Nondestructive Food Evaluation: Techniques to Anyaluze Properties and Quality (Food Science and Technology)

Nondestructive Food Evaluation

FOOD SCIENCE AND TECHNOLOGY A Series of Monographs, Textbooks, and Reference Books EDITORIAL BOARD

Senior Editors

Owen R. Fennema University of Wisconsin-Madison Marcus Karel Rutgers University (emeritus) Gary W. Sanderson Universal Foods Corporation (retired) Pieter Walstra Wageningen Agricultural University John R. Whitaker University of California-Davis Additives P. MichaelDavidson University of Tennessee-Knoxville Dairy science James L. Steele University of Wisconsin-Madison Flavor chemistry and sensory analysis John Thorngate University of Idaho-Moscow Foodengineering Daryl B. Lund ComellUniversity Health and disease Seppo Salminen University of Turku, Finland Nutrition and nutraceuticals MarkDreher Mead Johnson Nutritionals Processingandpreservation Gustavo W. Barbosa-Chnovas Washington State University-Pullman Safety and toxicology Sanford Miller University of Texas-Austin

1. Flavor Research: Principles and Techniques, R. Teranishi, 1. Hornstein, P. Issenberg, and E. L. Wick 2. Principles of Enzymology for the Food Sciences, John R. Whitaker 3. Low-TemperaturePreservation of Foods and Living Matter, OwenR. Fennema, William D. Powrie, and ElmerH. Marth 4. Principles of Food Science Part I: Food Chemistry, edited by OwenR. Fennema Part It: Physical Methods of Food Preservation, Marcus Karel, Owen R. Fennema, andD a y / 6. Lund 5. Food Emulsions,edited by Stig E. Friberg 6. Nutritional and Safety Aspects of Food Processing,edited by Steven R. Tannenbaum 7. Flavor Research: Recent Advances, edited by R. Temnishi, Robert A. Flath, and Hiroshi Sugisawa 8. Computer-AidedTechniques in FoodTechnology, editedbyIsrael SWUY 9. Handbook of Tropical Foods,edited by Harvey T. Chan I O . Antimicrobials in Foods, edited by Alfred Lany Branen and P. Michael Davidson

11. Food Constituents and Food Residues: Their Chromatographic Determination, edited by James F. Lawrence editedbyLewis D. Stegink 12.Aspartame:PhysiologyandBiochemistry, and L. J. Filer, Jr. 13.HandbookofVitamins:Nutritional,Biochemical,andClinicalAspects, edited by LawrenceJ. Machlin 14. Starch Conversion Technology, edited by G. M. A. van Beynum and J. A. Roels 15.FoodChemistry:SecondEdition,RevisedandExpanded, edited by Owen R. Fennema 16.SensoryEvaluationofFood:StatisticalMethodsandProcedures, Michael O'Mahony 17. Alternative Sweeteners, edited by Lyn O'Brien Nabors and Robert C. Gelardi 18. Citrus Fruits and Their Products: Analysis and Technology, S. V. Ting and Russell L. Rouseff 19. Engineering Properties of Foods,edited by M.A. Rao and S. S. H. Rizvi 20. Umami: A Basic Taste, edited by Yojiro Kawamura and Morley R. Kare 21. Food Biotechnology, edited by Dietrich Knorr 22.FoodTexture:InstrumentalandSensoryMeasurement, editedby Howard R. Moskowitz 23. Seafoods and Fish Oils in Human Health and Disease,John E. Kinsella 24. Postharvest Physiology of Vegetables, edited by J. Weichmann 25. Handbook of Dietary Fiber: An Applied Approach,Mark L. Dreher 26. Food Toxicology, PartsA and B, Jose M. Concon 27. Modem Carbohydrate Chemistry, Roger W. Binkley 28. Trace Minerals in Foods, edited by Kenneth T. Smith 29. Protein Quality and the Effects of Processing, edited by R. DixonPhillips and John W. Finley 30. Adulteration of Fruit Juice Beverages, edited by Steven Nagy, John A. Attaway, and MarthaE. Rhodes 31. Foodborne Bacterial Pathogens, edited by Michael P. Doyle 32. Legumes: Chemistry, Technology, and Human Nutrition, edited by Ruth H. Manhews 33.IndustrializationofIndigenousFermentedFoods, editedbyKeith H. Steinkraus 34. International Food Regulation Handbook: Policy0 Science 0 Law, edited by RogerD. Middlekauff and Philippe Shubik 35. Food Additives, edited by A. Lany Branen, P. Michael Davidson, and Seppo Salminen 36. Safety of Irradiated Foods, J. F. Diehl 37. Omega3 Fatly Acids in Health and Disease, edited by Robert S. Lees and Marcus Karel 38. FoodEmulsions:SecondEdition,RevisedandExpanded, editedby Kire Larsson and StigE. Friberg 39.Seafood:EffectsofTechnologyonNutrition, George M. Pigonand Barbee W. Tucker 40. Handbook of Vitamins: Second Edition, Revised and Expanded, edited by LawrenceJ. Machlin

41.HandbookofCerealScienceandTechnology, Klaus J. Lorenzand Karel Kulp 42. Food Processing Operations and Scale-up, Kenneth J. Valentas, Leon Levine, andJ. Peter Clark 43. Fish Quality Control by Computer Vision, edited by L. F. Pau and R. Olafsson 44. Volatile Compounds in Foods and Beverages,edited by Henk Maarse 45. Instrumental Methods for Quality Assurance in Foods, edited by Daniel Y. C. Fung and RichardF. Matthews 46. Listeria, Listeriosis, and Food Safety,Elliot T. Ryser and Elmer H. Marth 47. Acesulfame-K, edited by D. G. Mayer andF. H. Kemper 48. Alternative Sweeteners: Second Edition, Revised and Expanded, edited by Lyn O'Brien Nabors and Robert C. Gelardi 49. Food Extrusion Science and Technology, edited by Jozef L. Kokini, ChiTang Ho, and MukundV. Karwe 50. Surimi Technology, edited by Tyre C. Lanier and Chong M. Lee 51. Handbook of Food Engineering,edited by Dennis R. Heldman and Daryl B. Lund 52. Food Analysis by HPLC, edited by Leo M. L. Nollet 53.FattyAcids in FoodsandTheirHealthImplications, editedbyChing Kuang Chow 54. Clostridium botulinum: Ecology and Controlin Foods, edited by Andreas H. W. Hauschild and KarenL. Dodds 55. Cereals in Breadmaking: A Molecular Colloidal Approach,Ann-Charlotte Eliasson andKire Larsson 56. Low-Calorie Foods Handbook,edited by Aaron M. Altschul 57. Antimicrobials in Foods: Second Edition, Revised and Expanded,edited by P. Michael Davidson and Alfred Larry Branen 58. Lactic Acid Bacteria, edited by Seppo Salminen and Atte von Wright 59. Rice Science and Technology,edited by Wayne E. Marshall and James 1. Wadsworth 60.FoodBiosensorAnalysis, editedbyGabrieleWagnerandGeorgeG. Guilbault 61. Principles of Enzymology for the Food Sciences: Second Edition, John R. Whifaker 62. Carbohydrate Polyesters as Fat Substitutes, edited by Casimir C. Akoh and Barry G. Swanson 63. Engineering Properties of Foods: Second Edition, Revised and Expanded, edited by M. A. Rao and S. S. H. Rimi 64. Handbook of Brewing, edited by William A. Hardwick 65.AnalyzingFood for NutritionLabelingandHazardousContaminants, edited by IkeJ. Jeon and William G. lkins 66. Ingredient Interactions: Effects on Food Quality, edited by Anilkumar G. Gaonkar 67.FoodPolysaccharidesandTheirApplications, editedby Alisfair M. Stephen 68. Safety of Irradiated Foods: Second Edition, Revised and Expanded, J. F. Diehl 69. Nutrition Labeling Handbook, edited by Ralph Shapiro

70.Handbookof Fruit ScienceandTechnology:Production, COmpOSitiOn, Storage, and Processing,edited by D. K. Salunkhe andS. S. Kadam 71.FoodAntioxidants:Technological,Toxicological,andHealthPerspectives, edited by D. L. Madhavi, S. S. Deshpande, and D. K. Salunkhe 72. Freezing Effects on Food Quality,edited by Lester E. Jeremiah 73.HandbookofIndigenousFermentedFoods:SecondEdition,Revised and Expanded,edited by KeithH. Steinkraus 74. Carbohydrates in Food, edited by Ann-Charlotte Eliasson 75.BakedGoodsFreshness:Technology,Evaluation,and Inhibition of Staling, edited by RonaldE. Hebeda and HenryF. Zobel 76. Food Chemistry: Third Edition, edited by Owen R. Fennema 77.HandbookofFoodAnalysis:Volumes 1 and 2, edited by Leo M. L. Nollet 78. Computerized Control Systems in the Food Industry, edited by Gauri S. Mittal 79. Techniques for Analyzing Food Aroma,edited by Ray Marsili 80. Food Proteins and Their Applications, edited by Srinivasan Damodaran and Alain Paraf 81. Food Emulsions: Third Edition, Revised and Expanded,edited by Stig E. Friberg and K5re Larsson 82. Nonthermal Preservation of Foods, Gustavo V. Barbosa-Canovas, Usha R. Pothakamury, Enrique Palou, and BarryG. Swanson 83. Milk and Dairy Product Technology, Edgar Spreer 84.AppliedDairyMicrobiology, editedby €/mer H. MarthandJames L. Steele 85.LacticAcidBacteria:MicrobiologyandFunctionalAspects:Second Edition, Revised and Expanded,edited by Seppo Salminen and Atte von Wright 86. Handbook of Vegetable Science and Technology: Production, Composition,Storage,andProcessing, editedby D. K.Salunkheand S. S. Kadam 87. Polysaccharide Association Structures in Food, edited by Reginald H. Walter 88. Food Lipids: Chemistry, Nutrition, and Biotechnology, edited by Casimir C. Akoh and DavidB. Min 89. Spice Science and Technology, Kenji Hirasa and Mitsuo Takemasa 90. Dairy Technology: Principles of Milk Properties and Processes, P. Walstra, T. J. Geurts, A. Noomen, A. Jellema, and M. A.J. S. van Boekel 91. Coloring of Food, Drugs, and Cosmetics, Gisbert Otterstafter 92. Listeria, Listeriosis,andFoodSafety:SecondEdition,Revisedand Expanded, edited by Elliot T. Ryser and Elmer H. Marth 93. Complex Carbohydrates in Foods, edited by Susan Sungsoo Cho, Leon Prosky, and Mark Dreher 94. Handbook of Food Preservation, edited by M. Shafiur Rahman 95. International Food Safety Handbook: Science, International Regulation, and Control, edited by Kees van der Heijden, Maged Younes, Lawrence Fishbein, and Sanford Miller 96.FattyAcids in FoodsandTheirHealthImplications:SecondEdition, Revised and Expanded,edited by Ching Kuang Chow

97. Seafood Enzymes: Utilization and Influence on Postharvest Seafood Quality, edited by Norman F. Haard and BenjaminK. Simpson 98. Safe Handling of Foods, edited by Jeffrey M. Farber and Ewen C. D. Todd 99. Handbookof Cereal Science andTechnology: Second Edition, Revised and Expanded, edited by Karel Kulp andJoseph G. Ponte, Jr. 100. Food Analysis byHPLC: Second Edition, Revised and Expanded, edited by Leo M. L. Nollet 101. Surimi and Surimi Seafood, edited by Jae W. Park 102. Drug Residues in Foods: Pharmacology, Food Safety, and Analysis, Nickos A. Botsoglou and Dimitrios J. Fletouris 103. Seafood and Freshwater Toxins: Pharmacology, Physiology, and Detection, edited by Luis M. Botana 104. Handbook of Nutrition and Diet, Babasaheb B. Desai 105. Nondestructive Food Evaluation: Techniques to Analyze Properties and Quality, edited by Sundaram Gunasekaran 106. Green Tea: Health Benefits and Applications, Yukihiko Hara

Additional Volumes in Preparation Alternative Sweeteners: Third Edition, Revised and Expanded, edited by Lyn O’Brien Nabors Handbook of Dietary Fiber, edited by Susan Sungsoo Cho and Mark Dreher Food Processing Operations Modeling: Design and Analysis, edited by Joseph lrudayaraj Handbook of Microwave Technology for Food Applications, edited by Ashim K. Datta and R. C. Anantheswaran Applied Dairy Microbiology: Second Edition, Revised and Expanded, edited by Elmer H. Marthand James L. Steele Food Additives: Second Edition, Revised and Expanded, edited by John H. Thorngate 11, Seppo Salminen, and Alfred Larry Branen

Nondestructive Food Evaluation Techniques to Analyze Properties and Quality

edited by

Sundaram Gunasekaran University of Wisconsin-Madison Madison, Wisconsin

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Preface

In the food industry, we are inherently limited by our inability to objectively, consistently, and accurately test food quality by our faculties of sight, sound, of developtouch, taste, and smell. Fortunately, however, through many years ment, we have sensors that assist, andin many cases replace, human evaluations. Nonetheless, on-line control of food processes remains a major challenge in designing processes to consistently produce high-quality foods. The recent development of new sensors and measuring techniques has created several new opportuin thisveryimportantaspectoffood nities to assistthefoodindustry manufacturing. Rapid, nondestructive, and on-line food quality evaluations can improve plant productivity and cost-effectiveness. Therefore, it is a very critical issue for the food industry. This book is a comprehensive treatise on most of the nondestructive methods for food quality evaluation and is designed to serve as asinglereferencesourcefortheindustryandacademia.Emphasishasbeen placed on the new and emerging methods and applications. Nondestructive Food Evaluation is an edited volume with contributions from active researchers and experts in their topic areas. The bookis divided into 10 chapters, each focusing on a major nondestructive techique, including optical, magnetic, ultrasonic, mechanical, and biological methods. Each chapter informs the reader of significant advances and offers insights for possible future trends in the nondestructive method. iii

iv

Preface

Optical techniques are presented under four topical headings (Chapters1of electromagnetic spectrum: visible, IR, NIR, and FTIR; computer vision; delayed light and fluorescence; and x-ray tomography. Chapter 5 introduces the basic principles of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI). NMR and MRI are nondestructive techniques that can be used to probe the physical and chemical properties and anatomical structure of biological materials. Therefore, the quality parameters associated with certain physical and chemical properties of foods can be evaluatedby NMR and MRI. Use of NMR and MRI in analysis of water mobility, glass transition in foods is described. process, distributionof water and fat, and internal blemishes Sound waves are transmitted through materials as compressions and rarefactions in their physical structure. Hence, it is often possible to relate the ultrasonic properties of a material to useful information about its macroscopic and microscopic composition. In Chapter 6, the physics of high-frequency sound is introduced, and applications of ultrasonic properties to monitor food quality are described. Mechanical methods of nondestructive food evaluation include low-intensity impact (tapping) and vibration testing and high-pressure air impingement (Chapters 7 and 8). One of the most recent techniques used to evaluate food texture is the small-amplitude oscillatory strain test, popularly known as dynamic 5%) is used to study the material testing. In this test, a very small strain (less than structure-function relationships. Since food structure is the basis for its texture, this method offers the advantageof obtaining fundamental information about the eating quality of foods. A taste panel traditionally measures many subjective food quality factors such as aroma andtaste.Recent developments in providing objective, instrumented evaluations of such subjective factors are presented in Chapter 9, “Biosensors in Food Quality Evaluation.” A good example of such class of sensor is the “electronic nose,” which mimics the human sense of smell. The integration of multiple gas sensors and artificial intelligence has led to a new science of machine olfaction. Biosensors offer the advantage of rapid detection of bioactive componentsthatcanbemeasured and controlledtoensurefoodquality and safety. In food quality analysis and control, the data collected often are subjective and ill-conditioned. To infer useful information out of such data sets requires methods other than those traditionally used. Chapter 10 describes some of these data analysis procedures, such as neural networks, fuzzy logic, pattern recognition, and statistical process control. I would like to thankallthe contributors and the Marcel Dekker, Inc., production staff for their enthusiastic and timely support in bringing this project to fruition.

4) to cover the wide span

Sundaram Gunasekaran

Contents

Preface Contributors

1.

Optical Methods: Visible, NIR, and FI'IR Spectroscopy Sundaram Gunasekaran and Joseph Irudayaraj

2. Computer Vision Suranjan Panigrahi and Sundaram Gunasekaran 3.

Delayed Light Emission and Fluorescence Sundaram Gunasekaran and Suranjan Panigrahi

4.

X-Ray Imaging for Classifying Food Products Based on Internal Defects Ernest W. Tollner and Muhammad Afzal Shahin

5 . Nuclear Magnetic Resonance Techniques and Their Application in Food Quality Analysis R. Roger R u m and Paul L. Chen

...

111

vii

1

39

99

137

165 V

vi

Contents

6. Ultrasonics John Coupland and David Julian McClernents

217

7. Firmness-MeasurementMethods Yen-Con Hung, Stan Prussia, and Gabriel 0. I . Ezeike

243

8. LinearViscoelasticMethods M . Mehrnet Ak and Sundararn Gunasekaran

287

9.Biosensors in FoodQualityEvaluation Sudhir S. Deshpande

335

10. New Techniques for Food Quality Data Analysis and Control Jinglu Tan

319

Index

417

Contributors

M. Mehmet Ak Department of Food Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey Paul L. Chen Department of Biosystems and Agricultural Engineering, University of Minnesota, St. Paul, Minnesota John Coupland Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania Sudhir S. Deshpande Signature Bioscience, Inc., Burlingame, California Gabriel 0.I. Ezeike Center for Food Safetyand Quality Enhancement, Department of Food ScienceandTechnology,TheUniversity of Georgia,Griffin, Georgia SundaramGunasekaran Department of BiologicalSystemsEngineering, University of Wisconsin-Madison, Madison, Wisconsin Yen-Con Hung Center for Food Safety and Quality Enhancement, Department of Food Science and Technology, The University of Georgia, Griffin, Georgia vii

viii

Contributors

Joseph Irudayaraj Department of Agricultural andBiologicalEngineering, The Pennsylvania State University, University Park, Pennsylvania David Julian McClements Department of Food Science, University of Massachusetts, Amherst, Massachusetts Suranjan Panigrahi Department of Agriculture and Biosystems Engineering, North Dakota State University, Fargo, North Dakota Stan Prussia Department of Biological and Agricultural Engineering, The University of Georgia, Griffin, Georgia R. Roger Ruan Department of Biosystems and Agricultural Engineering, University of Minnesota, St. Paul, Minnesota Muhammad Afzal Shahin Grain Research Laboratory, Canadian Grain Commission, Winnipeg, Manitoba, Canada Jinglu Tan Department of Biological and Agricultural Engineering, University of Missouri, Columbia, Missouri Ernest W. Tollner Department of BiologicalandAgriculturalEngineering, Driftmier Engineering Center, The University of Georgia, Athens, Georgia

Optical Methods: Visible, NIR, and FTlR Spectroscopy Sundaram Gunasekaran University of Wisconsin-Madison, Madison, Wisconsin

Joseph lrudayaraj The Pennsylvania State University, University Park, Pennsylvania

1.

