. Most experiments are made by measuring the relaxation time of protons, usually of the water molecule. There are many factors, including internal tissue conditions and machine settings, that affect relaxation times of protons. Before they can interpret results realistically, investigators should be aware of these conditions and the type of machine settings needed to obtain proton excitation. Nevertheless, MRI is a technique that will be used increasingly by investigators studying processes in living plant tissues. Its usefulness will increase as new techniques are developed and existing techniques are refined. LITERATURE CITED Alger, J. R. 1993. An introduction to MR spectroscopic imaging: syllabus of advanced magnetic resonance spectroscopy course. Soc. Mag. Res. 12th Sci. Mtg. Bacic, G., and S. Ratkovic. 1984. Water exchange in plant tissue studied by proton NMR in the presence of paramagnetic centers. Biophys. J. 45:767-776. Balla, Y. 1., N. G. Bahadze, and Y. G. Sharimanov. 1985. Detection of two states of water in plant tissues by proton magnetic relaxation. Biophysics 30:522-528. Bottomley, P. A., H. H. Rogers, and T. H. Foster. 1986. NMR imaging shows water distribution and transport in plant root system in situ. Proc. Nat. Acad. Sci. (USA) 83:87-89. Bottomley, P. A., H. H. Rogers, and S. A. Prior. 1993. NMR imaging of root water distribution in intact Vida faba 1. plants in elevated atmospheric CO 2 , Plant Cell Env. 16: 335-338. Bowtell, R. W, G. D. Brown, P. M. Glower, M. MeJury, and P. Mansfield. 1990. Resolution of cellular structures by NMR microscopy at 11.7 T. Phil. Trans. Royal Soc. London. (A) 333:457-467. Bowtell, R. W., J. C. Sharp, G. D. Brown, M. MeJury, and P. M. Glower. 1992. NMR microscopy at 500 MHZ: cellular resolution in biosystems. p. 427-439. In: B. Bliimich and W. Kuhn (eds.), Magnetic resonance microscopy: methods and application in material science, agriculture and biomedicine. VCH Publishers, Weinheim. Brennan, R. M. et al. 1993. Freezing events in flowers of Ribes nigrum L. revealed by NMR microimaging. J. Hort. Sci. 68:919-924. Brown, D. P., T. K. Pratum, C. Bledsoe, E. D. Ford, J. S. Cothern, and D. Perry. 1991. Noninvasive studies of conifer roots: nuclear magnetic resonance (NMR) imaging of Douglasfir seedlings. Can. J. For. Res. 21:1559-1566.

260

M. FAUST, P. WANG, AND J. MAAS

Brown, J. M., W. C. Fonteno, D. K. Cassel, and G. A. Johnson. 1986b. Computer tomography and magnetic resonance imaging of water and root distribution for containergrown plants. HortScience 21:738. Brown, J. M., G. A. Johnson, and P. J. Kramer. 1986a. In vivo magnetic resonance microscopy of changing water content in Pelargonium hortorum roots. Plant PhysioI. 82:1158-1160. Brown, J. M., J. F. Thomas, C. P. Cofer, and G. A. Johnson. 1988 Magnetic resonance microscopy of stem tissues of Pelargonium hortorum. Bot. Gaz. 149:253-259. Buban, T., and M. Faust. 1994a. New aspects of bud dormancy in apple trees. Acta Hort. 395:105-111. Buban, T., and M. Faust. 1994b. Contributions for understanding bud dormancy in apple trees. Hort. Sci. 26:21-25. Callaghan, P. T. 1991. Principles of nuclear magnetic resonance microscopy. Clarendon Press, Oxford. Callaghan, P. T. and C. D. Eccles. 1988. Diffusion-limited resolution in nuclear magnetic resonance microscopy. J. Magn. Res. 78:1-8. Callaghan, P. T., C. J. Clark, and 1. C. Forde, 1994. Use of static and dynamic NMR microscopy to investigate the origins of contrast in images of biological tissues. Biophys. Chem. 50:225-235. Callaghan, P. T., and Y. Xia. 1991. Velocity and diffusion imaging in dynamic NMR microscopy. J. Magn. Reson. 91:326-352. Case, T. A., C. H. Durney, D. C. Ailion, A. G. Cutillo, and A. H. Morris. 1987. A mathematical model of diamagnetic line broadening in lung tissue and similar heterogeneous systems: calculations and measurements. J. Magn. Reson. 73:304-314. Chen, P., M. J. McCarthy, and R. Kauten. 1989. NMRfor internal quality evaluation of fruits and vegetables. Trans. Am. Soc. Agr. Eng. 32:1747-1753. Chen, P., M. J. McCarthy, R. Kauten, Y. Sarig, and S. Han. 1993. Maturity evaluation of avocados by NMR methods. J. Agr. Eng. Res. 55:177-187. Cho, S. 1., R. 1. Stroshine, 1. C. Baianu, and G. W. Krutz. 1993. Non-destructive sugar content measurements of intact fruit using spin-spin relaxation time (Tz) measurements by pulsed lH magnetic resonance. Trans. Am. Soc. Agr. Eng. 36:1217-1221. Clark, C. J., and S. K. Forbes. 1994. Nuclear magnetic resonance imaging of chilling injury in 'Fuyu' persimmon (Diospiros khaki). N. Z. J. Crop Hort. Sci. 22:209-215. Clark, C. J., P. D. Hockings, D. C. Joyce, and R. A. Mazucco. 1997. Application of magnetic resonance imaging to pre- and postharvest studies of fruit and vegetables. Postharvest BioI. Techn. (in press). Clark, C. J. and J. S. MacFall. 1996. Magnetic resonance imaging of persimmon fruit (Diospiros khaki) during storage at low temperature and under modified atmosphere. Postharvest BioI. Techn. 9:97-108. Colire, c., E. 1. Rumeur, J. Gailier, J. D. Certaines, and F. Larher. 1988. An assessment of proton nuclear magnetic resonance as an alternative method to describe water status of leaf tissues in wilted plants. Plant PhysioI. Biochem. 26:767-776. Connelly, A., J. A. B. Lohman, B. C. Loughman, H. Quiquampoix, and R. G. Ratcliffe. 1987. High resolution imaging of plant tissues by NMR. J. Expt. Bot. 38:1713-1723. Duce, S. L., T. A. Carpenter, 1. D. Hall, and B. P. Hills. 1992a. An investigation of the origins of contrast in NMR spin echo images of plant tissue. Magn. Reson. Imaging 10:289-297. Duce, S. L., T. A. Carpenter, and 1. D. Hall. 1992b. Nuclear magnetic resonance imaging of fresh and frozen courgettes. J. Food Eng. 16:165-172. Eccles, C. D., and P. T. Callaghan. 1986. High resolution imaging. The NMR microscope. J. Magn. Reson. 68:393-398.

3. THE USE OF MAGNETIC RESONANCE IMAGING IN PLANT SCIENCE

261

Eccles, C. D., P. T. Callaghan, and C. F. Jenner. 1988. Measurements of the self-diffusion coefficient of water as a function of position in wheat grain using nuclear magnetic resonance imaging. Biophys. J. 53:77-81. Faust, M., D. Uu, M. M. Millard, and G. W. Stutte. 1991. Bound versus free water in dormant apple buds-A theory for endo-dormancy. HortScience 26:887-890. Faust, M., D. Liu, M. J. Line, and G. W. Stutte. 1994. Conversion of bound water to free water in endo-dormant buds of apple is an incremental process. Acta Hort. 395:113-118. Frahm, J., M. L. Gyngell, and W. Hanicke. 1992. Rapid scan techniques. p. 165-203. In: D. D. Stark and M. G. Bradley (eds.), Magnetic resonance imaging. 2nd ed., vol. 1. Mosby Yearb., St. Louis, Missouri. Gamble, G. R 1994. Non-invasive determination of freezing effects in blueberry fruit tissue by magnetic resonance imaging. J. Food Sci. 59:571-573. Gassner, G. 1989. Magnetic resonance imaging in agricultural research. p. 405-428. In: P. E. Pfeffer and W. V. Gerasimowicz (eds.), Nuclear magnetic resonance in agriculture. CRC Press, Boca Raton, Florida. Goodman, B. A, B. Williamson, andJ. A Chudek. 1992a. Non-invasive observation of the development of fungal infection in fruit. Protoplasm 166:107-109. Goodman, B. A., B. Williamson, and J. A Chudek. 1992b. Nuclear magnetic resonance (NMR) microimaging ofraspberry fruit: further studies on the origin ofthe image. New Phytol. 122:529-535. Goodman, B. A, B. Williamson, and J. A Chudek. 1993. Identification of the distribution of sugars in grapes using chemical shift selective NMRmicroscopy. Magn. Reson. Imaging 11:1039-1041. Goodman, B. A, B. Williamson, E. J. Simpson, J. A. Chudek, G. Hunter, and D. A M. Prior. 1996. High field NMR microscopic imaging of cultivated strawberry fruit. Magn. Reson. Imaging 14:187-196. Gusta, L. V., D. B. Fowle, P. Chen, D. B. Russel, and D. G. Stout. 1979. A nuclear magnetic resonance study of water in cold-acclimating cereals. Plant Physiol. 63:627-634. Hall, L. D., V. Rajanayagam, W. Stewart, and P. R Steiner. 1986. Magnetic resonance imaging in wood. Can. J. For. Res. 16:423-426. Halloin, J. M., T. G. Cooper, E. J. Potchen, and T. E. Thompson. 1993. Proton magnetic resonance imaging of lipid in pecan embryos. J. Am. Oil Chern. Soc. 70:1259-1262. Harrison, L. G., S. D. Luck, B. D. J. P. Munasignhe, and L. D. Hall. 1988. Magnetic resonance imaging approaching microscopic scale: maturation stages of Acetebularia mediterranea reproductive caps. J. Cell Sci. 91:379-388. Heil, J. R, P. Chen, F. G. Mitchell, B. Zion, and M. J. McCarthy. 1991. Use of magnetic resonance for testing peach, nectarine, and plum maturity and ripening characteristics. Rep. Research Projects for Calif. Peaches and Nectarines. Calif. Fruit Testing Agreement, Sacramento. Hills, B. P., and S. L. Duce. 1990. The influence of chemical and diffusive exchange on water proton transverse relaxation in plant tissues. Mag. Reson. Imaging 8:321:331. Hinshaw, W. S. 1976. Image formation by nuclear magnetic resonance: sensitive-point method. J. Appl. Phys. 47:3709-3721. Hinshaw, W. S., P. A Bottomley, and G. N. Holland. 1979. A demonstration of the resolution ofNMR imaging in biological systems. Experimentia 35:1268-1269. Ishida, N., T. Kobayashi, M. Koizumi, and H. Kano. 1989. lH-NMR imaging of tomato fruits. Agr. BioI. Chern. 53:2363-2367. Ishida, N., M. Koizumi, and H. Kano. 1994. Ontogenic changes in water in cherry tomato fruits measured by nuclear magnetic resonance imaging. Scientia Hort. 57:335-346. Jenner, c. F., Y. Xia, C. D. Eccles, and P. T. Callaghan. 1988. Circulation of water within wheat grain revealed by nuclear magnetic resonance micro-imaging. Nature 336:399-402.

262

M. FAUST, P. WANG, AND J. MAAS

Johnson, G. A., J. Brown, and P. J. Kramer. 1987. Magnetic resonance microscopy of changes in water content in stems of transpiring plants. Proc. Nat. Acad. Sci. (USA) 84:2752-2755. Johnson, G. A., L. W. Hedlund, G. P. Cofer, and S. A. Suddarth. 1992. Magnetic resonance microscopy in life sciences. Rev. Magn. Reson. Med. 4:187-219. Joyce, D. c., P. D. Hockings, R. A. Mazucco, A. J. Shorter, and 1. M. Brereton. 1993. Heat treatment injury of mango fruit revealed by nondestructive magnetic resonance imaging. Postharv. BioI. TechnoI. 3:305-311. Kano, H., N. Ishida, T. Kobayashi, and M. Koizumi. 1990. IH-NMR imaging analysis of changes of free water distribution in barley and soybean seeds during maturation. Japan J. Crop Sci. 59:503-509. Koizumi, M., N. Ishida, and H. Kano. 1992. Imaging Mn z+ uptake of a maize shoot by an NMR microscope. Bot. Mag. Tokyo 105:667-671. Kramer, P. J., J. N. Siedow, and J. S. MacFalL 1990. Nuclear magnetic resonance on plants. p. 403-432. In: Y. Hasimoto, P. J. Kramer, H. Nonami, and B. R. Strain (eds.), Measurement techniques in plant science. Academic Press, New York. Kuchenbrod, K, A. Haase, R. Benkert, H. Schneider, and U. Zimmerman. 1995. Quantitative NMR microscopy on intact plants. Magn. Reson. Imaging 13:447-455. Kuhn, W. 1990. NMR microscopy-fundamentals, limits and possible applications. Angewandte Chemie (Int. Edition in English) 29:1-19. Lai, C. M., and P. C. Lauterbur. 1981. True three-dimensional image reconstruction by nuclear magnetic resonance zeugmatography. Phys. Med. BioI. 26:851-856. Lai, H. M., and S. J. Schmidt. 1991. Proton, deuterium and oxygen-17 nuclear magnetic resonance relaxation studies of lactose- and sucrose-water systems. J. Agr. Food. Chem. 39:1921-1926. Lauterbur, P. C. 1977. Spatially-resolved studies of whole tissues, organs and organisms by NMR zeugmatography. p. 32:3-335. In: R. A. Dwek, 1. D. Campbell, R. K Richards, and R. J. P. Williams (eds.), NMR in biology. Academic Press, New York. LeBehan, D. 1995. Diffusion and perfusion magnetic resonance imaging. Raven Press, New York. Liu, D., M. M. Millard, G. W. Stutte, H. Norman, and M. Faust. 1990. State of water in apple buds. Proc. XIII Int. Hort. Congr. 1:497. Liu, D., M. Faust, M. M. Millard, M. J. Line, and G. W. Stutte. 1993. State of water in summer dormant apple buds determined by proton magnetic resonance. J. Am. Soc. Hort. Sci. 118:632-637. Llinas, M., K. Wuterich, W. Schwotzer, and W. Von Philipsborn. 1975. Nuclear magnetic reesonance of living cells. Nature 257:817-818. Maas, J. L., M. J. Line, M. M. Millard, and G. J. Galletta. 1992. Nuclear magnetic resonance imaging: a different view of strawberry fruit. Adv. Strawberry Res. 11 :64-66. Maas, J. L., and M. J. Line. 1995a. Mobile proton magnetic resonance (T z) microimaging of strawberry fruit. Hungarian Agr. Res. 4:4-7. Maas, J. L., and M. J. Line. 1995b. Nuclear magnetic resonance microimaging of strawberry flower buds and fruit. HortScience 30:1090-1096. Maas, J. L., and M. M. Millard. 1993. Nuclear magnetic resonance microimaging of strawberry fruit. Acta Hort. 348:375-377. MacFaU, J. S., and J. A. Johnson. 1994. The architecture of plant vasculature and transport as seen with magnetic resonance microscopy. Can. J. Bot. 72:1561-1573. MacFall, J. S., J. A. Johnson, and P. J.Kramer. 1990. Observation of a water depletion region surrounding loblolly pine roots by magnetic resonance imaging. Proc. Nat. Acad. Sci. (USA) 87:1203-1207.

3. THE USE OF MAGNETIC RESONANCE IMAGING IN PLANT SCIENCE

263

MacFall, J. S., G. A. Johnson, and P. J. Kramer. 1991. Comparative water uptake by roots of different ages in seedling loblolly pine (Pinus taeda 1.). New Phytol. 119:551-560. MacFall, J. S., P. Pfeffer, D. B. Rolin, J. R. MacFall, and G. A. Johnson. 1992. Observation of the oxygen diffusion barrier in soybean nodules with magnetic resonance imaging. Plant Physiol. 100:1691-1697. MacFall, J. S., P. Spaine, R. Doudrick, and G. A. Johnson. 1994. Alteration in growth and water-transport process in fusiform rust gall of pine, determined by magnetic resonance microscopy. Phytopathology 84:288-293. Mansfield, P., and 1. 1. Pykett. 1978. Biological and medical imaging. J. Magn. Reson. 29:355-373. Mansfield, P., and P. G. Morris. 1982. NMR imaging in biomedicine. Academic Press, New York. Masuch, G., J. T. Franz, H. Marshmann, D. Grob, and A. Kettrup. 1991. lH-NMR microimaging and correlated SEM studies of spruce needles from healthy and declined forest sites. Int. J. Environ. Anal. Chem. 45:179-191. Matyac, C. A., G. P. Cofer, J. E. Bailey, and G. A. Johnson. 1989. In situ observations ofrootgall formation using NMR imaging. J. Nematol. 21:131-134. McCain, D. C., and J. 1. Markley. 1992. A nuclear magnetic resonance imaging technique designed for studies of water in plant leaves. J. Struct. BioI. 108:195-201. McCarthy, M. J. 1994. Magnetic resonance imaging in foods. Chapman & Hall, New York. McCarthy, M. J., B. Zion, P. Chen, S. Ablett, A. H. Darke, and P. J. Lillford. 1995. Diamagnetic susceptibility changes in apple tissue after bruising. J. Sci. Food Agr. 67:13-20. McDougall, G. J., B. A. Goodman, and J. A. Chudek. 1992. Nuclear magnetic resonance (NMR) micro imaging of stems of Unum usitatissimum. J. Agr. Sci. 9:157-164. Metzler, A., M. Izquierdo, A. Ziegler, W. Kockenberger, E. Komor, M. von Kienlin, A. Haase, and M. Decorps. 1995. Plant histochemistry by correlation peak imaging. Proc. Nat. Acad. Sci. (USA) 92:11912-11915. Millard, P., and J. A. Chudek. 1993. Imaging the vascular continuity of Prumus avium petioles during leaf senescence using nuclear magnetic resonance spectroscopy. J. Expt. Bot. 44:599-603. Millard, M. M., D. Liu, M. J. Line, and M. Faust. 1993. Method for imaging the states of water by nuclear magnetic resonance in low water containing apple bud and stem tissues. J. Am. Soc. Hort. Sci. 118:628-631. Millard, M. M., O. B. Weisz, D. T. Krizek, and M. Line. 1995. Magnetic resonance imaging (MRI) of water during cold acclimation and freezing in winter wheat. Plant Cell Environ. 18:535-544. Morris, P. G, H. E. Darceuil, A. Jasinski, A. K. Jha, D. J. McIntyre, and D. H. Northcote. 1990a. NMR microscopy of the germinating castor bean. Phil. Trans. Royal Soc. London. (A) 333:487-493. Morris, P. G., A. Jasinski, and D. J. McIntyre. 1990b. NMRmicroscopy of plants. p. 171-183. In: P. J. Duke and A. G. Michette (eds.), Modem microscopies: techniques and applications. Plenum Press, New York. Omassa, K., M. Onoe, and H. Yamada. 1985. NMR imaging for measuring root system and soil water content. Environ. Control BioI. 23:99-102. Pearce, R. B., S. Sumer, S. J. Doran, T. A. Carpenter, and 1. D. Hall. 1994. Non-invasive imaging of fungal colonization and host response in the living sapwood of sycamore (Acer pseudoplatonus 1.) using nuclear magnetic resonance. Physiol. Mol. Plant. Pathol. 45:359-384. Pech, J. c., A. Latche, M. H. Andrieu, J. Raynal, and J. Pradere. 1990. In vivo monitoring of fruit ripening by proton NMR imaging. Proc. XXIII Int. Hort. Congress 1:635.

264

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Pfeffer, P. K, and W. V. Gerasimowicz. 1989. Nuclear magnetic resonance in agriculture. CRC Press, Boca Raton, Florida. Pope, J. M. 1992. Applications of chemical shift microscopy to non-invasive histochemistry of plant materials. p. 441-457. In: B. Bliimich and W. Kuhn (eds.), Magnetic resonance microscopy: methods and application in material science, agriculture and biomedicine. VCH Publishers, New York. Pope, J. M., D. Jonas, and R RWalker. 1993. Applications ofNMR micro-imaging to the study of water, lipid, and carbohydrate distribution in grape berries. Protoplasm 173:177-186. Pope, J. M., H. Rumpel, W. Kuhn, R Walker, D. Leach, and V. Sarafis. 1991. Applications of chemical shift selective NMR microscopy to the noninvasive histochemistry of plant materials. Magn. Reson. Imaging 9:357-363. Pope, J. M., R R Walker, and T. Kron. 1992. Artifacts in chemical shift selective imaging. Magn. Reson. Imaging 10:695-698. Ratcliffe, R G., and J. K. M. Roberts. 1990. Recent applications of NMR to higher plants and algae. Magn. Reson. Med. BioI. 4:77-79. Rogers, H. H., P. A. Bottomley, and T. H. Foster. 1985. Application of nuclear magnetic resonance imaging to plant root studies. Proc. Int. Conf Soil Dynamics. 5:1152. Rogers, H. H., and P. A. Bottomley. 1987. In situ nuclear magnetic resonance imaging of roots: influence of soil type, ferromagnetic particle content and soil water. Agron. J. 79:957-965. Rollins, A., J. Barber, R Elliott, and B. Wood. 1989. Xenobiotic monitoring in plants by 19F and IH nuclear magnetic resonance imaging and spectroscopy. 1989. Plant PhysioI. 91:1243-1246. Rowland, L. J., D. Liu, M. M. Millard, and M. J. Line. 1992. Magnetic resonance imaging of water in flower buds of blueberry. HortScience 27:339-341. Saltveit, M. K 1991. Determining tomato fruit maturity with non-destructive in vivo nuclear magnetic resonance microscopy. Postharv. BioI. Technol. 1:153-159. Sarafis, V., H. Rumpel, J. Pope, and W. Kuhn. 1990. Non-invasive histochemistry of plant materials by magnetic resonance microscopy. Protoplasm 159:70-73. Saraifs, V., J. Pope, and Y. Sarig. 1992. Some aspects of NMR microscopy applications in plant science and agriculture. p. 459-476. In: B. Bliimich and W. Kuhn (eds.), Magnetic resonance microscopy: methods and application in material science, agriculture and biomedicine. VCH Publishers, New York. Schaefer, J., K O. Stejksal, and C. F. Beard. 1975. Carbon-13 nuclear magnetic resonance analysis of metabolism in soybeans labeled by CO 2 , Plant PhysioI. 55:1048-1053. Schmidt, S. J. and H. M. Lai. 1991. Use ofNMR and MRI to study water relations in foods. p. 405-453. In: H. Levine and L. Slade (eds.), Water relations in foods. Plenum Press, New York. Snijder, A. J., s. M. Glidewell, and B. A. Goodman. 1995. NMR microimaging of healthy and diseased potato tubers. p. 136. Abstr. III Int. Conf. Magn. Reson. Microscopy, Wurzburg, Germany. Sonego, L. R Ben-Arie, J. Raynal, and J. C. Pech. 1995. Biochemical and physical evaluation of textural characteristics of nectarines exhibiting woolly breakdown: NMR imaging, x-ray computed tomography and pectin composition. Postharv. BioI. TechnoI. 5:187-198. Song, H. P., J. B. Litchfield, and H. D. Morris. 1992. Three-dimensional microscopic MRI of maize kernels during drying. J. AgI. Eng. Res. 53:51-69. Southon, T. K, A. Mattsson, and R A. Jones. 1992. NMR imaging of roots: methods for

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reducing the soil signal and for obtaining a 3-dimensional description of the roots. Physiol. Plant. 86:329-334. Southon, T. E. and R. A. Jones. 1992. NMR imaging of roots: effects after root freezing of containerised conifer seedlings. Physiol. Plant. 86:329-334. Spaine, P, J. S. MacFall, and G. A. Johnson. 1994. Magnetic resonance microscopy of water movement through fusiform rust galls of pine. p. 11-16. In: Proc. 4th Southern Station Chemical Sciences Meeting. Research and Applications of Chemical Sciences and Forestry. Forest Service General Technical Rep. SO-104. Stark, D. D., and M. G. Bradley. 1992. Magnetic resonance imaging, 2nd ed. Mosby Yearb., St. Louis. Stejskal, E. 0., and J. E. Tanner. 1965. Spin diffusion measurements: Spin-echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42:288. Stout, D. G., R. M. Cotts, and P. 1. Steponkus. 1977. The diffusional water permeability of Elodea leaf cells as measured by nuclear magnetic resonance. Can. J. Bot. 57:1623-1631. Stout, D. G., and P. 1. Steponkus. 1978. Nuclear magnetic resonance relaxation times and plasmalemma water exchange in ivy bark. Plant Physiol. 62:63 6-641. Sugiura, T., M. Yoshida, J. Magoshi, and S. Ono. 1995. Changes in water status of peach flower buds during endodormancy and ecodormancy measured by differential scanning calorimetry and nuclear magnetic resonance spectroscopy. J. Am. Soc. Hort. Sci. 120:134-138. Suzuki, K., T. Tajima, S. Takano, T. Asano, and T. Hasegawa. 1994. Nondestructive methods for identifying injury to heat-treated papaya. J. Food Sci. 59:855-857. Tamiya, T., T. Miyuazaki, H. Ishikawa, N. Iriguchi, T. Maki, J. J. Matsumoto, and T. Tsuchiya. 1988. Movement of water in conjunction with plant movement visualized by NMR imaging. J. Biochem. 104:5-8. Van As, H., and T. J. Schaafsma. 1984. Noninvasive measurement of plant water flow by nuclear magnetic resonance. Biophys. J. 45:469-472. Veres, J. S., G. A. Johnson, and P. J. Kramer. 1991a. In vivo magnetic resonance imaging of Blechnum fems: Changes in T1 and N(lH) during dehydration and rehydration. Am. J. Bot. 78:80-88. Veres, J. S., G. P. Cofer, and G. A. Johnson. 1991b. Distinguishing plant tissues with magnetic resonance microscopy. Am. J. Bot. 78:1704-1711. Veres, J. S., G. P. Cofer, and G. A. Johnson. 1993. Magnetic resonance imaging of leaves. New Phytol. 123:769-774. Walter, 1., R. Callies, and R. Altenburger. 1992. Studies of plant systems by in vivo NMR spectroscopy. p. 573-610. In: J. D. De Certaines, V. M. M. J. Bovee, and F. Podo (eds.), Magnetic resonance spectroscopy in biology and medicine. Pergamon Press, Oxford. Walter, 1., A. Balling, U. Zimmerman, A. Hasse, and W. Kuhn. 1989. Nuclear magnetic resonance imaging of leaves of Mesembryanthemum crystallinum 1. plants grown in high salinity. Planta 178:524-530. Wang, P. c., and S. J. Chang. 1986. Nuclear magnetic resonance imaging of wood. Wood Fiber Sci. 18:308-314. Wang, S. Y., M. Faust, and M. J. Line. 1994. Apical dominance in apple (Malus domestica Borkh.): the possible role of indole-3-acetic acid (IAA). J. Am. Soc. Hort. Sci. 19:1215-1221. Wang, C. Y, and P. C. Wang. 1989. Nondestructive detection of core breakdown in 'Bartlett' pears with nuclear magnetic resonance imaging. HortScience 24:106-109. Wang, C. Y., and P. C. Wang. 1992. Differences in nuclear magnetic resonance images between chilled and non-chilled zucchini squash. Env. Expt. Bot. 32:213-219.

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Wang, S. Y., P. C. Wang, and M. Faust. 1988. Nondestructive detection of watercore in apple with nuclear magnetic resonance imaging. Scientia Hort. 35:227-234. Warmund, M. R, J. M. Brown, K. 1. Schaffer, and B. H. Barritt. 1990. Magnetic resonance imaging of graft unions of Mark apple rootstock. p. 186. In: Proc. XXII Int. Hort. Congo Firenze. Warmund, M. R, B. H. Barritt, J. M. Brown, K. 1. Schaffer, and B. R Jeong. 1993. Detection of vascular discontinuity in bud unions of'Jonagold' apple on Mark rootstock with magnetic resonance imaging. J. Am. Soc. Hort. Sci. 118:92-96. Williamson, B., B. A. Goodman, J. A. Chudek, and D. J. Johnston. 1992a. Nuclear magnetic resonance (NMR) microimaging of soft fruits infected by Botryis cinerea. p. 140-144. In: K. Verhoeff, N. E. Malathrakis, and B. Williamson (eds.), Recent advances in Botrytis research. Pudoc Sc. Publ., Wageningen. Williamson, B., B. A. Goodman, and J. A. Chudek. 1992b. Nuclear magnetic resonance (NMR) micro-imaging ofripening red raspberry fruits. New Phytol. 120:21-28. Williamson, B., B. A. Goodman, and J. A. Chudek. 1993. The structure of mature gooseberry (Ribes grossularia) fruits revealed non-invasively by NMR microscopy. Micron. 24:337-383. Williamson, B., B. A. Goodman, J. A. Chudek, G. Hunter, and J. A. B. Lohman. 1994. The vascular architecture of the fruit receptacle of red raspberry determined by 3D NMR microscopy and surface-rendering techniques. New Phytol. 128:39-44. Xia, J., and P. T. Callaghan. 1992. "One-shot" velocity microscopy: NMR imaging of motion using a single phase-encoding step. Mag. Reson. Med. 23:138-153. Xia, J., V. Sarafis, E. O. Campbell, and P. T. Callaghan. 1993. Non-invasive imaging of water flow in plants by NMR microscopy. Protoplasm 173:170-176. Zimmerman, u., A. Haase, D. Langbein, and F. Meizner. 1993. Mechanism oflong-distance water transport in plants: a re-examination of some paradigms in the light of new evidence. Phil. Trans. Royal Soc. London (B) 341:19-31. Zimmerman, U., J. Rygol, A. Balling, G. Klork, A. Metzler, and A. Haase. 1992. Radial turgor osmotic pressure profiles in intact and excised roots of Aster tripolium. Plant Pysiol. 99:186-196. Zion, B., P. Chen, and M. J. McCarthy. 1995. Detection of bruises in magnetic resonance images of apples. Computers and Electronics in Agr. 13:289-299.