INTRODUCTION

Food quality may be defined as the compositeof those characteristics that differentiate individual units of a product and have significance in determining the degree of acceptability of that unit by the buyer (1). The qualityof many products may be judged by the colors they display or fail to display. It is particularly and vegetables, critical in cases of food and biological products such as fruits cereal grains, and processed foods. The primary goal of quality control is to maintain a consistent standard of quality at a reasonable cost and at levels and tolerances acceptable to buyers. Human evaluation has been the primary method of quality assessment for operations such as grading and sorting of food products, but such evaluation can hardly provide a general standardon a large scale and wide range of operations. Factors such as eye fatigue, lackof color memory, variationsin color discriminain lightingconditions tionbetweenindividuals,personalbias,andvariations greatly influence an individual’s decision when determining the qualityof a certain product. Moreover, the human eye is greatly limited by its perceptions in a very narrow band of the vast electromagnetic spectrum. Some quality attributes, external and internal defects, and compositional factors are more readily detect(UV) and infrared able in the region outside the visible range, e.g., ultraviolet (IR). This has led to considerable research in developing instruments sensitive 1

Gunasekaran and lrudayaraj

2

to a broad band of the electromagnetic spectrum and in establishing indices of quality for various food and biological materials. The nondestructive nature of optical methods has made them particularly attractive in on-line quality evaluation involving a large number of samples for processing operations. IR radiation that are In this chapter optical methods based on visible and routinely used in quality evaluation and control of several food products are discussed. A major emphasis isplaced on nondestructive quality evaluation methods for agricultural and biological materials that are useful in evaluating maturity and/or ripeness, detecting external and internal defects, and composition analysis.

II. LIGHT ANDCOLORMEASUREMENT Among the properties widely used for analytical evaluation of materials, color to possess a specific is unique in several aspects. While every material can be said property such as mass, no material is actually colored as such. Color is primarily in a an appearance property attributed to the spectral distribution of light and, way, is related to some source of radiant energy (the illuminant), to the object to which the color is ascribed, and to the eye of the observer. Without light or the illuminant, color does not exist. Therefore, several factors that influence the (2): radiation subsequently affect the exact color that an individual preceives Spectral energy distribution of light Conditions under which the color is viewed Spectral characteristics of the object with respect to absorption, reflection, and transmission Sensitivity of the eye Thus, in reality, color is in the eye of the observer, rather than in the “colored” object. The property of an object that gives it a characteristic color is its light-absorptive capacity. Since lightis the basic stimulus of colors, it is important to consider the electromagnetic spectrum (Fig. 1). Several optical methods have been developed based on radiation from different regions of this spectrum. Radiation is one of the basic physical processes by which energy is transferred from one place to another. Propagation of radiation through free space is thesamefortheentireelectromagneticspectrum, i.e., radiation of all wavelengths-from the shortest gamma rays to the longest radio waves-travels with small partof the electromagthe same speed in vacuum. Visible light forms aonly netic spectrum, with a spectral range from approximately 390 nm (violet) to 750 nm (red). The sensitivity of the eye varies even within this narrow visible range. Under conditions of moderate-to-strong illumination, the eye is most sensitive to yellow-green light of about 550 nm.

Optical Methods

3

Fig. 1 The electromagnetic spectrum.

If the spectral distribution throughout the visible region is unequal, then the sensation of color is evokedby radiant energy reaching the eye's retina.An equal spectral distribution makes the light appear as white. The unequal distribuof the source itself, tion responsible for color sensation may be characteristic such as flame spectra composed of one or more monochromatic wavelengths, or may result from selective absorptionby the system, which appears colored. The latter includes several systems that show selective absorption for light and exhibit color as a resultof reflection or transmissionof unabsorbed incident radiant energy (Fig. 2). The three basic factors required in color sensation include the radiator or illuminant, the object, and the observer. The radiant energy emitted by the radiator is characterized by its spectral quality, angular distribution, and intensity. Hutchings (3) lists the following material properties and lighting of the scene as affecting the total appearanceof the object: Material properties: Optical properties (spectral, reflectance, transmission) Physical form (shape, size, surface texture) Temporal aspects (movement, gesture, rhythm) Lighting of the scene: Illumination type (primary, secondary, tertiary) Spectral and intensity properties; directions and distributions Color-rendering properties

lrudayaraj Gunasekaran and

4

Reflection Specular Radiation Incident

\

f

Medium 1 , n,

n Y

Medium 2, n2

Light Scattering and Absorption

Diffuse kkmission\

\r

Regular Transmission

Fig. 2 Schematic representation of interaction of light with matter. 8, = angle of incidence, eR = angle of reflectance, OT = angle of transmittance, n,, n2 = refractive index of medium 1 and 2, respectively.

A.

Color Specification

There are three characteristics of light by which a color may be specified: hue, saturation, and brightness. Hueis an attribute associatedwith the dominant wavelength in a mixture of light waves, i.e., it represents the dominant color as perceived by an observer. Saturation refers to relative purity or the amount of white light mixed with a hue. Brightness is a subjective term, which embodies the chromatic notion of intensity. Hue and saturation taken together are called chromaticity. Therefore, a colormay be characterized by brightness and chromaticity (4).

B. CIE System The Commission de Internationale de 1’Eclairage (CIE) defined a system of describing the color of an object based on three primary stimuli: red (700 nm), green (546.1 nm), and blue (435.8 nm). Because of the structure of the human eye, all colors appear as different combinations of these. The amounts of red, form any given color are called the “tristimulus” green, and blue needed to values, X, Y, and Z, respectively. Using theX, Y, and Z values, a color is represented by a set of chromaticity coordinates or trichromatic coefficients,x, y, and z, as defined below:

Optical

Z

x =

5

X X + Y + Z

Y y = x + Y + z

z =

X + Y + Z

(1)

+

It is obvious from the equations above thatx y + z = 1. The tristimulus values for any wavelength can be obtained from either standard tables or figures. A plot that representsall colors in x (red)-y (green) coordinatesis known as a chromaticity diagram. For a givenset of x and y, z is calculated from the above equations. Therefore, colors are generally specified in terms of Y, x, and y. There are a number of color metrics based on the CIE system. They include CIE Lightness, CIELUV, CIELAB, etc. In the food industry, the CIELAB system has been popular. For example, objective measurements of color using the CIELAB color parameters such as L* (lightness), a* (redness), and hue angle have been used to evaluate pork quality on-line in a industrial context (5,6). Other color models, suchas the RGB, CMY, and HSI, etc., are very similar to the CIE system, and numerical representation of a color in one system can be converted into another (4).

C. MunsellSystem and Atlas The Munsell color-order system isway a of precisely specifying colorsand showing the relationships among colors. Every color has three qualities or attributes: hue, value, and chroma. A set of numerical scales with visually uniform steps for each of these attributes has been established. The Munsell Book of Color displays a collection of colored chips arranged according to these scales. Each chip is identified numerically using these scales. The color of any surface can be identified by comparing it to the chips under proper illumination and viewing conditions. The color is then identified by its hue, value, and chroma. These attributes are given the symbols H, V, and C and are written in a form H V/C, which is called the Munsell notation. Using Munsell notations, each color has a logical relationship toall other colors. This opens up endless creative possibilities in color choices, as well as the ability to communicate those color choices prein food cisely. The Munsell systemis the color order system most widely quoted industry literature (3). Food products for which the U.S. Department of Agriculture (USDA) recommends matching Munsell discs to used be include dairy products such as milk and cheese, egg yolks, beef, several fruits, vegetables, and fruit juices (3). Other color atlases and charts are available for use in the food industry, such as the Natural Color System and Atlas, Royal Horticultural Society Charts, etc. (3). These atlases and charts are used for visual comparison of a product color with that of a standard color (diagram), which is still commonly practiced in the food industry. The evaluationof potato chip color is a very good example.

Gunasekaran and lrudayaraj

6

111.

INTERACTION OF LIGHTWITHMATTER

A.

PhysicalLaws

When light falls on an object, it may be reflected, transmitted, or absorbed (Fig. 2 ) . Reflected light is the partof the incident energy that is bouncedoff the object surface, transmitted light passes through the object, and absorbed light constitutes the part of the incident radiant energy absorbed within the material. The degree to which these phenomena take place depends on the nature of the material and on the particular wavelength of the electromagnetic spectrum being used. Commonly, optical properties of a material can be defined by the relative magnitudes of reflected, transmitted, and absorbed energy at each wavelength. Conservation of energy requires that sum of the reflected (IR), transmitted (IT), and absorbed (IA) radiation equals the total incident radiation (I). Thus,

I

=

IR

+ IT + IA

(2)

According to its transmittance properties, an object may be transparent, opaque, or translucent. Almost all food and biological products may be considered to be opaque, although most transmit light to some extent at certain wavelengths (7). The direction of a transmitted ray after meeting a plane interface between any two nonabsorbing media can be predicted based on Snell’s law:

(3)

n2 sin 8, = n , sin 8,

The attenuation of the transmitted rayin a homogeneous, nondiffusing, absorbing medium is defined by Beer-Lambert’s law:

where k is a constant and n is the number of molecules in the path of the beam. of the sample and the thickness b Since n is proportionaltoconcentrationc through which the radiation passes, Eq. (4) is rewritten as: (5)

lOg(IT/I) = abc

The ratio IT/Iis known as the transmittance T and is related to absorbance A as: A = log( 1/T) From Eqs. (5) and (6), absorbance A can also be written A = abc

as:

(7)

where a is called the absorptivity. [If c is expressed in mol/L and b in cm, a is replaced by the molar absorptivity, E (L/mol . cm).] Various constituents of food products can absorb a certain amount of this radiation. Absorption varies with the constituents, wavelength, and path length of the light (8). The absorbed energy can be transformed into other forms of

Optical Methods

7

energy such as fluorescence, phosphorescence, delayed light emission, heat, etc. Optical density (OD) is more commonly used to describe absorption. OD has the same definition as that of A, i.e., OD = log( l/T)],but it is used for applications where the transmitted ray is attenuated by both geometrical means (scatter) and absorption. The advantages of using the OD scale are as follows (9). First, the analysis is simpler, i.e., an optical density difference, AOD, is equivalent to a transmittance ratio. The differences are easier to compute. (Note: AOD(A B) = log( 1/RA) - log( 1/RH) whereA and B are wavelengths at which the measurements are made.) Second, the logarithmic plot permits a wider of range intensities, i.e., a transmittance scale of 1 to 100 is two orders of magnitude, while an OD scalemay cover five orders of magnitude. Finally, there aislinear relationship between OD and the concentration of an absorbing substance. Reflection is a complex action involving several physical phenomena. Depending on how light is reflected back after striking an object, reflection may be 2). Reflection defined as regular or specular reflection and diffused reflection (Fig. from a smooth, polished surface is called “specular” or “regular.” It mainly produces the gloss or shine of the material (1,2,10,1I). The basic law of specular reflection states that the angle at which a ray is incident to a surface must equal the angle at which it is reflected off the surface. Fresnel equations define the phenomenon of specular reflection. The intensityof parallel Ril and perpendicular RI components of the reflected light are:

RII

=

[

(n2/nl)?cos 8, - [(nz/nl)’- sin? 8J1/? (n2/nI)>cos 8, + [(nz/nl)?- sin2 81]1/2 cos 8, - [(nz/nl)?- sin? 8,]’/? cos el + [(n?/n,)?- sin2

The regular reflectance R = Ri RI, and hence

+ R:

and for normal incidence (8 =

OO),

Rii =

where n , and n z are refractive index of the medium and object, respectively; and 8, is the incidence angle (Fig. 2).If the material is absorbing, the refractive index is a complex number n( 1 - ik), where n is the real part of the complex number and k is an absorption constant, and the regular reflectance is written as: =

[I.?

(n?

-

nJ2+ ( n M ]

+ n,)? + (nzk)2

(1 1)

When the incident light is reflected from a surface evenly at all angles, the object appears to have a flat or dull finish termed “diffuse reflection.” No rigor-

and

8

Gunasekaran

lrudayaraj

ous theory has been developed for diffuse reflectance, but several phenomenological theories have been proposed, the most popular being the Kubelka-Munk theory (12). The Kubelka-Munk model relates sample concentration to the intensity way Beer-Lambert’s of the measured spectrum in a manner analogous to the law relates band intensities to concentration for transmission measurements. The Kubelka-Munk function f(R,) is generally expressed as: f(RJ =

(1 - R-)2 - k S 2R,

”

where R, = absolutereflectance of an infinitelythicklayer,k = absorption coefficient, and s = scattering coefficient. Kubelka-Munk theory predicts a linear relationship between spectral data and sample concentration under conditionsof constant scattering coefficient and infinite sample dilution in a nonabsorbing matrix such as KBr (potassium bromide). Hence, the relationship can only be applied to highly diluted samples in a nonabsorbing matrix. In addition, the scattering coefficient is a function of particle size, so samples must be prepared to a uniform fine size if quantitatively valid measurements are desired. It is not easy to quantify diffuse reflectance measurements since sample transmission, scattering, absorption, and reflection all contribute to the overall effect. By reducing particle size and dilution in appropriate matrices, surface reflection that can give strong inverted bands is reduced and the spectra more closely resemble transmission measurements. Typically, quantitative diffuse rein log( 1/R) units, analogous to absorbance flectance measurements are presented log( 1/T) units for transmission measurements. Bands increase logarithmically with changes in the reflectance values.By comparison, bandsin spectra displayed of reflectance. This differin Kubelka-Munk unitsvary as a function of the square ence emphasizes strong absorbance bands relative to weaker bands. The diffuse reflectance may be measured with respect to a nonabsorbing standard and converted to produce a nearly linear relationship with concentration c as follows (13): log(R’/R) = log(l/R)

+ log(R’) = a d s

(13)

where R’ and R = reflectance of the standard and the sample (R’ > R), a = absorptivity, c = concentration, and s = scattering coefficient. For monochromatic radiation, log R’ is constant and may be ignored, and Eq. (13) may be written as (12):

+

(14) log(l/R) (s/a) c =k where k = absorption coefficient. It should be noted that s is not a constant but depends on a number of properties of the sample such as particle size (s is in-

Optical Methods

9

versely proportional to particle size) and moisture content. Equation (14) is the basis for near-infrared(NIR) spectroscopic analysisof foods (14).In food materials, the primary factor that influences light reflection is a phenomenon known as scattering or diffusion (2,7,10).If the surface of incidence is rough, incident light will be scattered in all directions. Since the incident rays strike a rough surface more than once before being reflected, they would be expected to have a lower total reflectance than those reflected from a smooth surface (15). In classical optics, diffuse reflection was thought be to responsible for color. It was also commonly believed that colors of natural objects, such as foods and plant foliage, are seen by means of light reflected off their surfaces. Birth (15) recognized that the light must be transmitted through pigment within the cells in order to produce a colored appearance. Since most food materials are optically in all directions. Only nonhomogeneous, light entering such material is scattered about 4-5% of the incident radiationis reflected off the surfaceof these materials as regular reflectance (7,16). The remaining radiation transmits through the surface and encounters small interfaces in the cellular structure and reflects back. A large fraction of this reflected radiation from within the material is scattered back to the surface through the initial interface. This type of reflection is termed as “body reflectance” (7). The body reflectance is nearly always diffuse and is the most significant form of reflectance for foods. Some part of the transmitted light diffuses deeper into the material and may eventually reach the surface some distance away from the incident point.

B. FactorsAffectingDiffuseReflectanceSpectralData Diffuse reflectance spectroscopy offers exceptional versatilityin sample analysis. This versatility results from both its sensitivity and optical characteristics. Classically, diffuse reflectance has been used to analyze powered solids in a nonabsorbing matrix of an alkali halide such as KBr. The sample is typically analyzed at low concentrations, permitting quantitative presentation of the datain KubelkaMunk units. This technique yields spectra that are qualitatively similar to those produced by conventional transmittance or pellet methods. However, they exhibit higher sensitivity for quantificationand are less subject to scattering effects that cause sloping baselines in pellet measurements. Several factors determine band shape and relative/absolute intensity in diffuse reflectance spectroscopy through their effect on the reflection/absorbance phenomena specific to the sample. These include: Refractive index of the sample Particle size Sample homogeneity Concentration

10

1.

Gunasekaran and lrudayaraj

RefractiveIndex

Refractive index affects the results via specular reflectance contributions to diffuse reflectance spectra. With organic samples, the spectra display pronounced changes in band shape and relative peak intensities, resulting in nonlinearity in For some inorthe relationship between band intensity and sample concentration. ganic samples, strong specular reflection contributions can even result in complete band inversions. This overlayof diffuse reflectance and specular reflectance by diluting spectra, as well as the resulting spectral distortions, can be minimized the sample in a nonabsorbing matrix. In addition, accessory design can help reduce specular reflectance contributions.

2. ParticleSize Particle size is a major consideration when performing diffuse reflectance measurements of solids. The bandwidth is decreased and relative intensities are dramatically altered as particle size decreases. These effects are even more pronounced in spectra of highly absorbing inorganic materials with high refractive indices. For these samples, specular contributions can dominate thefinal spectra if the particle size is too large. To acquire a true diffuse reflectance spectrum, it is necessary to uniformly grind the sample and dilute it in a fine, nonabsorbing matrix. Similar preparation must be applied to the nonabsorbing matrix material in order to provide and “ideal” diffuse reflector for background analysis and as a support matrix for the sample.

3. SampleHomogeneity The Kubelka-Munk model for diffuse reflectance is derived for a homogeneous sample of infinite thickness. However, some sample analysis methods, especially of sample onto a powdered those designed for liquid samples (e.g., deposition supporting matrix), can result in a higher concentration of sample near the analysis surface. In these circumstances, variations in relative peak intensities may be noticed. In particular, more weakly absorbing wavelengths tend to be attenuated it is at higher sample concentrations. To avoid these peak intensity variations, necessary to distribute the analyte as uniformly as possible within the nonabsorbing background matrix.

4.

Concentration

One particularly important advantage of diffuse reflectance spectroscopy, especially in comparison to transmittance measures, is its extremely broad sampleanalyzing range. While it is theoretically possible to acquire usable diffusereflectance spectraon samples of wide-ranging concentrations, practical considerations often complicate the analysis process. With high concentration samples, espe-

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11

cially those with a high refractive index, one can expect a dramatic increase in the specular contribution to the spectral data. As a result, some sample data may be uninterpretable without adequate sample dilution. Even when samples can be measured satisfactorily at high concentrations, it is advisable to grind the sample to a very uniform and fine particle size to minimize both specular reflectance and sample scattering effects, which adversely affect quantitative precision. Alternative methods of sample analysis in diffuse reflectance include:

of a solid in the presence of a nonabEvaporation of volatile solutions sorbing supporting matrix Deposition of a liquid sampleor dissolved solid onto the surface of a nonabsorbing supporting matrix asin analysis of liquid chromatography eluent Direct analysis of certain solid samples, which has been successfully employed on a broad array of sample types including starch, wool cloth, paper, plant leaves, pharmaceutical tablets, and cedar wood siding

IV.

NIRAND FTlR SPECTROSCOPY

A.

Near-InfraredSpectroscopy

IR spectroscopy has been used as an analytical technique for almost a century (17). The IR region of the spectrum spans 0.780-1000 ym and has been divided into near-, mid-, and far-IR subregions. The most widely used are NIR, which is from about 1 to 2.5 ym, and mid-IR, which is from 2.5 to 14.9 pm (18). In IR studies, the frequency is often expressed in wave number (cm"), which is the inverse of wavelength when expressed in centimeters. IR spectroscopy is a form of vibrational spectroscopy but arises from an interaction of IR radiation with molecular bonds within a sample (19). Any sample will absorb at a certain wavelength, depending on the characteristics of the chemical entities present.The IR spectrum would reveal the particular absorption band(s), which can be related to the constituents present. It can be shown that the IR radiation of frequency and energy hu can supply energy required for a transition provided that:

where 2) = frequency, h = Planck's constant, k = force constant, p = reduced mass = (m, + m,)/(m, + m?), and m , and mz = mass of two atoms joined by the bond being studied. The above equation shows that the absorption frequency for a given bond For depends upon its strength and the masses of the atoms forming the bond. example, though the bonds C - 0 and C = O haveidenticalreducedmasses,

Gunasekaran and lrudayaraj

12

C -0 absorbs at a different frequency thanC=O. The C =O bond has a higher force constant and a higher absorption frequency. The C-H bond has a much lower reduced mass and absorbs even at high frequencies. IR spectroscopy can thus be used to determine which functional groups are present in a sample. Different functional groups in foods absorb IR radiation at different wavelengths (Table1). The constituents also affect the overall spectrum since scattering depends on the ratio of the refractive index of the material n , to that of the surrounding medium n2. For example, as the moisture content increases, so does the partial pressure of water vapor around the particles. Since the refractive index of water is greater than that of air, it leads to a decrease in n,/nz. Hences, the scattering of these procoefficient, and therefore log(1/R) increase (14). The overall effect cesses is that s becomes anew unknown for each sample. Therefore, the analytical utilization of diffuse reflectance spectra must be carried out an onempirical basis. Also, since s varies in a complex mannerwith wavelength, background correction in the case of diffuse reflectance spectra is difficult, although the basic principles are the same as those outlined earlier. The artof NIR spectroscopy of scattering samples lies in selecting measurement and reference wavelengths at which s is nearly equal, so that the s/a term in Eq. (14) becomes a constant. Osborne and Fearn (12) presented the theory of NIR spectrophotometry in much detail. Commercial NIR instruments are manufactured using oneof three geometries to collect reflected light from samples: integrating sphere, large solid angle detector, and small detector. The large solid angle detector offers good collection efficiency, simplicity of construction, and minimum interference from specular reflectance.

Table 1 CharacteristicFunctionalGroups ofFoodComponents

component Chemical Food functionality Wavelength Water, carbohydrates Unsaturated fat Fats, proteins, carbohydrates Fats Pectin Fatty acids, acetic acid Water Protein Protein Carbohydrates, fats Source: Adapted from Ref. 19

of band 0 -H stretch C -H of cis double bond

C-H C=O, ester C=O, ester C =0, acidic 0 - H (bend) C =0, amide I N -H, amide I1

c-0, c-c

3600-3200 3030 3000-2700 1745 1725 1600- 1700 1640 1650 1550 1400-900 (complex)

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B. FourierTransformInfraredSpectroscopy Fourier transform infrared (FTIR) spectroscopy is based on the Michelson interferometer configuration designed a century ago (1 8,20,2I). It is used to detect radiation in the mid-IR region. Fourier transform instruments obtain the data by using interferometry while they calculate the spectrum by using Fourier transform mathematics. The resultis increasing sensitivity of measurement.The interferometer consists of a fixed mirror, a movable mirror, and a beamsplitter (Fig. 3). The beamsplitter transmits half the incident IR radiation to the movable mirror and reflects the other half to the fixed mirror. The speed of the movable mirror is monitored by a laser. The two mirrors reflect the two light beams back to the beamsplitter and then the beams recombine. When the distance from the fixed to the beammirror to the beamsplitter equals that from the movable mirror back splitter, the amplitudes of all frequencies are in phase and recombine construca condition called zero tively. There is no difference between the two beams, retardation. As the movable mirror is moved away from the beamsplitter (retarded), the difference between the two beams is generated because the two beams travel different distances within the interferometer before recombining.A pattern of constructive and destructive interference results, which depends on the position of the movable mirror and the frequency of the retardation. The intensity of the

Y Source

i

t

Movable Mirror

Fig. 3 Schematicdiagram of theMichelsoninterferometer.

14

Gunasekaran and lrudayaraj

radiation is altered in a complex pattern as a functionof mirror movement. Thus, the output radiation is modulated by the interferometer. Such recombined and modulated IR radiation is directed through the sample compartment to the detector. It will generate a continuous electrical signal called an interferogram at the detector.Acomputerisused to changetheinterferogramintoasingle-beam spectrum by a Fourier transform. In FTIR spectroscopy, the (background) spectrum of the source is first collected and stored at the computer. Then the sample is placed in the sample compartment, and the spectrum is collected proportional to the background spectrum to obtain the desired transmission spectrum. FTIR is a rapid, precise qualitative technique for identifying and verifying chemical compounds in foods with spectral multiplexing and optical throughput (22,23). The procedure to prepare samples is not as complicated as that of traditional wet chemical procedures (24). With these advantages, FTIR can also be used to determine food and food ingredient authentication (25). Powerful modern data processing techniques, especially multivariate analysis, have been applied to extract useful information from spectral data. However, water can be a problem in F U R measurements because it absorbs strongly in regions about 3300 cm"' of other chemical groups. Thus, and 1600 cm", which overlap the absorption some sampling procedures and computer software tools must be applied to overcome such problems.

1. AttenuatedTotalReflectionSpectroscopy Attenuated total reflection (ATR) spectroscopy is one of the most powerful FTIR methods for biological and liquid sample analysis. It is fast and yields a strong signal even with small traces of the target molecule. Reflection occurs when a beam of light passes from a dense to a less dense medium. Total reflection occurs when the incident angle is greater than a critical angle (Fig. 4). A light beam actually penetrates a small distance into the less dense medium before reflection occurs(18). The depthof penetration varies from a fraction of a wavelength up to several wavelengths. The depth of penetration depends on the wavelength of the incident radiation, the index of refraction of the two media, and the angle of the incident beam with respect to the interface. Such penetration radiation is called the evanescent wave. When the less dense medium absorbs the evanescent radiation, attenuation of a beam occurs at different wavelengths of the absorption bands. This is referred to as attenuated total reflectance. In the ATR method, the sampleis placed in contact against a special optical crystal, which is called an internal reflectance element. An IR beam from the spectrometer focused onto the beveled edgeof a setof mirrors is reflected through the crystal, usually numerous times, and then is directed to the detector. Penetration d, is calculated as (26):

15

Optical Methods

"2

Fig. 4 Illustration of attenuated total reflectance. 8, = angle of incidence. 8, angle, n , , 11: = refractive index of crystal and surrounding, respectively.

=

critical

where h = wavelength of the radiation in the internal reflectance element, 0 = angle of incidence, n,p = ratio of the refractive indices of the sample vs. internal reflectance element, and n p = refractive index of the internal reflectance element. For a typical ATR setup, d, is in the range between 10 and 20% of the wavelength used (26). For quantitative purposes, FTIR-ATR can only be used for homogeneous samples (27). Thus, the penetration depth is typically 0.1-5 pm (28). When the incident angle is changed, the penetration depth can also be changed. Factors affecting the determination in an ATR experiment include wavelength of the IR radiation, refractive index of the crystal and sample, depth of penetration, effective path length, 670-4000 cm" angle of incidence, efficiency of sample contact, and ATR crystal material (29). 2. Diffuse Reflectance Infrared Fourier Transform Spectroscopy

Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) is a sampling technique developed forIR analysis of powder materials and turbid liquids. This technique has been applied for the analysisof pharmaceutical (30) and food (3 1 ) samples. DRIFTS is also utilized for quantitative (32) and qualitative(33,34) determination of the composition of samples (forages and coffee, respectively) with an accuracy equal to or better than that found using NIR spectra. DRIFTS has been used primarily in UV-visible spectroscopy studies where the beam energy is high enough for proper detection. Diffuse reflectance studies found little use in the caseof classical scanned IR beam. The advantages brought

16

Gunasekaran and lrudayaraj Two elliptical mirrors

IR beam source

’

Optical detector

Fig. 5 Schematic of DRIFTS with diffuse reflectance accessory.

by introducing Fourier transform methods (sensitivity and good signal-to-noise to IR studies as ratio) allowed the application of diffuse reflectance technique well. The most commonly used device for collecting diffusely reflected UV-visible and even NIR radiation froma sample is an integrating sphere whose interior is coated witha diffusing, nonabsorbing powder suchas MgO (magnesium oxide) or BaSOJ (barium sulfate) (Fig. 5). The sample and detector are usually held at the surface of the sphere, and the measured spectrum is independent, to a good approximation, of the spatial distribution of the reflected light and the relative position of the sample and the detector. DRIFTS offers several advantagesas a sample analysis technique, such as: Minimal or no sample preparation Very high sensitivity Applicability across a wide range of sample concentrations Ability to analyze most nonreflective materials including highly opaque or weakly absorbing materials Ability to analyze irregular surfaces or coatings such as polymer coatings or glass fibers; suitability for very large, intractable samples through the use of specialized sampling devices

V.

QUALITYEVALUATIONOFFOODPRODUCTS

A.

MaturityandRipenessEvaluation

The ripening of fruits is associated with changes in color, flavor, and texture that lead to the state at which the fruit is acceptable to eat. Readily apparent phenom-

Optical

17

ena associated with the ripening of most fruits, among others, include changesin color, which involve loss of chlorophyll leading to the unmaskingof underlying pigments and the synthesis of new pigments. Ripening is regarded as an indication of senescence accompanied by several physiological and chemical changes (12,13). It may also represent a process requiring synthesisof specific enzymes. Most fruits exhibit an increased reflectance and reduced absorbance in the 670 nm region because of the loss of chlorophyll. This hasbeenthe single most (35). The mechanismof color important criterionin optically judging fruit quality in Hutchings changes in fruits and vegetables during ripening is further discussed (3). Determining degree of maturity by surface color evaluation, however, has its limitations. In many fruits, external color changes donot reflect internal ripeness. Similarly, external color evaluation cannot truly differentiate between immature and mature green fruits. Such a distinction is important because mature green fruits eventually ripen while immature fruits will not. Where skin color does not truly represent fruit quality or does not sufficiently indicate the stage of maturation, internal flesh color can serve as an index of quality (36). The success of establishing a valid index of quality largely depends on the appropriateness of the optical property measured. Basic requirements for a successful optical measurement are that the magnitude of relative measurement should vary as greatly as possible over the full range of maturation, andthe change in the measurement between consecutive stages of maturity should be great enough to permit precise color differentiation. In addition, the nature of the measurement criterion should also be formulated judiciously so as to permit measurements insensitive to variations in the product and the measurement system. Over the years, the measurement criterion has taken various forms, which can be broadly grouped under the following four classes: 1.

Single wavelength measurement-the optical property of the object at a particular wavelength is considered as an index. 2. Difference measurement-the change in an optical property of the object at two wavelengths is measured. This is an indication of the average slope of the curves in the region between the two wavelengths. The region should be chosen so as to obtain the maximum possible change. The effectof variations in the object and apparatus are largely attenuated in this kind of measurement (37). 3. Ratio measurement-the ratio of an optical property at two or more wavelengths is the chosen criterion. Generally, this is independent of the sensitivitity of the measuring instrument. 4. Combination measurement-any combination of the above categories is used. For instance, to evaluate the maturityof tomatoes, Birth et al. (38) suggested the form

18

Gunasekaran and lrudayaraj

To identify diseased potatoes, Muir et al. (39) used

where the subscripts A, B, C, and D represent wavelengths at which the transmittance T and reflectance R measurements are made. In apples, major changes in spectral characteristics were found to occurin the visible region. As the fruits mature, the percentage of reflectance increases at about 676nm and greatly decreases between 400 and 600 nm (35). Peak reflectance was observed at 800 nm, but it decreased thereafter, presumably due to absorption by water in the NIR region (35). Since the water content in the skin is not likely to change with maturity, the reflectance would not change in the NIR range. A ratio measurement, RSKO/Rh2",was found to be the best for red varieties of apples. The Golden Delicious variety can be evaluated for maturity at a single wavelength in the 550-620 nm region. Yeatman and Norris (40) suggested an OD difference (AOD) between 740 and 695 nm for several varieties. As in apples, themost noticeable maturity-induced changein peaches is the band at 675 nm, indicating a decrease in absorbance in a fairly narrow wavelength decrease in chlorophyll content (41). A decrease in absorbance at 675 nm was also accompanied by a smaller decrease over a wider wavelength area beginning with at about 550 nm. This latter decrease gradually shifted to larger wavelengths increasing ripeness of peaches. Although measurements suchas those described above are useful to follow maturation and ripening within varieties, they are less suitable as a general maturity index for the whole class of fruits. Color differences among varieties of in wavelength of peak transmittance, peachesarelikelytocausedifferences which might not specifically be related to the stage of maturation. The optical density differences between two wavelength readings may be better suited for in such purposes. As mentioned earlier, such differences overcome differences fruit size and variations induced by instrumental factors. Sidwell et al. (41) studied three such AOD values-AOD (700-720), AOD (700-740), and AOD (700750)-for peaches harvested at different stages of maturity. Such measurements were apparently stable and were not influenced by the chlorophyll content. The maturity index AOD (700-740) yielded the highest correlation ( r = 0.957) with eating quality peaches. (Note: The numbers in parenthesis following AOD and subscripts used with T and R refer to wavelength in nanometers. This notation continues in the rest of the text.)

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The CIE tristimulus values X, Y, and Z or the chromaticity coordinates x, of objects, can also be y, and z, which are normally used to express the color used to fonnulate general maturity indices for various fruits. This is particularly true if the reflectance variation related to the stage of maturation occurs in the (35) studiedtherelationshipbetweenthese visibleregion.BittnerandNorris parameters and the picking date for several varieties of apples, peaches, and pears. ReflectanceratiosRSxo/RhzoandR670/R730seemedpromising as indicatingthe stage of maturation for most of fruit varieties investigated. For fruits like tomatoes, which are processed into several forms (e.g.. puree, juice, or paste), internal color is far more important than skin color. Birth et al. (38) reported a nondestructive measurement of internal colorof tomatoes by speca minimum transmittance at 670 tral transmittance. Green tomatoes exhibited nm, which increased by more than five decades as they matured to the red stage, corresponding to loss of chlorophyll. On the other hand, an equivalent relative decrease in transmittance at 550 nm was observed, corresponding to the increase in lycopene, the characteristic red pigment of tomatoes. The measurement Th2,)/ Th7[) was found to change on the order of 30: 1 as tomatoes ripened from yellow to full red color. These two wavelengths (620 and 670 nm) were specifically selected because the spectral transmittance change was the greatest at 620 nm as the fruit developed a redder color. At 670 nm, the transmittance value was high enough that an extremely sensitive instrument was not required. Similarly, T520/TSJs value could distinguish externally green tomatoes with internal amber color from tomatoesthat were green throughout. Combining the above two criteto indicate ria, Birth et al. (38) proposed a new ratio, (T670 - Ts2~J/(Thz,)TSJS), a very high correlation ( r = internal color of tomatoes. This new index gave 0.95) with the color of the extracted juice. The Agtron Grade G given by Eq. (1 6) is used by the food industry in California to grade raw tomatoes based on reflectance measurements made using the Agtron colorimeter (2):

+

Maturity and other quality indices for several other fruits and vegetables have been developed following a similar pattern. A detailed list is available in Gunasekaran et al. (42). IR and NIR methods can beused to determine the sugarsin fruits that have a thin skin, such as apples, peaches, prunes, or cherries, which, in turn, can be correlated to their stateof ripeness (43). Sensors basedon this principle have been developed. However, this method is ineffective for thick-skinned fruits (44,45). Optical properties have been used to evaluate maturity in peanuts. Kramer et al. (46) investigated light absorption properties of Virginia-type cured, un-

20

Gunasekaran and lrudayaraj

roastedpeanuthalveswithoutskins.AOD(480-510)wasfoundusefulasan indicator of peanut maturity. Due to the absorption band at 445 and 470 nm of the pigment xanthophyll in immature peanuts, the above measurement values also found werehigherforimmaturepeanutsthanformaturepeanuts.They the tastepanelratingof offgoodcorrelationbetweenAOD(480-587)and flavor in peanuts. Beasley and Dickens (47) indicated that oil extracted from as amaturityindex.Theyreportedthatoilfrom peanutscouldalsobeused mature peanuts generally transmitted more light at about 425, 456, and 480 nm than oil from immature peanuts. Apart from xanthophyll, p-carotene and lubein to were also suggested as influencing the spectral properties, which are related maturity. Gloss characteristics of a number of fruits and vegetables have been determined. Unwaxed oranges, bananas, and onions have significantly lower gloss than eggplant, green pepper,and tomato (48). Commercially mature eggplants are glossier than green tomatoes and apples. This is partly explained by differencesin epicuticular wax structure. The lamellae-type wax covering the eggplant reflects light more efficiently than the amorphous wax layer covering the tomato and the as they large overlapping platelets of the apple (49). Bananas also lose gloss mature. This is probably due to epicuticular wax from the surface and possibly also due to an increase in surface roughness caused by water transfer by osmosis to shriveling, separationof epidermal cells, from the skin to the pulp, which leads and the appearance of longitudinal cracks (50).