4

Postharvest Technology and Utilization of Almonds Mario Schirra C.N.R.-Istituto per la Fisiologia della Maturazione e della Conservazione del Fruito delle Specie Arboree Mediterranee Localita Palloni 09170 Oristano, Italy

1. Introduction A. Historical B. Dietary Uses C. World Production II. Kernel Analysis A. Lipids B. Proteins C. Carbohydrates D. Vitamins E. Minerals F. Cyanogenic Glycosides III. Postharvest Operations A. Handling B. Drying C. Shelling D. Bleaching E. Storage F. Aflatoxins

*Research supported by Mi.R.A.A.F-CASMEZ. Subproject 4-Almond. Paper 571. The author is indebted to D. E. Kester, Prof. Emeritus, Department ofPomology, University of California, Davis (USA) and to Dr. C. Grasselly, Institute National de la Recherche Agronomique, Montpellier (France), for critical reviews of the paper and to Prof. G. Barbera, Istituto di Coltivazioni Arboree, University of Palermo (Italy) and to Dr. Laura De Palma, Istituto di Coltivazioni Arboree, University of Bari (Italy), for helpful discussion and suggestions. Horticultural Reviews, Volume 20, Edited by Jules Janick ISBN 0-471-18906-5 © 1997 John Wiley & Sons, Inc. 267

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IV. Utilization A. Direct Consumption B. Processing 1. Whole Kernel 2. Chopped or Milled Almonds 3. Marzipan and Almond Paste 4. Almond Milk 5. Almond Butter, Margarine, and Edible Oil C. Almond Oil 1. Preparation and Characteristics 2. Utilization D. Essential Oils E. Byproducts 1. Kernel Wastes 2. Almond Tegmen and Hulls 3. Almond Shells Furfural Fertilizers Charcoal, Activated Carbon, and Fuels Xylose and Xylitol Other Byproducts V. Future Prospects Literature Cited

I. INTRODUCTION

The almond (Prunus dulcis Miller [D.A. Webb] syn. P. amygdalus Batsch) (Kester and Gradziel1996) consists of two types, the sweet edible almond and the bitter almond which is mainly used in the manufacture of flavoring extracts (Monastra 1979; Grasselly and Crossa-Raynaud 1980). Sweet almond varieties are classified as hard or stone shell and soft or paper shell, depending on shell texture (Kester et al. 1980). The fruit is an ovoid or elongated drupe with a dry or meaty exocarp, green in color, sometimes with red undertones, generally fuzzy but sometimes smooth. The epicarp and mesocarp together form the hull which, on ripening, splits open spontaneously, thus exposing the endocarp, or shell. Shell characteristics (hardness and degree of lignification, shape, size, thickness, type, and number of pores) are strictly related to cultivar. The shell contains one or two white seeds covered with a smooth or rippled tegmen that varies in color from ochre to brown (Casella 1968; Godini et al. 1979). The weight of the fruit and its different parts is extremely variable. The fresh weight of the whole fruit varies from 10 to 30 g; the hull may represent from 40 to 700/0 of the weight,

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the shell from 20 to 45%, the seed from 10 to 20%, and the seminal tegmen from 6.0 to 8.4%. The hulls of paper-shell cultivars are heavier and larger in percentage (Godini 1982). Relationships between size, shape, and weight in almonds have been described by Kester (1965). This review summarizes the literature relevant to the postharvest technology and utilization of almonds. A. Historical

The almond originated in the mountainous regions of Central Asia where even today some wild forms can be found which, through reciprocal hybridization, may have produced the cultivated form. Another hypothesis is that the common almond is contemporary with the wild form and, like it, originated from unknown parent plants (Grasselly and Crossa-Raynaud 1980). The time and circumstances under which the almond arrived in the Mediterranean basin are unknown, but its origin is almost surely Asia Minor on the basis of linguistic evidence and connections with the Greek myths and more ancient oriental mythologies (Petino 1942). The earliest mention of almonds is found in the Bible (Gen. 43,11) where they are mentioned many times in various contexts. One famous story is that of the patriarch Israel who ordered his children to take a gift of typical Palestinian products, including almonds, to offer to Joseph in Egypt. There are numerous mentions of almonds by Theophrastus and Dioscorides (Bianca 1872). Three intact almond fruits and a few fragments of hull were found in a ship wrecked off the coast of Majorca around the fourth century B.C. and were quite similar to the 'Pou' cultivar grown on Majorca (Socias 1988). Toward the middle of the second century B.C., Cato mentioned the Avellanae grecae, the almonds we know today. The Latin name (Amygdalus) attributed by Pliny, Palladius, and Columella is the literal translation from the Greek of the name handed down by Theophrastus and Dioscorides. The almond was thus already known and grown in ancient Greece, in the fourth century B.C. as it was in Rome in the first century A.D. It appears that the almond was fairly widely cultivated and the grafting technique was known to imperial Romans (CoIls et al. 1985). In all times, almonds have been highly considered both as food and as a medicine. Hippocrates in the fifth century and Pliny in the first century B.C., described their therapeutic properties (Petino 1942). The ancient Romans prepared an ointment from the bitter almond to protect dogs from insect bites.

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Scribonius Largus, a Roman physician, called bitter almonds Amigdali amari. In his medical prescriptions, which date back to the first half of the first century A.D., bitter almonds and their oil were frequently prescribed for headaches, tumors, and pains of the bladder. In his treatise on pharmacology, Dioscorides considered the almond highly effective against liver, kidney, and respiratory disorders, adding that it was sufficient to eat five or six almonds to avoid getting drunk. In addition, Dioscorides recommended almond oil as an emollient and a remedy for bladder stones. The abundant use of almonds in pharmacology explains why, in the prices listed in an edict issued by Diocletian in 301 A.D., almonds appear as a medicinal product sold already shelled. Arab doctors prescribed the eating of almonds as an emollient for the throat, to fortify the stomach and chest, and for purifying and medicating the intestines. Arab pharmacopoeia included almonds among aphrodisiacs and foodstuffs with sexual connotations (Qureshi et al. 1989). In 716, almonds were mentioned by Epicean authors in a charter promulgated by King Chilperic II, of France in the monastery of Corbie. In 812, Charlemagne ordered the introduction of almond trees (Amandulari) into the imperial plantations. Toward the end of the Middle Ages, almond cultivation was introduced into the Palatinate (Germany). Around the middle of the 14th century, Marino Sanuto wrote that almonds represented an important commodity in trade between Venice and Alexandria. The production of almonds in the Greek archipelago, which was then under Christian domination, was noteworthy. In the 17th century, the almond was introduced into America by the Spanish, who began cultivation' mostly in California, where the first efforts at intensive cultivation were made between the 18th and 19th centuries. Consumption of almonds was enormous in the Middle Ages. In an inventory drawn up in 1372 for Jeanne D'Ivrea, Queen of France, 500 pounds of almonds and only 20 pounds of sugar were listed. Almonds are mentioned in many recipes including the cookbooks of King Richard II of England (1390). In 1421, the Templars on Cyprus used almondbased products. Documents dating back to 1407 inform us about the production of marzipan in the town of Lubeck (Germany). The preparation of this important almond-based foodstuff, which went under the name of Marci Panis, that is, the bread of Saint Mark, was entrusted to the pharmacists (Alessandroni 1984). In 1542, Boorde mentioned almond milk and butter as alternatives to meat during Lent. English manuscript recipes dating back to the 14th and the 15th centuries reveal a large consumption of sweets, spices, and almond milk by aristocrats (Sass 1981). Almond milk or milklike derivatives were extensively used as an enrich-

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ing and thickening agent during Lent when the consumption of cream, milk, and eggs was prohibited. B. Dietary Uses

The almond is among the so-called "mendicant" fruits, a reference to the four monastic orders (the Augustinians, the Carmelites, the Dominicans, and the Franciscans), who accepted only figs, raisins, hazel nuts, and almonds as offerings. It was a wise choice since these four crops contain very well-balanced nutrients (Lucas 1942; Bevilotti 1942; Casares and Lopez 1952; Lopez-Andreu et al. 1981). Almonds are low in sodium and sugar and rich in dietary fiber and calcium, even though in part as oxalate (Linder 1966; Souty et al. 1973; Meena et al. 1987) and thus not completely available (Rose and MacLeod 1923; Poneros-Schneier and Erdman 1989). One hundred grams of almond kernels (fresh wt) contain from 350 to 500 mg of phosphorus as compared to 250 mg in fish and 700 mg in cheese. Almonds contain 20 to 50 mg/l00 g iron as compared to 3 mg for spinach and 9 mg for beef liver. The bioavailability of iron in almonds is low, less than 10% of the total (Reddy and Hotwani 1993), probably because of the high content of polyphenols and phytate, two known inhibitors of iron absorption (Macfarlane et al. 1988; Hazell and Johnson 1987). Almonds are one of the richest natural sources of magnesium. Its intestinal absorption from almonds is high-equal to soluble magnesium acetate (Fine et al. 1991). Oligoelements and liposoluble vitamins (Saura-Calixto et al. 1988) together with a diastatic enzyme, emulsin (Bridel and Arnold 1921; Helferich and Kleinschmidt 1961; Heyworth and Walker 1961; Haisman et al. 1967), complete the nutritional wealth of almonds (Kuzio 1977; Paul and Southgate 1979; Havigorst 1986; Moricone and Pedicino 1986). The caloric value of almonds ranges from about 500 to 600 cal/l00 g for the edible part (Istituto Nazionale della Nutrizione 1973), mostly because of their high lipid content. Almonds are cholesterol free (Cowan et al. 1963; Segerstrom 1990). A diet high in monounsaturated fat from almonds has been shown to lower plasma cholesterol level (Spiller et al. 1992) and is beneficial in reducing the risk of coronary heart disease (Abbey et al. 1994). However, the quality of kernel lipid is not always related to the degree of unsaturation since the oil of certain cultivars, although less unsaturated than others, shows a higher content of linoleic acid (Polesello and Rizzolo 1989), which is essential for nutrition (Deuel 1957; Kinsell et al. 1958; CantaJora 1982).

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Almonds are also rich in proteins (Souty et al. 1971,1973; Carpena et al. 1980) which provide all eight of the essential amino acids, although methionine and lysine are present in limited concentrations from a nutritional viewpoint (FAD/OMS 1966). Cysteine concentration is low in almond proteins but its value is higher than that recommended by FAD/WHO and also higher than in certain foodstuffs of animal origin, such as cow's milk and human milk. The high content of glutamic acid, arginine, and especially glycine is noteworthy (Nassar et al. 1977; SauraCalixto et al. 1981, 1982c). The lysine/arginine ratio is important from a nutritional viewpoint since it has been directly associated with serum cholesterol levels (Sanchez and Hubbard 1989,1991). Previous studies on almond proteins, physicochemical properties, and biological value were performed by Morgan et al. (1923), and Barre (1951, 1953a, b), Barre and Wormser (1955). Studies on the availability of riboflavin from some foods, including almonds, were carried out by Everson et al. (1948, 1952). Studies have been performed on extractability (Esteban et al. 1985) and almond protein solubility characteristics, polypeptide composition, and in vitro digestibility (Sathe 1993). The latter investigation suggested that, since almond protein contains a good balance of essential amino acids with the exception of methionine, and they are easily hydrolyzed by common digestive proteases, production of high-quality protein hydrolysates (with respect to essential amino acid balance) for food application may be possible. Processing, such as roasting, may alter almond proteins (Hall et al. 1958; Girard et al. 1961). In vitro digestibility of roasted almond protein decreased markedly when roasted above 210°C (Fuse et al. 1984). Components contributing to the flavor of roasted almond have been identified by Takei and Yamanishi (1974). Symptoms of almond allergy have been noticed by Ortolani et al. 1989. The allergenicities of raw or processed almonds (blanched, blanched roasted, and almond butter) were examined and compared by immunoblotting using sera from eight almond-allergic individuals (Bargman et al. 1992). C. World Production

The almond, one of the most ancient nut trees, is the most important nut species. In 1993/94, world almond production was 337,000 tonnes. In 1994/95, it reached the record high of over 427,000 t (Anon. 1996). The leading almond-producing country is the United States (central valleys of California) which, with a production of over 276,000 t of kernels, accounts for over 65% of the world's supply. The second almond-

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producing area includes the European countries bordering on the Mediterranean Sea. Among these, Spain is the largest producer, accounting for 15 to 20% of total world production. The other producer countries include Italy (5%), Greece (5%), Morocco (2%), and Portugal (2%), followed by Tunisia, Turkey, Algeria, and some eastern European countries. In the last several decades, almond production in the Mediterranean countries has remained practically constant in absolute terms (about 75,000 t), but has undergone a sharp decrease in percentage of world production, which has stabilized at about 25 %. The increase in Spanish production has compensated for the large decrease in production in Italy, where a progressive abandonment of almond growing has occurred (Monastra 1988, 1991). In European countries, almonds are cultivated mostly on non-irrigated areas represented by poor lands receiving little attention from farmers. Production suffers from significant yield and quality fluctuations from season to season. Conversely, since 1966 the United States has had a rapid increase in almond growing areas, and production rose to first place in world standings in just a few decades (Monastra 1991). Highly adapted, crosscompatible cultivars (Kester et al. 1992, 1994), rootstocks (Kester and Micke 1992), favorable soil and climate, intensive management systems, abundant water supply and fertilization (Kester and Micke 1984; Kester and Gradzie11996), mechanized harvesting (Connell 1994), and the use of domestic honeybees to improve cross-pollination (Loper et al. 1985) have contributed to the success of almond growing in California. Thanks to an efficient marketing system, the U.S. exports over 50% of its production (Anon. 1996). II. KERNEL ANALYSIS

Kernel moisture remains very high during the first two months after fruit set and it then decreases rapidly until about two weeks before full maturity. Moisture content reaches values close to 15% at harvesting (Hawker and Buttrose 1980; Schirra and Nieddu 1992).

A. Lipids Lipids are the main constituents of almonds, generally exceeding 40% (dry weight basis). Lipid content and composition mainly depend on the cultivar (Lotti et al. 1965; Garcia Olmedo and Marcos Garcia 1971; Meheran and Filsoof 1974; Romojaro et al. 1977; Filsoof et al. 1976;

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Beuchat and Worthington 1978; Dugo etal. 1979; Munshi et al. 1988; Romojaro et al. 1988a) and the stage of growth (Galoppini and Lotti 1962; Carrante and Cucurachi 1967; Munshi et al. 1982; Saura-Calixto et al. 1983f; Munshi and Sukhija 1984; Soler et al. 1988; Schirra and Nieddu 1992). Oil composition is also affected by geographic area of production (Barbera et al. 1994a), as cold climates cause an increase in unsaturated fatty acids (Meara 1952), and, to a lesser extent, by tree age (Saura-Calixto et al. 1983b) and agricultural practices such as irrigation (Souty et al. 1973; Romojaro and Berenguer 1984; Schirra et al. 1988), rootstocks (Schirra et al. 1993; Barbera et al. 1994b), and growth regulators (Munshi and Sukhija 1986). Oleic and linoleic acids together account for over 90% of the fatty acids of kernel lipids. Oleic and linoleic acids are negatively correlated (Saura-Calixto et al. 1981; Schirra et al. 1993). In ripe almonds, fatty acids are present mostly as triglycerides (Violante 1966; Nassar et al. 1977). The unsaponifiable fraction of almond oil shows a wide range of variation, depending on cultivar and area of production (Polesello and Rizzolo 1989). The main components of this fraction are sterols, methylsterols, aliphatic alcohols, triterpene alcohol, hydrocarbons, and liposoluble vitamins. Sterols are mainly represented by fJ-sitosterol and, to a much lesser extent, by campesterol. Minor sterols (Li5-avenasterol, stigmasterol and Li7-stigmasterol) have been detected in some cultivars (Colombini et al. 1979; Garcia Olmedo et al. 1978a; Dugo et al. 1979; Brieskorn and Betz 1987). Among methylsterols, obtusifoliol, gramisterol, citrostadienol and three unidentified compounds have been detected (Garcia Olmedo et al. 1978b). In the unsaponifiable fraction, different aliphatic alcohols and seven triterpenes, four of which have been identified as cycloarthenol, fJ-amirin, cycloarthanol, and lupeal, have been detected (Garcia Olmedo et al. 1978c). Among aliphatic hydrocarbons, 20 compounds have been identified, in which -C zz predominates (Garcia Olmedo et al. 1978d). B. Proteins Protein content ranges from about 18 to 24% and consists of albumin, globulin, glutelin, and prolamin (Ekpenyong 1969; Saura-Calixto et al. 1982c; Lopez-Andrea et al. 1984, 1985a; Riquelme et al. 1985). Nonproteic nitrogen (NPN) is present at about 0.2%. The predominant amino acids in NPN are aspartic acid, glutamic acid, proline, arginine, and alanine. The polypeptides of almond meal NPN contain 65 % mol glycine (Wolf 1995), suggesting the presence of glycine-rich proteins that are structural elements of plant cell walls. Elemental composition of protein

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bodies in almond seeds has been investigated by EDX analysis (LoU and Buttrose 1978). Youle and Huang (1981) studied the role of albumin and globulin in different oil seeds, including almond. Kernels with the highest oil content were found to be lowest in protein (Saura-Calixto et al. 1983e). The lipid/protein ratio is important for the confectionery industry, especially for marzipan production, since it influences water absorption by the almond paste: the higher the lipid content, the less the water absorption (Alessandroni 1980). A method to distinguish oils from different origins in oleaginous material has been developed by analyzing specific components in the glycoprotein fraction (Fournier et al. 1993). Prats Moya and Berenguer Navarro (1994) characterized some almond cultivars using the composition of free amino acids. C. Carbohydrates Soluble sugars are present in relatively low concentrations in almonds, varying from 3 to 8% (Souty et al. 1971; Godini et al. 1979; Schirra et al. 1993), but are sufficient to make them sweet-tasting. Nonreducing sugars include sucrose and raffinose, representing 90% and 7%, respectively, of total sugars. Reducing sugars glucose, fructose, sorbitol, and inositol are also present in very low concentrations (Ramic et al. 1971; Vidal-Valverde et al. 1979; Saura-Calixto et al. 1980, 1984; Romojaro et al. 1988b; Soler et al. 1989; Fourie and Basson 1990). Polysaccharides represent approximately 3 to 6% of dry matter. Starch has been detected only in some cultivars (Kumar et al. 1990, 1994). Fiber (cellulose, hemicellulose, and lignin) is present in extremely variable quantities (Saura-Calixto et al. 1983a,c,d; Lopez-Andreu 1985b; Lintas and Capelloni 1992) with pectic substances as accompanying polysaccharides, impurities, or linked as side groups to the acid chain (Vidal-Valverde 1982). This component has been studied in various parts of the fruit, especially in connection with the use of waste almonds (Sequeira and Lew 1970; Saura-Calixto and Cafiellas 1982b). Structural studies on hemicellulose polysaccharides from seeds and hulls have been conducted by Kandeel et al. (1982). Englyst and Cummings (1988) described an improved method for measuring dietary fiber in plant foods, including almonds. D. Vitamins

Liposoluble vitamins such as tocopherols (vitamin E) are present in fairly large concentrations in almonds, about 400 mg/100 g of oil (Lam-

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bersten et al. 1962; Slover et al. 1969; Gertz and Herrmann 1982; Piironen et al. 1986; Polesello et al. 1990). The large amount of total tocopherols in almonds, mainly a-tocopherol (the most active antioxidant as compared to /3, 8 and y-tocopherol), accounts for their good keeping quality. Total tocopherol concentration gradually decreases during storage, probably as a result of its inhibitory function during auto-oxidation (Salvo et al. 1986; Fourie and Basson 1989a; Senesi et al. 1991). Almonds contain water-soluble vitamins such as B1 (thiamine) and Bz (riboflavin) groups in concentrations of about 160 and 120.ug/g of the edible part, respectively (Polansky and Murphy 1966), as well as nicotinic acid (niacin) from 14.7 to 34.1.ug/g and biotin from 1.2 to 9.0 .ug/g (James 1952; Polesello and Rizzolo 1989). Vitamins of the B6 group are present in the amount of about 80 .ug/g and in the form of pyridoxine, pyridoxal, and pyridoxamine, both free or combined as phosphates (Polansky and Murphy 1966; Polesello and Rizzolo 1989; Hoppner et al. 1994). Processing (toasting and peeling) causes a partial degrading of the vitamin B6 • These losses are on the order of 24 to 26% in toasted almonds and 12% in peeled almonds (Daoud et al. 1977). Fumigation did not affect thiamine and riboflavin contents in the kernel (Siesto 1956). Ascorbic acid and vitamin P are present at very low levels (Shehab and EI-Zoheiry 1974: Stepanova 1972). Rose and Macleod (1922), De Caro and Franceschini (1939), Secchi (1979) and Woodroof (1982a) reported the presence of vitamin A in almond kernels but this has not been confirmed by Saura-Calixto et al. (1988) and Polesello and Rizzolo (1989).

E. Minerals The mineral content of kernels, as evaluated by percentage of ash, represents about 3% dry matter depending on cultivar, location, climate, and agricultural practices (Saura-Calixto and Cafiellas 1980; Saura-Calixto et al. 1981; Schirra et al. 1988). The predominant minerals are potassium and phosphorus, which together represent over 70% of the total. Phosphorus and calcium show a negative correlation, while positive correlations have been observed with chlorine, sodium, copper, and nitrogen (Saura-Calixto and Cafiellas 1982a). Such relationships support the hypothesis of a copper deficiency conditioning protein synthesis in vegetables (Barcelo et al. 1980). Furr et al. (1979) found 39 mineral elements in almonds, including selenium and chromium. These two elements were also detected by Giri (1990). The evolution of the main macro- and microelements in different parts of the fruit during growth and ripening has been investigated (Carrante and Giannelli 1965; Bosch Arifio et al. 1976a,b; Schirra et al. 1994).

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F. Cyanogenic Glycosides

Amygdalin, a cyanogenic glycoside, ranges from 2.5 to 3.5% (dry wt) of bitter almond seeds. Amygdalin has also been detected at low levels in some sweet almond cultivars (McCarty et al. 1952; Welkerling de Tacchini et al. 1972; Barbera et al. 1988; Usai and D'hallewin 1992). Amygdalin is an association of gentiobiose and mandelic acid (fJ-gentiobiose of the nitrile of a-hydroxyphenylacetic mandelonitrilic acid). On hydrolysis, it is split by the enzymes amygdalase and prunase into glucose and benzaldehyde (Conn 1980). Hydrocyanic acid accumulation begins five days after fruit set (DAF) , reaching a maximum concentration of 13 ,umol/fruit at 25 DAF and then decreasing to a final content of 6 to 13 ,umol/fruit. Studies on bitter almond fruit development (Frehner et al. 1990) showed that in almond kernels the monoglucoside prunasin (Schwarzmaer 1972) is present at anthesis, while the corresponding diglucoside amygdalin is present at maturity. Hydrocyanic acid (HCN) in almonds may represent a danger to the consumer (Plahl 1924; Talon 1946; Rossler 1955; Sturm and Hanssen 1967). Its minimum lethal dose is 0.5 mg/kg of body weight. Processing and cooking affects the release of HCN after ingestion (Fomunyam et al. 1985). In the international trade of sweet almonds, 3% bitter kernels are permitted. Bitter almonds are used in the preparation of many foodstuffs, including macaroons, to which they confer a good taste and flavor much prized by many consumers (Rothea 1945). The content of hydrocyanic acid in macaroons ranges from 8 to 166 mg/kg. In Germany, bitter almonds are used to prepare a type of marzipan containing up to 12% of total weight of almonds (Corradi and Micheli 1982). Many methods !lave been developed to determine amygdalin and cyanide content in almond seeds and foodstuffs (Mandenius et al. 1983; Kupchella and Syty 1984; LaCourse and Krull 1987). In international trade, the percentage of bitter kernels in sweet almond lots is currently evaluated by sensory analysis. Such a laborious, time-consuming, and very expensive method could conveniently be replaced by chemical methods (Arya and Madhura 1986; Blumenthal and Kiss 1970). III. POSTHARVEST OPERATIONS

A. Handling

Harvesting operations must be timed to optimize kernel quality (Holmberg 1978). Early harvesting helps to avoid early autumn rains that delay harvesting and decrease quality by increasing both insect and mold

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damage (Connell 1994). After harvesting, almonds must be cleaned by removing foreign material and reducing loose-shell kernels, since small amounts of foreign matter may provide habitats for insects (Arthur 1989). Almonds are then placed in burlap bags or directly on trailers and removed from the grove for hulling. Hulling is carried out mechanically but hand hulling still survives in cases of small quantities of very high quality almonds. Hulling machines consist of a hopper and a cylindrical chamber with slotted walls, at the center of which a motor-driven shaft with bristles rotates. Fruit fall by gravitation from the hopper into the cylinder where, by friction caused by the rotating shaft, the hulls are detached from the shells. Hulls are expelled through the slits in the cylinder, while the shells come out on the side opposite the hopper and are placed in baskets or bags to be dried in the sun. It is a good practice to hull cultivars with shells of different hardness separately, since they require different rotating speeds in the huller. B. Drying The kernels of freshly harvested almonds have a high moisture content and must therefore be dried immediately (Godini 1982; Moreira et al. 1988). Almonds take from three to four days to dry and must be stirred several times a day and covered at night. Drying is considered complete when kernels make a noise on hitting the inside shell walls; at this stage, mean moisture content is 7 to 80/0. Unshelled almonds are stored in cool, dry rooms until they are processed or sold.

c.