6. Detection of External and Internal Defects

7. Fruits and Vegetable Products Optical properties of fruits and vegetables are affected by external and internal defects, including mechanical injuries that occur during harvesting and postharvest handling as well as certain microbial diseases. Some common problems encountered in mechanical fruit harvesting include skin damageor bruising, as well as association of nonedible, unwanted parts such as stem and calyx. Tender fruit varieties that make up a very large portion of fresh market fruit production are especially susceptible to bruising during mechanical harvesting. Fruit color and bruise appearance may change substantially between the time of harvest and final grading. By measuring light reflected from such defects and comparing it with that reflected from the undamaged surface, defective fruits can be sortedout from normal fruits. Similarly, measuring transmittance characteristics of fruits could help identify several internal defects. Bruising on apples is a major problem in grading operations. With the increasing use of mechanical hurvesters, the numbers of bruises and other surface defects are expected to increase. Attempts to develop an automatic apple bruise

Optical Methods

21

detection device to sort out bruised fruit beganin the early 1970s. While studying the rate of discoloration for impact injuries versus the changesin selected phenolic compoundsin bruised apple pulp, Ingle and Hyde( 5 l ) observed a consistently (52j reported a lower reflectance at 600 nm for bruised apple pulp. Woolley decrease in the NIR diffuse reflectance when water was used to replace air in the intercellular air spacesin plant tissues. Since an apple bruise primarily consists of crushed cells surroundedby free liquid, measuring NIR reflectance seems promis(53) ing to detect bruising in apples. Using a similar technique, Brown et al. extensivelystudiedtheNIRreflectance of threevarieties of freshandstored apples. At all wavelengths between 700 and 2200 nm, bruised apple skin exhibited a less average reflectance than unbruised skin. It is thought that reflectance a combifor a bruised surface is less than that for an unbruised surface because of nation of cell destruction in the bruise (fewer rigid cell walls to scatter light), an in unaltered water-air relationship in the tissue, and a gradual chemical change the cell material. The differences in reflectance at 800, 1200, and 1700 nm or the ratio of reflectance (unbruised to bruised) at some wavelength between 1400 and 2000 nm may be useful in optical bruise detection (53). Reflectance properties of apple tissue cannot reliably predict bruise depth, but two-wavelength derivative models distinguish between good and bruised tissue better than nonderiva( 5 5 ) alsoinvestigatedtypicalreflectanceproperties of tivemodels(54).Reid three varieties of apples for use in automatic trimming operations. He determined that a detector sensitive in the wavelength range from 400 to 450 nm could be used for bruise detection, and one sensitive in the 725-800 nm region could be used for stem and calyx detection. in Reflectance properties have also been used to detect water core defect apples. a heat-initiated disorder. In affected tissue, intercellular spaces are filled with liquid or cells become swollen so as to eliminate air spaces, resulting in a translucent or water-soaked appearance. Moderately water-cored apples are difficult to sort out visually from sound apples. The inability to detect such disorders not only causes a marketing problem butalso prevents investigation of the development of the disorders in intact fruits. Olsen et al. (56) were among the first to use the differencein optical density at wavelengthsof 760 and8 15 nm to measure water core concentration in apples. Birth and Olsen (37) made a more definitive study of this technique to detect water core in Delicious apples. This technique takes advantageof the physical changesthat occur in apple tissue that affect lightscattering properties. Since the air spaces are eliminated in water-cored tissue. it scatterslesslightthannormaltissue.Water-coredtissuethustransmitsmuch more energy. The relative optical density values indicated a water absorption band at 760 nm. Also, there wererelatively few substances in normal apples that absorbed energy at about 800 nm, so it was considered the best wavelength rein gion. An optical density difference of AOD (760-840) gave the best results identifying water-cored apples.

22

Gunasekaran and lrudayaraj

Felsenstein and Manor (57) studied certain surface defects of oranges that are characterized by color change. They reported that within the vicinity of 667 nm, there was a difference of at least 17% in the intensity of light reflectance from the surface of a good orange and one with a blemish. Gaffney (58) investias plug, oleocellosis, rots, gated fruit grade color and several surface defects such molds, windscar, scabs, thorn scratches, etc. of oranges and grapefruit. Several of these defects affect the keeping quality of fruits, whereas others detract from to be sorted out. The surface appearance and lower the grade. Such fruits have Hamlin, Pineapple, and Valencia varieties of oranges and Marsh and Thompson varieties of grapefruit exhibited definite differences in reflectance characteristics in the wavelength band of 650-700 nm according to surface color. The wavelength band 550-610 nm was sensitive to changes in surface color or nature due to various defects. A color index has been developed to evaluate the quality of orange juice. The color number (CN) is defined in terms of the tristimulus values X, Y, and Z as (59):

X Z CN = 56.5 - - 18.4 Y Y

+ 48.2 I

-

Y

- 8.57

(18)

Applying principal component and discriminant analysis of NIR reflectance a simple, practical spectra over a wavelength range of 1100-2498 nm offers method of detecting 10% pulp wash in orange juice and sugar-acid mixturewith an accuracy of 90% (60). Visible and UV absorption and fluorescence and emission characteristics of alcoholic solutions of frozen orange concentrates and single strength orange juices can give qualitative detection and quantitative approximation of orange pulp wash in orange juice (61). The absorbance sum at 443, 325, and 280 nm and ratio of absorbance at 443/325nm can provide an estimate of the percentage total citrus material, orange juice, pulp wash, and dilution of the sample. UV-visible absorption and room temperature fluorescence excitation and emission spectra have been adopted as the official first action to detect adulteration of Florida orange juice with pulp wash (62). NIR spectroscopy givesa good idea of fruit content, particularly with strawberry jams, for which peaks are obtained at 770 nm and 1090 nm. FTIR can distinguish the fruit type in fruit purees (63). It can also detect whether fresh or freeze-thawed fruit was used to make puree, the level of ripeness in some cases (e.g., raspberry but not strawberry), fruit variety (e.g., in apples), and any added sulfur dioxide (64). An FTIR method to determine fruit content of jam has been reported (65). The FTIR spectra can reliably and reproducibly distinguish between jams of differing fruit content. Furthermore, the spectra are characteristics of fruit and can act as fingerprints for different fruit types. These methods, therefore, have good potential to verify product authenticity and to detect adulteration.

Optical Methods

23

The AOD (810-710) was suggested as a nondestructive criterion to detect in the vicinity hollow heart disease in potatoes based on the brown substances of the void (66). This measurement is also capable of indicating other potato discolorations such as black spots and greening. Porteous et ai. (67) identified diffuse reflectance at wavelengths between 590 and 890 nm and the bands near 1 100 and 1400 nm as being the most significantin detecting a number of diseases and defects such as bacterial soft rot, blight, common scab, dry rot. gangrene, in greening, and skin spot. The wavelengths suggested to detect these defects their order of importance are 650, 710, 1410, 630, 750, and 830nm. In a similar investigation, Muir et ai. (39) observed that diseased tubers have progressively reduced diffuse reflectance for several diseases of potatoes at shorter wavelengths up to about 800 nm and increased with wavelengths greater than 1100 nm due to water absorption. The wavelength bands between 590 and 750 nm and the bands near 950, 1150, 1350, 1470, and 1850 nm were found useful in detecting various diseases studied. Mechanical methods for separating potatoes from other materials are not very successful because they are similar in their mechanical properties. Differences in reflectance of light and IR radiation by potatoes, stones, and soil clods offer a possible way of separating them. Palmer (68) reported a red-to-blue ratio, Rxlxr ,,)-91Xl/R32s ,,, as a very successful criterion in differentiating potatoes from soil clods. This ratio was found to be unaffected by the size of the object, its distance from the sensors, and glass. Virtually perfect sorting efficiency was not unusual. While verifying these results, Story (69) also included the IR radiation properties to separate potatoes from stones and soil clods. The results indicated a higher reflectance over the 600that potatoes, like other plant material, showed 1300 nm region and lower reflectance outside this region. Accordingly, the ratio of the reflectance in the 600- 1300nm region to that in the 1500-2400 nm region was found to be a more distinctive indicator than the red-to-blue ratio used by Palmer (68).

2. Food Grains Separating foreign material such weed as seeds from thatof other grain cropsis an important operation in the grain industry. Hawk et al. (70) studied the reflectance characteristics of 12 grains. Their results indicated that the difference in reflectance between grains in the IR region is small and the greatest differences occur between 450 and 750 nm. The different reflectance properties were used in evaluating grain samples for admixture grain, i.e., grains other than the primary one. 1 ) used light reflectance measurementsto detect exterGunasekaran et al. (7 nal cracks in individual kernels of corn. Using a laser light (632.8 nm), they could detect cracks smaller than 1 mm. The defect detection accuracy was 100% for broken, chipped, and starch-cracked kernels and 80% for surface-split kernels.

24

Gunasekaran and lrudayaraj

Johnson (72) used an absorbance difference measurement (AX(X,-Aq3(,) to determine heat-damaged, sprouted, frosted, badly ground-damaged, and badly weather-damaged yellow corn kernels. Though larger differences between damage groups were recorded in the 650-750 nm region than in the 800-1000 nm region, the latter one was chosen for measurement to minimize effects of natural color differences in corn kernels. Birth (73) observed that the slope of the transmittance curves between 750 and 1000 nm was in direct proportion to the amount of smut on wheat samples. Therefore, excluding the water absorptionband at 970 nm, optical density differences at any two wavelengths in the above range could be indicativeof the total smut content. The actual smut spore content gave a correlation of 95% with the measurement of AOD (800-930). The degree of milling is one of the principal factors in determining the grade of rice. It is a measure of the extent to which germ and bran layers have been removed from the endosperm. Extensive milling is required for complete removal of the germ and bran layers, which would consequently result in an increased percentage of broken kernels. Stermer et al. (74) reported the spectral transmission of rice with various degrees of milling. Greater changes were observed at approximately 660 and 850 nm. Accordingly, the ratio measurement, Txso/Tohr,, was suggested as the criterion. In another method, use was madeof the fact that protein as well as oil is primarily located in the outer layers of rice to establish a criterion indicative of the degreeof milling of rice (75).The oil absorption bands at 928, 1215, and 1725 nm highly correlated with surface and total lipids. A higher correlation with total lipids ( r = -0.85) than with surface lipids ( r = -0.58) was observed, presumably because1R energy penetrates rice kernels sufficiently to be absorbed by all the lipids present in the kernel. Beerwinkle and Stermer (76) utilized this translucence difference between normal and abnornlal kernels to sort milled rice optically. With this feature included, the efficiency of a conventional rice sorter was improved by 50-70%. Stermer (77) developed objective measurements of the color of milled white rice, which could be used as an indicator of rice grade, the degree of parboiling, and the extent of starch gelatinization in parboiled rice.

3. Animal Food Products Unlike fruits and vegetables where pigments are the dominant factors influencing their appearance. several factors affect the spectral properties of meat products. Apart from pigments (predominantly myoglobin), factors like cellular structure, surface roughness, and homogeneity can equally affect the appearance of meat samples ( 15.78). Forexample, McDougall(78) observedno difference in pigment or PSE, and normal) concentration between two qualities (pale-soft-exudative, of pork muscle which appear different. He also found that the myoglobin and

Optical

25

hemoglobin selectively absorb light, while structural and myofibrillar protein absorb relatively less light but cause more scattering. This suggests that the general optical quality standards for meat products should take factors other than pigments into consideration. Davis et al. (79) reported that the interaction between light and muscle pigments could provide a nondestructive means of evaluating pork muscle quality. They investigated the reflectance spectra of longissimus muscle from pork loins of the qualities PSE, normal, and dark, firm, and dry (DFD). A pork quality at 633 and index (PQI) was suggested based on light reflectance measurement 627 nm as follows:

PQI

- 1.67 - 254log

-

(RI)

(RI,)

+ 258log

-

This yielded a correlation of 0.8 with visual rating of quality and 0.86 with the that the measurements involving OD of Hart extract. Davis et al. (79) commented pigments might be affected by the chemical reactions involving porcine myoglobin and by other external factors such as bacterial growth and oxidation. The light-scattering property of muscle, independent of the above factors, was suget (36) reported gested as a desirable factor in evaluating muscle quality. Birth al. a high correlation between the scatter coefficient at 632 nm and the OD of Hart extract. The presence of blood and meat spots is one of the most common defects found in eggs. Its incidence may range from less than 1% to nearly 100% (80). One of the earliest attempts to develop a spectrophotometric technique to detect blood in eggs is credited to Dooley(SI), who developed a device to automatically detect eggs containing blood using radiation in the region of 1260-1400 nm. However, Brant et al. (80) identified three absorption bands for blood at 415, 541, and 575 nm in the visible region. Their method of detecting blood spots i n white shell eggs was based on the relative transmittance measurement between 555 and 565 nm. Although a success rate of 97.5% was reported, this measurement was specific to the color of the eggshell. Norris and Rowan (82) applied a similar technique to detect blood spots regardless of shell color. Based on the relative absorbance measurements at 577 and 600 nm, they could detect 70% of all eggs having blood spots from 3 to 6 mnl in diameter and 100% having spots larger than 6 mm in diameter.

C. Composition Analyses Composition analyses of food materials are very important as quality indices for a variety of food materials. Such evaluation is normally performed by NIR and FTIR spectroscopy.

Gunasekaran and lrudayaraj

26

7.

MoistureContent

The concept of direct spectrophotometric measurement of moisture content of food grains was introduced by Norris and Hart (83). In the initial development diffusetransmittancewasused.However,thediffusereflectancetechniqueis more popular and is now an accepted technique for rapid analysis of grains and oilseeds. IR absorption spectroscopy is one of the most versatile methods of determiningmoisture in a variety of substances-gases,liquids,andsolids.By employing suitable wavelengthsat which maximum absorptionis expected, fairly reliable, repeatable measurements can be made. The relative advantages and disadvantages of some commonly used moisture determination methods are compared in Table 2. 1R spectroscopy for cereal grains has been investigated in the 700-2400 nm region. Absorption bands of 970, 11 80, 1450, and 1940 nm have been observed. The moisture content is estimated by comparing the depth of the band of interest with that for the standard concentration of water (85). Reflectance and transmittance of grain samples donot change greatly with moisturecontent(86).Nevertheless,thewaterabsorptionbandsat760,970, I 180, 1450, and 1940 nm were investigated for spectrophotometric measurement ofgrainmoisturecontent(87). The measurementcriterion AOD (970-900) closely predicted moisture contentof ground wheat samples. In general, it should be possible to measure the moisture content of a wide range of materials using the absorption band at 1940 nm on a uniform, thin sample 1-3 mm thick without any interfering bands. However, for moisture contents greater than 20%, the absorption at 1940 nm is difficult to measure. Hence the 970 nm band should give greater accuracy. The moisture content of whole, intact peanut cotyledons was also spectrophotometrically determined (87), and OD (970-900) predicted the peanut moisture content within ?0.7%. 2. Lipids/Fats

Goulden (88) first used IR radiation to measure fat, protein, and lactose in milk. Fat measurement was based on absorbance at 1724 cm" (fatA) by ester carbonyl groups of fat molecules. Protein measurement was based on absorbance at 1538 cm" by peptide bonds of protein molecules, and lactose measurement was based on absorbance at 1042 cm" by hydroxyl groups of lactose molecules. NIR spectroscopy can be used to analyze moisture, fat, protein, and total solids in cheese (89,90). Rodriguez-Otero et al. (90) used NIR reflectance spectroscopyto analyze fat, protein, and total solids in cheese without any sample treatment. Norris (91) studied light absorption characteristics of ground beef samples. Of the observed absorption bands at 540, 575, 640, and 760 nm, he selected the of ground beef. The one at 760 nm as a criterion to estimate the fat content other absorption bands were rejected because they were closely related to light absorption by blood. A fat absorption band at 928 nm was also reportedby Massie

50

Table 2 Advantages and Disadvantages of S o m e Common Moisture Determination Methods Method Oven drying

Chemical method. Karl Fisher

Advantages Standard conventional method Convenient Relative speed and precision Accommodates more samples Attains desired temperature more rapidly One of the standard methods More accurate and precise than other methods Useful for determining water in oils and fats by preventing samples from oxidizing Very rapid once apparatus is set up (within minutes)

IR absorption

Can perform multicomponent analysis Most versatile and selective Nondestructive

NIR reflectance

Rapid Precise Nondestructive No extraction required Minimal sample preparation High sensitivity due to large dielectric constant of water Convenient to industrial operations with the continuous measurement system Universal aplicability

Dielectric capacitance

Source: Ref. 83

Disadvantages

E

Temperature varies due to particle size, sample moisture. position in oven, etc. Difficult to remove bound water Loss of volatiles Decomposition of sample (e.g., sugar) Chemicals of higher purity should be used to prepare reagents Titration endpoint may be difficult to determine Reagent is unstable and should be standardized before use Titration apparatus should be protected from atmospheric moisture due to extreme sensitivity of reagent to moisture Accurate on calibration against reference standard Dependent on temperature Dependent on homogenizing efficiency of sample Absorption band of water is not specific Reflectance data affected by particle size, shape, packing density, and homogeneity Hydroxyl group interferes with amine group Temperature dependent Equipment is expensive Affected by sample texture, packing, mineral content, temperature, moisture distribution, and acid salts Calibration difficult far beyond sample pH 2.7-6.7 Difficult to measure bound water at high frequencies

2

z

5 0 Q u)

28

(92).Fromthespectralreflectancedata, formula:

Gunasekaran and lrudayaraj

heproposedthefollowingempirical

where A andBareconstants.Comparisons of fatcontentestimated bythis methodwiththevaluesobtained by chemicalanalysis(Soxhletprocedure) -+ 1.98% fat. yielded a correlation of 0.82, and the measurements are within NIR spectroscopy has been used to determine the sum of dimer and polymer triglycerides and acid value to evaluate frying oils (93). This is a rapid, lowcost technique to assess whether a sample complies with food legislation. FTIR spectroscopy is another interesting approach to authenticate extra virgin olive oil (94). The problems associated with identifying adulterated oils are complicated by the ever-changing nature of adulteration techniques and the numberof procedures that can pass undetected through official quality control. Some oils can be added to other oils without being detected by routine physical and chemical characteristics due to their fatty acid composition. In such cases, gas-liquid chromatography (GLC) analysis of fatty acids proves useful. IR spectra between 300 and 357 cm-l and 770to 1175 cm" can distinguish between oils of peanut, sesame, sunflower, etc. Peanutoil has a characteristic band at 9 13 cm",sunfloweroilat847,andsesameoilat812and 913 cm". IR spectra oil and its between 4000 and 850 c1n-l have shown differences between olive adulterant, rapeseed oil. Differences have been noted at 3100 and 1750 cm" and of the differential spectrum from 1400 to 1300 cm", the most striking feature being the negative peaks at 1130 and 1080 cm" with a characteristic contour from 900 to 1200 cm". Thesecharacteristicspersist in a mixture containing in as little as 10% rapeseed oil. These differences are attributed to differences unsaturated fatty acids, particularly oleic and linoleic acids (95). In contrast to NIR, FTIR has much to offer the analyst because specific bands may be assigned to specific chemical entities. Statistical correlation methods are not always necessary, but they are not excluded and may be required in very complicated mixtures (63). This techniques has been widely used to deter(96), meat (97), sweetened condensed mine fat, moisture, and protein in butter milk (98), and other high-fat products (99). It has also been used to monitor the oxidation of edible oils (100) and to determine the level of tram-unsaturation in fat (101). By combining attenuated total reflectance and mid-IR spectroscopy with statistical multidimensional techniques, Safer et al. (102) obtained relevant information from mid-IR spectraof lipid-rich food products. Wavelength assignments for typical functional groups in fatty acids were made for standard fatty acids.