Shelling

Unshelled almonds are first graded to facilitate product classification as well as to reduce moisture. Unshelled almonds are placed in hoppers from which they pass through a series of rating cylinders which classify them on the basis of size and send them, thus separated, to different shelling units. Shelling, once a manual operation, is now carried out by special shelling and separating machines operated by compressing fruits between rollers turning in opposite directions and separating the fruit parts by blowing (Godini 1982). These shelling machines have now reached a satisfactory degree of reliability. Separation of shell fragments from nuts, however, is most successful when shells are tender and light (with shelled yield equal to or higher than 40%) because of the high difference in weight between nut and shell fragments. After leaving the sheller-separator, the product goes to vibrating tables where separation is completed by hand and the kernels damaged during mechanical

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shelling are set aside as waste. Features of almond-shelling machines and problems due to almond fruit characteristics (size, shape, hardness) have been described by Guarella and Pellerano (1988, 1989). D. Bleaching Kernels can be marketed directly, peeled, or sent to automated production lines for the processing and packaging of whole, split, sliced, chopped, and milled almonds. Bleaching or peeling is accomplished by dipping the kernels in hot water (1 to 2 min dip in 80 to 90°C water), followed by chilling in cold water and passing between rubber rolls, moving in opposite directions, which squeezes off the skin (Pierce 1928; Ramsay and Norris 1931; George 1985). This process is less successful in cultivars with quite thin tegmen (since they adhere tightly to the cotyledons), with batches having a high percentage of double kernels (since they are irregular in shape), and also with batches of heterogeneous nuts (since the thicker ones are broken during the peeling operation). The loss following peeling is considerable. Seed and embryonic seed coats represent 10 to 12% of the entire product and 13 to 15% of waste almonds. Bleached almonds, which are still wet, are sent through belt dryers where they are brought back to previous humidity levels after 3.0 to 3.5 h at about 60°C. Dry matter losses during processing (peeling, grinding, refining, drying, and roasting) could be reduced by standardizing all the operations (Obchinnikova and Selezneva 1986). Electronic selectors, operating on the basis of color difference between tegmen and cotyledons, send the insufficiently peeled almonds back to the beginning of the cycle. E. Storage Before utilization, large amounts of almonds are usually stored at room temperature and ambient humidity (shelf-life conditions) and removed throughout the year for shelling and processing. Unshelled almonds can be stored for up to two years but exposure to sunlight reduces storage life. Shelling reduces kernel storage life by making them more susceptible to insects and molds (Wells 1951; Harris et al. 1972). Almonds readily absorb odors and should never be stored with commodities such as onions, apples and other fruits, or fish. Foreign odors may be removed from a storage room by ventilation or treatment with ozone or activated carbon. Disinfecting and disinfestation operations should be carried out frequently (Spitler et al. 1975), and almond organoleptic characteristics (appearance, color, odor, and taste) should be inspected periodically. If

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possible, almonds should be analyzed periodically to evaluate keeping quality (Fourie and Basson 1989b; Schirra 1992). Pre- or postharvest treatments with methyl bromide or hydrogen phosphide fumigation (Nelson et al. 1978) are usually performed to prevent or reduce damage by almond pests, one of the most serious problems for the almond industry (Goodin 1980; Curtis et al. 1984; Navarro et al. 1986). Integrated pest management and innovative farming practices such as orchard sanitation, the use of natural naval orangeworm predators, and/or use of insect traps (Rice et al. 1984) offer promising solutions as an alternative to pesticides (Curtis 1983; Cunnigham 1991). One of the means proposed to reduce postharvest losses and increase crop quality is to harvest the fruits two to three weeks before the normal date, when nuts are fully dried and hulls have split open (Micke et al. 1966: Connell et al. 1989). However, mid-season almonds are more susceptible to mold development than those harvested late (Mirocha and Wilson 1961; King and Schade 1986). Gamma ray irradiation has been effective in controlling insects and mold development (Sattar et al. 1989) but adversely affects oil quality since the rate of lipid peroxidation increases during storage (Sattar et al. 1990). It has been demonstrated that there are no significant sensory quality effects when almonds are irradiated at the beginning of the storage period (Narvaiz et al. 1993) but after six months, flavor intensity slightly decreased in almonds irradiated with the higher gamma radiation doses. The keeping quality of almonds greatly depends on the kernel moisture content (Micke et al. 1966; Labavitch 1978; Schirra and Agabbio 1989) and especially on water activity Q w (King et al. 1983) since available residual water is found to be inversely related to fat percentage (Beuchat 1978). Kernel moisture must be less than 7% in order to protect the nut (Kader 1985). Relative humidity (RH) inside storage rooms also affects almond shelf life. In general, relative humidity should be below 75%. Shelled almonds can be stored for seven to eight months at room temperature with RH below 70% as long as they are properly dried. Storage for longer periods requires temperatures below 10°C (Ryall and Pentzer 1974). Results of chemical analyses, flavor and taste ratings, visual examination, and microbiological evaluation of unshelled almonds stored under different temperature and humidity conditions for up to 14 months have been reported by Hadorn et al. (1981). Minor changes occurred under cool, dry conditions (50-60% RH), while under higher humidity (70-80% RH) deterioration occurred due to enzymatic reactions and mold development. Storage at o°C and 60 to 75 % RH gives good results for 15 to 16 months even with shelled almonds (Salunkhe and Desay 1986). Controlled atmosphere (CA) assures main-

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taining the quality of shelled almonds and delays the occurrence of offflavor and off-taste, even at relatively high storage temperatures (Wright 1941; Guadagni et al. 1978). CA also has been approved for dry almonds but is still experimental. CA is effective in reducing insect development during storage (Storey and Soderstrom 1977; Soderstrom and Brandl 198Z, 1984). Storability of shelled almonds may be improved by packaging (Wells and Barber 1959). Small, airtight packages allow storage of shelled and peeled almonds at room temperature for up to eight months (Senesi et al. 1990, 1991). Storage for longer periods requires refrigeration. On the other hand, peeled kernels rich in a-tocopherol, such as the 'Supernova' kernel, can be maintained in good condition for up to one year, even in transparent polyethylene film pouches (Rizzolo et al. 1994). Packaging in ventilated polyethylene pouches allows keeping the quality of green almonds for up to 10 days at room temperature (30-35°C; 30-60% RH), 60 days at 8 to 10°C and 80 to 85% RH, and 105 days at 0 to ZOC and 70 to 75% RH (Teotia et al. 1993). F. Aflatoxins Many microorganisms occur on almonds (Phillips et al. 1976, 1979; 1980; Purcell et al. 1980) and may spread to involve the entire hull while the fruit is on the tree or on the ground (Mirocha and Wilson 1961). Among these, Aspergillus flavus and Aspergillus parasiticus may produce toxic metabolites called aflatoxins, among the most dangerous substances for animals and humans (Dupaigne 1978; Jimenez et al. 1991). The high toxicity of aflatoxins poses serious problems to the safety of almond-based foods and feed since contamination has been proved in many countries (Korpinen 1971; Schade et al. 1974; Fuller et al. 1977; Gelda and Luyt 1977; Pensala et al. 1977; Velez Gonzalez and Xirau Vayreda 1979; Qutet et al. 1983; Kershaw 1985; Tutelyan et al. 1989; Wood 1989; Haydar et al. 1990; Gradziel and Wang 1994). However, the incidence of aflatoxins on almonds can be greatly reduced by commercial sorting procedures and by removing insect-damaged kernels, which are more susceptible to infection than sound nuts (Phillips et al. 1976). Aflatoxins are sensitive to processing (Bassler 1980; Schade et al. 1981) and to a number of chemical and physical treatments (Marth and Doyle 1979). Oven drying and autodaving degraded aflatoxins by more than 65% (Bilgrami et al. 1984). Important chemical changes, such as decreases in sugars and ascorbic acid and increases in proteins and phenols, have been detected during aflatoxin contamination (Bilgrami et al. 1983). A large amount of research work (Fuller et al. 1978; Cauderay

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1979; Stanley et al. 1979; Schade and King 1984; Gilbert and Shepherd 1985; Tutelyan et al. 1989; Henderson et al. 1989; Wilson and Romer 1991; Horwitz et al. 1993; Trucksess et al. 1994) has provided sensitive

analytical methods for aflatoxin detection.

IV. UTILIZATION

A. Direct Consumption Immature almond fruit can be consumed as soon as they set on the limb and are consumed whole, including the pericarp. In the past, green almonds were traded in Italy, especially on the Florentine market (Bianca 1872). Almonds are still consumed in this way in Sicily, France, and some Scandinavian countries as soon as the cotyledon appears (Polesello and Rizzolo 1989) and in Turkey up to hardening of the cotyledons (June). Dried almonds, both processed and unprocessed, are the most common form of consumption. With the exception of a small percentage of paper-shell almonds which are sold whole, the product is generally shelled before marketing. Paper-shell cultivars have been found to be unsuitable for the making up of gift baskets of mixed nuts and dried fruit because of the fragility of their shells, which tend to break and spoil the decorative effect. The almonds most in demand are the large ones with smooth, regular shells, with no chipping or other damage, and with the nut light in color. From the organoleptic viewpoint, almonds with strong aroma, fully developed nuts, and low oil content are preferable (Godini et al. 1979; Testoni 1989). B. Processing 1. Whole Kernel. The product is marketed in the following ways (Francaviglia 1958): (1) salted, smoked, and flavored; packaged alone; or mixed together with other nuts or dried fruit; (2) toasted or candied, vacuum-packed in small packages, and (3) as confectionery products

used to decorate cakes, in typical sweets ("torrone" and "panforte" in Italy), sugar coated for use at weddings or used for making other candies such as nougats. Uniform and high-quality kernels, without doubles, are required by the confectionery industry (Loreti and Xiloyannis 1978).

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One of the most suitable almond cultivars for the confectionery industry is 'Pizzuta D'Avola' (Ferrara 1967; Barbera 1991). 2. Chopped or Milled Almonds. Almond flour is obtained by milling the peeled and blanched nut. To preserve aroma and reduce exchanges of humidity, the flour can be placed in a mold with a thin layer of malt dextrin (Anon. 1976a,b). This is the base product for the preparation of an extremely wide range of confectionery products such as ice cream, desserts, flavored milk-based beverages, Bavarian cream, macaroons, "pazienze," "tegole," "petit fours," almond rochers, "piani di mandorle," "frangipane," candies, snacks, icings, almond and mixed stuffings, baked products, frozen foods, dairy products, and syrups (Custer 1968; Calapaj and D'Amore 1972; Engwell 1977; Antoniazzi 1978; Anon. 1979; Alessandroni 1980; Payne 1981; Hannigan 1982; Woodroof 1982b; Alessandroni 1984; Anon. 1987; Matsunobu et al. 1987; Hayhou 1987; Van Wagner 1988; Segerstrom 1990; Forestier 1991; Pouquet 1991; Liem 1991). Because of its refined taste and aroma (Rizzolo et al. 1991b, 1992), the almond is also a basic ingredient in the so-called "salty" cuisine (soups, sauces, stuffings, seasonings for salads, meat or fish dishes, and sandwich spreads). Among the most typical gastronomic specialities are amber fish with almonds "caponata" made in the traditional way, chicken with almonds, meatballs with almond sauce, Ragusa-style tripe and San Bernardo sauce (Sicily), not to mention tagliatelle with poppies (an Eastern dish), Madrid soup (Spain), Moroccan eggs (Morocco), carp with almonds (Portugal), and rice with almonds (U.S.). A coating (a mixture of sucrose, dextrin alcohol and water) to prevent agglomeration and preserve crispness was patented by Kobayashi and Hisamatsu (1977). Salim et al. (1986) investigated the effect ofpreservatives on the shelf life of almond seed cake syrup. 3. Marzipan and Almond Paste. Marzipan originated in the Arab countries and spread first to Spain, then into Italy and from there to Germany, where it became widely consumed. Marzipan and almond dough are terms often misused as being synonymous. There is, however, a difference. Marzipan is obtained by boiling, while almond dough is generally prepared by simple blending (Alessandroni 1984). Marzipan is made with granulated sugar and has a much finer consistency than almond dough. Although its production entails some difficulty, it is better tasting and lasts longer. On the other hand, almond dough is made with icing sugar and peeled almonds; both these ingredients are blended

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with egg white. It is not cooked and sometimes maintains a coarse structure. On kneading, it often becomes oily; that is, it releases fatty matter. Sometimes, in the preparation of almond dough, colors and aromas based on almond, orange, and lemon oil or rose water are added. Marzipan is higher priced than almond dough. The almonds must be sweet, but with a small amount of bitter almond. They must be thoroughly cleaned. Marzipan and almond dough are put to different uses. The most common are the sculpting into the shape of flowers, fruit, vegetables and whatever else the imagination suggests; the preparation of petit fours, and the fillings of long-lasting cakes and their icing. In the preparation of flowers, as in the classic case of rose petals, the work involves a certain skill and requires a good deal of practice. It is easier to prepare fruits by pressing with simple, manual presses for marzipan. In this case, once the marzipan is molded into the form, it is finished by hand and left to dry before being decorated. Oil and ash concentration have been proposed as indicators to evaluate the almond content in marzipan (Houlbrooke 1963). An alternative method of analysis of the almond content in marzipan has been suggested by Firth (1969). 4. Almond Milk. Almond milk is a many-times-filtered emulsion of sugar and peeled and mashed sweet almonds (Hofer-Massard, 1926), also known as "Virgin's Milk" and "Genoese Bomb" because of its high nutritional value (Cotta Ramusino et al. 1961; Bath 1982). When almond milk is used in dietetic products, its lipid amount has to be reduced to make it quite similar to cow's milk in calorie content (Cotta Ramusino et al. 1961). It is considered a vegetable milk substitute (Ujsaghy 1940), recommended in cases of intolerance to cow's milk (Moricone and Pedicino 1986). A cheese- and yoghurt-like derivative of almonds has been patented as a milk substitute (Wakana and Okubo 1979; Yotsuhashi et al. 1986). Almond milk can be stabilized by adding a solution containing pectin and citric acid and sterilizing by heat treatment (Baudot 1967). 5. Almond Butter, Margarine, and Edible Oil. Because of the great expansion of almond production in California in the last few years, research and marketing experts in the almond industry have been devoting more attention to new ways of using almonds in new candy product formulations, in basic and flavored almond butters (Nolan 1982; Duxbury 1989), and in edible oil and margarine (Axer 1982,1983). Edible oil and margarine manufacture may also be obtained from bitter almond following removal of HCN (Abd EI-Aal et al. 1987).

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C. Almond Oil 1. Preparation and Characteristics. According to the Official Italian Pharmacopoeia (Farmacopea Ufficiale della Repubblica Italiana 1972), sweet almond oil (Prunus amygdalorum oleum) is the fatty oil obtained by the cold-pressing of mature seeds of Prunus amygdalus, var. dulcis D.C. On the market, however, it is also possible to find the so-called "Olio di Prima Pressione Raffinato" (refined first press oil), obtained by cold-pressing stale and waste almonds and other byproducts of the confectionery industry, with poorer characteristics (Richert and Mulart 1967). Yield varies from 43 to 56% for sweet almonds and from 36 to 50% for bitter almonds. Almond oil is quite fluid, yellow in color, all but odorless and has a pleasant, sweetish taste. Almond oil is more stable than other oils under normal storage conditions. Even at high temperature, oxidation occurs only after long storage (Riquelme and Romojaro 1989). When rancid, almond oil acquires an unpleasant, astringent taste. It should be stored in small, airtight containers kept in cool, dark places. 2. Utilization. The pharmacological, dietetic, and cosmetological properties of almond oil are well known (Vaughan 1970; Hotellier and Delaveau 1972). Because of its anti-inflammatory and analgesic properties, it is used in dermatology to treat erythremia and skin blemishes (Nagamoto et al. 1988), for the treatment of acne (Yee 1990), in the preparation of antiphlogistic ointments (Vaughan 1970), and for hair and skin preparations (Ansmann 1986). Less known is the use of almond oil in sedating earache and in softening and alleviating pain caused by inflamed hemorrhoids (Clark et al. 1967). It is also used in emulsion form as a laxative and against forms of bronchial catarrh. In the cosmetics industry, almond oil is used in the preparation of creams, perfumes, soaps, shampoos, and lotions. Over 280 cosmetic formulations are known with concentrations of up to 50% of almond oil (Fisher 1983). Stanchzuk-Rozycka and Elert (1974) patented a self-polishing wax emulsion containing almond oil for footwear. The price of almond oil is several times that of the oils commonly used as food and, for this reason, considerable effort has been expended in the past to establish the characteristics of the genuine oil and/or to devise methods for detecting adulteration (Franr;ois et al. 1960; Richert and Mulart 1967; Carballido and Prestamo 1970; Gutfinger and Letan 1973; Hallabo et al. 1977; Bertin 1980; Salvo et al. 1980; Rugraff et al. 1982; Fournier et al. 1993). Almond oil retains its quality for about one year at room temperature, or two years with storage at 4°C (Salvo et al. 1986). The stability of almond oil for use in parenteral solutions was investi-

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gated by Hizon and Huick (1956). Almond oil is often adulterated by adding peach and apricot oil (Demanze et al. 1982; Olliver et al. 1993). Bruno (1965) described a procedure to characterize oil from sweet almond tegmen. D. Essential Oils

Essential oil is commercially known as bitter almond oil. It can be obtained from the partially defatted press cake of bitter almond kernels, by enzymatic hydrolysis, and by steam distillation. Hydrocyanic acid is eliminated by washing in alkali and rectifying. Yield ranges from 0.5 to 0.7% with respect to the treated boards. In commercial oils, benzaldehyde percentage ranges from 85 to 87%. Besides benzaldehyde, the essence of bitter almond also contains numerous aromatic compounds that confer upon it a higher degree of refinement than that of pure benzaldehyde. The pure essence is a colorless, highly unstable liquid with a strong bitter almond smell. It must be kept in small, hermetically sealed glass containers and stored in dry, dark places. It can be stabilized with ethyl alcohol in concentrations above 10% (Arctander 1960). It is extensively used in baked goods, candies, and sugar fondants, with or without fixatives or flavoring material such as vanillin, anisic alcohol, benzaldehyde-phenylglycidates, etc. to improve stability and tenacity of the oil (Arctander 1960). Because of its scarcity and consequent high cost, bitter almond oil is replaced by synthetic benzaldehyde. Legally, there are no restrictions on this substitution since this has been included among the natural aromas. Adulterations are quite frequent and many methods have been proposed for detecting benzaldehyde in almond oil, its derivatives, and foodstuffs (Schmidt De Gil and De Falconi Catalini 1977; Kupchella and Syty 1984; Lucas and Sotelo 1984; LaCourse and Krull 1987; Butzenlechner et al. 1989). Benzaldehyde content in almond syrups has been determined by Stacchini and Boniforti (1965). Small amounts of benzaldehyde can also be detected in kernel of sweet almonds, depending on the rootstock (Welkerling de Tacchini et al. 1972). The inheritance of kernel flavor in almonds is determined by one gene with two alleles. SS is sweet flavored, ss is bitter, and the heterozygote Ss is slightly bitter (Heppner 1926; Dicenta and Garcia 1993). E. Byproducts

Large amounts of almond wastes from kernels (kernel waste, crude almond oil, almond flour, almond meal), tegmen, hulls, and shells are the raw material for almond industry byproducts (Sala Catala 1942).

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1. Kernel Wastes. Byproducts obtained from ripe almond kernels after the extraction of oil contain proteins, lipids, water, carbohydrates, fiber, minerals, and vitamins. They are commonly used as supplement to animal feed thanks to their high protein content (Longhi 1952). Almond flour has been proposed for the human diet as a protein supplement in cereal flours and baked products, as well as in the preparation of dietary and confectionery products (Pominski et al. 1985). Orchard preharvest sprays with crude almond oil in water resulted in lower insect egg deposition and a lower percentage of infested mummy nuts as compared with untreated controls (Van Steenwyk and Barnett 1987). Almond meals are used in dermatological abrading cleansers for the treatment of skin blemishes (Miles Laboratoires Inc. 1966), in cosmetics (Guillot et al. 1981), and in medicated soaps (Fisher 1983); they are commended as a cleansing agent for those who are allergic to soaps (Schwarz 1940) and cosmetics (Bonnet 1974). Finely ground almond can be used to stimulate hair regrowth and as a dandruff treatment when the hair roots have not dried up (Latif 1979). Anthelmintic substances can be obtained from bitter almonds (Saito 1954; Hoshi 1953). Reznikova (1956) carried out studies on antigens from almond meal for serological tests for syphilis. From sweet and bitter almond kernel mixed extracts can be prepared a cream with antifungal activity (Krishna Rao and Vimala Devi 1992).

2. Almond Tegmen and Hulls. The possibility of using the tegmens and hulls as supplemental livestock feed has been known for a long time (Hilpert and Kruger 1939; Cruess et al. 1947; Weir 1951) and much information is available in the literature concerning their composition and feeding value (Velasco et al. 1965; Bath 1972; Ohanesian et al. 1973; Sanchez-Vizcaino and Rios 1978; Bath et al. 1980). Investigations have also been carried out to evaluate the nutritive value of hulls in the diet of ruminants (Saura-Calixto and Cafiellas 1982b; Bath 1983; Aguilar et al. 1984), hogs (Calvert and Parker 1985), horses, and lactating swine (Petty and Rodick 1988). Reed and Brown (1988) studied the digestibility of almond hull in diets of lactating goats and its influence on yield and quality of milk. They concluded that mixtures of almond hulls and urea can be substituted for the alfalfa feed and are accounted for the lower costs without losses in milk production or significant changes in milk composition. Investigations on chemical changes and in vitro digestibility of almond hulls (Fadel 1990) showed that neutral detergent fibers are digested or solubilized at earlier heating (4 to 5 h at 120°C) but digestibility decreased to zero following prolonged heating. Carbohydrate content in almond hulls is higher than 25% (Cruess

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1947; Robert and Robert 1970; Sequeira and Lew 1970). They include glucose (14.4%), fructose (8.8%), sucrose (5.3%), sorbitol (4.6%), and inositol (2.5%) (Sequeira and Lew 1970). Analyses have been carried out on mineral composition (Saura-Calixto et al. 1980), dietary fiber (SauraCalixto et al. 1983c), proteins (Saura-Calixto and Cafiellas 1982), mineral content and amino acid composition (Saura-Calixto and Cafiellas 1982b), and tegmen oil (Saura-Calixto et al. 1985). Ryugo and Labavitch (1978) analyzed gums and mucilagens and Buttery et al. (1980a) identified 44 compounds in the essential oil extracted from hulls. The extensive use of pesticides in intensive production of almonds has raised serious toxicological and environmental concerns resulting in regulations limiting exposure for dairy animals and poultry. Decontamination of pesticide residues in almond hull meal and nuts has been proposed by Archer (1969, 1970). Alternative uses of hulls include the feasibility of converting hulls to pure alcohol (Anon. 1980). Rabinowitz (1991) patented a microbiological method for recovering myo-inositol and other sugars and sugar alcohol from dry almond hulls for nutritional uses. Almond hulls have also been studied as possible navel orangeworm attractants (Van Steenwyk and Barnett 1987) and growth inhibitors (Buttery et al. 1980b). Almond hulls have been proposed as fertilizer (Phillip 1925), to make soaps (De Dominicis 1920), and as a source of commercial vegetable tannin (Kedlaya and Selvarangan 1962; Cruess et al. 1947). Almond hulls can also be used as a humic material that could improve the physical and chemical structure of soil. 3. Almond Shells

Furfural. The residue of almond shells after acid hydrolysis is 35% (dry weight basis), indicating high lignification (Sinner et al. 1979). The carbohydrate fraction is mainly represented by cellulose and hemicellulose (Phillips and Goss 1940; Saura-Calixto et al. 1983c, 1984; Preston and Sayer 1992), which constitute a potential source of furfural with a yield of about 20% (Ciusa and Di Taranto 1956). By treating them with boiling nitric acid, the pentosans (polypentoses) present in the shell are hydrolyzed to pentoses which, by dehydration and cyclization, produce furfural (2-furancarboxyaldehyde or furfurylic aldehyde), which is an important intermediate for the chemical industry and is used as a selective solvent in the refining of vegetable oils and lubricants and in the extraction of butadiene from cracking gas (Nanni Marchino 1975). Natta et al. (1956) studied acetic acid as a byproduct of the furfural manufacture process from almond shells. Fractionation processes of

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residual lignocellulosics of almond shells have been investigated by Montane et al. (1993) and Martinez et al. (1995).

Fertilizers. A considerable amount of solid residue is produced in the furfural process. Part of this is used as fuel to generate the steam needed in the process and the remainder may be used as a potential source of humic-based fertilizers after oxidation with nitric acid (Riera et al. 1991, 1992). Investigations have revealed that certain organisms adapt to utilize almond shell polysaccharides much more than other organic residues (Martin et al. 1965). The mineral composition and availability of some macroelements of shells and hulls, as well as the feasibility to utilize the epicarp and mesocarp as a potassium fertilizer, were studied by Gomez et al. (1983, 1987, 1989). Charcoal, Activated Carbon, and Fuels. Many studies have been devoted to the conversion of almond shells to prepare charcoal and fuel gas (Jefferson 1936; Singer 1939; Choroszy et al. 1981; Davis et al. 1981; Brown et al. 1984; Font et al. 1986, 1988; Mercant and Petrie 1992), liquid hydrocarbon gas (Kuester 1984; Kuester et al. 1985; Hanson 1991), and the activated carbons with high surface area hardness and low ash content (Cotroneo 1938; Gonzales-Vilchez et al. 1979a,b; Lopez-Gonzales et al. 1976a,b; Berenguer-Merelo et al. 1977; Linares-Solano et al. 1980). Investigators have studied the influence of different activating agents in the preparation of activated carbon from almond shells (RodriguezReinoso et al. 1981; Ruiz-Bevie et al. 1984a,b; Torregrosa and Martin Martinez 1991), activated carbon structure characterization (Gergova et al. 1992; Guzel and Tel 1993), and almond shell pyrolysis (Parodi and Zanella 1990). When prepared in gninular form, activated carbon from almond shells is hard and easily recycled. In different forms it can be used to adsorb zinc, cadmium, and copper (Ferro-Garcia et al. 1988), to remove ortophosphates (Ferro-Garcia et al. 1990a) and lead from water (Ferro-Garcia et al. 1990b), or to remove some air-polluting hydrocarbons (Domingo-Garcia et al. 1990,1991) as well as adsorbents in gas and liquid phases (Linares-Solano et al. 1980). Activated carbon from almond shells is also used in the food industry (sugar refining, wine making), in making protective masks, and in the pharmaceutical industry. Design data were provided by Choroszy et al. (1981) for a mobile pyrolysis system to convert industrial, municipal, and agricultural wastes, including almond wastes, into clean energy sources as well as to reduce significantly the volume of waste material. Xylose and Xylitol. Methods have been developed for producing lowmolecular-weight sugars like xylose (Pou-Ilinas et al. 1990) and xylitol

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(Nobile 1971) (a nonfermentable and low insulin-dependent natural sugar with a potential caries inhibitor) from almond shells (Cutress et al. 1992). Xylose is suitable for the production of various materials including foodstuffs for diabetics, chewing gum, explosives, and toothpaste (Cestaat 1988). A dentifrice containing almond seeds and shells for cleaning teeth, preventing caries and tooth diseases, as well as for relieving some of the symptoms caused by dental diseases has been patented (Hakeem 1983; Wahmi 1983). Inositol and sorbitol can be recovered from water extracts of almond shells (Williams and Rabinowitz 1984).

Other Byproducts. A substance which permanently seals holes <0.08 mm, inhibits rust formation and corrosion, provides water pump lubrication, and is compatible with normal antifreeze substances has been patented by Hatch (1962) and Lasswell and Monier (1967). A fluid obtained from destructive distillation of almond shells has shown bactericidal properties, especially against Pseudomonas pyrocyanea (Sachdeva 1968). This antimicrobial agent was clinically tested with success as an antiseptic in the control of postoperative wound infections (Gupta et al. 1971). The antipseudomonas activity in preparation from almond shells has been shown by Bhatia (1977). Many other possibilities for the use of almond shells can be found in the literature. Patents have been registered on inert laminate composites (Mengard 1991) and plastic composites having a woodlike appearance (Stavrakellis 1988). Finely ground shells can be used as glue extenders in the plywood industry (Williamson and Lathrop 1949; Ayers 1988), for preparing phenolic molding compounds (Clark 1949), as an abrasive in cosmetics (Bouillon et al. 1992; Guenter et al. 1992), and as a material suitable for making toes, heels, and other parts of shoes (Agger and Arnold 1990). Gulati and Gaur (1988) selected cellulosic fungi to produce sugars by enzymatic hydrolysis of cellulosic wastes, including almond shells. Sreenath and Joseph (1983) isolated streptomyces cultures for production of xylanases. Other possibilities of using almond shells include fuel briquettes (Rodgers 1936), sandblasting, and garden mulch. In the past, bakers used to spread powdered almond shells over the floors of their ovens to keep the bread from sticking (C. Grasselly, pers. comm.). V. FUTURE PROSPECTS

Almonds are tasty nuts of high nutritional value; they can be eaten fresh and used in many other ways (Teotia et al. 1987). From earliest times,

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almonds have been used as an ingredient in a wide range of delicacies produced in the home, in small enterprises, and now on an industrial scale. They are used as an ingredient in decorating and flavoring sweets and other gastronomic specialities. Nonfood uses, especially in the pharmaceutical and cosmetics industries, are of great importance. There is an awareness of the potential to utilize almond wastes as a raw material in the preparation of a large number of byproducts. During the last 20 years, world almond consumption has greatly increased, not only in producing countries, but also in many importing countries. The largest importer of almonds is Germany which, in the three-year period from 1988 to 1990, with 28,500 t, accounted for 28.2% of all imports. The continuous increase in imports to Germany is caused by the increase in internal consumption and the development of the socalled "second-hand" market in Hamburg, which deals with the reexportation of almonds to the other European countries (Baccarella 1991). Other countries importing large quantities of almonds are France and Great Britain, which from 1988 to 1990 reached 11.1 and 6.7% of world imports, respectively. Large increases have been seen in the imports of all the other producing countries of the European Community. The trade currents of almonds are essentially between economically developed countries with high per capita incomes and large consumption of foodstuffs with an elastic demand. This phenomenon can be related to improvements in the standard of living and changes in eating habits, determined by economic development and the transformations that have taken place in the social organization of populations, in turn brought about by technological changes and the organization of labor, and by lifestyles and the quality of social life. It is for this reason that world almond consumption has become widespread in the area of the developed countries of the West and, just recently, in the countries of the former Soviet Union, and in some countries of the Far East, while in other parts of the world, consumption is low and limited to the producing countries. The large increase in almond supply will pose serious challenges in the future. Economic growth is a precursor to increased almond consumption in potentially important markets such as those of Eastern Europe and developing countries. These markets, with their enormous populations, offer considerable potential for market expansion of almonds. Promotional programs highlighting the health and dietetic benefits of almond consumption will support growth prospects in the near future.