Optical Methods

29

Absorption bands around 1745 cm" due to carbonyl group, 2853 and 2954 cm" due to C-H stretch, 3005 and 960 cm" due to C = C bonds, 1160 cm" due to C - 0 bonds, and 3450 and 1640 cm" due to 0 - H bonds were observed. Water strongly absorbs in the region of 3600-3000 cm-l and at 1650 cm" in butter and margarine, allowing one to rapidly differentiate them as a function of their water content. Principal component analysis was used to emphasize the difference between spectra and to rapidly classify 27 commercial samples of oils, butter, and margarine. Belton et al. (103) studied the components of fat, protein, and sugar in confectionery products usingFTIR spectroscopy coupled with photoacoustic and attenuated totalreflectancedetectionmethods.Theyconcluded that peaks at 1744, 1477- 1400, 1240, and 1 195- 1 129 cm" could be from an ester carbonyl group, C-H bond, and C - 0 stretching of fat, respectively. Peaks at 1650 and 1540 cm" are from protein, and those at 1128 to 952 cm" are from sugars. 3000 cm" and at 1650 cm" in Water is strongly absorbed between 3600 and fat-rich foods (96,103). Principal component analysiswas used to emphasize the differences between spectra and to rapidly classify each sample (96). Usually wavelength assignments for typical functional groupsin fatty acids A) forestercarbonylgroups areabsorptionbandsaround1745cm"(fat in methylene (R(CO)OR/OH), 2930 and 2853 cm"' (fat B) for C-H stretch groups, and 1 160 cm" for C - 0 bonds of lipid (104). IR spectroscopy hasalso been used to detect adulterationof fat with lowerquality/cost oil. Attempts have been made to detect concentrations of less than 10% of vegetable or animal fat in butter fat by GLC in conjunction with IR spectroscopy (percentage transmission at 967 and 948 cm", denoted as T967 and T948 and assigned to isolated trans and conjugated cis-trans isomers) can reliably distinguish butter from its adulterant substitute fats (95).

3. ProteinContent Proteins have three characteristic absorbance bands in the mid-IR spectrum (104). Two of these, amide I (about 1600- 1700 cm") and amide 111 (about 1200- 1400 cm"), are sensitive to polypeptide backbone conformation and might be able to I band is moreintense, but it distinguishbetweenproteins(105).Theamide overlaps with an intense water deformation band at 1645 cm". The amide 111 band, although less intense, is not overlapped by water absorptions. This band has been used as a tool to detect adulterations of NDM with SPC (106). Wheat protein content and grain hardness can be rapidly determined by IR and NIR spectroscopy (107). The NIR and Brabender hardness tester results correlate significantly with percentage of dark hard and vitreous grains as shown by commercial red winter wheats which have similar protein contents. NIR spec-

Gunasekaran and lrudayaraj

30

troscopy has also been used to differentiate between hard red winter and hard red spring wheat. Examination of the principal component factors has indicated that hardness, protein level, and the interaction of water with protein and other constituents are responsible for correct classification based on NIR (108). IR absorption can be used to determine the protein content in whole milk at 6460 nm, which is the absorption maximum for the peptide bond. Other components of milk such as lactose and fat can be simultaneously measured at their respective absorption bands at 5730 and 9597 nm. Water absorbs significantly at 1000-5000 cm”. Therefore, it interferes excessively with protein absorption bands in the IR spectrum. For protein determination in milk, this couldbe alleviated by using a double-beam spectrophotometer with water in the reference cell and milk in the sample cell (109). NIR spectroscopy is a popular method for determining protein in cereal products primarily due to its speed, simplicity of operation, safety, and low operating cost. To avoid excessive interference by starch, fat, and water, a wavelength of 4590 cm” corresponding to a combined vibration of amide groups is chosen to quantify protein components ( 1 IO).

VI.

SUMMARYAND FUTURE TRENDS

Optical propertiesof food and biological materials vary widely and are dependent upon many factors. Quality evaluation based on these properties requires accurate optical property values. Undera given setof conditions, precise values can probably be obtained for any particular substance by careful measurements. However, good estimates can be made for many materials on the basis of the investigations already reported. Such optical property estimates will permit a quick evaluation of various properties that have servedas quality indices and help in the selection of those most suitable for any particular application. Generally, nondestructive quality evaluationof food and biological materials focuses on three major aspects: maturity and/or ripeness evaluation, internal For maturity evaluation, and external defect detection, and composition analysis. the interaction of various pigments and the changes associated with them during maturation are taken as the prime indicator. The importance of chlorophyll in this regard has been adequately established. Both external and internal defects have been found to affect the normal propertiesof interaction of light with products in consistent ways. Hence, defect detection is accomplished by comparing optical propertiesof a normal product with thoseof the defective ones. Moisture, protein, fat or oil content, and other compositions have been analyzed based on certain absorption bands in the electromagnetic spectrum. The wide variety of sizes, shapes, and textures of food products makes most commercial instruments difficult to use for measuring optical characteristics

Optical

31

since special geometric designs are needed. With technological advancements such as fiber optics, laser, etc., this problem has been partially overcome. Fiber optic technology has the advantage of detecting light of extremely low intensity by providing a good light seal and by eliminating the effects of variations such as fruit size and distance from the light source. This has also been found to be very useful in high-speed operations. Low-power lasers can be of great help because the laser beam is highly directional, coherent,very bright, and has a welldefined beam diameter that can be focused down to small sizes to detect very local defects. The spectrophotometric techniques so far investigated rely heavily on ema better engineering approach, interaction pirical data and statistical analyses. For of lightwithagriculturalproductsshouldbeanalyzedanalytically.Chenand to Nattovetty (1 11) indicated that if a mathematical model can be developed represent the distribution of diffused light in fruit, the effectsof various parameters could be studied more thoroughly. Many food products may be considered as turbid or translucent, i.e., the incident light energy does not traverse the object in a rectilinear manner but is scattered from its original direction of travel. Thus, not only the absorption of energy within the sample, but also the scatter and changes in the direction of travelof energy within the sample must be considered. Mathematical description of scatter, diffuse reflectance, and diffuse transmittance are extremely valuable in providing a means of obtaining insight into the interaction between light and biological materials where multiple scattering predominates. The NIR and FTIR techniques are finding significantly increased usein the food industry for quality evaluation and control applications. The ability of such a variety of foods have made techniques to provide compositional data rapidly for possible some on-line quality evaluationsthat were traditionally done off-line in a laboratorysetting.Determination of adulteratedentities in manyliquidand in computer and powdered foods is a very good example. With new advances to findevenwider opticaltechnology,theNIRandFTIRmethodsarelikely applications. The optical methods will also become more commonplace in rapid microbial testing for improved food safety.

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FRvan de Voort, AA Ismail. Proximate analysis of foods by mid-FTIR spectroscopy. Trends Food Sci Techno1 2: 13- 17, 199 1. 23. FR van de Voort. Fourier transform infrared spectroscopy applied to food analysis. Food Res Int 25:397-403, 1992. 24. NS Letzelter,RHWilscon,ADJones, G Sinnaeve. Quantitative determination of 22.

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42. S Gunasekaran, MR Paulsen, GC Shove. Optical methods for nondestructive quality evaluation of agricultural and biological materials. J Agric Eng Res 32(3):209, 1985. 43. B Zion, P Chen, MJ McCartney. J Sci Food Agric 67:423-429, 1995. 44. V Bellon, SI Cho, GW Krutz, A Davenel. Food Control 3(1):45-48, 1992. 45. V Bellon, G Rabatel, C Guizard. Food Control 3( l):49-54, 1992. 46. AH Kramer. JE Gates, KD Demoree, AP Sidwell. Spectrophotometric investigations on peanuts with particular reference to estimation of maturity. Food Technol 17(8):90,1963. 47. EO Beasley,JW Dickens. Light transmissionof peanut oilas an objective measurement related to quality of raw peanuts. ASAE Paper No. 67-809, American Society of Agricultural Engineers, St. Joseph, MI, 1967. 48. A Nussinovitch, G Ward,E Mey-Tal. Gloss of fruit and vegetables. Lebensm Wiss Technol29(1):184-186,1996. 49. G Ward, A Nussinovitch. Gloss properties and surface morphology relationships of fruits. J Food Sci 61(5):973-977, 1996. SO. G Ward, A. Nussinovitch. Peel gloss as a potential indicator of banana ripeness. Lebensm Wiss Technol 29:289-294, 1996. 51. M Ingle, JF Hyde. The effect of bruising on discoloration and concentration of phenoleic compounds in apple tissue. Proc Am SOC Hort Sci 93:738, 1968. by leaves. Plant Physiol 47: 52. JT Woolley. Reflectance and transmittance of light 656,1971. 53. GK Brown, LJ Segerlind, R Summitt. Near-infrared reflectance of bruised apples. Trans Am SOC Agric Eng 17: 17, 1974. 54. WK Bilanski, CL Pen,DR Fuzzen. Apple bruise detection using optical reflectance parameters. Can Agric Eng 26(2): 1 1 1-1 14, 1984. 55. WS Reid. Optical detection of apple skin, bruise, flesh, stem, and calyx. J Agric Eng Res 21:291, 1976. 56. KL Olsen, HA Schomer,GS Birth. Detection and evaluationof water core in apples by light transmittance. Washington State Hort Assoc Proc 58:195, 1962. 57. G Felesenstein, G Manor. Feasibility study into the development of an improved photoelectric device for sorting citrus fruits for surface defects. Trans An1 SOC Agric Eng 16: 1006, 1973. 58. EE Gaffney. Reflectance properties of citrus fruits. Trans Am SOC Agric Eng 16: 310. 1973. of Food Analysis. Vol. 59. C Calvo. Optical properties. In: LML Nollet, ed. Handbook I . New York: Marcel Dekker, 1996. 60. M Twomey,G Downey, PB McNulty.The potential of NIR spectroscopy for detection of the adulteration of orange juice. J Sci Food Agric 67(1):77-84, 1995. 61. DR Petrus, NA Dunham. Methods for detection of adulteration in processed citrus products. In: S Nagy, JA Attaway, eds. Citrus Nutrition and Quality. ACS Symposium Series 143, 1980, pp 395-421. 62. DR Petrus, JA Attaway. J Assn Official Anal Chem 68(6):1202-1206. 1985. 63. PS Belton, AM Saffa, RH Wilson. Use of Fourier transform infrared spectroscopy for quantitative analysis:a comparative study for different detection methods. Analyst 112:1117-1120,1987.

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64. M Deferenz.EK Kemsley, RH Wilson. Use of infrared spectroscopy and chemometrics for the authentication of fruit purees. J Agric Food Chem 43( I):l09-113, 1995. 65. RH Wilson. Fourier transform mid-infrared spectroscopy for food analysis. Trends Anal Chem 9(4): 127- 13 I , 1990. 66. GS Birth. A nondestructive technique for detecting internal discoloration in potatoes. Am Potato J 3753, 1960. The identification of diseases and defects in 67. RL Porteous, AV Muir, RL Wastie. potato tubers from measurements of optical spectral reflectance. J Agric Eng Res. 26:151,1981. 68. J Palmer. Electronic sortingof potatoes, and clods by their reflectance.J Agric Eng Res6:104,1961. 69. AG Story. Spectral reflectance of light and infrared radiation by potatoes, stones, andsoilclods.In:JJGaffney, ed. QualityDetection in Foods. St. Joseph,MI: American Society of Agricultural Engineers, 1976, p 83. 70. ALHawk,HHKaufmann,CAWatson.Reflectancecharacteristicsofvarious grains. ASAE Paper No. 69-357, American Society of Agricultural Engineers, St. Joseph, MI, 1969. 71. S Gunasekaran, MR Paulsen, GC Shove. A laser optical system for detecting corn kernel defects. Trans Am Soc Agric Eng 29( 1):294-298, 304, 1986. 72. RM Johnson. Determining damagein yellow corn. Cereal Sci Today 7( I ) : 14, 1962. 13. GS Birth. Measuring smut content of wheat. Trans Am Soc Agric Eng Res 3:19, 1960. 74. RA Stermer, HW Schroeder, AW Hartstack, CH Kingsolver. A rice photometer for measuring the degree of milling of rice. Rice J 67(5):24, 1962. 75. RA Stermer, CA Watson, E Dikeman. Infrared spectra of milled rice. ASAE Paper No.76-3030,AmericanSocietyofAgriculturalEngineers, St. Joseph,MI, 1976. 76. RBeenvinkle, RA Stermer.Adevicetofacilitateopticalsortingofmilledrice based on translucence difference. ASAE Paper No. 71-373, American Society of Agricultural Engineers, St. Joseph, MI. 197 I . 77. RA Stermer. An instrument for objective measurement of degree of milling and color of milled rice. Cereal Chem 45:358. 1968. 78. DB McDougall. Characteristics of appearance of meat.1. The luminous absorption, scatter, and internal transmittance of the lean of bacon manufactured from normal and pale pork. J Sci Food Agric 21568, 1970. 79. CE Davis, GS Birth, WE Townsend. Analysis of spectral reflectance for measuring pork quality. J Animal Sci 46:634, 1978. 80. AW Brant, KH Norris, G Chin. A spectrophotometric method for detecting blood in white-shell eggs. Poultry Sci 32:357, 1953. 81, WD Dooley. Method and apparatus for detecting the presence of blood in an egg. U.S. Patent 2,321,899 (1943). 82. KH Norris, JD Rowan. Automatic detection of blood in eggs. Agric Eng 43(3): 154.1962. 83 KH Norris, JR Hart. Direct spectrophotometric determination of moisture content of grain and seeds. In: A Wexler, ed. Humidity and Moisture: Measurement and

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ControlinScienceandIndustry.NewYork:ReinholdPublishingCorporation, 1965. 84. YW Park. Determination of moisture and ash contents of food.In:LMLNollet, ed. Handbook of Food Analysis. Vol. 1. New York: Marcel Dekker, 1996. 85. EKarmas.FoodTechno134(1):52,1980. 86. DR Massie, KH Norris. Spectral reflectance and transmittance properties of grain in the visible and near-infrared. Trans Am SOC Agric Eng 8598, 1965. 87. KH Norris, JR Hart. Direct spectrophotometric determination of moisture content of grains and seeds. Proceedings of 1963 International Symposium on Humidity and Moisture, Principles and Methods of Measuring Moisture in Liquid and Solids. Vol. 4. New York: Reinhold Publ., 1963, p 19. 88. JDS Goulden. Quantitative analysis of milk and other emulsions by infrared absorption.Nature191:905-912,1961. 89. MM Pierce, RL Wehling. Comparison of sample handling and data treatment methods for determining moisture and fat in Cheddar cheeseby near-infrared spectroscopy. J Agric Food Chem 42:2831-2835, 1994. 90. JL Rodriguez-Otero, M Hermida, A Cepeda. Determination of fat, protein and total solids in cheese by near-infraredreflectancespectroscopy. J AssnOfficialAnal Chem Int 8:802-806, 1995. 91. KH Norris.Measuringlighttransmittancepropertiesofagriculturalcommodities. Agric Eng 39:640, 1958. 92. DR Massie. Fat measurementof ground beef with a gallium arsenide infrared emitter.ASAEPaperNo.73-6503,AmericanSocietyofAgriculturalEngineers, St. Joseph, MI, 1973. 93. AJ Boot, AJ Speck. J Assn Official Anal Chem Int 77(5): 1 184-1 189, 1994-95. 94. YW Lai, EK Kemsley, RH Wilson. Quantitative analysis of potential adulterants of extra virgin olive oil using infrared spectroscopy. Food Chem 53(1):95-98, 1995. 95.RSSinghal, PR Kulkarni, DV Rege.HandbookofIndicesofFoodQualityand Authenticity. Cambridge, England: Woodheard Publishing Ltd., 1997. 96. FR van de Voort, J Sedman, G Emo. A rapid FTIR quality control method for fat and moisture determination in butter. Food Res Int 25:193-198, 1992. 97. B Dion, M Ruzbie, FR van de Voort, AA Ismail, .IS Blais. Determination of protein and fat in meat by transmission Fourier transform infrared spectrometry. Analyst 119:1765-1771,1994. 98. N Nathier-Dufour, J Sedman, FR van de Voort. A rapid ATR/FTIR quality control method for the determinationof fat and solidsin sweetened condensed milk. Milchwissenschaft 50:462-466, 1995. 99. FRvan de Voort, J Sedman, AA Ismail. A rapid FTIR quality-control method for determining fat and moisture in high-fat products. Food Chem 48:213-221. 100. FR van de Voort, AA Ismail, J Sedman, G Emo. Monitoring the oxidation of edible oils by Fourier transform infrared spectroscopy. J Am Oil Chem SOC 71:243-253, 1994. 101. F Ulberth, HJ Haider. Determination of low level trans-unsaturation infats by Fourier transform infrared spectroscopy. J Food Sci 57:1444-1447, 1992. 102. M Safer, D Bertrand, P Robert, MF Devaux, C Genot. Characterization of edible

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106. 107. 108.