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LITERATURE CITED Abbey, M., M. Noakes, G. B. Belling, and P. J. Nestel. 1994. Partial replacement of saturated fatty acids with almonds or walnuts lowers total plasma cholesterol and lowdensity lipoprotein cholesterol. Am. J. Clin. Nutr. 59:995-999. Abd El-Aal, M. H., E. G. Gomaa, and H. A. Karara. 1987. Bitter almond, plum and mango kernels as sources of lipids. Fett Wiss. Technol. 89:304-306. Agger, R. T., and B. Arnold. 1990. Poly(hexamethylene adipate)-based reinforcingstiffening materials. British United Shoe Machinery Ltd.; Bostick Ltd. Eur. Pat. Appl. EP 448,294. (Chern. Abstr. 1991, 115:257968f.) Aguilar, A. A., N. E. Smith, and R. L. Baldwin. 1984. Nutritional value of almond hulls for dairy cows. J. Dairy Sci. 67:97-103. Alessandroni, A. 1980. Le mandorle. Panif. Pasticc. 8(5):67-71. Alessandroni, A. 1984. Produzione della pasta di mandorle:aggiornamenti. Panif. Pasticc. 12(4):79-83. Anon. 1976a. Encapsuled flavourings retain volatile, delicate flavour nuts. Food Prod. Dev. 10(7):40. Anon. 1976b. Natural almond powder modified to improve stability, flow, and dispersion. Food Prod. Dev. 10(7):40. Anon. 1979. Ingredients & flavors-the supply picture, future trends. Dairy Field 162(12):68-70, 72. Anon. 1980. Hulls to alcohol. Calif. Grower & Ranch. 35(Sept.):1. Anon. 1987. Selection of nut pastes. 1. Almond praline and noisette. Confect. Prod. 53:479--481. Anon. 1996. Almond market status. Cracker (April):34, 35,40. Ansmann, A. 1986. Cosmetic preparations containing almond protein hydrolysate. Ger. Offen. DE 3,445,919. (Chern. Abstr. 1986, 105:120510z.) Antoniazzi, F. 1978. II marzapane e la pasta di mandorle. Panif. Pasticc. 6(9):63-67. Archer, T. E. 1970. Kelthane residues on almond hull meal exposed to ultraviolet light irradiation. Bul. Environ. Contam. Toxicol. 5:247-250. Archer, T. E. 1969. Kelthane decontamination studies on almond hulls. J. Agr. Food Chern. 17:1070-1074. Arctander, S. 1960. Perfume and flavour materials of natural origin. Elizabeth Publ., New York. Arthur, F. H. 1989. Effects of cleaning peanuts on insects damage, insect population growth and insecticide efficacy. Peanut Sci. 16:100-105. Arya, S. S., and C. V. Madhura. 1986. Benzaldehyde as a measure of bitter kernels in sweet almonds. J. Food Sci. Technol. India 23(2):100-101. Axer, J. 1982. Almond butter. Manufact. Confect. 62(12):57-60. Axer, J. 1983. Nut butters may aid marketers to increase consumption. Almond Facts 48(4):62-65. Ayers, J. W. 1988. Nutshell flour extenders for plywood-and laminating adhesives. U.S. Patent 4,778,530. (Chern. Abstr. 1989, 110:96794a.) Baccarella, A. G. 1991. Aspetti tecnici, economici e di mercato del mandorlo in Sicilia. p. 73-123. In: A. G. Baccarella, G. Chironi, and G. Barbera. Quaderni di Ricerca e di Sperimentazione, 40. Un. Cam. Comm. Ind. Art. Agr. Reg. Sicilia, Palermo. Barbera, G. 1991. Aspetti tecnici della mandorlicoltura in Sicilia. p. 153-188. In: A.G. Baccarella, G. Chironi, and G. Barbera. Quaderni di Ricerca e di Sperimentazione, 40. Un. Cam. Comm. Ind. Art. Agr. Reg. Sicilia, Palermo.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

293

Barbera, G., L. De Palma, T. La Mantia, F. Monastra, and M. Schirra. 1994a. Qualitative and quantitative responses of 'Ferragnes' and 'Tuono' almond cultivars to environmental growing conditions in southern Italy. Acta Hort. 373:125-128. Barbera, G., L. Di Marco, G. Fatta Del Bosco, and P. Inglese. 1988. Behaviour of 26 almond cultivars growing under rainfed and semiarid conditions in Sicily. Rapport EUR 11709, p. 17-32. 7° Colloque du Grempa, Reus (Terragone), Espagne, 17-19 Juin 1987. Barbera, G., L. Di Marco, T. La Mantia, and M. Schirra. 1994b. Effect of rootstock on productive and qualitative response of two almond varieties. Acta Hort. 373:129-134. Barcelo, J., G. Nicolas, B. Sabater, and R Sanchez. 1980. Fisiologia Vegetal. Ed. Piramide. Madrid, p. 196-200. Bargman, T. J., J. H. Rupnow, and S. L. Taylor. 1992. IgE-Binding proteins in almonds (Prunus amygdalus) identification by immunoblotting with sera from almond-allergic adults. J. Food. Sci. 57:717-720. Barre, R 1951. Preparation and purification of the proteins of almonds. Bul. Soc. Chim BioI. 34:1473-1482. Barre, R, and G. Wormser. 1955. Proteins of the seeds of the almond tree. Ann. Pharm. France 13:736-744. Barre, R 1953a. Chemical composition of almond globulin and albumin. Bul. Soc. Chern. BioI. 35:907-912. Barre, R 1953b. Some physicochemical properties of the proteins of almonds. Bul. Soc. Chern. BioI. 35:899-906. Bassler, R 1980. Investigation offoodstuffs and oil seeds for aflatoxin. Qual. Plant. Plant Foods Hum. Nutr. 30(3-4):271-282. Bath, D. L. 1972. What is the value of almond hulls as a dairy feed? Dairy Tales 2, 9 Bath, D. L. 1982. Reducing fat in milk and dairy products by feeding. J. Dairy Sci. 65:450-453. Bath, D. L. 1983. Update on the feeding value of almond hulls. Berkeley Coop. Ext. Servo Univ. Calif. 13, 5. Bath, D. L., J. R Dunbar, J. D. King, S. L. Berry, R O. Leonard, and S. E. Olbrich. 1980. Byproducts and unusual feedstuffs in livestock ratios. West. Reg. Ext. PubL 39, Univ. Calif., Davis, USA. Baudot, G. 1967. Sterilizable coconut and almond milk. Fr. Patent 1,550,401. (Chern. Abstr. 1970, 72:11516k.) Berenguer-Merelo, c., J. de D. Lopez-Gonzales, F. Martinez-Vilchez, and F. RodriguezReinoso. 1977. Compared adsorptive properties of active carbons from olive stones and almond shells. Extr. Abstr. Program. Bienn. Conf. Carbon Abstr. 13:357-358. Bertin, M. 1980. Distinction analytique entre l'huile de noisette et l'huile d'amande douce en cosmetologie. Parf. Cosmet. Aromes 33(7-8):31-3~i. Beuchat, L. R, and R E. Worthington. 1978. Fatty acid composition of tree nut oils. J. Food Technol. 13:335-358. Beuchat, L. R 1978. Relationship of water activity to moisture content in tree nuts. J. Food Sci. 43:754-755, 758. Bevilotti, V. 1942. SuI valore alimentare ricostruttivo di alcuni semi oleosi. Quaderni Nutriz.8:378-383. Bhatia, V. N. 1977. Some studies on Pseudomonas aeruginosa with special reference to antimicrobial sensitivity. J. All India Inst. Med. Sci. 3(2):124-128. Bianca, G. 1872. Monografia del Mandorlo Comune, sua Storia e sua Coltivazione in Sicilia. Stamperia Lorsnaider, Palermo, Italy. Bilgrami, K. S., A. Singh, and K. S. Ranjan. 1984. Detoxification of aflatoxins in dry fruits and spices through light and heat treatments. Nat. Acad. Sci. Lett. (India) 7:273-274.

294

M. SCHIRRA

Bilgrami, K. S., K. K. Sinha, and A. Singh. 1983. Chemical changes in dry fruits during aflatoxin elaboration by Aspergillus flavus Link, es Fries. Curro Sci. 52:960-964. Blumenthal, A., and G. Kiss. 1970. Chemischer nachweis von rohen bittermandeln. Mitt. Geb. Lebensmitt. Hyg. 61(5-6):394-395. Bonnet, 1. 1974. Cosmetic. Fr. Demande 2,219,774. (Chern. Abstr. 1975, 82:175141s.) Bosch Arino, F. De A., B. Gomez Martinez, and J. M. Santiago Perez. 1976a. Variacion durante el periodo de maturacion, del contenido hidrico en eje embrional, episperma y cotiledones de la almendra; de lipido en estos ultimos y studio en este"de los contenidos en Ca, Fe, Mg, Mn, Na y Zn. Anal. Quim. 73:842-844. Bosch Arino, F. De A., B. Gomez Martinez, and J. M. Santiago Perez. 1976b. Variacion en el contenido de elementos inorganicos en distintas partes de la semilla de almendra. Anal. Quim. 73:844-848. Bouillon, G., G. Daniel, H. Denzer, R Pepmoeller, and M. Franzer. 1992. Bleached stone-fruit kernel and nut fruit shells as abrasive for cosmetics. German Offen De 4,038,076. (Chern Abst. 1992, 117:55700t.) Bridel, M., and R Arnold. 1921. Use of different precipitating agents in the precipitation of emulsin of almonds. J. Pharm. Chern. 23:161-168. Brieskorn, C. H., andR Betz. 1987. Die steroide dermandel-semenschale. Fett. Wiss. Technol. Fat Sci. Technol. 89:230-235. Brown, M. D., L. K. Mudge, and E. G. Baker. 1984. Catalysts for gasification of biomass. p. 125-126. In: Proc. 6th Symp. Biotechnol. Fuels Chern. May 15-18 Gatlinburg, TN. Bruno, S. 1965. Vapor phase chomatography and infrared spectroscopy in the characterization of oil from sweet almond tegmen. Farm. Ed. Prat. 20(2):85-89. Buttery, R G., R M. Seifert, W. F. Haddon, and R E. Lundin. 1980a. 2-hexyl-3-methylmaleic anhydride an unusual volatile component of raisins and almond hulls. J. Agr. Food Chern. 23:1336-1338. Buttery, R G., E. 1. Soderstrom, R M. Seifert, 1. C. Ling, and W. F. Haddon. 1980b. Components of almond hulls: possible navel orangeworm attractants and growth inhibitors. J. Agr. Food. Chern. 28:353-356. Butzenlechner, M., A. Rossmann, and H. 1. Schmidt. 1989. Assignment of bitter almond oil to natural and synthetic sources by stable isotope ratio analysis. J. Agr. Food Chern. 37:410-412. Calapaj, R, and G. D'Amore. 1972. Produzione di mandorle, uso e commercio. Ann. Fac. Econ. Comm. Univ. Messina 101:79-95. Calvert, c., and K. Parker. 1985. Almond hulls produce unexpected results in hog trials. Calif. Agr. 39(3-4):14-15. Cantafora, A. 1982. Ruolo dell'acido linoleico nell'alimentazione umana:aspetti teorici e pratici. Ind. Alim. 21:454-458. Carballido, A., and G. Prestamo. 1970. Caracters espectrofotometricos de los aceites extraidos de almendras, avellanas, cacahutes y pinones. Anal. Bromatol. 22:81-100. Carpena, 0., F. J. Lopez-Andreu, and R M. Esteban. 1980. Apportaciones al elstudio de la determinacion cuantitativa de proteinas totales en semillas de almendra. Las Ciencias 45:191-196. Carrante, V., and A. Cucurachi. 1967. Andamento della inoliazione, caratteristiche chimico-fisiche e composizione acidica dell'olio di mandorle. Agricoltura 4(4):1-8. Carrante, V., and D. Giannelli. 1965. Variazioni nella composizione di alcuni organi della pianta del mandorlo (Amygdalus communis, var. dulcis). Ind. Tip. Trizio Bari, Italy. Casares, R, and C. Lopez. 1952. Estudio bromatologica de los almendras dulces espafiolas. Anal. Bromat. 7:71-85. Casella, G. 1968. Su alcune caratteristiche dei tegumenti seminali nel mandorlo in relazione al calo della resa in sgusciato. Ann. Fac. Agr. Univ. Bari. 22:52-62.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

295

Cauderay, P. 1979. Rapid chemical confirmation method for aflatoxins B1 and G1 by direct acetylation on a thin layer plate before chromatography. J. Assoc. Offic. Anal. Chern. 62:197. Cestaat, Centro Studi Sull'agricoltura, L'ambiente e il Territorio 1988. Impiego dei Sottoprodotti Agricoli e Agroindustriali in Campo Energetico e Chimico-industriale. Stampa Ippolit. dell'Orso Roma. Vol. 3, p. 148-150. Choroszy, M. A., R. S. Davis, and R. H. Rosen. 1981. Fluidized bed gasification system. 1:200-221. In: Proc. Fluid Combust. Conf. Jan. 28-30, 1991, Cape Town USA. Energy Res. Inc., Cambridge, MA. Ciusa, R., and M. Di Taranto. 1956. Materiale pugliese per la produzione del furfurolo. Chimica Ind. 38:383-384. Clark, C. G., G. R. Giles, and J. C. Goligher. 1967. Results of conservative management of internal hemorroids. British. Med. J. 2(5543):12-14. Clark, T. F. 1949. Agricultural residues in plastics. III. Evaluation of residue flours as fillers in thermosetting phenolics. Modern Plastics 26(12):111-115, 164-165. ColIs, D., C. Descamps, and C. Grasselly. 1985. Des amandes sur une epave antique. Options Mediterraneennes 1:105-106. Colombini, M., M. C. Vanoni, and G. Amelotti. 1979. Olio di noci, nocciole, mandorle, avocado: composizione sterolica. Riv. It. Sost. Grasse 56:392-393. Conn, E. E. 1980. Cyanogenic compounds. Ann. Rev. Plant Physiol. 31:433-451. Connell, J. H., J. M. Labavitch, G. S. Sibbett, W. O. Reil, W. H. Barnett, and C. Heintz. 1989. Early harvest of almonds to circumvent late infestation by navel orangeworm. J. Am. Soc. Hort. Sci. 114:595-599. Connell, J. H. 1994. Almond harvest operations in California, maintaining nut quality. Acta Hort. 373:241-247. Corradi, c., and G. Micheli. 1982. SuI contenuto di acido cianidrico totale degli amaretti. Ind. Alim. 21:459-465,472. Cotroneo, C. 1938. Attivazione meccanica dei carboni attivi. Rend. Accad. Sci. 8:44 -62. Cotta Ramusino, F., R. Intonti, and A. Stachini. 1961. Analisi dellatte di mandorle e dello sciroppo di orzata. Bol. Lab. Chim. Provo 12:491-504. Cowan, J. W., Z. 1. Sabri, F. 1. Rinnu, and J. A. Campbell. 1963. Evaluation of protein in Middle Eastern diets 1. Almonds. J. Nutrit. 81:235-340. Cruess, W. V., J. H. Kilbuck, and E. Hahl. 1947. Utilization of almond hulls. Fruit Prod. J. 26:363-365. Cunningham, S. 1991. Almonds: an update. Manufacturing Confectioner (May):145-149. Curtis, C. E., R. K. Curtis, and K. L. Andrews. 1984. Progression of navel orangeworm (Lepidoptera pyralidae) infestation damage of almonds on the ground and the tree during harvest. Environm. Entomol. 13:146-149. Curtis, C. E. 1983. Pest management of insects on almonds. Almond Facts 48(7/8):35-38. Custer, E. W. 1968. Fruits nuts and flavourings in ice cream. Am. Dairy Rev. 30(10):118, 162-165. Cutress, T., P. T. Owell, C. Finidori C., and F. Abdullah. 1992. Caries preventive effect of high fluoride and xylitol containing dentifrices. J. Dent. Child. 59:313-318. Daoud, H. N., M. W. Miller, and B. S. Luh. 1977. Effect of commercial processing on vitamin B6 retention in almond. Can. Inst. Food Sci. Technol. J. 10:244-246. Davis, D. A., A. Vigil Samuel, and G. Tehobanoglous. 1981. Evaluation of residual char from the gasification of solid wastes as a substitute for powdered activated carbon. Biotechnol. Bioeng. Symp. 11:211-224. De Caro, L., and J. Franceschini. 1939. Contenuto in vitamina A e B1 di noci, nocciuole, mandorle e arachidi. Quaderni Nutriz. 6(1):82-86. De Dominicis, A. 1920. The ash of the hull of the almond as an industrial product. Ann. Scuola Agr. Portici (Italy) 15(2):1-11.

296

M. SCillRRA

Demanze, Ch., L. Rugraff, and A. Karleskind. 1982. Etude des huiles oIeiques a usages cosmetique et pharmacologique. 2e partie: Adulteration des huiles de noisette et d'amande par diverses huiles oleiques. Parf. Cosmet. Arom. 47(10-11):63-66, 69. Deuel, H. J., JI. 1957. The lipids. Their chemistry and biotechnology. Vol III. Biochemistry, biosynthesis, oxidation, metabolism and nutritional value. Interscience Publ., New York. Dicenta, F., and J. E. Garcia. 1993. Inheritance of the kernel flavour in almond. Heredity 70:308-312. Domingo-Garcia, M., I. Fernandez-Morales, F. J. Lopez-Garzon, and C. Moreno-Castilla, 1991. Use of activated carbons obtained from agricultural byproducts for the adsorption of some hydrocarbons. Langmuir 7:339-343. Domingo-Garcia, M., I. Fernandez-Morales, F. J. Lopez-Garzon, C. Moreno-Castilla, and M. J. Prados-Ramirez. 1990. The dynamic adsorption of several hydrocarbons on active carbons. J. ColI. Interf. Sci. 136:160-167. Dugo, G., I. Stagno D'alcontres, A. Cotroneo, F. Salvo, and Giacomo Dugo 1979. Composizione dell'olio di mandorle. Nota I: acidi grassi, idrocarburi e steroli di alcune varieta di mandorle dolci siciliane. Riv. It. Sost. Grasse 56:201-203. Dupaigne, P. 1978. Les micotoxines et les fruits. Fruits 33(7-8):505-519. Duxbury, D. D. 1989. Almond butter versatility, flavor and nutrition. Food Proc. 50:28-30. Ekpenyong, T. 1969. Amino acid content of seeds of orchard crops. J. Sci. Food. AgI. 20:608-610. Englyst, H. N., and J. H. Cummings. 1988. Improved method for measurement of dietary fiber as non-starch polysaccharides in plant foods J. Assoc. Offic. Anal. Chern. 71:808814. Engwell, W. L. 1977. Dairy Notes. (Sept.) Coop. Ext. Univ. Calif., Berkeley, CA. Esteban, R, F. S. Lopez-Andreu, and O. Carpena. 1985. Protein extractability of almond (Prunus amygdalus Bath) seed. J. Sci. Food Agric. 36:485-490. Everson, G., E. Pearson, and R Matteson. 1952. Biological availability of certain foods as sources of riboflavin. J. NutI. 46:45-53. Everson, G., E. Wheeler, H. Walker, and W. J. Caulfield. 1948. Availability of riboflavin of ice cream, peas, and almonds judged by urinary excretion of the vitamin by women. J. NutI. 35:209-223. Fadel, J. C. 1990. Changes during heating in chemical composition and in-vitro digestion of four commodities. J. Sci. Food Agr. 53:395-404. FAa/OMS. 1966. Necesidades de proteinas. Informe FAa 37, Informe tecnico OMS 301, Roma. Farmacopea Ufficiale della Repubblica Italiana. 1972. Olio di mandorle dolci, Amygdalorum oleum. p. 749-750. VIII Ed., Roma. Ferrara, E. 1967. Le cultivar di mandorlo e l'industria dei confetti. Bari Econ. II(13):27-30. Ferro-Garcia, M. A., F. Carrasco-Marin, J. Rivera-Utrilla, E. Utrera-Hildago, and C. MorenoCastilla. 1990a. The use of activated carbon columns for the removal of orthophosphate ions from aqueous solutions. Carbon 28:91-95. Ferro-Garcia, M. A., J. Rivera-Utrilla, 1. Bautista-Toledo, and M. D. Mignorance. 1990b. Removal of lead from water by activated carbons. Carbon 28:545-552. Ferro-Garcia, M. A., J. Rivera-Utrilla, J. Rodriguez-Gordillo, and I. Bautista-Toledo. 1988. Adsorption of zinc, cadmium, and copper on activated carbons obtained from agricultural by-products. Carbon 26:363-373. Filsoof, M., M. Meheran, and F. Farrohi. 1976. Determination and comparison of oil characteristics in Iranian almond, apricot and peach nuts. Fette Seif. Anstrichmitt. 78:150-151.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

297

Fine, K. D., C. A. Santa Ana, J. L. Porter, and J. S. Fordtran. 1991. Intestinal absorption of magnesium from food and supplements. J. Clin. Invest. 88(2):396-402. Firth, F. O. 1969. Analysis of marzipan. J. Assoc. Pub. Anal. 7(1):22-23. Fisher, K T. 1983. Final report on the safety assessment of sweet almond oil and almond meal. Cosmetic Ingredient Review (CIR) Expert Panel (USA). J. Am. ColI. Toxicol. 2(5):85-99. Fomunyam, R T., A. A. Adegbola, and O. L. Oke. 1985. The stability of cyanohydrins. Food Chern. 17:221-225. Font, R, A. Marcilla, J. Devesa, and E. Verdu. 1988. Gaseous hydrocarbons from flash pyrolysis of almond shells. Ind. Eng. Chern. Res. 27:1143-1149. Font, R, A. Marcilla, E. Verdu, and J. Devesa. 1986. Fluidized-bed flash pyrolysis of almond shells. Temperature influence and catalysts screening. Ind. Eng. Chern. Prod. Res. Dev. 25:491-496. Forestier, D. 1991. Almonds in bakery foods. Europ. Food Drink Rev. (Spring):55, 57, 59 Fourie, P. c., and D. S. Basson. 1989a. Changes in the tocopherol content of almond, pecan and macadamia kernels during storage. J. Am. Oil Chern. Soc. 66:1113-1115. Fourie, P. C., and D. S. Basson. 1989b. Predicting occurrence ofrancidity in stored nuts by means of chemical analyses. Lebensm. Wiss. Technol. 22:251-253. Fourie, P. c., and D. S. Basson. 1990. Sugar content of almond, pecan and macademia nuts. J. Agr. Food Chern. 38:101-104. Fournier, N., C. Arzouyan, and J. Estienne. 1993. Electrophorese d'une fraction glycoproteique des oleagineux obtenue par separation affine. Analusis 21:39-43. Francaviglia, G. 1958. Sistemi di lavorazione e norme per l'esportazione delle mandorle. Tecnica Agr. 10(1-2):431-448. Frangois, M. T., S. Meinrich, and M. Mihailov. 1960. A reaction charactaristic of almond oil and of kernel oil. Ann. Pharm. Franc. 18:773-775. Frehner, M., M. Scalet, and E. E. Conn. 1990. Pattern of the cyanide-potential in developing fruits. Implications for plants accumulating cyanogenic monoglucisides (Phaseolus lunatus), cyanogenic diglycosides in their seeds (Linum usitatissimum, Prunus amygdalus). Plant Physiol. 94:28-34. Fuller, G., B. E. Mackey, and G. Stanley. 1978. The problems of sampling almonds and walnuts for aflatoxin contamination. Gesundheitsgefaehrdung Aflatoxine Arbeitstag 291-302. Fuller, G., W. W. Spooncer, A. D. King, Jr., J. Schade, and B. Mackey. 1977. Survey of aflatoxins in California tree nuts. J. Am. Oil Chern. Soc. 54:231A--:-234A. Furr, A. K, L. H. MacDaniels, L. E. St. John, Jr., W. H. Gutenmann, I. S. Pakkala, and D. J. Lisk. 1979. Elemental composition of tree nuts. Bul. Environ. Cont. Toxicol. 21:392-396. Fuse, M., F. Hayase, and H. Kato. 1984. Digestibility of proteins and racemization of amino acid residues in roasted foods. Nippon Eyo Sho Kuryo Gokkaishi 37:349-354. Galoppini, c., and G. Lotti. 1962. La maturazione delle mandorle con particolare riferimento alIa frazione lipidica. Olearia (Sett.-Dic.):164-167. Garcia Olmedo, R, A. Carballido, and A. Diaz Marquina. 1978b. Contribuci6n al estudio de los aceites de frutos secos espanoles. Composici6n de insaponificable. II. Fracci6n metil ester6lica. Anal. Bromatol. 30:175-186. Garcia Olmedo, R, A. Carballido, and A. Diaz Marquina. 1978c. Contribuci6n al estudio de los aceites de frutos secos espanoles. Composici6n del insaponificable. III. Fracci6n alcoh6lica. Anal. Bromat. 30:205-234. Garcia Olmedo, R, A. Carballido, and A. Diaz Marquina. 1978a. Contribuci6n al estudio de los aceites de frutos secos espanoles. Composici6n de insaponificable. 1. Fracci6n de esteroles. Anal. Bromatol. 30:63-89.

298

M. SCHIRRA

Garcia Olmedo, R., A. Carballido, and A. Diaz Marquina. 1978d. Contribucion al estudio de los aceites de £rutos secos espaiioles. Composicion de insaponificable. IV. Fraccion de hidrocarburos. Anal. Bromatol. 30:267-286. Garcia Olmedo, R., and M. A. Marcos Garcia. 1971. Contribucion al estudio de los aceites defrutos secos espaiioles-composicion acidica. Anal. Bromatol. 32:233-258. Gelda, C. S., and 1. J. Luyt. 1977. Survey oftotal aflatoxin content in peanuts, peanut butter, and other foodstuffs. Ann. Nutr. Aliment. 31(4-6):477-483. George, K. A., 1985. Skin removal apparatus for almonds. (Proctor Swarch Inc. U.S. Patent 4,537,122 (FSTA 18:8V143, 1986.) Gergova, K., A. Galushko, N. Petrov, and V. Monkova. 1992. Investigation of the porous structure of activated carbons prepared by pyrolysis of agricultural by-products in a stream of water vapor. Carbon 30:721-727. Gertz, c., and K. Herrmann. 1982. Zur Analytyk der tocopherole und tocotrienole in lebensmittein. Z. Lebens. Unter. Forsch. 174:390-394. Gilbert, J., and J. M. Shepherd. 1985. A survey of aflatoxins in peanut butters, nuts and nut confectionery products by HPLC with fluorescence detection. Food Addit. Contam. 2:171-183. Girard, P., J. Guezennec, M. Th. Lanteaume, and A. M. Le Clerc. 1961. La valeur nutritive des proteines d'amandes. Ann. Nutrit. Aliment. 16:1-10. Giri, J., 1990. Evaluation of selenium and chromium content in plant foods. Plant Food Hum. Nutrit. 40:49-53. Godini, A., E. Ferrara, and A. Reina. 1979. Composizione chimica e caratteri estetici e organolettici dei semi di una vasta popolazione di cultivar di mandorlo pugliesi. "II Miglioramento della Coltura del Mandorlo e del Nocciolo. Proc. Nat. Symp. 'Aspetti Genetici e Tecnici' p. 331-339. Messina and Siracusa Nov. 29-30, Dec. 1,1979. Godini, A., 1982. Raccolta. p. 131-150. In: "II Mandorlo. REDA, Collana Frutticoltura anni 80, Roma. Gomez, 1., F. Burlo, J. Mataix, and B. Gomez. 1987. Contenido en oligoelementos y disponibilidad de K, Na, Ca y Mg en el residuo vegetal constituido por el mesocarpio-epicarpio del £ruto del almendro (Prunus amygdalus). Rev. Agroqufm. Tecnol. Aliment. 27: 131-138. Gomez, 1., B. Gomez, and J. Mataix. 1989. Evaluacion mediante un sistema de E.U.F. de la fertilidad potasica de un suelo compostado con piel de almendra. Agrochim. 33:209-218. Gomez, B., F. Burlo, 1. Gomez, and J. Mataix. 1983. Nota previa. Contenido mineral del subproducto agricola constituido por el conjunto mesocarpio-epicarpio del £ruto del aImendro (PTllnus amygdalus 1.). Rev. Agroqufm. Tecnol. Aliment. 23:439-442. Gonzalez-Vilchez, P., A. Linares-Solano, J. de D. Lopez-Gonzales, and F. RodriguezReinoso. 1979a. The reaction of active carbon with air at 350°C. Extr. Abstr. ProgramBiennal Conf. Carbon 14:18-19. Gonzalez-Vilchez, P., A. Linares-Solano, J. De D. Lopez-Gonzalez, and F. RodriguezReinoso. 1979b. The controlled reaction of active carbons with air at 350°C. 1. Reactivity and changes in surface area. Carbon 17:441-446. Goodin, P. 1980. Almond Moth recapture rates. Agric. Res. U.S.D.A. Sci. Educ. Adm. 29(3). Gradziel, T. M., and D. C. Wang. 1994. Susceptibility of California almond cultivars to aflatoxigenic Aspergillus flavus. HortScience 29:33-35. Grasselly c., and P. Crossa-Raynaud. 1980. L'Amandier. In: Commerce et Utilization de l'Amande. Maisoneuve G.P. et Larose, Paris. Cap. 10. p. 397-415. Guadagni, D. G., E. 1. Soderstrom, and C. 1. Storey. 1978. Effect of controlled atmosphere on flavor stability of almonds. J. Food Sci. 43:1077-1080.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

299

Guarella, P., and A Pellerano. 1988. Impianti per la prima lavorazione industriale delle mandorle in Puglia. Orientamenti e prestazioni. Riv. Ing. Agr. 979-988. Guarella, P., and A Pellerano. 1989. Structural and operating features of devices for mechanical almond shelling. Agr. Eng. 1877-1882. Guenter, B., D. Guenter, D. Horst, P. Reinmar, and F. Martin. 1992. Bleached stone-fruit kernel and nut-fruit shells as abrasives for cosmetics. Ger. Offen DE 4,038,076 (Chern. Abstr. 1992, 117:5570ot.) Guillot, G. P., J. V. Giuffret, M. C. Martini, J. F. Gonnet, and G. Soule. 1981. Results obtained with 110 samples of various origin. Raw materials used in cosmetics. Inf. Chim. 213:239-249. Gulati, S. 1., and A. C. Gaur. 1988. Enzymic hydrolysis of cellulose from agricultural and industrial wastes. BioI. Wastes 25:309-313. Gupta, U., U. N. Bhatia, P. Venugopal, and J. R Talwar. 1971. In vitro susceptibility of Pseudomonas pyocianea strains to a new antimicrobial agent derived from almond (Prunus amygdalis) shells. Indian J. Med. Res. 59:1002-1005. Gutfinger, T., and A Letan. 1973. Detection of adulteration of almond oil with apricot through determination of tocopherols. J. Agr. Food Chern. 21:1120-1123. Guzel, F., and Z. Tel. 1993. The characterization of the micropore structures of some activated carbons of plant origin by nitrogen and carbon dioxide adsorptions. Sep. Sci. Technol. 28:1609-1627. Hadorn, H., T. Keme, J. Kleinert, M. Messerli, and K. Ziirker. 1981. The behaviour of some nuts under different storage conditions (II). CCB Rev. Choc. Confect. Bakery 6(4):3-6,8, 10. Haisman, D. R, D. J. Knight, and M. J. Ellis. 1967. The electrophoretic separation of the f3-glucosidases of almond 'emulsin.' Phytochemistry 6:1501-1505. Hakeem, W. V. R 1983. Dentifrice. U.S. Patent 4,374,824. Hall, P. A, G. J. Moore, B. Gunning, and B. B. Cook. 1958. The nutritive value of fresh and roasted, California-grown nonpareil almonds. J. Agr. Food Chern. 6:377-382. Hallabo, S. AS., F. A El-Wakeil, and M. Morsi. 1977. Chemical and physical properties of apricot kernel, apricot kernel oil, and almond kernel oil. Egypt. J. Food Sci. 3(1-2):1-6. Hannigan, K. J. 1982. Nutritious beverage drink based on almond powder. Food Eng. 54:105. Hanson, J. 1. 1991. Agricultural waste fired fluid bed combustor. Proc. 11th Int. Conf. Fluid. Bed Combust, Delano, California. 3:1105-1109. Harris, N. K, D. K Westcott, and A. S. Enick. 1972. Rancidity in almonds: shelf life studies. J. Food Sci. 37:824-827. Hatch, V. O. 1962. A stop leak composition. U.S. Patent 3,034,998 (Chern. Abstr. 1963, 57:3541i.) Havighorst, C. R 1986. Those amazing almonds. Food Eng. 57:88-91. Hawker, J. S., and M. S. Buttrose. 1980. Development of the almond nut (Prunus dulcis (MilL) D.A Webb). Anatomy and chemical composition of fruit parts from anthesis to maturity. Ann. Bot. 46:313-321. Haydar, M., 1. Benelli, and C. Brera. 1990. Occurrence of aflatoxins in Syrian foods and foodstuffs: a preliminary study. Food Chern. 37:261-268. Hayhow, S. 1987. Peanut's butter brother: almond, cashew and sesame butter. Veg. Times 113(1):36-39. Hazell, T., and 1. T. Johnson. 1987. In vitro estimation of iron availability from a range of plant foods: influence of phytate, ascorbate and citrate. British. J. Nutr. 57:223-233. Helferich, B., and T. Kleinschmidt. 1961. Sweet almond emulsins. Z. Physiol. Chern. 324:211-219.

300

M. SCHIRRA

Henderson, J. C., S. H. Kreutzer, A. A. Schidt, C. A. Smith, and W. R. Hagen. 1989. Flotation separation of aflatoxin contained in grain or nuts. Procter and Gamble Co. U.S. Patent 4.795.651 (Chern. Abstr. 1989, 111:767771h.) Heppner, M. 1926. Further evidence on the factor for bitterness in the sweet almond. Genetics 11:605. Heyworth, R., and P. G. Walker. 1961. Identity off)-gucosidase and f)-galactosidase in sweet almond emulsin. Biochem J. 80:44. Hilpert, R. S., and W. Kruger. 1939. Untersuchungen uber pflanzliche samenschalen. Berichte Deutschen Chemischen Gesellschaft. 72:400-404. Hizon, R. P., and C. L. Huick. 1956. The stability of almond and corn oils for use in parenteral solutions. J. Am. Pharm. Assoc. 45:145-150. Hofer-Massard, H., 1926. Concentrated almond and milk syrup for use in foods or beverages. Brit. Patent 282,088 (Chern. Abstr. 1928, 22:1415.) Holmberg, D., 1978. Almond harvest. p. 143-145. In: Micke Wand Kester, D. (eds), Almond Orchard Management. Div. Agr. Sci. Univ. CA Publ. 4092. Horwitz, W., R. Albert, and S. Nasheim. 1993. Reliability of mycotoxin assays. An update. J. Am. Off. Anal. Chern. Int. 76:461-491. Hoppner, K, B. Lamp, and K O'Grady. 1994. Biotin content in vegetables and nuts available on the Canadian market. Food Res. Int. 27:495-497. Hoshi, N. 1953. Anthelmintic. Jp. Patent 848. (Chern. Abstr. 1954,48:2332.) Hotellier, F., and P. Delaveau. 1972. Huiles a interet pharmaceutique, dietetique et cosmetologique: amande, noyau, noisette. Ann. Pharm. Fran<;;;aises 30:495-502. Houlbrooke, A. 1963. Determination ofthe almond content ofthe marzipan. Composition of almonds. J. Assoc. Public Analysts 1:33. Istituto Nazionale della Nutrizione 1973. Composizione in alcuni principi nutritivi e valore alimentare degli alimenti comunemente consumati in Italia. Sez. Graf. 1st. Naz. Nutriz, Roma. James, D. P. 1952. Nicotinic acid, pantothenic acid, and biotin in fruits, vegetables, and nuts. Brit. J. Nutrition 6:341-356. Jefferson, B. R. 1936. Thermal and physical properties of fuel briquets made from agricultural and other waste products. Agr. Eng. 17:199-204. Jimenez, M., R. Mateo, A. Querol, T. Huerta, and K Hernandez. 1991. Mycotoxins and mycotoxigenic molds in nuts and sunflower seeds for human consumption. Mycopathology 115:121-127. Kader, A. A. 1985. Postharvest handling systems: tree nuts. p. 170-173. In: Postharvest Technology of Horticultural Crops. Coop. Ext. Univ. California Div. Agr. Nat. Resourc. Special Publ. 3311. Kandeel, K M., M. M. El-Sayed, M. M. El-Sadek, and G. A. Yacout. 1982. Structural studies on two hemicellulosic polysaccharides from almond (Amygdalus communis). Carbohydr. Res. 110:261-266. Kedlaya, K J., and R. Selvarangan. 1962. Tanning materials of Afghanistan. Bul. Central Leather Res. Inst. Madras (India)8:434-435. Kershaw, S. J. 1985. Aflatoxins in imported edible nuts: some data 1982-84. J. Food Technol. 20:647-649. Kester, D. K, W. Miche, D. Rough, D. Morrison, and R. Curtis. 1980. Almond variety evaluation. Calif. Agr. (Oct.):4-7. Kester, D. K 1965. Size, shape and weight relationship in almond kernels. J. Am. Soc. Hort. Sci. 87:204-213. Kester, D. K and T. M. Gradziel. 1996. Almonds. p. 1-94. In: J. Janick andJ. N. Moore (eds.), Fruit breeding. Vol. III Nuts. Wiley, New York.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

301

Kester, D. E. 1994. Almond cultivar and Breeding programs in California. Acta Hart. 373:13-28. Kester, D. E. and W. C. Micke. 1984. The California almond industry. Fruit Var. J. 38:85-94. Kester, D. E. and W. C. Micke. 1992. Almond rootstock research in California. Rapport EUR 14081 FR, p. 251-254. VIII Reunion Grempa 26-27 Juin, Nimes-France. Kester, D. E. and W. C. Micke, T. Gradziel, and S. Weinhaum. 1992. Almond cultivar research in California. Rapport EUR 14081 FR, p. 67-72. VIII Reunion Grempa 26-27 Juin, Nimes-France. King, A. D., Jr., and J. E. Schade. 1986. Influence of almond harvest, processing and storage on fungal population and flora. J. Food Sci. 51:202-205, 215. King, A. D., Jr., W. U. Halbrook, G. Fuller, and L. C. Whitehand. 1983. Almond nutmeat moisture and water activity and its influence on fungal flora and seed composition. J. Food Sci. 48:615-617. Kinsell, L. W., G. D. Michaels., R. W. Frisley, and S. Splitter. 1958. Essential fatty acids, lipids metabolism and vascular disease. p. 125-146. In: H. M. Sinclair (ed.), Essential fatty acids. Butterworths Scientific Publ., London. Kobayashi, K., and K. Hisamatsu. 1977. Coating agent for roasted nuts and grains. Jp. Patent 77 38,107 (Chern. Abstr. 1978, 88:36132a.) Korpinen, E. L. 1971. Occurrence of aflatoxin in ground nuts, some other nuts and industrial proteins imported into Finland. Nord. Hyg. Tidskr. 53(2):60-69. Krishna Rao, R. V., and M. Vimala Devi. 1992. Herbal antifungal cream. 1st World Congr. Med. Arom. Plants Hum. Welf. 19-25 July 1992, Maastricht, the Netherlands. (Abstr.). Kuester, J. L., C. M. Fernandez, Wang, T. C. and G. Heat. 1985. Liquid hydrocarbon fuel potential of agricultural materials. p. 875-895. In: Proc. Int. Conf. Fundam. Termochem. Biomass. Converso (1982). Kuester, J. L. 1984. Diesel fuel from biomass. Energy Biomass Wastes 8:1435-1459. Kumar, K., D. K. Uppal, Y. R. Chanana and P. P. S. Minhas. 1990. Kernel quality of promising almond (Prunus amygdalus) cvs in plains of N. India. Proc. XXIII Int. Hort. Congr. Vol 2, Abstract 3318. Kumar, K., K. L. Ahuja, and D. K. Uppal. 1994. Kernel quality of almonds (Prunus amygdalus Batsch) in terms of oil content, fatty acid composition and phospholipid content. J. Food Sci. Technol. 31:335-337. Kupchella, L., and A. Syty. 1984. Determination of cyanogenic glycoside in seeds by molecular absorption spectrometry in the gas phase. J. Assoc. Offic. Anal. Chern. 67:188-191. Kuzio, W. 1977. Nutmeat roundup. Candy Ind. 142(6):25-42. Labavitch, J. M. 1978. Relationship of almond maturation and quality to manipulations performed during and after harvest. p. 145-150. In: W. Micke and Kester D. (eds.), Almond orchard management. Div. Agr. Sci. Univ. Calif. LaCourse, W. L., and 1. S. Krull. 1987. Photoelectrochemical detection of benzaldehyde in foodstuffs. Anal. Chern. 59:49-53. Lambertsen, G., H. Myclestad, and O. R. BrCBkklan. 1962. Tocopherols in nuts. J. Sci. Food Agr. 13:617-620. Lasswell, T. c., and J. L. Monier. 1967. Stop-leak composition for automobile cooling systems. U.S. Patent 3,322,548. (Chern. Abstr. 1967, 67:92572x.) Latif, A. 1979. Formulation for treating hair. Brit. Patent 2,000,971. (Chern. Abstr. 1980, 92:31000h.) Liem, Kh Hoo. 1991. Process for preparation of nut based delicacies. Nd. Patent 8.901.633A (FSTA 1991, 8v47.) Linares-Solano, A., J. de D. Lopez Gonzales, M. Molina Sabio, F. Rodriguez Reinoso. 1980.

302

M. SCHIRRA

Active carbons from almond shells as adsorbents in gas and liquid phases. J. Chern. Technol. Biotechnol. 30:65-72. Lindner, M. W. 1966. Calcium content of "Mountain" almonds and other fruit kernels. Dische Lebensmitt. Rdsch. 62:270-272. Lintas, c., and M. Cappelloni. 1992. Dietary fiber content of Italian fruit and nuts. J. Food Compos. Anal. 5:146-151. Longhi, S. 1952. High-protein animal feed. It. Patent 470,433. (Chern. Abstr. 1953, 47:10766.) Loper, G. M., R. Berdel, and R. W. Thorp. 1985. Improving honeybee pollination efficiency in almonds. California Agr. 39(11):19-20. Lopez-Andreu, F. J., R. M. Esteban, and O. Carpena. 1985b. Fiber determination in almond seeds (Prunus amygdalus). Grasas Aceites 36:42-44. Lopez-Andreu, F. J., R. M. Esteban, A. Hernanz, and O. Carpena. 1981. El fruto de almendro en dietetica. p. 325-332. In: ResUrn. IV. Congr. Nac. Quim. Sanit. Madrid, Espana. Vol. II. Lopez-Andreu, F. J., R. M. Esteban Alvarez, and O. Carpena. 1984. Estudio de fracciones proteinicas de diferentes variedades de almendra (Prunus amygdalus). Anal. Edaf. Agrobiol. 43:291-298. Lopez-Andreu, F. J., R. M. Esteban, J. G. Collado, and O. Carpena. 1985a. Proteinas y aminoacidos del fruto del almendro (Prunus amygdalus, Batsch). Comparaci6n entre cultivares. Fruits 40:491-494. Lopez-Gonzales, J. de D., C. Berenguer, and F. Rodriguez-Reinoso. 1976a. The adsorptive properties of activated almond shell chars. p. 63-66. In: Carbon '76. 2nd Int. Carbon Conf. Prep. Lopez-Gonzales, J. de D., F. Rodriguez-Reinoso, and C. Berenguer-Merelo. 1976b. Activated charcoal from almond shells. Sp. Patent 426,439. (Chern. Abstr. 1977, 86:108735h.) Loreti, F., and C. Xiloyannis. 1978. Osservazioni sulle caratteristiche tecnologiche di alcune cultivar di mandorlo. Riv. Ortoflorofrutt. It. 62:147-154. Lott, J. N. A., and M. S. Buttrose. 1978. Thin sectioning, freeze facturing, energy dispersive x-ray analysis and chemical analysis, in the study of inclusions in seed protein bodies: almond, brazil nut, and quandong. Can. J. Bot. 56:2050-2061. Lotti, G., V. Averna, and E. Bazan. 1965. Composizione lipidica e caratteristiche analitiche delle mandorle della Sicilia. Olearia 19:181-188. Lucas, B., and A. Sotelo. 1984. A simplified test for the quantitation of cyanogenic glucosides in wild and cultivated seeds. Nutr. Rep. Int. 29:711-719. Lucas, 1. M. 1942. Almonds, "manna" for Americans, are valued assets to the diet. Hosp. Management 68:53-54. Macfarlane, B. J., W. R. Bezwoda, T. H. Bothwell, R. D. Baynes, J. E. Bothwell, A. P. MacPhail, R. D. Lamparelli, and F. Mayet. 1988. Inhibitory effect of nuts on iron adsorption. Am. J. Clin. Nutr. 47:270-274. Mandenius, C. F., 1. Buelow, and B. Danielsson. 1983. Determination of amygdalin and cyanide in industrial food samples using enzymic methods. Acta Chern. Scand., SeT. B B37(8):739-742. Marth, E. H., and M. P. Doyle. 1979. Update on molds: Degradation of aflatoxin. Food Technol. 42:81-87. Martin, J. P., J. O. Ervin, and R. A. Shepherd. 1965. Decomposition and binding action of polysaccharides from Azotobacter Indicus (Beijerinckia) and other bacteria in soil. Soil Sci. Soc. Proc. 29:397-400. Martinez, J. M., J. M. Granado, D. Montane, J. Salvado, and X. Farriol. 1995. Fractionaction of residuallignocellulosics by dilute-acid prehydrolysis and alcaline extraction: application to almond shells. Biores. Technol. 52:59-67.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

303

Matsunobu, A, S. Horishita, and T. Yamada. 1987. Almond nut paste for beverages and desserts (Ton. Co. Ltd). U.S. Patent 4,639,374. (Dairy Sci. Abstr. 1987,4988.) McCarty, C. D., J. V. Lesley, and H. B. Frost. 1952. Bitterness (benzaldehyde content) of kernels of almond-peach F 1 hybrids and their parents. Proc. Am. Soc. Hort. Sci. 59:254-258. Meara, M. L. 1952. The component acids of an English almond oil. Chern. Ind. Lond. 8:667-668. Meena, B. A, K. P. Umapathy, N. Pankaja, and J. Prakash. 1987. Soluble and insoluble oxalates in selected foods. J. Food Sci. Technol. India I 24:43-44. Meheran, M., and M. Filsoof. 1974. Characteristics ofIranian almond nuts and oils. J. Am. Oil Chern. Soc. 51:433-434. Mengard, M. 1991. Inert laminate composites and products based on them. (Impressions en Continu S.A). Fr. Demande 2,654,980. (Chern. Abstr. 1991, 115:257963a.) Mercant, A A., and M. A Petrie. 1992. Preparation and characterization of activated carbons from scrap tires, almond shells, and Illinois coal. Chern. Eng. Commun. 118:251-263. Micke, W. c., D. E. Kester, A D. Rizzi, and C. V. Carlson. 1966. Early harvesting of almonds. Calif. Agr. 20(12):14-16. Miles Laboratories Inc. 1966. Dermatological abradant cleanser. Brit. Patent 1,045,041. (Chern. Abstr. 1967, 66:5770n.) Mirocha, C. J., and E. E. Wilson. 1961. Hull rot almonds. Phytopathology 51:843-847. Monastra, F. 1979. II mandorlo. Collana Frutticoltura Moderna, Edagricole, Bologna, Italy. Monastra, F. 1988. Frutta secca a un bivio: abbandono 0 rilancio? Frutticoltura 1-2:43-49. Monastra, F. 1991. II mandorlo nel mondo e Ie iniziative per il rilancio del settore. p. 27-32. In: Atti Convegno Nazionale suI Mandorlo. Possibile evoluzione della Mandorlicoltura in Sicilia. Sept. 29-30. Agrigento, Italy. Montane D., J. Salvad6, X. Farriol, and E. Chornet. 1993. The fractionation of almond shells by thermomechanical acqueous-phase (TM-AV) pretreatment. Biomass Bioen. 4:427-437. Moreira, R. G., M. E. Castell-Perez, and F. W. Bakker-Arkema. 1988. Desecacion de almendras a altas temperaturas. Rev. Agroquim. Tecnol. Alimentos. 28:509-518. Morgan, A F., M. B. Newbecker, and E. Bridge. 1923. Biological food tests. V. The biological value of almond proteins and of almond oil. Am. J. Physiol. 67:173-192. Moricone, A, and V. Pedicino. 1986. Dizionario dietetico degli alimenti. Ed. GarzantiVallardi, Milano, Italy. Munshi, S. K., A. S. Dhatt, and P. S. Sukhija. 1988. Lipid compositional studies in almond (Prunus amygdalus Batsch) kernels. J. Res. (Punjab Agr. Univ.) 25:257-264. Munshi, S. K., and P. S. Sukhija. 1984. Compositional changes and biosynthesis oflipids in the developing kernels of almond (Prunus amygdalus Batsh). J. Sci. Food Agric. 35:689-697. Munshi, S. K., and P. S. Sukhija. 1986. Effect ofGA3 , ABA and CK on biosynthesis oflipids in the kernels of almond (Prunus amygdalus Batsh). Indian J. Hortic. 43(1-2):23-28. Munshi, S. K., S. P. Sukhija, and 1. S. Bahatia. 1982. Lipid biosynthesis in developing kernels of almond (Prunus amygdalus Bash). Phytochemistry 22:79-83. Nagamoto, N., H. Noguchi, K. Nanba, H. Nakanura, and M. Mizuno. 1988. Active components having anti-inflammatory and analgesic activities from armeniacae semen, Pruni japonicae semen and almond seeds. Shoyakugaku Zasshi 42(1):81-88. Nanni Marchino, M. L. 1975. Chimica e prodotti secondari dellegno e dei cascami analoghi. p. 752-754. In: M. Giua, (ed), Trattato di Chimica Industriale Vol. 6, USES, Firenze, Italy.

304

M. SCIDRRA

Narvaiz, P., G. Lescano, and E. Cairiyama. 1993. Irradiation of almonds and cashew nuts. Food Sci. Technol. 25:232-235. Nassar, A. R, B. S. EI-Tahawi, and S. A. S. El-Deen. 1977. Chromatographic identification of oil and amino acid constituents in kernels of some almond varieties. J. Am. Oil Chem. Soc. 54:553-556. Natta, G., E. Beati, M. Pegoraro, and G. Guglielmi. 1956. Ricerche suI recupero dell'acido acetico sottoprodotto dell'estrazione del furfurolo da materiali vegetali. Chimica Ind. 38:6-10. Navarro, S., E. Donahaye, and M. Calderon. 1986. Development of the carob moth, spectrobates ceratoniae, on stored almonds. Phytoparasitica 14:177-186. Nelson, H. D., W. H. Barnett, C. A. Ferris. 1978. Fumigation of hull in the farm. Coop Univ. California Ext., Fresno. Nobile, L. 1971. Xylose and xylitol from waste cellulosic materials. Fr. Demande 2,047,193. (Chem. Abstr. 1972, 76:15913t.) Nolan, A. L. 1982. Flavoured almond butter. Food Eng. 54(11):57. Obchinnikova, A. S., and V. 1. Selezneva 1986. Standardization of dry matter losses during the processing of nuts including groundnuts. Khebopekarnaya Konditerskaya Promyshlennosti. 6:21. (Food. Sci. Technol. Abstr. 19, 7J205, 1980.) Ohanesian, N., S. A. L. Lesperance, and J. A. McCormick. 1973. Energy value of alfalfa and almond hulls fed to cattle. Proc. Ann. Meet. Am. Soc. Anim. Sci. West Sect. 24:336 (Abstr.) Olliver, D., J. M. Dordonnat, T. Garetier, S. Barridas, C. Tisse, and M. Guerere. 1993. Differentiation entre amandes et amandons. Ann. Fals. Exp. Chim. 86(918):149-160. Ortolani, C., M. Ispano, E. A. Pastorello, R Ansaloni, and G. C. Magri. 1989. Comparison of results of skin prick tests (with fresh foods and commercial food extracts) and RAST in 100 patients with oral allergy syndrome. J. Allerg. Clin. Inmmunol. 83:683-686. Parodi, E., and E. Zanella. 1990. Fast pyrolysis of almond shells and grape husks in an entrained flow reactor. Energy Biomass Wastes 13:879-895, Paul, A. A., and D. A. T. Southgate. 1979. The composition of foods. Elsevier/North Holland Biomedical Press, Amsterdam. Payne, W. F. 1981. Use of almonds in confections. MC Manual Confect. 61(10):37-41. Pensala, 0., A. Niskanen, and S. Lahtinen. 1977. The occurrence of aflatoxin in nuts and nut products imported to Finland for human consumption during the years 1974-1976. Nord. Veter. 29(7-8):347-355. Petino, A. 1942. II mandorlo. Dai tempi antichi ai nostri giorni. A.P.E. Catania, Italy. Petty, S. H., and A. V. Rodick. 1988. Dry matter digestibility of diets containing almond hulls fed to horses. Proc. Ann. Meet. West Sect. Am. Soc. Anim. Sci. 39:162-164. Phillip, L. 1925. Fertilizer. U.S. Patent 1,322,817. (Chem. Abstr. 1920, 14:308.) Phillips, D. J., B. Mackey, W. R Ellis, and T. N. Hansen. 1979. Occurrence and interaction of Aspergillus flavus with other fungi on almonds. Postharv. Pathol. Mycotoxins. 69:829-831. Phillips, D. J., S. L. Purcell, and G. L. Stanley. 1980. Aflatoxins in almonds. Dep. Agr. Sci. Educ. Adm. West. Reg. Univ. Calif. Coop. Ext. Servo 20(Nev.). Phillips, D. J., M. Uota, D. Monticelli, and C. Curtis. 1976. Colonization of almond by Aspergillus flavus. J. Am. Soc. Hort. Sci. 101:19-23. Phillips, M., and M. G. Goss. 1940. The composition of certain nut shells. J. Assoc. Offic. Anal. Chem. 23:662-665. Pierce, G. W. 1928. Apparatus for bleaching almonds. U.S. Patent 1,382,850 (Chem. Abstr. 1921, 15:3416.) Piironen, V., E. L. Syvaoja, P. Varo, K. Salminen, and P. Koivistoinen. 1986. Tocopherols

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

305

and tocotrienols in finnish foods: vegetables, fruits, and berries. J. Agr. Food Chern. 34:742-746. Plahl, W. 1924. Detection of bitter almonds in the sweet and its hygienic significance. Z. Nahr. Genussm. 48:241-243. Polansky, M. M., and E. W. Murphy. 1966. Vitamin B6 components in fruits and nuts. J. Am. Diet. Assoc. 48:109-111. Polesello, A., C. Baldo, M. Minnini, and A. Rizzolo. 1990. Chemical composition and physical characteristics of some sweet almond cultivars. In: Proc. XXIII Int. Hort. Congr. Vol. 2. (Abstr. 3310.) Polesello, A., and A. Rizzolo. 1989. Caratteristiche nutrizionali e utilizzazione industriale delle mandorle. Frutticoltura 51(4):43-50. Pominski, J., H. M. Pearce, Jr., and R. E. Ritter. 1985. Development of partially defatted almonds: laboratory evaluations. J. Food Sci. 50:716-718. Poneros-Schneier, A. G., and J. W. Erdman, Jr. 1989. Bioavailability of calcium from sesame seeds, almond powder, whole wheat bread, spinach and nonfat dry milk in rats. J. Food Sci. 54:150-153. Pou-Ilinas, J., J. Cafiellas, H. Driguez, G. Excoffier, and M. R. Vignon. 1990. Steam pretreatment of almond shells for xylose production. Carbohydr. Res. 207:126-130. Pouquet, J. 1991. Almond paste products: its manifacture and novel patisserie product containing it. Fr. Patent 2,652,993 AI. (FSTA 1991, 11V74, 1991.) Prats Moya, M. S., and V. Berenguer Navarro. 1994. Caracterizaci6n de algunas variedades de almendra por su composicion en aminoacidos libres. Rev. Esp. Cienc. Tecnol. Alim. 34:218-227. Preston, C. M., and B. G. Sayer. 1992. What's in a nutshell: an investigation of structure by carbon-13 cross-polarization magic-angle spinning nuclear magnetic resonance spectroscopy. J. Agr. Food Chern. 40:206-210. Purcell, S. L., D. J. Phillips, and B. E. Machey. 1980. Distribution of Aspergillus flaVlls and other fungi in several almond growing areas of California. Phytophatology 70:926-929. Qureshi, S., A. H. Shah, M. Tariq, and A. M. Ageel. 1989. Studies on herbal aphrodisiacs used in Arab system of medicine. Am. J. Chinese Medic. 17:57-63. Qutet, S. M., A. M. Shehata El-Tabey, and A. S. Mesallam. 1983. Occurrence of aflatoxins in some Egyptian food crops collected from two coastal regions. Food Chern. 10:149-153. Rabinowitz, 1. N. 1991. Increasing the inositol yield from almond hulls. U.S. Patent 5,064,762. (Chern. Abstr. 1992, 116:57544y.) Ramie, S., D. Murko, and T. Pasic. 1971. Chromatographic determination of carbohydrates in amygdalus fruits. Chern Ind. 20(1):33-34. Ramsay, A. A., and G. W. Norris. 1931. Bleaching almonds. Agr. Gaz. N. S. Wales 42:153-154. Reddy, N. S., and M. S. Hotwani. 1993. In vitro availability of iron from selected nuts and oil seeds. Plant Food Hum. Nutrit. 43:247-250. Reed, B. A., and D. L. Brown. 1988. Almond hulls in diets for lactating goats: effects on yield and composition of milk, feed intake, and digestibility. J. Dairy Sci. 71(2):530-533. Reznikova, L. S. 1956. Antigens from plant materials for serological tests for syphilis. Vestnik Venerol. Dermatol. 30(6):43-45. (Chern. Abstr. 1957, 51:7952.) Rice, R. E., T. W. Johnson, J. C. Profita, and R. A. Jones. 1984. Improved attractant for navel orangeworm (Lepidoptera: Pyralidae) egg traps in almonds. J. Econ. Entomol. 77:13521353. Richert, c., and D. Mulart. 1967. Etude qualitative de l'huile d'amandes douces. Sopic Bv. M. Lause, Marseilles, France.