109. 1 10. 111.

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oils, butters and margarinesby Fourier transform infrared spectroscopy with attenuated total reflectance. J Am Oil Chem Soc 71:371-377, 1994. PS Belton,AM Saffa, RH Wilson. The potential of Fourier transform infrared spectroscopy for the analysis of confectionery products. Food Chem 28:53-61, 1988. RM Silverstein, FX Webster.In:SpectrometricIdentification of OrganicCompounds. 6th ed. New York: John Wiley & Sons, Inc., 1998, pp 71-1 1 1 . MR Nyden, GP Forney, K Chittur. Spectroscopic qualitative analysis of strongly interactingsystems:humanplasmaproteinmixtures.ApplSpectroscopy42(4): 588-594,1988. IV Mendenhall, RJ Brown. Fourier transform infrared determination of whey powder in nonfat dry milk. J Dairy Sci 74(9):2896-2900, 1991. SR Delwiche,G Weaver. Bread quality of wheat flour by near-infrared spectrophotometry: feasibility of modeling. J Food Sci 59(2):410-415, 1994. SR Delwiche, KH Norris. Classification of hard red winter wheatby near-infrared diffuse reflectance spectroscopy. Cereal Chem 70( 1):29-35, 1993. H Guillou,JP Pelissier, R. Grappin. Methods for quantitative determinationof milk proteins. Le Lait 66(2):143-175, 1986. JV Camp, A Huyghebaert. Protein. In: LML Nollet, ed. Handbook of Food Analysis. Vol. 1. New York: Marcel Dekker, 1996, p 277. P Chen. VR Nattovetty.Lighttransmittancethrough a region of an intact fruit. ASAE Paper No. 77-3506, American Society of Agricultural Engineers, St. Joseph, MI,1977.

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Computer Vision Suranjan Panigrahi North Dakota State University, Fargo, North Dakota Sundaram Gunasekaran University of Wisconsin-Madison, Madison, Wisconsin

1.

INTRODUCTION

Assessment or evaluation of food products is essential for ensuring their quality and safety. Rising consumer awareness and expectation for high quality along with strict legislation for food safetyhas necessitated quality evaluationof every food product being manufactured or processed. Consequently, the development and/or identification of advanced, reliable, fast, and cost-effective formsof nondestructive sensors and/or sensing techniques arein high demand. Computer vision is a powerful technique to extract and quantify features for food quality assessment and control. It offers the advantagesof accurate quantification of images and rapid data handling. Computer vision has been a proven technology for a varietyof nondestructive methods to evaluate food product characteristics ranging from dimensional measurements (length, width, shape, other geometrical attributes) to texture, color, defects and diseases, to three-dimensional analysis of food quality. With rapid advances in electronic hardware and other associated computer imaging technologies, the cost-effectiveness of computer vision systems has greatly improved.It is estimated thatby the year 2000, sales of machine vision systems in the North American food sector will be approximately $148 million (1 ).

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40 Gunasekaran

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COMPUTERVISION SYSTEMS

Computer vision is the science that develops the theoretical and algorithmic basis by which useful information about an object or scene can be automatically extracted and analyzed from an observed image, image set, or image sequence. Computer vision is also known as machine vision or computer imaging. It is a branch of artificial intelligence technique and deals with simulating human vision. 1): The essential components of a typical computer vision system are (Fig. Computer (analogous to the human brain) Sensor or camera (analogous to the human eyes) Illumination system (light source and illumination chamber, etc.) to facilitate image capture Frame grabber/digitizer to digitize the image information from the camera, monitor(s)

In today’s modern computer imaging system, the camera and frame grabber are joined together to form the digital camera. The use of digital cameras thus eliminates theneed to use a framegrabber in the computer. The imageis digitized in the camera and sent to the computer for further processing. More modern computer imaging systems do not need a separate monitor either. Images can be displayed directly on a high-resolution computer monitor. For special applications, many users still use a high-resolution display monitorto separately display images. Recognizing and extracting useful object features from image data are complex tasks involving a series of steps that can be grouped into three major parts

Frame Grabber/

I

Computer

Sensor/ Camera Light source

w

Illurnination

Chamber

Fig. 1 Schematic of a typicalcomputervisionsystem.

Display Monitor

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I

I

41

I

Fig. 2 Basic steps in digital image processing.

(Fig. 2): image acquisition, image processing, and image understanding. Image acquisition deals with such issueskomponents as illumination, camera, digitizer, etc. The image processing step encompasses preprocessing, segmentation, and feature extraction. The image understanding part consists of image recognition so as to make and interpretation. Eachof these steps must be carefully performed each subsequent step progressively easier and result inan improved end result. For example, a poorly formed or acquired image cannot provide good results with any amount of further processing. All the steps are closely linked with knowledge base available about the system studied and the featuresof interest.

111.

IMAGE ACQUISITION

A.

Digital Images

A digital image can be defined as a spatial representationof an object or scene. A digital monochrome image is a two-dimensional (2-D) light-intensity function, denoted by I(x,y), where the value or amplitude of intensity I at spatial coordinates (x,y) is typically proportional to the radiant energy received in the electroor detector (the camera) is sensitive in a small magnetic band to which the sensor area around the point (x,y). As far as the computer is concerned, the image is a matrix (x,y) of numerical values, each representing a quantized image intensity value. Each matrix entry is known as a pixel (short for picture element). The total number of pixels in an image is determined by the size of the 2-D array used in the camera. Most commonly used cameras have a spatial resolution of

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5 12 X 480 or 640 X 480. For best results it is important to match the spatial resolution of the camera to that of the frame grabber. The intensity of the monochrome image is known as the gray level. The limit on gray level is that it is positive and finite. The gray level interval (from low to high) is called a gray scale. A common practice is to shift this interval numerically to the interval (0,L) where the lowest value 0 represents pure black All intermediate values are and the maximum value L represents pure white. shades of gray varying continuously from black to white. For example, when an 8-bit integer is used to store each pixel value, gray levels range from 0 to 255 (i.e., 2" - I to 2' - I). Inferring an object's size, shape, position, orientation, and other attributes from the spatial distribution of gray levels requires the capability to infer which pixels belong to the object and which do not. Then, from the pixels that belong to the object, it requires the capability to identify the object features of interest. Algorithms have been developed to translate the gray levels of a pixel in a way that accentuates the desired information. In the case of color images, the image intensity is represented by three components representing red, green, and blue (RGB system) or hue, saturation, and intensity(HSI system). Further detailsof the color digital image are presented in a following section.

B. illumination The prerequisite for any vision application is that the features to be examined can be seen in the image. Therefore, despite all the progress in image analysis/ processing algorithms, the performance of the camera and illumination subsystem can greatly affect the reliabilityof a machine vision application. A well-designed lighting and illumination system can assist in the accuracy and success of image analysis by enhancing image contrast. Good lighting will improve feature discrimination,reduceprocessingtime,andreduceprocessinghardwarerequirements. Thus, it is almost always cheaper to improve lighting than image processing (2). Food materials are nonhomogeneous and randomly oriented; the raw materials may be dirty. Singulation of objects for examination is often difficult, so we have to cope with objects that touch and/or overlap, which may cause shading during image acquisition. Therefore, vision applications in the food industry present unusual challenges when designing proper illumination systems. Illumination consists of selecting appropriate light sources and identifying suitable configurations for the light sources so as to obtain the highest quality images. The geometryof the imaging system should be well known. This requirement is especially important for dimension measurements. When the viewing geometry is more complicated, either becauseof the nonplanar image surface or nonperpendicular imaging angle, measurements are more difficult and require

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determining the geometryof the imaging system. Elaborate discussions of different types of illumination techniques for general machine vision applications have been presented by other authors (3,4). Most lighting arrangements can be grouped as either front-lighting or back-lighting. The front-lighting option is best suited for obtaining surface characteristics of an object, while back-lighting is best for (5) subsurfacefeatures. For example, using back-lighting, Gunasekaran et al. examined internal stress cracks in corn kernels and Upchurch and Throop (6) detected watercore in apples. The appropriatenessof a well-designed illumination system can be evaluated by the suitability of acquired images for successful further processing. The most commonly used illuminations system configurations are summarizedin Fig. 3. Associated advantages and disadvantages of these techniques are compared in Table 1. A wide variety of light sources and lighting arrangements are available (2). Most general computer vision applications are implemented using either incandescent or florescent lighting. However, use of polarizers and polarized light can improve the light intensity contrast, eliminate unwanted glare, and minimize diffuse reflectance (7). This is especially suitable for transparent and translucent objects. Since an object’s color dependson illumination, color measurements are easily affected by changesin the color temperatureof an incandescent bulb. Thus, or colorvalues,requiresa measuringbrightnessinformation,suchasdensity very stable illumination source and sensor. Bright specular reflections may cause saturation, blooming, or shifts in image magnification. Sometimes the color of two objects will appear similar under one light source but much different under another. So, a number of light sources of different spectral responses must sometimes be tried when attempting to maximize image contrastfor the best possible results. For multiple or brightly colored fruits and vegetables, a multiple spectral lighting system is needed to assure accuracy over a large spectral range. Spectral reflectance properties of products should be considered when developing an appropriateilluminationsystem(lightingandviewinggeometries,lightsources, (8). The specand sensor components) to obtain maximum discrimination power tral output of different light sources can be obtained from respective manufacturers. For on-line evaluations where speed of operation becomes an important look the same criterion, global uniformity (i.e., the same type of feature should wherever it appears in the image) is essential. This means that brightness and color values are the same and thus it requires uniform, consistent image illumination (9). Furthermore, the optomechanical constructionof a camera and illuminator should withstand environmental conditions suchas mechanical vibrations and dust common in industrial applications. Strobe lightingis useful for on-line applications to virtually arrest themotion to aid in acquiring images without worrying about image “blur” due to image motion. The strobe repetition rate should be selected to match the speed of object motion. A strobe unit designed for machine

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Panigrahi and Gunasekaran

Light

Sources

Light Sources

Diffuser

Diffuser

kpiece

Fig. 3 Different configurations of illumination system for computer vision. (A) Diffuse front illumination, used for general top lighting. (B) Directional front illumination creates shadows and will not reflect into the camera if surface is highly reflective. (C) Light tent (cloudy day) is nondirectional, totally diffuse top lighting that produces illumination like that on a cloudy day (good for metal parts and electronic components). (D) Back-lighting through a collimating lens so that the light rays are pseudo parallel. (E) Dark field illumination in which incident light reflects away from the camera and illumination is created from specular reflections. (F) Diffuse back-lighting, in which light is on the opposite side of the part as the camera and goes through a diffusing material suchas lexan or opal glass. (G) Low-angle illumination, in which incident lighting is almost parallel to the surface of the part. (H) Polarized front illumination, involving front-lighting with a polarizer on the light and a cross-polarizer on the lens. (I) Polarized back-lighting, in which a polarizer and a cross-polarizer are on opposite sides of the part over some form of back-lighting. (J) Stmbed illumination, in which microsecond duration lighting is used to freeze the motion of moving parts. (K) Structured light, in which a plane of light generated via structured white light with focusing optics, or laser line converter,is used to show contour/ 3-D information of the part. (L) Coaxial lighting, in which the illumination is along the same path as the camera’s viewing path. (Courtesy of Machine Vision Association, Society of Manufacturing Engineers, Dearborn, MI.)

vision use must be able to withstand continuous operation with repetition rates

of 30 times a second (10). Fiber optics is the light guide that allows the transmission of radiant power through fibers (thin solid tubes) made of materials such as glass, fused silica, or plastic (13). Most applications of fiber optics until today have been in the areas of telecommunications and computer networking. However,with technological

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Computer Vision

L i g h t Soorcc

W orkpiece

(b)

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Panlgrahi and Gunasekaran

W orkpiece

Light

Sources

(r) Fig. 3 Continued

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r s

\I

Light Source

Light Source

Panigrahi and Gunasekaran

1

Cross-Polarizer

Translucent Part

1Polarizer

Light Source

(i)

(1) Fig. 3 Continued

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Computer Vision

Workpiece

(1)

ul 0

Table 1 Comparison of Different Illumination Systems Illumination System Diffuse front illumination

Directional front illumination

Light tent

Advantages Soft, fairly nondirectional Reduces glare on metallic surfaces Relatively easy to implement Easy to implement Good for casting shadows Fiber optic delivery in many configurations Eliminates glare Eliminates shadows

Collimated back lighting

Produces very sharp edges for accurate gauging

Dark field illumination

Illuminates defects Provides a high contrast image in some applications Easy to implement Creates silhouette of part Very high contrast image Low cost

Diffuse backlighting

Disadvantages Edges of parts may be fuzzy Low contrast on monocolor parts May create unwanted shadows Illumination is uneven Must surround workpiece Can be costly Size can be a problem Difficult to implement if material handling interferes May be too bright for camera without neutral density filters Does not illuminate flat smooth surfaces

Edges of parts may be fuzzy Difficult to impIement if material handling interferes

Low angle illurnination

Shows topological defects

Polarized front illurnination Polarized backlighting

Eliminates glare Highlights certain types of features or defects in translucent materials Relatively easy to implement

Strobed illumination

Structured light

Coaxial lighting

Crisp image with no blurring Can be area. fiber optic, or light emitting diode (LED) Very long lifetime Shows 3-D information Produces high contrast on most parts Laser frequency can be easily band pass filtered Eliminates shadows Uniform illumination across FOV

Single source will produce uneven lighting across surface Reduces amount of light into the lens significantly Only works for birefringent features Edges of parts may be fuzzy Difficult to implement if material handling interferes More costly than standard sources Requires accurate timing with camera Must be shielded from personnel Lasers above 5mW pose safety issue Hard to image on some metals and black rubber

Complicated to implement Harsh illumination for shiny surfaces

Source: Courtesy of Machine Vision Association. Society of Manufacturing Engineers. Dearborn. Michigan.

52 Gunasekaran

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advancements, fiber optics has been used for sensors as well as computer vision applications. For computer vision applications,fiber optics has been used mostly of fiber optics for illumination and image transmission. It is expected that the use as part of an image illumination subsystem of the computer vision system will dramatically increase in the future. In many computer vision applications, becauseof space and environmental or to constraints, it is necessary to provide illumination from remote locations transmit image information to a remotely located computer system. Under these conditions, the use of fiber optics is justified ( 1 1). Fiber optics is highly desirable under the following conditions ( 1 1 ) : Space for positioning the light source is restrictive. The application requires camera movement along with movement of the illumination. Multiple lighting with varied angle of incidence is required. Maintenance of temperature profile of heat sensitive objects is critical. Insertion of light through a small opening is required. The light source could be hazardous in an explosive environment. The application requires examination at micro or macro levels. Optical fibers used for light transmission, illumination, and image transmission can be of the high-loss type, made up of optical glass or plastics. High-loss type fibers are also cost-effective as compared to low-loss type fibers, which are used primarily for data transmission, communications networks and other sensing applications (12,13). Fiber optics operates on the principle of total internal reflection. Optical fibers exploit this phenomenonby encasing a cylindricalfiber in another cylindrical casing of lower refractive index (12,124). The outer casing is called “clad,” and the inner fiber is called “core” (Fig. 4). When light enters the fiber end at an angle A (incident angle), it undergoes refraction at the interface of the core and its surrounding. Generally, the core has a higher index of refraction (n,) than that of clad (11~). Under this condition, if the angle of incidence (A) is greater than or equal to A, (critical angle), the light will undergo total internal reflection (multiple times) at the core-clad interface. Finally, it will leave the fiber through the outer end ( 1 19). Using Snell’slaw for optical fiber, its numerical aperture(NA) can be determined from the refractive indices of the core (n,) and cladding (n?), respectively ( I 19):

NA

=

[(nf - n;)]’’’

NA

=

n sin (A,)

53

Computer Vision

Fig. 4 Illustration of totalinternalreflectance (Adapted with permission from Ref. 124.)

of incidentlight

inan

opticalfiber.

where n is the refractive index of the surrounding medium. Since for air n = I (for many cases, air is usedassurroundingenvironment), N A = sin A,. N A represents the ability of the fiber to accept light. Thus, the higher the NA, the greater is the amount of light entering into the fiber. Therefore, to allow more light through thefiber, consideration should be givento choose larger fiber (core) and N A ( 1 19). For both illumination and transmission applications, several criteria need to be considered before selecting the appropriate optical fiber for a given computer imaging system ( 120).