306

M. SCI-lIRRA

Riera, F. A., R. Alvarez, and J. Coca. 1991. Oxidation of hydrolyzed olive pits and almond shells with nitric acid in the production of humic-based fertilizers. Ind. Eng. Chem. Res. 30:267-270. Riera, F. A., R. Alvarez, and J. Coca. 1992. Humic fertilizer effects on the rate of nitrification in soil tests. Bioresour. Technol. 39(1):49-54. Riquelme, F., and F. Romojaro. 1989. Estudio de la estabilidad de la fraccion lipidica de almendra. Grasas Aceites. 40:26-29. Riquelme, F., F. Romojaro, J. L. Gimenez, and S. Llorente. 1985. Study on protein fraction in some almond varieties of the Spanish south-east. Fruit Sci. Rep. 12:55-59. Rizzolo, A., C. Baldo, and A. Polesello. 1991a. Application of high-performance liquid chromatography to the analysis of niacin and biotin in Italian almond cultivars. J. Chromatogr. 553:187-192. Rizzolo, A., E. Senesi, and C. Colombo. 1994. Studies on the storage of shelled and in-shell almonds. Acta Hort. 373:259-264. Rizzolo, A., A. Polesello, and S. Polesello. 1992. Use of headspace capillary GC to study development of volatile compounds in fresh fruit. J. High Res. Chromatogr. 15:472-477. Rizzolo, A., A. Polesello, and S. Polesello. 1991b. Volatile components in fresh fruit. Lab. 20005(8):116-122. Robert, M. S., and B. L. Robert. 1970. The carbohydrate composition of almond hulls. J Agr. Food Chem. 18:950. Rodgers, J. B. 1936. Thermal and physical properties of fuel briquets made fTom agricultural and other waste products. Agr. Eng. 17:199-204. Rodriguez-Reinoso, F., J. de D. Lopez-Gonzalez, A. Linares-Solano, and M. Molina-Sabio. 1981. The effect of different methods of activation on the porosity of active carbons obtained from agricultural by-products. p. 230-231. In: Program Proc. 15th Bienn. Conf. Carbon. June 22-23,1981, Philadelphia, Pennsylvania. (Abstr.) Romojaro, F., and T. Berenguer. 1984. L'influence de la variete et de l'irrigation sur la teneur en huile des fruits de l'amandier. Options Mediterraneennes. 2:185-188. Romojaro, F., J. E. Garcia, and F. J. Lopez Andreu. 1977. Estudio sobre la composicion quimica de variedades de almendra del sureste espanol. Anal. Edafol. Agrobiol. 36:121-131. Romojaro, F., F. Riquelme, J. L. Gimenez, and S. Llorente. 1988a. Fat content and oil characteristics of some almond varieties. Fruit Sci. Rep. 15(2):53-57. Romojaro, F., F. Riquelme, J. L. Gimenez, and S. Llorente. 1988b. Study on carbohydrate fractions in some almonds cultivars of the Spanish south-east. Fruit Sci. Rep. 15:1-6. Rose, M. S., and G. MacLeod. 1922. The almond as a source of vitamin A. Proc. Soc. ExptI. BioI. Med. 19:391-393. Rose, M. S., and G. MacLeod. 1923. Experiments on the utilization of the calcium of almonds by man. J. BioI. Chem. 57:305-315. Rossler, B. 1955. An unusual case of poisoning from the use of burnt almonds. Zucher-U. Sueswarenwirtch 8:1084-1085. Rothea, F. 1945. Use of bitter almonds in foods. Ann. Pharm. Franc. 3:155-156. Rugraff, L., C. Demanze, and A. Karleskind. 1982. Etude de huiles oleiques a usage cosmetique et pharmaceutique. 1'8 partie: Identification et evaluation de la nature de melanges d'huile d'amande et d'huile de noisette. Parfums, Cosmet. Arames 43(2-3):59, 60,63-67. Ruiz-Bevie, F., D. Prats Rico, and A. F. Marcilla Gomis. 1984a. Activated carbon from almond shells. Chemical activation. 1. Activating reagent selection and variables influence. Ind. Eng. Chem. Prod. Res. Dev. 23:266-269. Ruiz-Bevie, F., D. Prats Rico, and A. F. Marcilla Gomis. 1984b. Activated carbon from

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

307

almond shells. Chemical activation. 2. ZnCl z Activation temperature influence. Ind. Eng. Chern. Prod. Res. Dev. 23:269-271. Ryall, A 1., and W. T. Pentzer. 1974. Handling, tranportation and storage of fruits and vegetables. Vol 2. Fruits and tree nuts. AVI, Westport, CT. Ryugo, K., and J. Labavitch. 1978. Gums and mucilages in hulls of almonds. J. Am. Soc. Hort. Sci. 103:568-570. Sachdeva, y. 1968. A new antimicrobial agent from almond (Prunus amygdalus) shells. India J. Physiol. Pharmacol. 12:207-212. Saito, M. 1954. Anthelmintic. Jp. Patent 647 (Chern. Abstr. 1954,48:14130.) Sala Catala, F. 1942. Our agricultural wastes and the problem of fuel in Spain. Afinidad 19:369-374. Salim, U. R, M. A Shaneen, and M. J. Siddique. 1986. Effects of preservatives on the shelflife of almond seed cake syrup. J. Agr. Res., Pakistan 24:229-235. Salunkhe, D. K., and B. B. Desay. 1986. Postharvest Biotechnology of Fruits Vol II. CRC Press, Inc. Boca Raton, FL. Salvo, F., M. Alfa, and G. Dugo. 1986. Composizione dell'olio di mandorle. Nota III. Variazione di alcuni parametri chimici e chimico-fisici durante la conservazione. Riv. It. Sost. Grasse 63:37-40. Salvo, F., Giacomo Dugo, I. S. D'alcontres, A Cotroneo, and G. Dugo. 1980. Composizione dell'olio di mandorle. Nota II. Differenziazione dell'olio di mandorle dolci da sue miscele con olio di semi di pesco e di albicocco. Riv. It. Sost. Grasse. 57:24-26. Sanchez, A., and R W. Hubbard. 1989. Dietary Protein Modulation of serum Cholesterol: the Aminoacid Connection. p. 247-273. In: M. Friedman (ed), Absorption and utilization of aminoacids. Vol. II. CRC Press Inc. Boca Raton F1. Sanchez, A, and R W. Hubbard. 1991. Plasma amino acids and insulin/glucagen ratio as an explanation for the dietary protein modulation of artheriosclerosis. Medical Hypotheses. 35:324-329. Sanchez-Vizcaino, E., and R M. Rios. 1978. Valores nutritivos del tegumento de la almendra. Proc. 3rd World Congr. Anim. Feed. 7:487-491, Madrid, Spain. Sass, J. 1. 1981. The preference for sweets, spices, and almond milk in late medieval English cuisine. p. 253-260. In: A Fenton and T. M. Owen (eds), Food in perspective. J. Donald Publ. Ltd., Edimburgh. Sathe, S. K. 1993. Solubilization, electrophorectic characterization and in vitro digestibility of almond (Prunus amygdalus) proteins. J. Food Biochem. 16:249-264. Sattar, A., M. Jan, A. Ahamad, M. Wahid, and I. Khan. 1989. Irradiation disinfestation and biochemical quality of dry nuts. Acta Aliment. 18:45-52. Sattar, A, J. Mohammad, A Saleem, M. Jan, and A Ahamad. 1990. Effect of fluorescent light, gamma radiation and packages on oxidative deterioration of dry nuts. Sharad J. Agr. 6:235-240. Saura-Calixto, F., J. Cafiellas, and A Garcia-Raso. 1983a. Determination of hemicellulose, cellulose and lignin of dietary fibre and fibre of several seed hulls. Data comparison. Z. Lebensm.-Unters Forsch. 177:200-202. Saura-Calixto, F., J. Cafiellas, and A Garcia-Raso. 1985. Characteristics and fatty acid composition of almonds, tegument oil-eomparison with almond kernel oil. Fette. Seif. Anstrich.87:4-6. Saura-Calixto, F., J. Cafiellas, and 1. Soler. 1983b. Age of almond trees:its influence on fruit quality and quantity. Proc. 6th Int. Congr. Food Sci. Technol, Dublin. Saura-Calixto, F., M. Bauza, F. Martinez de Toda, and A Argamenteria. 1981. Amino acids, sugars, and inorganic elements in the sweet almond (Prunus amygdalus). J. Agr. Food Chern. 29:509-511.

308

M. SCHIRRA

Saura-Calixto, F., and J. Cafiellas. 1980. Determinacion de elementos inorganicos en almendra. Part II. B, S, Cl, y N. Anal. Bromatol. 32:375-380. Saura-Calixto, F., and J. Cafiellas. 1982a. Mineral composition of almond varieties (Prunus amygdalus). Z. Lebensm.-Unters Forsch. 174:129-131. Saura-Calixto, F., and J. Cafiellas. 1982b. Chemical composition of hulls of the sweet almond (Prunus amygdalus). J. Sci. Food Agr. 33:336-339. Saura-Calixto, F., J. Cafiellas, and M. Bauza. 1980. Carbohidratos solubles en variedades de almendra dulce (Prunus amygdalus). Anal. Bromatol. 32:263-270. Saura-Calixto, F., J. Cafiellas, and J. Garda Raso. 1983c. Contents of detergent-extracted dietary fibers and composition of hulls, shells, and teguments of almonds (Prunus amygdalus). J. Agr. Food Chern. 31:1255-1259. Saura-Calixto, F., J. Cafiellas, and J. Garda Raso. 1984. Gas chromatographic analysis of sugars and sugar-alcohols in the mesocarp, endocarp and kernel of almond fruit. J. Agr. Food Chern. 32:1018-1020. Saura-Calixto, F., J. Cafiellas, and De Toda F. Martinez. 1982c. A chemical study ofthe protein fraction of mediterranean sweet almond varieties (Prunus amygdalus). Z. Lebensm.Unters Forsch. 175:34-37. Saura-Calixto, F., J. Cafiellas, and L. Soler. 1983d. Dietary fibre and components of the nitrogen-free extract of almond kernels. J. Sci. Food Agr. 34:1419-1422. Saura-Calixto, F., J. Cafiellas, and L. Soler. 1983e. Correlations between oil and protein contents in nuts and oil seeds. Anal. Bromat. 34:23. Saura-Calixto, F., J. Cafiellas, and L. Soler. 1988. La almendra. Composicion, variedades, desarollo y maduracion. INIA, Madrid. Saura-Calixto, F., L. Soler, and J. Cafiellas. 1983f. Morphological and compositional changes during development and maturation of the almond (Prunus amygdalus). Agrochimica 28:175-184. Schade, J. E., and A. D. King, Jr. 1984. Fluorescence and aflatoxin content of individual almond kernels naturally contaminated with aflatoxin. J. Food Sci. 49:493-497. Schade, J. E., A. D. King, Jr., B. E. Mackey, W. U. Halbrook, and G. Fuller. 1981. Sampling diced almonds for aflatoxin. J. Am. Oil Chern. Soc. 58:852-857. Schade, J. E., K. Mc Greevy, A. D. King, Jr. and G. Fuller. 1974. Incidence of aflatoxin in California almonds. Appl. Microbiol. 29:48-53. Schirra, M., M. Mulas, and M. Mura. 1993. Chemical and technological characteristics of kernels of six almond cultivars grafted onto two rootstocks. Agr. Med. 123:200-208. Schirra, M., and M. Agabbio. 1989. Influence of irrigation on keeping quality of almond kernels. J. Food Sci. 54:1642-1645. Schirra, M., M. Mulas, G. Nieddu, and F. Virdis. 1994. Mineral content in 'Texas' almonds during fruit growth and ripening. Acta Hort. 373:207-214. Schirra, M., and G. Nieddu. 1992. Compositional changes in developing almond kernels in relation to rootstock and water supply. Ann. Fac. Agr. Univ. Sassari (1988-1992) 34:169-175. Schirra, M., G. Nieddu, M. Mulas, and 1. Chessa. 1988. The influence of cultural factors on the technological and nutritional values of almond fruit. Rapport EUR 11557 FR. 216-228. 7° Colloque du Grempa, Reus (Terragone), Espagne, 17-19 Juin 1987. Schirra, M. 1992. Influence of extraction methods on almond oil characteristics. Rapport EUR 14081 FR, p. 183-187. VIII Reunion Grempa 26-27 Juin, Nimes-France. Schmidt De Gil, J., and A. de Falconi Catalini. 1977. Metodo para determination de benzaldehydo en almendras dulces. Rev. Fac. CienciaAgr. Univ. Nac. Cuyo. 19(1-2):93-96. Schwarz, H. 1940. Almond meal. Seifensieder-Ztg 67:416. Schwarzmaer, U. 1972. Prunasin, daucosterin and sitosterol from the bitter seeds of Prunus amygdalus. Phytochemistry 11:2358.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

309

Secchi, G. 1979. I Nostri Alimenti. Hoepli, Milano (Italy), p. 483-484. Segerstrom, A. 1990. California almonds, world-famous ingredient. European Food Drink Rev. (Autumn):117, 120, 125, 126, 128, 130. Senesi, K, A. Rizzolo, and S. Sarlo. 1990. Almond quality during the storage under different packaging conditions. Proc. XXIII Int. Hort. Congr. Vol 1 n° 2583 (Abstr.). Senesi, K, A. Rizzolo, and S. Sarlo. 1991. Effect of different packaging conditions on peeled almond stability. It. J. Food Sci. 3:209-218. Sequeira, R. M., and R. B. Lew. 1970. The carbohydrate composition of almond hulls. J. Agr. Food Chern. 18:950-951. Shehab, S. K, and A. A. EI-Zoheiry. 1974. Vitamin C content ofrosaceae edible fruits. Ain Shams. Med. J. 25:525-532. Siesto, A. J. 1956. Influenza del bromuro di metile suI tasso di tiamina nelle farine "grasse" fumigate. Quaderni Nutriz. 16:122-129. Singer, M. 1939. The production and use of activated charcoal. Seifensieder-Ztg. 66:55-57, 75-77. Sinner, M., J. PuIs, and H. H. Dietrichs. 1979. Carbohydrate composition of nut shells and some other agricultural residues. Stark/Starke 31:267-269. Slover, H. T., J. Lehmann, and R. J. Valis. 1969. Vitamin E in foods: determination of tocoIs and tocotrienols. J. Am. Oil Chern. Soc. 46:417-420. Sodas, 1. C. R. 1988. A historical reference to almond trade. p. 267. Rapport EUR 11557 FR. 70 Colloque du Grempa, Reus (Terragone), Espagne, 17-19 Juin 1987. Soderstrom, K L., and D. G. Brandl. 1982. Antifeeding effect of modified atmospheres on larvae of the navel orangeworm and Indian meal moth (Lepidoptera: Pyralidae). J. Econ. Entomol. 75:704-705. Soderstrom, K L., and D. G. Brandl. 1984. p. 487-497. In: Modified atmospheres for postharvest insect control in tree nuts and dried fruits. p. 487-497. In: Proc. 3rd Int. Work. Conf. Stored-Product Entomol. Kansas State Univ., Manhattan, USA, Oct. 23-28, 1983. Soler, L., J. Caiiellas, and F. Saura-Calixto. 1988. Oil content and fatty acid composition of developing almond seeds. J. Agr. Food Chern. 36:695-697. Soler, 1., J. Caiiellas, and F. Saura-Calixto. 1989. Changes in carbohydrate and protein content and composition of developing almond seeds. J. Agr. Food Chern. 37:1400-1404. Souty, M., P. Andre, L. Breuils, and G. Jacquemin. 1971. Etude sur la qualite des amandes Amygdalus communis L. Variabilite de quelques caractres biochimiques. Ann. Technol Agr. 20:121-130. Souty, M., M. Raspail, G. Jacquemin, and L. Breuils. 1973. Etude de quelques caracteres biochimiques des fruits de l'amandier. Bul. Tech. Inf. 279:355-364. Spiller, G. A., D. J. A. Jenkins, L. N. Cragen, J. K Gates, O. Bosello, K Berra, C. Rudd, M. Stevenson, and R. Superko. 1992. Effect of a diet high in monounsaturated fat from almonds on plasma cholesterol and lipoproteins. J. Am. ColI. Nutr. 11:126-130. Spitler, G. H., P.L. Hartsell, and H. D. Nelson. 1975. Malathion protection of inshell almonds in bulk storage-pilot scale study. Public. Agr. Res. Servo U.S.D.A. Western Reg. ARS-W-26 (Jun.). Sreenath, H. K, and R. Joseph. 1983. Cultivation of Streptomyces cultures on agricultural by-products by surface (koji) method for production of xylanases. Chern. Mikrobiol. Technol. Lebensm. 8:46-51. Stacchini, A., and L. Boniforti. 1965. Determinazione gascromatografica della benzaldeide in sdroppi di mandorle. Ann 1st. Sup. Sanita.l(1/6):374-379. Stanchzuk -Rozycka, T., and A. Elert. 1974. Self-polishing wax emulsions for footwear. Pol. Patent 73,585. (Chern. Abstr. 1976, 84:46307t.) Stanley, G. 1., Jr., V. P. DiProssimo, and A. C. Koontz. 1979. Minicolumn detection method applied to almonds:collaborative study. J. Assoc. Offic. Anal. Chern. 62:136-140.

310

M. SCHIRRA

Stavrakellis, P. 1988. Plastic composites containing fruit kernels or shells and/or marble chips. Eur. Patent Appl. 292,628. (Chern Abs 110:96844s, 1989.) Stepanova, L. P. 1972. Vitamins in nuts grown as food. Tr. Vs. Semin. BioI. Activ. (Lech.) Vesh Chestvam Plodov Yagod, 4th (1970), 179-182 (Chern. Abstr. 1974, 81:90064y.) Storey, C. L., and E. L. Soderstrom. 1977. Mortality of navel orangeworm in a low oxygen atmosphere. J. Econ. Entomol. 70:95-97. Sturm, W., and E. Hanssen. 1967. Uber cyanwasserstoff in prunoideensamen und einigen anderen lebensmitteln. II. Mitteilung. Blausiiuregehalte in rohen und verarbeiteten bitteren mandeln. Toxikologische und lebensmittelrechtliche Uberlegungen. Z. Lebensmitt.-Unters. Forsch. 135:249-259. Takei, W., and T. Yamanishi. 1974. Flavor component ofroasted almond. Agr. BioI. Chern. 38:2329-2336. Talon, R. 1946. Use of bitter almonds as a food during 1940-1945. Cbimie Industrie 57:331-335. Teotia, M. S., S. Kaur, and S. K. Berry. 1987. Chemistry and technology of almonds. Indian Food Packer 41(5):49-90. Teotia, M. S., S. K. Berry, and R C. Sehgal. 1993. Studies on the post-harvest shelf-life of green almonds. Indian Food Packer 47(2):53-57. Testoni, A. 1989. I parametri della qualita della frutta fresca e secca. It. Agr. 4:123-136. Torregrosa, R, and J. M. Martin Martinez. 1991. Activation of lignocellulosic materials: a comparison between chemical, physical and combined activation in terms of porous texture. Fuel 70:1173-1180. Trucksess, M. W., M. E. Stack, S. Nashem, R H. Albert, and T. H. Romer. 1994. Multifunctional column coupled with liquid chromatography for determination of aflatoxins B1 , Bz, C1 and Cz in corn, almonds, Brazil nuts, peanuts and pistachio nuts:collaborative study. J. Assoc. Offic. Anal. Chern. Int. 77:1512-1521. Tutelyan, V. A., K. I. Eller, and V. S. Sobolev. 1989. A survey using normal-phase highperformance liquid chromatography of aflatoxins in domestic and imported foods in the USSR Food Addit. Contam. 6:459-465. Ujsaghy, P. 1940. Nitrogen and sulfur metabolism in healthy infants fed with vegetable protein (almond milk). Monatsschr. Kinderheilk. 81:225-235 (Chern. Abstr. 1941,35:5935.) Usai, M., and G. D'hallewin. 1992. Cyanogenic glycosides contained in different organs of bitter and sweet almond. p. 233-236. In: Rapport EUR 14081 FR, VIII Reunion GREMPA Nimes-France. Van Steenwyk, R A., and W. W. Barnett. 1987. Disruption of navel orangeworm (Leptidoptera: Pyralidae) oviposition by almond by-products. J. Econ. Entomol. 80:1291-1296. Van Wagner, L. R 1988. Almond: king of the California exports. Snack World 45:54-57. Vaughan, J. G. 1970. The structure and utilization of oil seeds. p. 49-53. Chapman & Hall, London. Velasco, M., C. Schoner, Jr., and G. P. Lofgreen. 1965. Composition and feeding value of almond hulls and hull-shell meal. Calif. Agr. 19(3):12-14. Velez Gonzalez, M., and M. Xirau Vayreda. 1979. Determination of aflatoxins in dry foods. Circ. Farm. 37(265):371-378. Vidal-Valverde, c., E. Rojas-Hildago, and S. Valverde-Lopez. 1979. Contenido en carbohidratos solubles de algunos frutos secos desecados y oleaginosos. Rev. CHn. Espanola 154(3-4):87-93. Vidal-Valverde, c., I. Blanco, and E. R Hildago. 1982. Pectic substances in fresh, dried, desiccated and oleaginous Spanish fruit. J. Agr. Food Chern. 30:832-835. Violante, P. 1966. La composizione gliceridica della sostanza lipidica dei semi del genere prunus. Ann. Fac. Sci. Agr. Univ. Napoli in Portici (Italy). Serie III. 30:417-429.

4. POSTHARVEST TECHNOLOGY AND UTILIZATION OF ALMONDS

311

Wahmi, H. V. R. 1983. Dentifrice. (Mathur Krishan Dyal). U.S. Patent 4,374,824. (Chern. Abstr. 1983, 98:149467p). Wakana, T., and K. Okubo. 1979. Milk substitute from nuts. Jpn. (Kibun Co., Ltd.) Kokai Tokkyo Koho 79,138,135. (Chern. Abstr. 1980, 92:109420n.) Weir, W. C. 1951. Almond hulls as feed. Calif. Agr. 5(9):9-13. Welkerling de Tacchini, E. M. L., E. Azar de Godoy, and A. B. Catalini de Falconi. 1972. Influencia del portainjerto en el contenido de benzaldehido en almendras dulces. Rev. Fac. Ciencias Agrarias Mendoza 18:53-54. Wells, A. W. 1951. The storage of edible nuts. USDA ARS Bur. PI. Ind. Rpt. 240. Wells, A. W., and H. R. Barber. 1959. Extending the market life of packaged shelled nuts. USDA Nat. Res. Repr. 329. Williams, D. E., and 1. N. Rabinowitz. 1984. Recovery of inositol and optionally sorbitol from acqueous extracts of almond shells. Fr. Patent 2,528,381 (Chern. Abstr. 1985, 102:80795w.) Williamson, R. V., and E. C. Lathrop. 1949. Agricultural residues as extenders in phenolic resin glues for plywood. Modern Plastics 27(2):111-112, 169-170, 172, 174. Wilson, T. J., and T. R. Romer. 1991. Use ofthe Mycosep multifunctional cleanup column for liquid chromatographic determination of aflatoxins in agricultural products. J. Assoc. Offic. Anal. Chern. 74:951-956. Wolf, W. J. 1995. Gel electrophoresis and amino acid analysis of the non-protein nitrogen fraction of defatted soybean and almond meals. Cereal Chern. 72:115-121. Wood, G. E. 1989. Aflatoxins in domestic and imported foods and feeds. J. Assoc. Offic. Anal. Chern. 72:543-548. Woodroof, J. G. 1982a. Composition and nutritive value of nuts. p. 49-80. In: Tree nuts: production, processing and products. 2nd ed. AVI, Westport, CT. Woodroof, J. G, 1982b. Almond. p. 139-180. In: Tree nuts: production, processing and products. 2nd ed. AVI, Westport, CT. Wright, R. C. 1941. Investigations on the storage of nuts. U.S.D.A. Tech. Bul. 770. Yee, S. M. 1990. Pharmaceutical solutions for treatment of acne. U.S. Patent 5,006,557. (Chern. Abstr. 1991, 114:254051v.) Yotsuhashi, K., T. Wakana, and K. Kitatsume. 1986. Manufacture of vegetable containing yoghurt-like foods. Jpn. Tokkio Koko 61 44,450. (Chern. Abstr. 1987, 106:31611m.) Youle, R. J., and A. H. C. Huang. 1981. Occurrence oflow molecular weight and high cysteine containing albumin storage proteins in oilseeds of diverse species. Am. J. Bot. 68:44-48.

Subject Index

A

Almond, postharvest technology and utilization, 267-311 Anatomy and morphology, magnetic resonance imaging, 77-86, 225-266 Apple, mealiness, 199-200

c Citrus, juice loss, 200 D

Dedication, Pratt,

Nut crops, almond postharvest technology and utilization, 267-311 p

Peach, wooliness, 198-199 Postharvest physiology and technology: almond,267-311 nondestructive quality evaluation, 1-120 quality evaluation, 1-120 texture in fresh fruit, 121-224

c., ix

F

Fruit: nondestructive postharvest quality evaluation, 1-120 texture, 121-224 Fruit crops, nondestructive postharvest quality evaluation, 1-120 M

Magnetic resonance imaging, 77-86, 225-266 N

Nondestructive quality evaluation of fruits and vegetables, 1-120

Q Quality evaluation: fruits and vegetables, 1-120, 121-224 nondestructive, 1-120 texture, 121-224 T Texture in fresh fruit, 121-224 Tomato, chilling injury, 199

v Vegetable crops: nondestructive postharvest quality evaluation, 1-120 texture in fresh fruit, 121-224 313

Cumulative Subject Index (Volumes 1-20) A

Abscisic acid: chilling injury 15:78-79 cold hardiness, 11:65 dormancy, 7:275-277 genetic regulation, 16:9-14,20-21 mechanical stress, 17:20 rose senescence, 9:66 stress, 4:249-250 Abscission: anatomy and histochemistry, 1:172-203

citrus, 15:145-182, 163-166 flower and petals, 3:104-107 regulation, 7:415-416 rose, 9:63-64 Acclimatization: foliage plants, 6:119-154 herbaceous plants, 6:379-395 micropropagation, 9:278-281, 316-317

Actinidia, 6:4-12 Adzuki bean, genetics, 2:373 Agaricus, 6:85-118 Agrobacterium tumefaciens, 3:34 Air pollution, 8:1-42 Almond: bloom delay, 15:100-101 in vitro culture, 9:313 postharvest technology and utilization, 20:267-311 Alocasia, 8:46, 57. See also Aroids Alternate bearing: chemical thinning, 1:285-289 fruit crops, 4:128-173

pistachio, 3:387-388 Aluminum: deficiency and toxicity symptoms in fruits and nuts, 2:154 Ericaceae, 10:195-196 Amorphophallus, 8:46, 57. See also Aroids Anatomy and morphology: apple flower and fruit, 10:273-308 apple tree, 12:265-305 asparagus, 12:71 cassava, 13:106-112 citrus, abscission, 15:147-156 embryogenesis, 1:4-21, 35-40 fig, 12:420-424 fruit abscission, 1:172-203 fruit storage, 1:314 ginseng, 9:198-201 grape flower, 13:315-337 grape seedlessness, 11:160-164 heliconia, 14:5-13 kiwifruit, 6:13-50 magnetic resonance imaging, 20:77-86, 225-266

orchid, 5:281-283 navel orange, 8:132-133 pecan flower, 8:217-255 petal senescence, 1:212-216 pollution injury, 8:15 Androgenesis, woody species, 10:171-173

Angiosperms, embryogenesis, 1:1-78 Anthurium, see Aroids, ornamental fertilization, 5:334-335 Antitranspirants, 7:334 cold hardiness, 11:65 315

CUMULATIVE SUBJECT INDEX

316

Apical meristem, cryopreservation,

Asexual embryogenesis, 1:1-78;

6:357-372

Apple: alternate bearing, 4:136-137 anatomy and morphology of flower and fruit, 10:273-309 bitter pit, 11:289-355 bioregulation, 10:309--401 bloom delay, 15:102-104 CA storage, 1:303-306 chemical thinning, 1:270-300 fertilization, 1:105 fire blight control, 1:423--474 flavor, 16:197-234 flower induction, 4:174-203 fruit cracking and splitting, 19:217-262

fruiting, 11:229-287 in vitro, 5:241-243; 9:319-321 light, 2:240-248 maturity indices, 13:407-432 mealiness, 20:199-200 nitrogen metabolism, 4:204-246 replant disease, 2:3 root distribution, 2:453--456 stock-scion relationships, 3:315-375

summer pruning, 9:351-375 tree morphology and anatomy,

2:268-310; 3:214-314; 7:163-168,171-173,176-177, 184, 185-187, 187-188, 189; 10:153-181; 14:258-259, 337-339

Asparagus: CA storage, 1:350-351 fluid drilling of seed, 3:21 postharvest biology, 12:69-155 Auxin: abscission, citrus, 15:161, 168-176 bloom delay, 15:114-115 citrus abscission, 15:161, 168-176 dormancy, 7:273-274 flowering, 15:290-291, 315 genetic regulation 16:5-6, 14, 21-22

geotropism, 15:246-267 mechanical stress, 17:18-19 petal senescence, 11: 31 Avocado: flowering, 8:257-289 fruit development, 10:230-238 fruit ripening, 10:238-259 rootstocks, 17:381--429 Azalea, fertilization, 5:335-337 B

12:265-305

vegetative growth, 11:229-287 watercore, 6:189-251 yield, 1:397--424 Apricot: bloom delay, 15:101-102 CA storage, 1:309 Aroids: edible, 8:43-99; 12:166-170 ornamental, 10:1-33 Arsenic, deficiency and toxicity symptoms in fruits and nuts, 2:154 Artemisia, 19:319-371 Artemisinin, 19:346-359 Artichoke, CA storage, 1:349-350

Babaco, in vitro culture, 7:178 Bacteria: diseases of fig, 12:447--451 ice nucleating, 7:210-212, 11:69-71

pathogens of bean, 3:28-58 tree short life, 2:46--47 wilt of bean, 3:46--47 Bacteriocides, fire blight, 1:450--459 Bacteriophage, fire blight control, 1:449--450

Banana: CA storage, 1:311-312 fertilization, 1:105 in vitro culture, 7:178-180

CUMULATIVE SUBJECT INDEX

317

Bean: CA storage, 1:352-353 fluid drilling of seed, 3:21 resistance to bacterial pathogens, 3:28-58

Bedding plants, fertilization,

in vitro, 18:87-169 micropropagation, 18:89-113 root physiology, 14:57-88 virus elimination, 18:113-123

c

1:99-100; 5:337-341

Beet: CA storage, 1:353 fluid drilling of seed, 3:18-19 Begonia (Rieger), fertilization, 1:104 Biennial bearing, see Alternate bearing Biochemistry, petal senescence, 11:15-43

Bioregulation, see Growth substances apple and pear, 10:309-401 Bird damage, 6:277-278 Bitter pit in apple, 11:289-355 Blackberry harvesting, 16:282-298 Black currant, bloom delay, 15:104 Bloom delay, deciduous fruits, 15:97 Blueberry: developmental physiology, 13:339-405

harvesting, 16:257-282 nutrition, 10:183-227 Boron: deficiency and toxicity symptoms in fruits and nuts, 2:151-152 foliar application, 6:328 nutrition, 5:327-328 pine bark media, 9:119-122 Botanic gardens, 15:1-62 Bramble, harvesting, 16:282-298 Branching, lateral: apple, 10:328-330 pear, 10:328-330 Brassicaceae, in vitro, 5:232-235 Breeding, see Genetics and breeding Broccoli, CA storage, 1:354-355 Brussels sprouts, CA storage, 1:355 Bulb crops, see Tulip genetics and breeding, 18:119-123

Cabbage: CA storage, 1:355-359 fertilization, 1:117-118 Cactus: crops, 18:291-320 reproductive biology, 18:321-346 Caladium, see Aroids, ornamental Calcifuge, nutrition, 10:183-227 Calciole, nutrition, 10:183-227 Calcium: bitter pit, 11:289-355 cell wall, 5:203-205 container growing, 9:84-85 deficiency and toxicity symptoms in fruits and nuts, 2:148-149 Ericaceae nutrition, 10:196-197 foliar application, 6:328-329 fruit softening, 10:107-152 nutrition, 5:322-323 pine bark media, 9:116-117 tipburn, disorder, 4:50-57 Calmodulin, 10:132-134, 137-138 Carbohydrate: fig, 12:436-437 kiwifruit partitioning, 12:318-324 metabolism, 7:69-108 partitioning, 7:69-108 petal senescence, 11:19-20 reserves in deciduous fruit trees, 10:403-430

Carbon dioxide, enrichment, 7:345-398, 544-545

Carnation, fertilization, 1:100; 5:341-345

Carrot: CA storage, 1:362-366 fluid drilling of seed, 3:13-14 Caryophyllaceae, in vitro, 5:237-239

CUMULATIVE SUBJECT INDEX

318

Cassava, 12:158-166; 13:105-129 CA storage, see Controlledatmosphere storage Cauliflower, CA storage, 1:359-362 Celeriac, CA storage, 1:366-367 Celery: CA storage, 1:366-367 fluid drilling of seed, 3:14 Cell culture, 3:214-314 woody legumes, 14:265-332 Cell membrane: calcium, 10:126-140 petal senescence, 11:20-26 Cellular mechanisms, salt tolerance, 16:33-69

Cell wall: calcium, 10:109-122 hydrolases, 5:169-219 ice spread, 13:245-246 tomato, 13:70-71 Chelates, 9:169-171 Cherry: bloom delay, 15:105 CA storage, 1:308 origin, 19:263-317 Chestnut: blight, 8:281-336 in vitro culture, 9:311-312 Chicory, CA storage, 1:379 Chilling: injury, 4:260-261,15:63-95 pistachio, 3:388-389 Chlorine: deficiency and toxicity symptoms in fruits and nuts, 2:153 nutrition, 5:239 Chlorosis, iron deficiency induced, 9:133-186

Chrysanthemum fertilization, 1:100-101; 5:345-352

Citrus: abscission, 15:145-182 alternate bearing, 4: 141-144 asexual embryogenesis, 7:163-168 CA storage, 1:312-313 chlorosis, 9:166-168

cold hardiness, 7:201-238 fertilization, 1:105 flowering, 12:349-408 honey bee pollination, 9:247-248 in vitro culture, 7:161-170 juice loss, 20:200 navel orange, 8:129-179 nitrogen metabolism, 8:181 rootstock, 1:237-269 Cloche (tunnel), 7:356-357 Coconut palm: asexual embryogenesis, 7:184 in vitro culture, 7:183-185 Cold hardiness, 2:33-34 apple and pear bioregulation, 10:374-375

citrus, 7:201-238 factors affecting, 11:55-56 herbaceous plants, 6:373-417 injury, 2:26-27 nutrition, 3:144-171 pruning, 8:356-357 Colocasia, 8:45, 55-56. See also Aroids Common blight of bean, 3:45-46 Compositae, in vitro, 5:235-237 Container production, nursery crops, 9:75-101

Controlled environment agriculture, 7:534-545. See also Greenhouse and greenhouse crops; Hydroponic culture; Protected crops, carbon dioxide Controlled-atmosphere (CA) storage: asparagus, 12:76-77, 127-130 chilling injury, 15:74-77 flowers, 3:98; 10:52-55 fruit quality, 8:101-127 fruits, 1:301-336; 4:259-260 pathogens, 3:412-461 seeds, 2:134-135 tulip, 5:105 vegetable quality, 8:101-127 vegetables, 1:337-394; 4:259-260 Copper:

CUMULATIVE SUBJECT INDEX

deficiency and toxicity symptoms in fruits and nuts, 2:153 foliar application, 6:329-330 nutrition, 5:326-327 pine bark media, 9:122-123

Corynebacterium f1accumfaciens, 3:33,46 Cowpea: genetics, 2:317-348 U.S. production, 12:197-222 Cranberry: fertilization, 1:106 harvesting, 16:298-311 Cryopreservation: apical meristems, 6:357-372 cold hardiness, 11:65-66 Cryphonectria parasitica, see

Endothia parasitica Crytosperma, 8:47, 58. See also Aroids Cucumber, CA storage, 1:367-368 Currant, harvesting, 16:311-327 Cytokinin: cold hardiness, 11:65 dormancy, 7:272-273 floral promoter, 4:112-113 flowering, 15:294-295, 318 genetic regulation, 16:4-5, 14, 22-23 grape root, 5:150, 153-156 lettuce tipburn, 4:57-58 petal senescence, 11:30-31 rose senescence, 9:66 D

Date palm: asexual embryogenesis, 7:185-187 in vitro culture, 7:185-187 Daylength, see Photoperiod Dedication: Bailey, L.H., 1:v-viii Beach, S.A., l:v-viii Bukovac, M.J., 6:x-xii Campbell, C.W., 19:xiii Cummins, J.N., 15:xii-xv Faust, Miklos, 5:vi-x

319

Hackett, W.P., 12:x-xiii Halevy, A.H., 8:x-xii Hess, c.E., 13:x-xii Kader, A.A., 16:xii-xv Looney, N.E., 18:xiii Magness, J.R, 2:vi-viii Moore, J.N., 14:xii-xv Pratt, c., 20:ix Proebsting, Jr., KL., 9:x-xiv Rick, Jr., C.M., 4:vi-ix Sansavini, S., 17:xii-xiv Smock, RM., 7:x-xiii Weiser, c.J., 11:x-xiii Whitaker, T.W., 3:vi-x Wittwer, S.H., 10:x-xiii Deficiency symptoms, in fruit and nut crops, 2:145-154 Defoliation, apple and pear bioregulation, 10:326-328 'Delicious' apple, 1:397-424 Desiccation tolerance, 18:171-213 Dieffenbachia, see Aroids, ornamental Dioscorea, see Yam Disease: and air pollution, 8:25 aroids, 8:67-69; 10:18; 12:168-169 bacterial, of bean, 3:28-58 cassava, 12:163-164 controlled-atmosphere storage, 3:412-461 control by virus, 3:399-403 cowpea, 12:210-213 fig, 12:447-479 flooding, 13:288-299 hydroponic crops, 7:530-534 lettuce, 2:187-197 mycorrhizal fungi, 3:182-185 ornamental aroids, 10:18 resistance, acquired, 18:247-289 root, 5:29-31 stress, 4:261-262 sweet potato, 12:173-175 tulip, 5:63, 92 turnip moasic virus, 14:199-238 yam (Dioscorea), 12:181-183

CUMULATIVE SUBJECT INDEX

320

Disorder, see Postharvest physiology bitterpit,11:289-355 fig, 12:477-479 pear fruit, 11:357-411 watercore, 6:189-251; 11:385-387 Dormancy, 2:27-30 blueberry, 13:362-370 release in fruit trees, 7:239-300 tulip, 5:93 Drip irrigation, 4:1-48 Drought resistance, 4:250-251 cassava, 13:114-115 Dwarfing: apple, 3:315-375 apple mutants, 12:297-298 by virus, 3:404-405 E

Easter lily, fertilization, 5:352-355 Embryogenesis, see Asexual embryogenesis

Endothia parasitica, 8:291-336 Energy efficiency, in greenhouses, 1:141-171; 9:1-52 Environment: air pollution, 8:20-22 controlled for agriculture, 7:534-545 controlled for energy efficiency, 1:141-171; 9:1-52 embryogenesis, 1:22,43-44 fruit set, 1:411-412 ginseng, 9:211-226 greenhouse management, 9:32-38 navel orange, 8:138-140 nutrient film technique, 5:13-26 Epipremnum, see Aroids, ornamental

Erwinia: amylovora, 1:423-474 lathyri, 3:34 Essential elements: foliar nutrition, 6:287-355 pine bark media, 9:103-131 plant nutrition 5:318-330

soil testing, 7:1-68 Ethylene: abscission, citrus, 15:158-161, 168-176 apple bioregulation, 10:366-369 avocado, 10:239-241 bloom delay, 15:107-111 CA storage, 1:317-319, 348 chilling injury, 15:80 citrus abscission, 15:158-161, 168-176 cut flower storage, 10:44-46 dormancy, 7:277-279 flowering, 15:295-296, 319 flower longevity, 3:66-75 genetic regulation, 16:6-7, 14-15, 19-20 kiwifruit respiration, 6:47-48 mechanical stress, 17:16-17 petal senescence, 11:16-19, 27-30 rose senescence, 9:65-66 F Feed crops, cactus, 18:298-300 Fertilization and fertilizer: anthurium, 5:334-335 azalea, 5:335-337 bedding plants, 5:337-341 blueberry, 10:183-227 carnation, 5: 341-345 chrysanthemum, 5:345-352 controlled release, 1:79-139; 5:347-348 Easter lily, 5:352-355 Ericaceae, 10:183-227 foliae plants, 5:367-380 foliar, 6:287-355 geranium, 5:355-357 greenhouse crops, 5:317-403 lettuce, 2:175 nitrogen, 2:401-404 orchid, 5:357-358 poinsettia, 5:358-360 rose, 5:361-363 snapdragon, 5:363-364

CUMULATIVE SUBJECT INDEX

soil testing, 7:1-68 trickle irrigation, 4:28-31 tulip, 5:364-366 Vaccinium, 10:183-227 Fig: industry, 12:409-490 ripening, 4:258-259 Filbert, in vitro culture, 9:313-314 Fire blight, 1:423-474 Flooding, fruit crops, 13:257-313 Floricultural crops. See individual crops fertilization, 1:98-104 growth regulation, 7:399-481 heliconia, 14:1-55 postharvest physiology and senescence, 1:204-236; 3:59-143; 10:35-62; 11:15-43

Florigen, 4:94-98 Flower and flowering: alternate bearing, 4:149 apple anatomy and morphology, 10:277-283

apple bioregulation, 10:344-348 aroids, ornamental, 10:19-24 avocado, 8:257-289 blueberry development, 13:354-378

cactus, 18:325-335 citrus, 12:349-408 control, 4:159-160, 15:279-334 development (postpollination), 19:1-58

fig, 12:424-429 grape anatomy and morphology, 13:354-378

honey bee pollination, 9:239-243 induction, 4:174-203; 254-256 initiation, 4:152-153 in vitro, 4:106-127 kiwifruit, 6:21-35; 12:316-318 orchid, 5:297-300 pear bioregulation, 10:344-348 pecan, 8:217-255 perennial fruit crops, 12:223-264 phase change, 7:109-155

321

photoperiod, 4:66-105 pistachio, 3:378-387 postharvest physiology, 1:204-236; 3:59-143; 10:35-62; 11:15-43

postpollination development, 19:1-58

protea leaf blackening, 17:173-201 pruning, 8:359-362 raspberry, 11:187-188 regulation in floriculture, 7:416-424

rhododendron, 12:1-42 rose, 9:60-66 senescence, 1:204-236; 3:59-143; 10:35-62; 11:15-43; 18:1-85

sugars, 4:114 thin cell layer morphogenesis, 14:239-256

tulip, 5:57-59 water relations, 18:1-85 Fluid drilling, 3:1-58 Foliage plants: acclimatization, 6:119-154 fertilization, 1:102-103; 5:367-380 Foliar nutrition, 6:287-355 Freeze protection, see Frost, protection Frost: apple fruit set, 1:407-408 citrus, 7:201-238 protection, 11:45-109 Fruit: abscission, 1:172-203 citrus, 15:145-182 apple anatomy and morphology, 10:283-297

apple bioregulation, 10:348-374 apple bitter pit, 11:289-355 apple flavor, 16:197-234 apple maturity indices, 13:407-432

apple ripening and quality, 10:361-374

avocado development and ripening, 10:229-271

CUMULATIVE SUBJECT INDEX

322

Fruit (cont'd) bloom delay, 15:97-144 blueberry development, 13:378-390

cactus physiology, 18:335-341 CA storage and quality, 8:101-127 chilling injury, 15:63-95 cracking, 19:217-262 diseases in CA storage, 3:412-461 drop, apple and pear, 10:359-361 fig, 12:424-429 kiwifruit, 6:35-48; 12:316-318 maturity indices, 13:407-432 navel orange, 8:129-179 nectarine, postharvest, 11:413-452 nondestructive postharvest quality evaluation, 20:1-120 peach, postharvest, 11:413-452 Pear: bioregulation, 10:348-374 fruit disorders, 11:357-411 pear maturity indices, 13:407-432 pear ripening and quality, 10:361-374

pistachio, 3:382-391 quality and pruning, 8:365-367 ripening, 5:190-205 set, 1:397-424; 4:153-154 set in navel oranges, 8:140-142 size and thinning, 1:293-294; 4:161

softening, 5:109-219; 10:107-152 splitting, 19:217-262 strawberry growth and ripening, 17:267-297

texture, 20:121-224 thinning, apple and pear, 10:353-359

tomato parthenocarpy, 6:65-84 tomato ripening, 13:67-103 Fruit crops: alternate bearing, 4:128-173 apple bitter pit, 11:289-355 apple flavor, 16:197-234 apple fruit splitting and cracking, 19:217-262

apple growth, 11:229-287 apple maturity indices, 13:407-432

avocado flowering, 8:257-289 avocado rootstocks, 17:381-429 berry crop harvesting, 16:255-382 bloom delay, 15:97-144 blueberry developmental physiology, 13:339-405 blueberry harvesting, 16:257-282 blueberry nutrition, 10:183-227 bramble harvesting, 16:282-298 cactus, 18:302-309 carbohydrate reserves, 10:403-430 CA storage, 1:301-336 CA storage diseases, 3:412-461 cherry origin, 19:263-317 chilling injury, 15:145-182 chlorosis, 9:161-165 citrus abscission, 15:145-182 citrus cold hardiness, 7:201-238 citrus flowering, 12:349-408 cranberry harvesting, 16:298-311 currant harvesting, 16:311-327 dormancy release, 7:239-300 Ericaceae nutrition, 10:183-227 fertilization, 1: 104-1 06 fig, industry, 12:409-490 fireblight, 11:423-474 flowering, 12:223-264 foliar nutrition, 6:287-355 frost control, 11:45-109 grape flower anatomy and morphology, 13:315-337 grape harvesting, 16:327-348 grape nitrogen metabolism, 14:407-452

grape purning, 16:235-254, 336-340

grape root, 5:127-168 grape seedlessness, 11:164-176 grapevine pruning, 16:235-254, 336-340

honey bee pollination, 9:244-250, 254-256

jojoba, 17:233-266

CUMULATIVE SUBJECT INDEX

in vitro culture, 7:157-200; 9:273-349 kiwifruit, 6:1-64; 12:307-347 longan, 16:143-196 lychee, 16:143-196

muscadine grape breeding, 14:357-405

navel orange, 8:129-179 nectarine postharvest, 11 :413452

nondestructive postharvest quality evaluation 20:1-120 nutritional ranges, 2:143-164 orange, navel, 8:129-179 orchard floor management, 9:377-430

peach origin, 17:331-379 peach postharvest, 11:413-452 pear fruit disorders, 11:357-411 pear maturity indices, 13:407432

pecan flowering, 8:217-255 photosynthesis, 11 :111-15 7 Phytophthora control, 17:299-330 pruning, 8:339-380 rambutan, 16:143-196 raspberry, 11:185-228 roots, 2:453-457 sapindaceous fruits, 16:143-196 short life and replant problem, 2:1-116

strawberry fruit growth, 17:267-297

strawberry harvesting, 16:348-365 summer pruning, 9:351-375 Vaccinium nutrition, 10:183-227 water status, 7:301-344 Fungi: fig, 12:451-474 mushroom, 6:85-118 mycorrhiza, 3:172-213; 10:211-212

pathogens in postharvest storage, 3:412-461

truffle cultivation, 16:71-107 Fungicide, and apple fruit set, 1:416

323

G

Garlic, CA storage, 1:375 Genetics and breeding: aroids (edible), 8:72-75; 12:169 aroids (ornamental), 10:18-25 bean, bacterial resistance, 3:28-58 bloom delay in fruits, 15:98-107 bulbs, flowering, 18:119-123 cassava, 12:164 chestnut blight resistance, 8:313-321

citrus cold hardiness, 7:221-223 embryogenesis, 1:23 fig, 12:432-433 fire blight resistance, 1:435-436 flowering, 15:287-290, 303-305, 306-309,314-315

flower longevity, 1:208-209 ginseng, 9:197-198 in vitro techniques, 9:318-324; 18:119-123

lettuce, 2:185-187 muscadine grapes, 14:357-405 mushroom, 6:100-111 navel orange, 8:150-156 nitrogen nutrition, 2:410-411 plant regeneration, 3:278-283 pollution insensitivity, 8:18-19 potato tuberization, 14:121-124 rhododendron, 12:54-59 sweet potato, 12:175 tomato parthenocarpy, 6:69-70 tomato ripening, 13:77-98 tree short life, 2:66-70 Vigna, 2:311-394 woody legume tissue and cell culture, 14:311-314 yam (Dioscorea), 12:183 Genetic variation: alternate bearing, 4:146-150 photoperiodic response, 4:82 pollution injury, 8:16-19 ternperature-photoperiod interaction, 17:73-123 Geophyte, see Bulb, tuber

CUMULATIVE SUBJECT INDEX

324

Geranium, fertilization, 5:355-357 Germination, seed, 2:117-141, 173-174

Germplasm preservation: cryopreservation, 6:357-372 in vitro, 5:261-264; 9:324-325 Gibberellin: abscission, citrus, 15:166-167 bloom delay, 15:111-114 citrus, abscission, 15:166-167 cold hardiness, 11:63 dormancy, 7:270-271 floral promoter, 4:114 flowering, 15:219-293, 315-318 genetic regulation, 16:15 grape root, 5:150-151 mechanical stress, 17:19-20 Ginseng, 9:187-236 Girdling, 4:251-252 Glucosinolates, 19:99-215 Graft and grafting: incompatibility, 15:183-232 phase change, 7:136-137, 141-142 rose, 9:56-57 Grape: CA storage, 1:308 chlorosis, 9:165-166 flower anatomy and morphology, 13:315-337

harvesting, 16:327-348 muscadine breeding, 14:357-405 nitrogen metabolism, 14:407-452 pollen morphology, 13:331-332 pruning, 16:235-254, 336-340 root, 5:127-168 seedlessness, 11:159-187 sex determination, 13:329-331 Gravitropism, 15:233-278 Greenhouse and greenhouse crops: carbon dioxide, 7:357-360, 544-545

energy efficiency, 1:141-171; 9:1-52

growth substances, 7:399-481 nutrition and fertilization, 5:317-403

pest management, 13:1-66 Growth regulators, see Growth substances Growth substances, 2:60-66. See also Abscisic acid; Auxin; Cytokinins; Ethylene; Gibberellin abscission, citrus, 15:157-176 apple bioregulation, 10:309-401 apple dwarfing, 3:315-375 apple fruit set, 1:417 apple thinning, 1:270-300 aroids, ornamental, 10:14-18 avocado fruit development, 10:229-243

bloom delay, 15:107-119 CA storage in vegetables, 1:346-348

cell cultures, 3:214-314 chilling injury, 15:77-83 citrus abscission, 15:157-176 cold hardiness 7:223-225; 11:58-66

dormancy, 7:270-279 embryogenesis, 1:41-43; 2:277-281

floriculture, 7:399-481 flower induction, 4:190-195 flowering, 15:290-296 flower storage, 10:46-51 genetic regulation, 16:1-32 ginseng, 9:226 grape seedlessness, 11:177-180 in vitro flowering, 4:112-115 mechanical stress, 17:16-21 meristem and shoot-tip culture, 5:221-227

navel oranges, 8:146-147 pear bioregulation, 10:309-401 petal senescence, 3:76-78 phase change, 7:137-138, 142-143 raspberry, 11:196-197 regulation, 11:1-14 rose, 9:53-73 seedlessness in grape, 11:177-180 triazole, 10:63-105

CUMULATIVE SUBJECT INDEX

H

Halo blight of beans, 3:44-45 Hardiness, 4:250-251 Harvest: flower stage, 1:211-212 index, 7:72-74 lettuce, 2:176-181 mechanical of berry crops, 16:255-382

Hazelnut, see Filbert Heliconia, 14:1-55 Herbaceous plants, subzero stress, 6:373-417

Herbicide-resistant crops, 15:371-412

Histochemistry: flower induction, 4:177-179 fruit abscission, 1:172-203 Histology, flower induction, 4:179-184. See also Anatomy and morphology Honey bee, 9:237-272 Horseradish, CA storage, 1:368 Hydrolases, 5:169-219 Hydroponic culture, 5:1-44; 7:483-558

Hypovirulence, in Endothia parasitica, 8:299-310

325

tree short life, 2:52 tulip, 5:63, 92 Integrated pest management, greenhouse crops, 13:1-66 In vitro: abscission, 15:156-157 apple propagation, 10:325-326 artemisia, 19:342-345 aroids, ornamental, 10:13-14 bulbs, flowering, 18:87-169 cassava propagation, 13:121-123 cellular salinity tolerance, 16:33-69

cold acclimation, 6:382 cryopreservation, 6:357-372 embryogenesis, 1:1-78; 2:268-310; 7:157-200; 10:153-181

environmental control, 17:123-170

flowering, 4:106-127 flowering bulbs, 18:87-169 pear propagation, 10:325-326 phase change, 7:144-145 propagation, 3:214-314; 5:221-277; 7:157-200; 9:57-58, 273-349; 17:125-172

thin cell layer morphogenesis, 14:239-264

woody legume culture, 14:265-332

I

Ice, formation and spread in tissues, 13:215-255

Ice-nucleating bacteria, 7:210-212; 13:230-235

Industrial crops, cactus, 18:309-312 Insects and mites: aroids, 8:65-66 avocado pollination, 8:275-277 fig, 12:442-447 hydroponic crops, 7:530-534 integrated pest management, 13:1-66

lettuce, 2:197-198 ornamental aroids, 10:18

Iron: deficiency chlorosis, 9:133-186 deficiency and toxicity symptoms in fruits and nuts, 2:150 Ericaceae nutrition, 10:193-195 foliar application, 6:330 nutrition, 5:324-325 pine bark media, 9:123 Irrigation: drip or trickle, 4:1-48 frost control, 11:76-82 fruit trees, 7:331-332 grape root growth, 5:140-141 lettuce industry, 2:175 navel orange, 8:161-162 root growth, 2:464-465

CUMULATIVE SUBJECT INDEX

326

J Jojoba, 17:233-266 Juvenility, 4:111-112 pecan, 8:245-247 tulip, 5:62-63 woody plants, 7:109-155 K

Kale, fluid drilling of seed, 3:21 Kiwifruit: botany, 6:1-64 vine growth, 12:307-347 L

Lamps, for plant growth, 2:514-531 Leaves: apple morphology, 12:283-288 flower induction, 4:188-189 Leek: CA storage, 1:375 fertilization, 1:118 Leguminosae, in vitro, 5:227-229; 14:265-332 Lemon, rootstock, 1:244-246. See also Citrus Lettuce: CA storage, 1:369-371 fertilization, 1:118 fluid drilling of seed, 3:14-17 industry, 2:164-207 tipburn, 4:49-65 Light: fertilization, greenhouse crops, 5:330-331 flowering, 15:282-287, 310-312 fruit set, 1:412-413 lamps, 2:514-531 nitrogen nutrition, 2:406-407 orchards, 2:208-267 ornamental aroids, 10:4-6 photoperiod,4:66-105 photosynthesis, 11:117-121 plant growth, 2:491-537

tolerance, 18:215-246 Longan, see Sapindaceous fruits Lychee, see Sapindaceous fruits M

Magnesium: container growing, 9:84-85 deficiency and toxicity symptoms in fruits and nuts, 2:148 Ericaceae nutrition, 10:196-198 foliar application, 6:331 nutrition, 5:323 pine bark media, 9:117-119 Magnetic resonance imaging, 20:77-86, 225-266 Male sterility, temperaturephotoperiod induction, 17:103-106 Mandarin, rootstock, 1:250-252 Manganese: deficiency and toxicity symptoms in fruits and nuts, 2:150-151 Ericaceae nutrition, 10:189-193 foliar application, 6:331 nutrition, 5:235-326 pine bark media, 9:123-124 Mango: alternate bearing, 4:145-146 asexual embryogenesis, 7:171-173 CA storage, 1:313 in vitro culture, 7:171-173 Mechanical harvest, berry crops, 16:255-382 Mechanical stress regulation, 17:1-42 Media: fertilization, greenhouse crops, 5:333 pine bark, 9:103-131 Medicinal crops: artemisia, 19:319-371 poppy, 19:373-408 Meristem culture, 5:221-277 Metabolism: flower, 1:219-223

CUMULATIVE SUBJECT INDEX

nitrogen in citrus, 8:181-215 seed,2:117-141 Micronutrients: container growing, 9:85-87 pine bark media, 9:119-124 Micropropagation, see In vitro; Propagation bulbs, flowering, 18:89-113 environmental control, 17:125-172 nuts, 9:273-349 rose, 9:57-58 temperate fruits, 9:273-349 tropical fruits and palms, 7:157-200 Microtus, see Vole Moisture, and seed storage, 2:125-132 Molybdenum nutrition, 5:328-329 Monocot, in vitro, 5:253-257 Monstera, see Aroids, ornamental Morphology: navel orange, 8:132-133 orchid, 5:283-286 pecan flowering, 8:217-243 Moth bean, genetics, 2:373-374 Mung bean, genetics, 2:348-364 Mushroom: CA storage, 1:371-372 cultivation, 19:59-97 spawn, 6:85-118 Muskmelon, fertilization, 1:118-119 Mycoplasma-like organisms, tree short life, 2:50-51 Mycorrhizae: container growing, 9:93 Ericaceae, 10:211-212 fungi,3:172-213 grape root, 5:145-146 N Navel orange, 8:129-179 Nectarine: bloom delay, 15:105-106 CA storage, 1:309-310

327

postharvest physiology, 11:413-452 Nematodes: aroids, 8:66 fig, 12:475-477 lettuce, 2:197-198 tree short life, 2:49-50 NFT (nutrient film technique), 5:1-44 Nitrogen: CA storage, 8:116-117 container growing, 9:80-82 deficiency and toxicity symptoms in fruits and nuts, 2:146 in embryogenesis, 2:273-275 Ericaceae nutrition, 10:198-202 fixation in woody legumes, 14:322-323 foliar application, 6:332 metabolism in apple, 4:204-246 metabolism in citrus, 8:181-215 metabolism in grapevine, 14:407-452 nutrition, 2:395, 423; 5:319-320 pine bark media, 9:108-112 trickle irrigation, 4:29-30 Nondestructive quality evaluation of fruits and vegetables, 20:1-120 Nursery crops: fertilization, 1:106-112 nutrition, 9:75-101 Nut crops: almond postharvest technology and utilization, 20:267-311 chestnut blight, 8:291-336 fertilization, 1:106 honey bee pollination, 9:250-251 in vitro culture, 9:273-349 nutritional ranges, 2:143-164 pistachio culture, 3:376-396 Nutrient: concentration in fruit and nut crops, 2:154-162 film technique, 5:1-44 foliar-applied, 6:287-355

CUMULATIVE SUBJECT INDEX

328

Nutrient (cont'd) media, for asexual embryogenesis, 2:273-281

media, for organogenesis, 3:214-314

plant and tissue analysis, 7:30-56 solutions, 7:524-530 uptake, in trickle irrigation, 4:30-31

Nutrition (human): aroids, 8:79-84 CA storage, 8:101-127 Nutrition (plant): air pollution, 8:22-23, 26 blueberry, 10:183-227 calcifuge, 10:183-227 cold hardiness, 3:144-171 container nursery crops, 9:75-101 embryogenesis, 1:40-41 Ericaceae, 10:183-227 fire blight, 1:438-441 foliar, 6:287-355 fruit and nut crops, 2:143-164 ginseng, 9:209-211 greenhouse crops, 5:317-403 kiwifruit, 12:325-332 mycorrhizal fungi, 3:185-191 navel orange, 8:162-166 nitrogen in apple, 4:204-246 nutrient film techniques, 5:18-21,

Opium poppy, 19:373-408 Orange, see Citrus alternate bearing, 4:143-144 sour, rootstock, 1:242-244 sweet, rootstock, 1:252-253 trifoliate, rootstock, 1:247-250 Orchard and orchard systems: floor management, 9:377-430 light, 2:208-267 root growth, 2:469-470 water, 7:301-344 Orchid: fertilization, 5:357-358 pollination regulation of flower development, 19:28-38 physiology, 5:279-315 Organogenesis, 3:214-314. See also In vitro; Tissue Ornamental plants: chlorosis, 9:168-169 fertilization, 1:98-104, 106-116 flowering bulb roots, 14:57-88 flowering bulbs in vitro, 18:87-169

foliage acclimatization, 6:119-154 heliconia, 14:1-55 orchid pollination regulation, 19:28-38

poppy, 19:373-408 protea leaf blackening, 17:173-

31-53

ornamental aroids, 10:7-14 pine bark media, 9:103-131 raspberry, 11:194-195 slow-release fertilizers, 1:79-139

o Oil palm: asexual embryogenesis, 7:187-188 in vitro culture, 7:187-188 Okra, CA storage, 1:372-373 Olive, alternate bearing, 4:140-141 Onion: CA storage, 1:373-375 fluid drilling of seed, 3:17-18