Range of operating wcrvelength: Most computer vision applications deal with the visible spectrum (380-700 nm). However, with the growth of it is important to know the the nonvisible computer imaging systems, range of the operating wavelength. Numerical aperture: The required numerical aperture of the optical fiber to be used. Muterid ofopticcI1,fiher: For illumination applications, high-quality optical glass is used for the fiber’s light transmitting core (13). For image transmission, glass fiber or other typesof low-loss fiber can also be used ( 12). For nonvisible imaging applications, the right type of materials need to be chosen such that the material can transmit electromagnetic energy

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Panigrahi and Gunasekaran

I

I

Chalcogenide

Fluoride

I

Glass

I

Enhanced Glass

1

Silica

I 0.1

0.2

0.3

Ultraviolet

I 0.5

I

I

0.7

Visible

I l l 1

I

I 2

3 Wavelength - (wm) .. .

5

IIIII 7

10

20

Near-infrared

Fig. 5 Opticalfibermaterialsandtheirspectraltransmission(Adaptedwithpermission fromRef. 12.)

within the operating wavelength range. Figure 5 shows different optical fiber materials and their spectral transmission characteristics (12). Overcrll length and trnnsnlissiorl loss: Though the type of application will determine the overall length of optical fiber, it is important to note that a transmission loss of energy (attenuation) occurs, which is dependent on the overall length of fiber used. Therefore,it is recommended to obtain the transmission loss of optical fibers for different lengths and to optimize the appropriate overall length of optical fiber ( 1 19). Activejber diatneter o r j b e r hurldle diameter: In computer vision applications, fiber bundles are used most often. A fiber bundle consists of several single (bare) strands of optical fiber thatarearrangedparallel to each other and are used mostly for image transmission. Noncoherent fiber bundles imply a random arrangementconsisting of optical fibers andareusedmostlyforillumination applications (12,13). Nevertheless, the overall active diameter of a fiber bundle is important because it affects the acceptance capability of optical fiber and is also related to the numerical aperture (12,13,1 19,124). Skeatlzirlg incrterial: This refers to the tubing or material that protects the fiber bundle (13). There are different types of sheathing: steel. poly (vinyl chloride) (PVC), aluminum, and others. It can be flexible, rigid, or semi-rigid (120). Sheathing on optical fiber greatlyaffects its performanceanddurability. The

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selection of sheathing depends on the operational environment, cable length, and bending radius of the fiber ( 1 3 ) . Minirnurn herding radius: Optical fibers are flexible. However,they should not be bent more than their recommended minimum bending radius, which can cause breakage in the fiber or even increase losses (12,13,124). It is advisable to get the minimum and optimum bending radii of optical fiberdfiber bundles from the manufacturer. A general rule of thumb is that bending a fiber more than 25 times its core or bundle diameter can damage the fiber (13). Environrnerlt: This refers to the environment in which the optical fibers will operate (120). Temperature, liquid, solid, and other parameters (such as exposure to explosive gases, radiation, etc.) will affect the materialof fiber optics, sheathing, and other manufacturing considerations ( 1 3,120). Plastic fibers can be or explosive environments, a used for applications below 80°C. For hazardous cold-light fiber optic system (eliminating the infrared component of light) can be used ( 1 3 ) . Rigid fiber systems (fiber rods), where fibers are completely fused to each other, are of particular value for measuring in and through liquids. An ordinary light-conditioning glass fiber willturnbrownveryquicklyunderthe influence of radioactivity. Typical “rad-hard” fibers are used that can withstand long periods of radiation exposure ( 13).

Camera C. The camera is the sensor of a computer imaging system used to capture image information. It functions similar to the eyes in human vision. Charged coupled device (CCD) cameras have been used for nearly all computer imaging applications since their introduction almost 25 years ago (14). There have been many developments basedonthe CCD technology.Recentlycomplementarymetaloxidesemiconductor(CMOS)technologyhasbeenintroduced(14,17).Many varieties of black-and-white and color cameras are commercially available. A black-and-white camera will suffice for food quality evaluationif attributes unrelated to color (dimensions, shape, other geometrical attributes) are to be evaluated. To evaluate color or color-related attributes, an appropriate color camera should be selected. Color cameras range in cost from several hundred to several thousand dollars. For higher-quality color images, three CCD color cameras perform better than one CCD color camera. Though most cameras available are of area-array types, line-scan cameras arealsoavailable in bothcolorandmonochromemodes.Area-arraycameras are useful for imaging 2-D scenes, while line-scan cameras offer high positional accuracy, rapid frame rate, and a wide dynamic range (14). They are available in resolutions that include 128, 256, 5 12, 1024, 2048, 4096, and 8 196 pixels per line. Many line-scan cameras have square pixels also (14). For some food product evaluation, the nature of the inspection might re-

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quire the camera to deal with low-light level images. This requirement can be fulfilled by using another variationof line-scan camera, “time-delay integration” (14). These cameras typically have 1024 lines and a number of stages or rows of sensors positioned side by side horizontally. Because these sensors also incorporate rows of photo elements, multiple views of objects can be captured. The electrical charge from each rowis transferred from row to rowin synchrony with the moving object to eliminate blurring. These cameras provide high signal-tonoise ratios as compared to their line-scan camera counterparts (14). For imaging 2-D scenes, the most commonly used cameras are either frame transfer or interline transfer CCD types. A frame transfer type camera has both a sensing array and a storage array. The storage array is positioned above the sensing array. At the end of a field period (1/60 s, RS-170 signal), the data are rapidly shifted vertically from the sensing array into the storage array. After the data have been shifted into the storage array, they are shiftedto a horizontal shift register, two lines at a time. The advantage of the frame transfer device is that is the entire area of the sensing array is sensitive to light. Their disadvantage that the transfer process takes longer and, thus, under certain conditions where objects move, error in image data may be introduced (1 5). Interline CCD type chips are used for most CCD cameras in the market. of their low cost and their They are attractive for many applications because ability to handle bright localized light overloads without streaking (15). They can image moving objects without blurring, provided the scene has sufficient light (1 5 ) . Recently, progressive scan technology based on CCD chips has created progressive scan cameras that can be used for imaging fast-moving objects (14). The emergence of these cameras has increased the potential of integrating computer-imaging techniques for high-speed, on-line applications in a cost-effective manner. Progressive scan means noninterlaced or sequential line-by-line scanning of the image information out of CCD as opposed to traditional interlaced fields (16). Although this technology has been available for some time, especially for frame-transfer type cameras, the recent introduction of cameras basedon interline transfer-based progressive scan devices has created new application advantages, especially for high-speed imaging (16). In these cameras, technology depends heavily on effective shuttering (16). The technological advantages of interline progressive scan ( 1 6) now can be applied to all types of high-speed and precision imaging applications related to food quality evaluation. Cameras for computer imaging that use charge injection devices (CIDs) are also available. These sensors differ significantly from typical CCD sensors. Although the sensing pixels are constructed of metal oxide, semi-conductor capacitor integrating sites, the collected photon charge is read out differently ( I 5). Major advantagesof CIDs includerandom access, radiation tolerance, nonbloom-

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ing, and adaptive exposure control (14). One disadvantage is that the read-out noise is higher than that from the conventional CCDs (15). New innovations are observedin the camera market based on advancements in CMOS technology. This has made possible the development of a single chip camera that could also be very cost-effective (17). For food quality evaluation where cost-effectiveness is much desired, it promises new applications. A new image sensing technique involving active pixel sensors manufactured on CMOS shows additional potential (14). In the design of an active pixel sensor, both the photo-detector and read-out amplifiers are integrated at the pixel site (14). These CMOS active pixel sensors are very new, and their integration needs to be assessed for different food quality evaluations. A survey of different types of cameras based on CMOS chips is given in Ref. 18. Improvements in CCD chips have also created additional opportunities for food applications. Thinned, back-illuminated CCDs (BCCDs) overcome the performance limitsof conventional front-illuminated CCDs by illuminating and collecting charge through the back surface (19). Cameras made up of BCCD show higher overall sensitivity than that of a traditional CCD, which makes themvery useful for fluorescent imaging and near infrared (NIR) imagingor imaging at the far end of the visible spectrum (700-1000 nm). Use of BCCD has allowed the capture of an image without using intensified CCD (19). As discussed earlier, it is important to match the application requirements with a camera’s capabilities when selectingan appropriate camera. The following parameters are also critical when selecting a suitable camera: Resolution of the camera Signal-to-noise ratio Signal output Minimum illumination required Analog or digital output Additional camera adjustment capabilities

D.

FrameGrabber

The basic function of a frame grabber or digitizer is to capture an image from a camera so that a computer can access the image. Generally, a frame grabber takes an analog video wave, samples it at specific intervals, and translates the as an array of picture information into a digital form (image), which is stored elements (pixels) (43). The selection of a frame grabber for a given computer vision application is very critical. The following describes the critical features to be considered in selecting a frame grabber.

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1.

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Video Input

It is critical to ensure that the output of an image source (analog camera, digital camera, VCR, etc.) matches the input of the frame grabber. The camera can be monochrome (black-and-white) or color providing standard outputs as RS-170 monochrome or National Television System Committee (NTSC) color signals (60 Hz video signals are used throughout North America and Japan) ( 1 18). The camera can also provide Comit’e Consultatif International Radio (CCIR, an international organization) monochrome or phase alteration line color signals (50 Hz or progressive video standard)( 1 18). Nowadays, other cameras, such as line-scan scan, are also available with nonstandard video outputs. It is also possible to take image information from a video cassette recorder (VCR) or digital camera. It is often thought that digital cameras do not need frame grabbers. However, most frame grabbers need an interface with a computer. It is recommended to verify the proper required interface of the digital camera. or of low Some video signals, including those from VCRs, can be noisy quality, resulting in blurry image acquisition due to missing or extraneous sync (synchronization)signals.Thus,specialtimingcircuitry is requiredonframe ( 1 18). grabbers to correct for missing, extraneous, or low-level sync pulses It is sometimes necessary to acquire monochrome images from color signals. The color (chrominance) content of these signals can cause interference patterns, which degrade the quality of the image. Thus, frame grabbers are provided with a hardware/software selectable chrominancefilter that produces better images ( 1 18). 2. Analog/DigitalCapability

Important aspects of analog/digital (A/D) capability arebriefly described below: CI. Spatial Resolution. umns ( 1 18).

The number of pixels represented as rows

X

col-

h. BrigktrwssResolution. Thisrepresentsthemaximumdiscrimination of a given capability of digital pixel value representing the brightness or color pixel. It is expressed by the resolution of the AID converter, which is expressed in number of bits (1 18). For a resolution of 12 bits, the resulting number of gray levels is 2”. For color images, three A/D converters simultaneously convert red, green, and blue information. For color frame grabbers, brightness resolution is determined by the sum of all the A/D converter’s resolutions, i.e., 3 X 11 bits. For example, a color frame grabber with an 8-bit A/D converter has a brightness resolution of 3 X 8 or 24 bits. The total number of colors is 2” or 16,777,2 I6 (25,43,118).

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c. Speed of A / D Cotwersiotl. The speed of A/D conversion in a frame grabber is expressed in megahertz. It is very important that the speed of A/D conversion is such that the required spatial resolutionof the image can be salnplcd by the frame grabber. For example, a 5 12 column X 480 row RS- I70 image signal needs 52.59 ps for each line. Each rowof the image of 5 12 columns requires 5 I2 A/D conversions. The time required will be ( 1 18):

52.59 ps/S 12 = 103 ns = 9.74 MHz

K

I O MHz

cl. Squrrre Pixels. Thegeometry of theimageshouldbesuch that the number o f pixels represents equal distance both horizontally and vertically. This is called a square pixel (20). Regardless of the image shape or resolution (201, it shouldbeverifiedfromthemanufacturer that the framegrabbergenerates square pixels. e. G r q Scale Noise. In the framegrabbercircuit,randomness of noise can cause variations in the digitization process (20). One way to define precision in the digitization process is by evaluating gray scale noise (20). Itis usually represented by the least significant bit (LSB), a binary number representing the gray scale value (20). For example, an 8-bit frame grabber uses an 8-bit binary number to represent each gray scale. A change in LSB of % 1 implies a change of 2 1 gray scale unit. A precision frame grabber typically has a gray scale noise of 0.7 LSB (20). Gray scale noise can be a problem for biological and food applications when dealing with low-contrast images (20). Applications such as analysis of defects (with subtle gray scale variations from adjacent good regions) can be affected by the gray scale noise (20).

3. SignalHandling/Conditioning The quality of an incoming video signal directly affects the qualityof a digitized image. Poor lighting conditions, signal loss due to long cables, irregular sync of camera are some parametersthat can cause signals, and gray scale nonlinearity poor quality in an incoming video signal. Well-designed frame grabbers can compensate for many of these problems by having different in-built capabilities (20). Gain control adjustments, offset adjustments, and sync timing are three very desirable capabilities for frame grabbers (20). (1. Guin Adjrrsftnents. Gain adjustments can help in many situations such as when adjustment of illumination is not possible and when the application depends on natural lighting (20). Other applications which are inherently of low contrast (typically food/biological applications) or those which deal with passive

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infrared imaging can also benefit from this feature. Thus, a gain control frame grabber provides more versatility (20).

in the

b.OnsetAdjustments. Offsetadjustment is verycritical,especially if the camera does not compensate for low or high lighting conditions. For best results, a frame grabber should have the capability to offset a video signal by 2 100% in small, precise increments (20). C. ProgrammableGainand Offset Controls. Becausegainandoffset adjustments are very complementary, it is desirable that a frame grabber have the capability for programmable control for gain and offset (20,118). The frame grabber that is to be used with resettable cameras should detect each horizontal sync pulse and instantly resynchronize its pixel timing. (Resettable cameras are mostly used for industrial applications such as inspecting parts on conveyer belts.) For such applications, a desirable frame grabber is one with proper sync-timing capability such as a crystal-controlled digital clock that can resynchronize instantly (20). To digitize video Another parameter to be considered is pixel jittering. images, frame grabbers sample analog video at uniform intervals on each line to determine the gray scale levelof each pixel (20). However, the inability of frame grabbers to precisely adjust the sampling points relative to horizontal sync causes pixel jitter (20). “Phase locked loop (PLL) is the traditional timing mechanism used for sync timing. PLL creates a clock from reference frequency.Pixel jitter, in other words, is the timing accuracy of this clock and is expressed in nanoseconds. Pixel jitter of PLL varies greatly dependingon design and implementation (21).” For example, a frame grabber with 2 5 ns implies a pixel positional error of 12.5% (20). Though frame grabbers are available that use both digital clock circulating and modified PLL for precision applications with pixel jitter of *2 ns, pixel jitter of ? 10 ns is reasonable. The selection of acceptable pixel jitter depends on the application (21).

4. SystemArchitecture for Integration Another important set of criteria is the integration of the frame grabber with the overall system. Criteria include the interfacing bus, in-board memory, video output support and digital I/O support (20). a. Inteflucirzg Bus. Thoughframegrabbersinitiallyweredesigned to be interfaced with computers using industry standard architecture (ISA), present technology allows using a higher speed bus. A peripheral component interconnect (PCI) bus is an integral part of today’s IBM-compatible, high-performance personal computer system.A PC1 bus has a theoretical bandwidth of 132 megabytes per second and a continuous band width of 70-80 megabytes per second. It can accommodate 32- and 64-bit data sizes(121). Although a PC1 bus has greater data handling and transfer capability than provided by an ISA/EISA bus(1 18,12 11, it of a given is important to evaluate the data output and handling requirements

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imaging application. Many frame grabber manufacturers offerPC1 bus mastering capability with frame grabbers. In this situation, the frame grabbers make the required requests for memory transfers, freeing the CPU for other processing tasks (22). It is also important to obtain additional information from the manufacto handle data. Some manufacturer on the adopted hardware desigdarchitecture turers provide an on-board memory/frame buffer (with PC1 bus), while others provide first-in, first-out (FIFO) memory (23). There are, of course, cheaper versions of frame grabbers with no memory at all. The selection of one type over another depends on the application (22,23).

6. Digital Input/Output. Forrealworldapplications, it isoftennecessary to correctly coordinate the timing of the image capture. One of the digital input/output (I/O) capabilities of the frame grabber would consist of a single output “strobe” and a single input “trigger” (20). The ability to generate a programmable width pulse is also desirable because camera control becomes more convenient with it (20). a single frame grabIf the application requires using multiple cameras with ber, the cameras are synchronized in a process called “genlocking” so that the frame grabber does not encounter different video timing (20). Although many cameras provide genlocking, variations and nonstandardizationof required camera input makes the realization of the process difficult. On the other hand, it is very convenient to genlock the cameras from the frame grabber. Thus, for such genlocking applications, the camera should supply horizontal and vertical drive outputs (20). Another desirable digital I/O capability of a frame grabber is “delayed trigger.” This capability is very helpful for real-time industrial applications and ensures that the interfaced camera will always capture images without failure (20). 5. MiscellaneousCharacteristics The following isa list of characteristics to considerin addition to those described already when selecting a frame grabber (20).

a 12 V output for supplying power Power output: some frame grabbers offer to a camera, thus eliminating the need for ora separate power supply to the camera (20) Many frame grabbers have in-built digital signal processors (DSP) or dedicated hardware to perform different image processing operations Support for operating systems and high-level programming languages such as C/C+ + and Pascal Visual Basic (20) Support for third-party programs/libraries for image processing (20) Technical support/warranty

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IV.

IMAGE PROCESSING

A.

Basic Steps

The basic steps in image processing are image preprocessing, segmentation, and feature extraction (Fig. 2). The purpose of image preprocessing or image conditioning is to enhance the quality of the acquired image, which is often degraded by distortion and noise in the optical and electronic systems of the input device. (24): noise reducImage preprocessing steps include one or more of the following tion, geometrical correction, gray level correction, and correction of defocusing. These steps are typically applied uniformly and are context independent. As thenameimplies,imagesegmentationrefers to theprocessofsegorobjects. menting or partitioningacompositeimageintocomponentparts Proper segmentation is very critical. Often, the first step in assuring successful segmentation is control of background uniformity. For monochrome images, segmentation normally is performed by examining the gray scale histogran-a bar chart of the number of pixels in the image at different gray levels. Segmentation algorithms are based on discontinuity or similarity of the gray level values. Discontinuities in image gray scale indicate sharp changesin image brightness such as background and object. In general, autonomous segmentation is one of the most difficult tasks in image processing (25). Macaire and Postaire (26) described a real-time adaptive thresholding to be used for on-line evaluation with line-scan cameras. Segmented image data constitute raw pixel data of the image boundary or or region a region of interest in the image. The image representation as boundary should be selected based on the intended application. For example, boundary representation is appropriate for image size and shape characterization. The region representation is suitable for evaluating image texture and defects. The feature extraction step is thekey in deciphering the require image data form the composite image information.The successof the feature extraction step of the previous steps, including image depends largely on the appropriateness acquisition. The “knowledge” of the feature under consideration is also critical at this stage in designing appropriate algorithms to extract information pertaining to the desired feature(s). Featureextractionfacilitatesobtainingsomequantitativeinformation of interest, which is then processed in conjunction with the knowledge base available for the feature studied.