201

rhododendron, 12:1-42 p

Paclobutrazol, see Triazole Papaya: asexual embryogenesis, 7:176-177 CA storage, 1:314 in vitro culture, 7:175-178 Parsley: CA storage, 1:375 drilling of seed, 3:13-14 Parsnip, fluid drilling of seed, 3:13-14

Parthenocarpy, tomato, 6:65-84

329

CUMULATIVE SUBJECT INDEX

Passion fruit, in vitro culture, 7:180-181

Pathogen elimination, in vitro, 5:257-261

Peach: bloom delay, 15:105-106 CA storage, 1:309-310 origin, 17:333-379 postharvest physiology, 11:413-452

short life, 2:4 summer pruning, 9:351-375 wooliness, 20:198-199 Peach palm (Pejibaye), in vitro culture, 7:187-188 Pear: bioregulation, 10:309-401 bloom delay, 15:106-107 CA storage, 1:306-308 decline, 2:11 fire blight control, 1:423-474 fruit disorders, 11:357-411 in vitro, 9:321 maturity indices, 13:407-432 root distribution, 2:456 short life, 2:6 Pecan: alternate bearing, 4:139-140 fertilization, 1:106 flowering, 8:217-255 in vitro culture, 9:314-315 Pejibaye, in vitro culture, 7:189 Pepper (Capsicum): CA storage, 1:375-376 fertilization, 1:119 fluid drilling in seed, 3:20 Persimmon: CA storage, 1:314 quality, 4:259 Pest control: aroids (edible), 12:168-169 aroids (ornamental), 10:18 cassava, 12:163-164 cowpea, 12:210-213 fig, 12:442-477 fire blight, 1:423-474

ginseng, 9:227-229 greenhouse management, 13:1-66 hydroponics, 7:530-534 sweet potato, 12:173-175 vertebrate, 6:253-285 yam (Dioscorea), 12:181-183 Petal senescence, 11:15-43 pH: container growing, 9:87-88 fertilization greenhouse crops, 5:332-333

pine bark media, 9:114-117 soil testing, 7:8-12, 19-23 Phase change, 7:109-155 Phenology: apple, 11:231-237 raspberry, 11:186-190 Philodendron, see Aroids, ornamental Phosphonates, Phytophthora control, 17:299-330 Phosphorus: container growing, 9:82-84 deficiency and toxicity symptoms in fruits and nuts, 2:146-147 nutrition, 5:320-321 pine bark media, 9:112-113 trickle irrigation, 4:30 Photoautotrophic micropropagation, 17:125-172

Photoperiod, 4:66-105,116-117; 17:73-123

flowering, 15:282-284, 310-312 Photosynthesis: cassava, 13:112-114 efficiency, 7:71-72; 10:378 fruit crops, 11:111-157 ginseng, 9:223-226 light, 2:237-238 Physiology, see Postharvest physiology bitter pit, 11:289-355 blueberry development, 13:339-405

cactus reproductive biology, 18:321-346

330

Physiology (cont'd) calcium, 10:107-152 carbohydrate metabolism, 7:69-108 cassava, 13:105-129 citrus cold hardiness, 7:201-238 conditioning 13:131-181 cut flower, 1:204-236; 3:59-143; 10:35-62 desiccation tolerance, 18:171-213 disease resistance, 18:247-289 dormancy, 7:239-300 embryogenesis, 1:21-23; 2:268-310 flower development, 19:1-58 flowering, 4:106-127 fruit ripening, 13:67-103 fruit softening, 10:107-152 ginseng, 9:211-213 glucosinolates, 19:99-215 heliconia, 14:5-13 juvenility, 7:109-155 light tolerance, 18:215-246 male sterility, 17:103-106 mechanical stress, 17:1-42 nitrogen metabolism in grapevine, 14:407-452 nutritional quality and CA storage, 8:118-120 orchid, 5:279-315 petal senescence, 11:15-43 photoperiodism, 17:73-123 pollution injury, 8:12-16 polyamines, 14:333-356 potato tuberization, 14:89-188 pruning, 8:339-380 raspberry, 11:190-199 regulation, 11:1-14 roots of flowering bulbs, 14:57-88 root pruning, 6:158-171 rose, 9:3-53 salinity hormone action, 16:1-32 salinity tolerance, 16:33-69 seed,2:117-141 seed priming, 16:109-141 subzero stress, 6:373-417

CUMULATIVE SUBJECT INDEX

summer pruning, 9:351-375 thin cell layer morphogenesis, 14:239-264 tomato fruit ripening, 13:67-103 tomato parthenocarpy, 6:71-74 triazole, 10:63-105 tulip, 5:45-125 vernalization, 17:73-123 volatiles, 17:43-72 watercore, 6:189-251 water relations cut flowers, 18:1-85 Phytohormones, see Growth substances Phytophthora control, 17:299-330 Phytotoxins, 2:53-56 Pigmentation: flower, 1:216-219 rose, 9:64-65 Pinching, by chemicals, 7:453-461 Pineapple: CA storage, 1:314 in vitro culture, 7:181-182 Pine bark, potting media, 9:103-131 Pistachio: alternate bearing, 4:137-139 culture, 3:376-393 in vitro culture, 9:315 Plantain, in vitro culture, 7:178-180 Plant protection, short life, 2:79-84 Plum, CA storage, 1:309 Poinsettia, fertilization, 1:103-104; 5:358-360 Pollen, desiccation tolerance, 18:195 Pollination: apple, 1:402-404 avocado, 8:272-283 cactus, 18:331-335 embryogenesis, 1:21-22 fig, 12:426-429 flower regulation, 19:1-58 fruit crops, 12:223-264 fruit set, 4:153-154 ginseng, 9:201-202 grape, 13:331-332 heliconia, 14:13-15

CUMULATNE SUBJECT INDEX

honey bee, 9:237-272 kiwifruit, 6:32-35 navel orange, 8:145-146 orchid, 5:300-302 petal senescence, 11:33-35 protection, 7:463-464 rhododendron, 12:1-67 Pollution, 8:1-42 Polyamines, 14:333-356 chilling injury, 15:80 Polygalacturonase, 13:67-103 Postharvest physiology: almond, 20:267-311 apple bitter pit, 11:289-355 apple maturity indices, 13:407-432 aroids, 8:84-86 asparagus, 12:69-155 CA storage and quality, 8:101-127 cut flower, 1:204-236; 3:59-143; 10:35-62 foliage plants, 6:119-154 fruit, 1:301-336 fruit softening, 10:107-152 lettuce, 2:181-185 low-temperature sweetening, 17:203-231 navel orange, 8:166-172 nectarine, 11:413-452 nondestructive quality evaluation, 20:1-120 pathogens, 3:412-461 peach,11:413-452 pear disorders, 11:357-411 pear maturity indices, 13:407-432 petal senescence, 11:15-43 protea leaf blackening, 17:173201 quality evaluation, 20:1-120 seed,2:117-141 texture in fresh fruit, 20:121-244 tomato fruit ripening, 13:67-103 vegetables, 1:337-394 watercore, 6:189-251; 11:385-387 Potassium: container growing, 9:84

331

deficiency and toxicity symptoms in fruits and nuts, 2:147-148 foliar application, 6:331-332 nutrition, 5:321-322 pine bark media, 9:113-114 trickle irrigation, 4:29 Potato: CA storage, 1:376-378 fertilization, 1:120-121 low temperature sweetening, 17:203-231 tuberization, 14:89-198 Propagation, see In vitro apple, 10:324-326; 12:288-295 aroids, ornamental, 10:12-13 cassava, 13:120-123 floricultural crops, 7:461-462 ginseng, 9:206-209 orchid,5:291-297 pear, 10:324-326 rose, 9:54-58 tropical fruit, palms 7:157-200 woody legumes in vitro, 14:265-332 Protea, leaf blackening, 17:173-201 Protected crops, carbon dioxide, 7:345-398 Protoplast culture, woody species, 10:173-201 Pruning, 4:161; 8:339-380 apple, 9:351-375 apple training, 1:414 chemical, 7:453-461 cold hardiness, 11:56 fire blight, 1:441-442 grapevines, 16:235-254 light interception, 2:250-251 peach,9:351-375 phase change, 7:143-144 root, 6:155-188 Prunus, see Almond; Cherry; Nectarine; Peach; Plum in vitro, 5:243-244; 9:322 root distribution, 2:456

Pseudomonas: phaseoIicola, 3:32-33, 39, 44-45

CUMULATIVE SUBJECT INDEX

332

Pseudomonas (cont'd) solanacearum, 3:33 syringae, 3:33,40; 7:210-212

Q Quality evaluation: fruits and vegetables, 20:1-120, 121-224

nondestructive, 20:1-120 texture in fresh fruit, 20:121-224 R

Rabbit, 6:275-276 Radish, fertilization, 1:121 Rambutan, see Sapindaceous fruits Raspberry: harvesting, 16:282-298 productivity, 11:185-228 Rejuvenation: rose, 9:59-60 woody plants, 7:109-155 Replant problem, deciduous fruit trees, 2:1-116 Respiration: asparagus postharvest, 12:72-77 fruit in CA storage, 1:315-316 kiwifruit, 6:47-48 vegetables in CA storage, 1:341-346 Rhizobium, 3:34,41 Rhododendron, 12:1-67 Rice bean, genetics, 2:375-376

Root: apple, 12:269-272 cactus, 18:297-298 diseases, 5:29-31 environment, nutrient film technique, 5:13-26 Ericaceae, 10:202-209 grape, 5:127-168 kiwifruit, 12:310-313 physiology of bulbs, 14:57-88 pruning, 6:155-188

raspberry, 11:190 rose, 9:57 tree crops, 2:424-490 Root and tuber crops: aroids, 8:43-99; 12:166-170 cassava, 12:158-166 low-temperature sweetening, 17:203-231

minor crops, 12:184-188 potato tuberization, 14:89-188 sweet potato, 12:170-176 yam (Dioscorea), 12:177-184 Rootstocks: alternate bearing, 4:148 apple, 1:405-407; 12:295-297 avocado, 17:381-429 citrus, 1:237-269 cold hardiness, 11:57-58 fire blight, 1:432-435 light interception, 2:249-250 navel orange, 8:156-161 root systems, 2:471-474 stress, 4:253-254 tree short life, 2:70-75 Rosaceae, in vitro, 5:239-248 Rose: fertilization, 1:104; 5:361-363 growth substances, 9:3-53 in vitro, 5:244-248

s Salinity: air pollution, 8:25-26 soils, 4:22-27 tolerance, 16:33-69 Sapindaceous fruits, 16:143-196 Scoring, and fruit set, 1:416-417 Secondary metabolites, woody legumes, 14:314-322 Seed: abortion, 1:293-294 apple anatomy and morphology, 10:285-286

conditioning, 13:131-181 desiccation tolerance, 18:196-203

CUMULATIVE SUBJECT INDEX

environmental influences on size and composition, 13:183-213 flower induction, 4:190-195 fluid drilling, 3:1-58 grape seedlessness, 11:159-184 kiwifruit, 6:48-50 lettuce, 2:166-174 priming, 16:109-141 rose propagation, 9:54-55 vegetable, 3:1-58 viability and storage, 2:117-141 Senescence: cut flower, 1:204-236; 3:59-143; 10:35-62; 18:1-85 petal, 11:15-43 pollination-induced, 19:4-25 rose, 9:65-66 whole plant, 15:335-370 Sensory quality, CA storage, 8:101-127 Shoot-tip culture, 5:221-277. See also Micropropagation Short life problem, fruit crops, 2:1-116 Small fruit, CA storage, 1:308 Snapdragon fertilization, 5:363364 Sodium, deficiency and toxicity symptoms in fruits and nuts, 2:153-154 Soil: grape root growth, 5:141-144 management and root growth, 2:465-469 orchard floor management, 9:377-430 plant relations, trickle irrigation, 4:18-21 stress, 4:151-152 testing, 7:1-68; 9:88-90 Soilless culture, 5:1-44 Solanaceae, in vitro, 5:229-232 Somatic embryogenesis, see Asexual embryogenesis Spathiphyllum, see Aroids, ornamental

333

Stern, apple morphology, 12:272-283 Storage, see Postharvest physiology, Controlled-atmosphere (CA) storage cut flower, 3:96-100; 10:35-62 rose plants, 9:58-59 seed,2:117-141 Strawberry: fertilization, 1:106 fruit growth and ripening, 17:267-297 harvesting, 16:348-365 in vitro, 5:239-241 Stress: benefits of, 4:247-271 climatic, 4:150-151 flooding, 13:257-313 mechanical,17:1-42 petal, 11:32-33 plant, 2:34-37 protection, 7:463-466 subzero temperature, 6:373-417 Sugar beet, fluid drilling of seed, 3:18-19 Sugar, see Carbohydrate allocation, 7:74-94 flowering, 4:114 Sulfur: deficiency and toxicity symptoms in fruits and nuts, 2:154 nutrition, 5:323-324 Sweet potato: culture, 12:170-176 fertilization, 1:121 Symptoms, deficiency and toxicity symptoms in fruits and nuts, 2:145-154 Syngonium, see Aroids, ornamental T

Taro, see Aroids, edible Temperature: apple fruit set, 1:408-411 bloom delay, 15:119-128

334

CUMULATIVE SUBJECT INDEX

Temperature (cont'd) CA storage of vegetables, 1:340-341

chilling injury, 15:67-74 cut flower storage, 10:40-43 cryopreservation, 6:357-372 fertilization, greenhouse crops, 5:331-332

fire blight forecasting, 1:456-459 flowering, 15:284-287, 312-313 interaction with photoperiod, 4:80-81

low temperature sweetening, 17:203-231

navel orange, 8:142 nutrient film technique, 5:21-24 photoperiod interaction, 17:73-123

photosynthesis, 11:121-124 plant growth, 2:36-37 seed storage, 2:132-133 subzero stress, 6:373-417 Texture in fresh fruit, 20:121-224 Thinning, apple, 1:270-300 Tipburn, in lettuce, 4:49-65 Tissue, see In vitro culture, 1:1-78; 2:268-310; 3:214-314; 4:106-127; 5:221-277; 6:357-372; 7:157-200; 8:75-78; 9:273-349; 10:153-181 dwarfing, 3:347-348 nutrient analysis, 7:52-56; 9:90

Tomato: CA storage, 1:380-386 chilling injury, 20:199 fertilization, 1:121-123 fluid drilling of seed, 3:19-20 fruit ripening, 13:67-103 galacturonase, 13:67-103 parthenocarpy, 6:65-84 Toxicity symptoms in fruit and nut crops, 2:145-154 Transport, cut flowers, 3:100-104 Tree decline, 2:1-116 Triazole, 10:63-105

chilling injury, 15:79-80 Trickle irrigation, 4:1-48 Truffle cultivation, 16:71-107 Tuber, potato, 14:89-188 Tuber and root crops, see Root and tuber crops Tulip, see Bulb crops fertilization, 5:364-366 in vitro, 18:144-145 physiology, 5:45-125 Tunnel (cloche), 7:356-357 Turfgrass, fertilization, 1:112-117 Turnip, fertilization, 1:123-124 Turnip Mosaic Virus, 14:199-238

u Urd bean, genetics, 2:364-373 Urea, foliar application, 6:332 V

Vaccinium, 10:185-187. See also Blueberry; Cranberry Vase solutions, 3:82-95; 10:46-51 Vegetable crops: aroids, 8:43-99; 12:166-170 asparagus postharvest, 12:69-155 cactus, 18:300-302 cassava, 12:158-166; 13:105-129 CA storage, 1:337-394 CA storage diseases, 3:412-461 CA storage and quality, 8:101-127 chilling injury, 15:63-95 fertilization, 1:117-124 fluid drilling of seeds, 3:1-58 greenhouse pest management, 13:1-66

honey bee pollination, 9:251-254 hydroponics, 7:483-558 low-temperature sweetening, 17:203-231

minor root and tubers, 12:184-188 mushroom cultivation, 19:59-97 mushroom spawn, 6:85-118

CUMULATIVE SUBJECT INDEX

nondestructive postharvest quality evaluation, 20:1-120 potato tuberization, 14:89-188 seed conditioing, 13:131-181 seed priming, 16:109-141 sweet potato, 12:170-176 tomato fruit ripening, 13:67-103 tomato parthenocarpy, 6:65-84 truffle cultivation, 16:71-107 yam (Dioscorea), 12:177-184 Vegetative tissue, desiccation tolerance, 18:176-195 Vernalization, 4:117; 15:284-287; 17:73-123

Vertebrate pests, 6:253-285 Vigna, see Cowpea genetics, 2:311-394 U.S. production, 12:197-222 Virus: benefits in horticulture, 3: 394-411 elimination, 7:157-200; 9:318; 18:113-123

335

photosynthesis, 11:124-131 trickle irrigation, 4:1-48 Watercore, 6:189-251 pear, 11:385-387 Watermelon, fertilization, 1:124 Weed control, ginseng, 9:228-229 Weeds: lettuce research, 2: 198 virus, 3:403 Woodchuck, 6:276-277 Woody species, somatic embryogenesis, 10:153-181

x Xanthomonas phaseoli, 3:29-32,41, 45-46

Xanthophyll cycle, 18:226-239 Xanthosoma, 8:45-46, 56-57. See also Aroids y

fig, 12:474-475 tree short life, 2:50-51 turnip mosaic, 14:199-238 Volatiles, 17:43-72 Vole, 6:254-274

Yam (Dioscorea), 12:177-184 Yield: determinants, 7:70-74; 97-99 limiting factors, 15:413-452

w

z

Walnut, in vitro culture, 9:312 Water relations: cut flower, 3:61-66; 18:1-85 desiccation tolerance, 18:171-213 fertilization, greenhouse crops,

Zantedeschia, see Aroids, ornamental Zinc: deficiency and toxicity symptoms in fruits and nuts, 2:151 foliar application, 6:332, 336 nutrition, 5:326 pine bark media, 9:124

5:332

fruit trees, 7:301-344 kiwifruit, 12:332-339 light in orchards, 2:248-249

Cumulative Contributor Index (Volumes 1-20) Abbott, J.A., 20:1 Adams III, W.W., 18:215 Aldwinckle, H.S., 1:423; 15:xiii Anderson, J.L., 15:97 Anderson, P.e., 13:257 Andrews, P.K., 15:183 Ashworth, E.N., 13:215 Asokan, M.P., 8:43 Atkinson, D., 2:424 Aung, L.H., 5:45 Bailey, W.G., 9:187 Baird, L.A.M., 1:172 Banks, N.H., 19:217 Barden, J.A., 9:351 Barker, A.V., 2:411 Bass, L.N., 2:117 Becker, J.S., 18:247 Beer, S.V., 1:423 Bennett, A.B., 13:67 Benschop, M., 5:45 Ben-Ya'acov, A., 17:381 Benzioni, A., 17:233 Bewley, J.D., 18:171 Binzel, M.L., 16:33 Blanpied, G.D., 7:xi Bliss, F.A., 16:xiii Borochov, A., 11:15 Bower, J.P., 10:229 Bradley, G.A., 14:xiii Brennan, R, 16:255 Broschat, T.K., 14:1 Brown, S. 15:xiii Buban, T., 4:174 Bukovac, M.J., 11:1 Burke, M.J., 11:xiii

Buwalda, J.G., 12:307 Byers, RE., 6:253 Caldas, L.S., 2:568 Campbell, L.E., 2:524 Cantliffe, D.J., 16:109; 17:43 Carter, G., 20:121 Carter, J.V., 3:144 Cathey, H.M., 2:524 Chambers, RJ., 13:1 Charron, e.S., 17:43 Chin, e.K., 5:221 Cohen, M., 3:394 Collier, G.F., 4:49 Collins, W.L., 7:483 Compton, M.E., 14:239 Conover, e.A., 5:317; 6:119 Coyne, D.P., 3:28 Crane, J.e., 3:376 Criley, RA., 14:1 Crowly, W., 15:1 Cutting, J.G., 10:229 Daie, J., 7:69 Dale, A., 11:185; 16:255 Darnell, RL., 13:339 Davenport, T.L., 8:257; 12:349 Davies, F.S., 8:129 Davies, P.J., 15:335 Davis, T.D., 10:63 DeGrandi-Hoffrnan, G., 9:237 De Hertogh, A.A., 5:45; 14:57; 18:87 Deikman, J., 16:1 DellaPenna, D., 13:67 Demmig-Adams, B., 18:215 Dennis, F.G., Jr., 1:395 337

338

Doud, S.L., 2:1 Duke, 5.0., 15:371 Dunavent, M.G., 9:103 Dyer, W.K, 15:371 Early, J.D., 13:339 Elfving, D.C., 4:1; 11:229 El-Goorani, M.A., 3:412 Esan, KB., 1:1 Evans, D.A., 3:214 Ewing, KK, 14:89 Faust, M., 2:vii, 142; 4:174; 6:287; 14:333; 17:331; 19:263 Fenner, M., 13:183 Fenwick, G.R, 19:99 Ferguson, A.R, 6:1 Ferguson, LB., 11:289 Ferguson, L., 12:409 Ferree, D.e., 6:155 Ferreira, J.F.S., 19:319 Fery, RL., 2:311; 12:157 Fischer, RL., 13:67 Flick, e.K, 3:214 Flore, J.A., 11:111 Forshey, e.G., 11:229 Fujiwara, K., 17:125 Geisler, D., 6:155 Geneve, RL., 14:265 George, W.L., Jr., 6:25 Gerrath, J.M., 13:315 Giovannetti, G., 16:71 Giovannoni, J.J., 13:67 Glenn, G.M., 10:107 Goffinet, M.e., 20:ix Goldschmidt, KK, 4:128 Goldy, RG., 14:357 Goren, R, 15:145 Goszczynska, D.M., 10:35 Grace, S.e., 18:215 Graves, e.J., 5:1 Gray, D., 3:1 Grierson, W., 4:247 Griffen, G.J., 8:291

CUMULATIVE CONTRIBUTOR INDEX

Grodzinski, B., 7:345 Guest, D.I., 17:299 Guiltinan, M.J., 16:1 Hackett, W.P., 7:109 Hallett, I.e., 20:121 Halevy, A.H., 1:204; 3:59 Hammerschmidt, R, 18:247 Hanson, KJ., 16:255 Harker, F.R, 20:121 Heaney, RK., 19:99 Heath, RR, 17:43 Helzer, N.L., 13:1 Hendrix, J.W., 3:172 Henny, RJ., 10:1 Hergert, G.B., 16:255 Hess, F.D., 15:371 Heywood, V., 15:1 Hogue, KJ., 9:377 Holt, J.S., 15:371 Huber, D.J., 5:169 Hutchinson, J.F., 9:273 Isenberg, F.M.R, 1;337 Iwakiri, B.T., 3:376 Jackson, J.K, 2:208 Janick, J., l:ix; 8:xi; 17:xiii; 19:319 Jensen, M.H., 7:483 Jeong, B.R, 17:125 Joiner, J.N., 5:317 Jones, H.G., 7:301 Jones, J.B., Jr., 7:1 Jones, RB., 17:173 Kagan-Zur, V., 16:71 Kang, S.-M., 4:204 Kato, T., 8:181 Kawa, L., 14:57 Kawada, K., 4:247 Kelly, J.F., 10:ix Khan, A.A., 13:131 Kierman, J., 3:172 Kim, K.-W., 18:87 Kinet, J.-M., 15:279

CUMULATIVE CONTRIBUTOR INDEX

King, G.A., 11:413 Kingston, C.M., 13:407 Kliewer, W.M., 14:407 Knight, RJ., 19:xiii Knox, RB., 12:1 Kofranek, A.M., 8:xi Korcak, RF., 9:133; 10:183 Kozai, T., 17:125 Krezdorn, A.H., 1:vii Lakso, A.N., 7:301; 11:111 Lamb, RC, 15:xiii Lang, G.A., 13:339 Larsen, RP., 9:xi Larson, RA., 7:399 Ledbetter, CA., 11:159 Li, P.H., 6:373 Lill, RK, 11:413 Lipton, W.J., 12:69 Litz, RK, 7:157 Lockard, RG., 3:315 Loescher, W.H., 6:198 Lorenz, O.A., 1:79 Lu, R, 20:1 Maraffa, S.B., 2:268 Marangoni, A.G., 17:203 Marini, RP., 9:351 Marlow, G.C, 6:189 Maronek, D.M., 3:172 Martin, G.G., 13:339 Mayak, S., 1:204; 3:59 Maynard, D.N., 1:79 McConchie, R, 17:173 McNicol, RJ., 16:255 Merkle, S.A., 14:265 Michailides, T.J., 12:409 Michelson, K, 17:381 Mika, A., 8:339 Miller, S.S., 10:309 Mills, H.A., 9:103 Mitchell, CA., 17:1 Mizrahi, Y., 18:291, 321 Molnar, J.M., 9:1 Monk, G.J., 9:1

339

Moore, G.A., 7:157 Mor, Y., 9:53 Morris, J.R, 16:255 Mills, H.A., 2:411 Monselise, S.P., 4:128 Murashige, T., 1:1 Murray, S.H., 20:121 Myers, P.N., 17:1 Nadeau, J.A., 19:1 Neilsen, G.H., 9:377 Nerd, A., 18:291, 321 Niemiera, A.X., 9:75 Nobel, P.S., 18:291 O'Donoghue, KM., 11:413 Ogden, RJ., 9:103 O'Hair, S.K., 8:43; 12:157 Oliveira, CM., 10:403 Oliver, M.J., 18:171 O'Neill, S.D., 19:1 Opara, L.U., 19:217 Ormrod, D.P., 8:1 Palser, B.F., 12:1 Parera, CA., 16:109 Pegg, K.G., 17:299 Pellett, H.M., 3:144 Perkins-Veazil, P., 17:267 Ploetz, RC, 13:257 Pokorny, F.A., 9:103 Poole, RT., 5:317; 6:119 Poovaiah, B.W., 10:107 Portas, C.A.M., 19:99 Porter, M.A., 7:345 Possingham, J.V., 16:235 Pratt, C., 10:273; 12:265 Preece, J.K, 14:265 Priestley, CA., 10:403 Proctor, J.T.A., 9:187 Quamme, H., 18:xiii Raese, J.T., 11:357 Ramming, D.W., 11:159

340

CUMULATIVE CONTRIBUTOR INDEX

Reddy, A.S.N., 10:107 Redgwell, RJ., 20:121 Reid, M., 12:xiii, 17:123 Reuveni, M., 16:33 Richards, D., 5:127 Rieger, M., 11:45 Rosa, KA.S., 19:99 Roth-Bejerano, N., 16:71 Roubelakis-Angelakis, K.A., 14:407 Rouse, J.L., 12:1 Royse, D.J., 19:59 Rudnicki, RM., 10:35 Ryder, KJ., 2:164; 3:vii

Stang, KJ., 16:255 Steffens, G.L., 10:63 Stevens, M.A., 4:vii Stroshine, RL., 20:1 Struik, P.c., 14:89 Studman, 19:217 Stutte, G.W., 13:339 Styer, D.J., 5;221 Sunderland, K.D., 13:1 Suranyi, D., 19:263 Swanson, B., 12:xiii Swietlik, D., 6:287 Syvertsen, J.P., 7:301

Sachs, R, 12:xiii Sakai, A., 6:357 Salisbury, F.B., 4:66; 15:233 San Antonio, J.P., 6:85 Sankhla, N., 10:63 Saure, M.C., 7:239 Schaffer, B., 13:257 Schneider, G.W., 3;315 Schuster, M.L., 3:28 Scorza, R, 4:106 Scott, J.W., 6:25 Sedgley, M., 12:223 Seeley, S.S., 15:97 Serrano Marquez, c., 15:183 Sharp, W.R, 2:268; 3:214 Shattuck, V.I., 14:199 Shear, C.B., 2:142 Sheehan, T.J., 5:279 Shirra, M., 20:267 Shorey, H.H., 12:409 Simon, J.K, 19:319 Sklensky, D.K, 15:335 Smith, G.S., 12:307 Smock, RM., 1:301 Sommer, N.F., 3:412 Sondahl, M.R, 2:268 Sopp, P.L, 13:1 Soule, J., 4:247 Sparks, D., 8:217 Splittstoesser, W.K, 6:25; 13:105 Srinivasan, c., 7:157

Tetenyi, P., 19:373 Tibbitts, T.W., 4:49 Timon, B., 17:331 Tindall, H.D., 16:143 Tisserat, B., 1:1 Titus, J.S., 4:204 Trigiano, RN., 14:265 Tunya, G.O., 13:105

c.r,

Upchurch, B.L., 20:1 van Doorn, W.G., 17:173; 18:1 Veilleux, RK, 14:239 Wallace, A., 15:413 Wallace, D.H., 17:73 Wallace, G.A., 15:413 Wang, c.Y., 15:63 Wang, S.Y., 14:333 Wann, S.R, 10:153 Watkins, C.B., 11:289 Watson, G.W., 15:1 Webster, B.D., 1:172; 13:xi Weichmann, J., 8:101 Wetzstein, H.Y., 8:217 Whiley, A.W., 17:299 Whitaker, T.W., 2:164 White, J.W., 1:141 Williams, KG., 12:1 Williams, M.W., 1:270 Wismer, W.V., 17:203

CUMULATIVE CONTRIBUTOR INDEX

Wittwer, S.H., 6:xi Woodson, W.R, 11:15 Wright, RD., 9:75 Wutscher, H.K., 1:237 Yada, RY., 17:203 Yadava, D.L., 2:1 Yahia, KM., 16:197

341

Yan, W., 17:73 Yarborough, D.K, 16:255 Yelenosky, G., 7:201 Zanini, K, 16:71 Zieslin, N., 9:53 Zimmerman, RH., 5:vii; 9:273 Zucconi, F., 11:1