6 . KnowledgeBase At all steps during image processing, interaction with the knowledge base enables more precise decision making. Thus, knowledge about the system being studied of an image-processingsystem.Withoutan shouldbeanintegralcomponent

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appropriate knowledge base, the vision system cannot “think” and make intelligent decisions (27). This problemis further complicated by the fact that the output of a vision sensor is a complex combination of many parameters: size, shape, texture, color, etc. Some requirements for intelligent decision making are (a) the ability to extract pertinent information from a background of irrelevant details, (b) the ability to learn from examples and generalize this knowledge and applyit in different circumstances, and (c) the ability to make inferences from incomplete information. of Expertsystems,neuralnetworks,andfuzzylogicaresomemethods building knowledge bases into computer memories, enabling them to recognize and interpret image data and to provide on-line control capabilities. The image understanding part of the computer vision systemis inherently tied with the completeness and accuracy of the valid knowledge base available for the product(s) and the feature(s) being studied. The successful image understanding step will lead to the ultimate goal-translating image analysis data into information useful for further action such as process/machine control. Applying neural networks and/or fuzzy logic in conjunction with computer vision systems is rapidly growfor quality sorting of fruits and vegetaing, and commercial systems are available bles (28).

C. PatternRecognition Pattern recognition at some level is fundamental to image analysis. A pattern is ina quantitative or structural description of an object or some other entity of is formed by one or more descriptors terest in an image. In general, a pattern (features). Pattern recognition by machine involves techniques for assigning patterns to their respective classes automatically and with as little human intervention as possible. In machine recognition of image patterns and shapes, generally two approaches are used: a statistical or decision-theory approach, in which features are extracted and subjectto statistical analysis, and a syntacticor structural approach, in which image primitives are selected and subjected to syntax analysis. The statistical or decision-theory approach is the traditional approach to 1960s. The system (Fig. 6) pattern recognition that has been studied since the consists of two parts: analysis and recognition. In the analysis part, a setof image features that are judged to be nonoverlapping (or as widely apart as possible) in the feature space is chosen (29). A statistical classifier (e.g., based on a fuzzy logic or neural network system) is designed and trained with the chosen set of features to obtain the appropriate classifier parameters. In the recognition part, an unknown image is filtered or enhanced in the preprocessing stage, followed by feature detection and classification. This approach, however, does not describe or represent structural informationin a pattern that is often desirable or necessary

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Gunasekaran Panigrahi and

Classification

t """_

RECOGNITION -------------""""" ANALYSIS

t "_ I

Sample Pattern

I Input Image Pattern

rL

-

Decomposition

-

t

4

,.

FPrimitive & Relation Recognition

.

RECOGNITION "--""""""""_ ANALYSIS

"1

-

I

Training

b

Selection

a

Syntax or Structural Analysis '

A

-

" " " ~ " " " " " " " " " " ~

il

Relation Selection

d

Grammatical or Structural lnferena3

b,

-

(8) Fig. 6 Pattern recognitionsystems: (A) statistical and (B) syntactic. (Adaptedfrom Ref. 29.)

for certain applications, as, for example, when the number of so classes large is or the given pattern isvery complex. In these circumstances, the numberof features required is probably very large, making the statistical approach impractical. In the syntactic or structural approach, complex patterns are decomposed so on, until meaningful into subpatterns and recursively into sub-subpatterns and primitive patterns (analogous to features in the statistical approach) can be reliably extracted from them (29) (Fig. 6B). This approach allows us to describe and represent the input pattern, in addition to classifying it into a specific class. This approach has attracted much attention in the recent development of pattern recognition research.

D. Image Morphology Image morphology refers to the geometric structure within an image, which inA general cludes size, shape, particle distribution, and texture characteristics.

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approach in analyzing image morphology is to transform the given image to another where the information represented in the transformed image is more easily understood. For example, investigationsof the shape of objectsin a binary image a minimal set of pixels often use thinning algorithms. Reducing an object to representing an invariant of the object’s geometrical shape is called thinning. A skeleton is a line-thinned caricature of the binary image that summarizes the shape and conveys information about its size, orientation, and connectivity (25). An image resulting from the thinning process has many fewer black pixels representing the object and is, therefore, easier to manipulate. If themaingoal of thinning is data reduction and exact reconstruction of the original image is not essential, many techniques are available that yield acceptable skeleton representations. However, if close or exact reconstruction is desired, care must be taken in choosing an appropriate algorithm. Morphological image-processing algorithms (thinning, region filling, thickening, pruning,etc.) remain a useful tool in image processing and computer vision. Some of the requirements for image thinning are (30): Connected image regions must thin to connected line structures. Approximate end-line locations should be maintained. Thinning result should approximate the medial lines. Extraneous spurs caused by thinning should be minimized. The morphological approach has been successfully applied to a wide variety of problems. The power and usefulness of some basic morphological processing algorithms have been illustrated by McDonald and Chen (31). Morphologicalprocessingforisolated,nontouchingobjectsiseasilydoneusing commercialpackages, which canperformobjectcounting and dimensional measurements, etc. However, touching and overlapping objects pose problems unique tothe products being examined. Thus. separate algorithms and procedures need to be developed. McDonaldand Chen (31) developeda morphological algorithm to separate connected muscle tissues in an image of beef ribeyes. Recently, Ni and Gunasekaran ( 3 2 ) used imagethinning in conjunction with a syntactic approach to evaluate the morphology and integrity of touching and overlapping cheese shreds (Fig. 7). The algorithm performed very well with less than 10% error in individual shred length measurements. Evaluation of an image skeleton was also used to characterize granular foods that may agglomerate (33). Smolarz et al. (34) used morphological image processing to define structural elements of extruded biscuits and then to discriminate biscuit type.

E. ShapeFeatureExtraction The statistical or decision-theory approach has been widely used for food shape it isoften featureextraction.Foodmaterialshapeisveryimportantbecause

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Gunasekaran Panigrahl and

Fig. 7 Morphological image processing for evaluating integrity of cheese shreds: (A) cheese shreds, (B) binarized image, (C) after image thinning. (Adapted from Ref. 32.)

closely related to quality. Due to the demands of high quality, automated food shape inspection has become an important need for the food industry. Due to large inhomogeneities of food materials, however, such invariant shape features cannot be used to detect local defects. In many cases, therefore, the invariant feature extraction methods cannot accurately distinguish between damaged and undamaged categories. Panigrahi et al. (35) evaluated invariant moments and fractal geometry for shape classificationof corn. Recently, variant shape extraction methods (position, orientation and scale) are gaining popularity for food material shape inspection (36). In the variant method, the edge contour of the inspected object is transformed to a given position, orientation, and scale. Then the shape features are extracted from every local edge point. Ding et al. (37) presented a statistical model-based variant feature extraction method for shape inspection of corn kernels. This was based on a reference shape, a transformed average shape of some undamaged corn kernels. After the reference shape was obtained, the shape of kernels being inspected was compared with the reference shape. (38) proposed a new algorithm with More recently, Ding and Gunasekaran improved ability to adjust object location, orientation, and scale to determine the edge contourof a numberof food materials for shape evaluation. This multi-index active model-based feature extractor is based on a reference shape comparison

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principle. The basic idea is to first transform and adjust of a set undamaged training objects to a certain location, orientation, and scale to obtain the “average of transformed good object edge contour” known as the reference shape. The second step is to transform and adjust each object to the same location, orientation, and scale. Then the shape of the objects under inspection can be compared with the reference shape to identify any significant deviations. Corn kernel and animal crackershapeinspectionisusedasexamplefoodmaterialstoillustratethis method. The reference shape contour of an undamaged product is indicated by the dotted linein Fig. 8. The line going through the geometrical center, the origin, and another (prespecified) point could be considered as the x-axis. Then a number of equal-angular locations are chosen, starting from the zero angle direction (xaxis). An arbitrary equal-angular location (ern]) and the corresponding radius (R[k]), the distance from origin to kernel edge, are shown on Fig. 8. A number of shape indices pertaining to object radius, curvature, continuity, symmetry etc. can be calculated and used for identifying damaged objects. The multi-index approach resulted in a more accurate identificationof damaged objects than the single-index approaches used in the past.

F. Image Texture Texture is characterized by the spatial distribution of gray levels in a neighborhood. For most image-processing purposes, texture is defined as repeating patterns of local variations in image intensity, which are too fine to be distinguished (30). Thus, a connected setof pixels as separate objects at the observed resolution

Fig. 8 Food shape evaluationto detectdamaged products by comparingobject and reference edge contours: (A) corn kernel, (B) animal cracker. “Object edge contour” has been converted into “Transformed edge contour” and comparedwith “Average of transformed good kernel edge contours.” (Adapted from Refs. 27 and 38.)

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satisfying a given gray-level property that occurs repeatedly in an image region constitutes a textured region. A simple example is a repeated pattern of dots on a white background. Image texture can be usedto describe such image properties as smoothness, coarseness, and regularity (25). There are three approaches to studying image texture characteristics: statistical, structural, and spectral. Statistical methods are used extensively in texture classification, identifying the given textured region from a given set of textured classes. Image data such as mean, standard deviation, and moment (a measureof the frequency of occurrence of pixels of a given gray level within a particular image region) are used to study smoothness, coarseness, graininess, etc. Techniques are also available to study additional image texture characteristics such as entropy (randomness) and uniformity. Structural techniques of image texture analysis deal with the arrangement of image primitives such as the descriptionof texture based on regularly updated parallel lines. Spectral methods are based on the Fourier transform to study the global periodicity of an image. For example, presence of high frequenciesin the frequency image may represent a coarse texture. Gonzalez and Woods (25) have further described the structural and spectral methods in some detail. Image texture analysis can be performed using either monochrome or color (39) usedgray-scaleimagecharacteristics to studybread imagedata.Zayas crumb grain properties. Tan et al. (40) used HSI space image texture properties to evaluate corn extrudate characteristics. Ruan et al. (41) performed texture analysis on RGB image data for evaluating wheat kernel features.

V.

COLORIMAGE PROCESSING

Color is an important property of biological and food products. Color variations play a major role in quality and disease evaluation. Modern computer imaging systems are capableof acquiring and processing color images. Although the discipline of computer imaginghision is nearly 40 years old, the color computer imaging technique is relatively young (10-12 years old). The basic differences between a gray level and a color computer imaging system arein the camera, frame grabber, and display monitor-the camera should of handling be a color camera, the frame grabbeddigitizer should be capable color information, and the display monitor should be a color monitor capable of displaying color information.

A.

ColorCoordinates

Color computer imaging systems represent color information in terms of color coordinates. Several types of color coordinates are found in color theory. How-

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ever, RGB and HSI havebeen extensively used for color image processing applications. Each color coordinate has three components. The combination of these components produces a color image.

1. RGB In the RGB color coordinate system, the three color components are the three primary or fundamental colors red, green, and blue. Different combinations of these primary colors produce various secondary colors. The Commission Internationale de 1’Eclairage (CIE) uses the spectral primary system RGB to define any color by combining red, green, and blue (light generated by a monochromatic light source at 700, 435.8, and 546.1 nm, respectively) (43). The chomaticities r, g, and b (normalized red, green, and blue) are defined as: r=R/R+G+B g=G/R+G+B b=B/R+G+B The RGB color coordinate system is commonly used in television, color cameras, and color display monitors. In the television industry, RGB signals are encoded into luminance (Y) and chrominance (I and Q) to minimize bandwidth for facilitating broadcast (43). RGB cameras and monitors have been used for computer graphics because RGB isa good system for generating and displaying images. Similarly, digitizers use three A/D converters to digitize the RGB signal. The digitized color imageis stored in three color components/buffers, which are mixed together only during display to show one composite color image. According to Travis (42), “RGB technology is fine for grabbing, storing, in RGB space is computationand displaying the image, but processing the image ally intensive and algorithmic implementation is complex. Moreover, RGB is a poor way to represent images basedon human vision because people do not think of color in terms of combinations of red, green, and blue.”For example, it would be difficult to look at a yellow cake and specify the percentages of red, green, and blue that combine to form the color of the cake.

2. HSI The HSI color coordinate system is an alternative to the RGB system. Hue (H) is defined as the attribute of color perception by means of which an object is judged to be red, yellow, green, or any other color. Intensity (I) represents the attribute of color perception by means of which an object is judged to reflect more or less light than another object. Saturation (S) represents the attribute of

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color perception that expresses the degree of departure from gray of the same lightness. Understanding and manipulating color images in HSI is much easier and less complicated than in RGB because the HSI system resembles the way we perceive color. Individual values of H, S, and I contain information that is meaningful for human color perception and can be analyzed independently. Thus, the algorithmdevelopment is lesscomplicated(43,122).Thefollowingempirical relationships between RGB and HSI color coordinates make it possible to switch between the two (25):

I = S

R + G + B 3

= 1

-

(6)

3 . -[mm(R,G,B)] I

H = cos”

[(R

(7)

I

[(R - G) + (R - B)]/2 G)’ + (R - B)(G - B)]”.5

-

where if B > G, then H = 2n: - H. Note that much digitizer/frame grabber and commercial image processing/ analysis software is able to convert color images from one set of coordinates to another. It is possible that some modifications might be incorporatedin the equations above for their hardware implementation. Some manufacturers even use different existing relationships to convert from RGB to HSI and vice versa. It is recommended that these empirical relationships be obtained from the manufacturers. In addition to RGB and HSI, other color coordinates have been used for color image processing applications. Details can be found in other publications (43,44).

B. Considerations for Color Imaging Systems Appropriate color camera and digitizers are definitely critical for color imaging systems. As emphasized earlier, selection of the appropriate light source (illumination source) is critical. In addition to several other considerations, when selecting a gray level-based imaging system, two more factors need to be taken into account when selecting light sources, especially for the color imaging system: color rendering index and color temperature/chromaticity of the light source. Color rendering involves the “property of light to reveal an object’s colors” (45). The color rendering index (CRI) of a light source “measures the degree of color shift that objects undergo when illuminated by the light source as compared to the color shift of the same object when illuminated by another light

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source” ( I 23). On a CRI scaleof 0- 100, 100 represents a source with the renderof 0 (zero) implies an illumination source ing capabilities of daylight (45). A CRI incapable of rendering color. Thus, the higher the CRI, the more vibrant or brilliant the color is. Light sources with a CRI of 80 or higher have excellent color rendering properties. A CRI of 70-80 implies a good color rendering property (45,123). Color temperature is the “absolute temperature of a black body radiator having a chromaticity equal to that of the light source” (123). It is expressed in of 3000 K (low) corresponds to warm or degrees Kelvin. A color temperature red-yellow appearances. Light sources at 3500 K provide neutral white light and those at 4100 K provide cool bluish light. Light sources witha color temperature of 5000 K give off daylight (45). Both CRI and the color temperature of a light source can be obtained from the manufacturer. For developing a color computer imaging system, i t is always recommended to select a light source with CRI above 85 and a color temperature close to 5000 K (123). Another secondary but important parameter is the stability of the color temperature of the light source over time. The color temperature of many light sources changes with time. Thus, it is recommended to select a light source whose color temperature does not change with time.

C.ColorCalibration Calibration of a color computer imaging system impliesthat all the critical comto ponents (i.e., camera, frame grabber, display monitor) should be calibrated handle, process, or display color information. Thus, calibration of a color computer imaging system includes the calibration of its components, such as a color camera, frame grabber, and display monitor. Many end users or developers, unfortunately, have not practiced color calibration. Often it is assumed that all components are working satisfactorily. In many cases, however, a small deviation of calibration of one component can introduce errors in the final result provided by the color computer imaging. For example, if a color camera looking at an orange puts out the color information as yellow or red instead of orange, then error is introduced.

1. DisplayMonitorCalibration Video test generators or test pattern generators available commercially can be The generatorsendsdifferentsingle-color connected to thedisplaymonitor. charts such as red, green, blue, etc. and white, black, or multiple color charts to the monitor to be displayed on thefull screen. The user thencan adjust the monitor for proper calibration for a wide range of color conditions or for user-desired specific color condition. At present, computer add-in boards are also available

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that can be used in place of stand-alone video test or pattern generators. A list of manufacturers of video test generators are listed in Ref. 46.

2. CameraCalibration A standard method to calibrate a camera’s output uses two pieces of test equipment: an NTSC vectroscope and a waveform monitor. Vectroscopes and waveform monitors are extensively used by the television industry, and they complement each other. Waveform monitors display the video signal to allow the user to measure its amplitude and time parameters. At the same time, the vectroscope can show the relationship between a chrominance signal and its reference burst (or phase) and gain distortion (46). or Under a given lighting condition, the camera can look at standard, single, multiple color bar charts. The camera output is sent simultaneously to a waveform monitor and an NTSC vectroscope. The waveform monitor measures the amplitude of a video signal, and the vectroscope shows the relationship between color information (46). Necessary adjustments canthen be madein the camera’s control unit to calibrate the camera. Note that an NTSC vectroscopeonly accepts NTSC composite input, which most analog cameras provide. If the camera does not have an NTSC output, individualRGBoutputscanbeinterfacedwithRGBinputs in any calibrated video display monitor. In this case, the NTSC vectroscope is not used. Using visual observation of the displayed color on a calibrated video monitor, proper adjustments can be made in the color camera.

3. FrameGrabberCalibration After the camera and display monitor are calibrated, the frame grabber can be calibrated. Standard single-color bar charts typically havea 94-96% reflectance value. Images can be acquired separately using single red, green, blue, white, and black charts. For red, green, and blue conditions, the average pixel values of the respective buffer for the digitized images should be about 252-255. Any deviation can be addressed by changing the gain and offsets of respective A/D converters of the frame grabber. Note that the frame grabber has three A/D converters for each of the red, green, and blue channels. The frame grabber should have programmable gain and offset adjustment capabilities. Similarly, the frame grabber can be adjusted under white (presence of all color) and black (absence of all color) conditions. Different researchers have related incorporationof calibration of different color imaging components or systems. Panigrahi (47) described calibration of a color imaging system for corn quality evaluation, and Hetzroni and Miles (48) described color calibration of RGB video images. Several researchers have reported additional procedural and mathematical techniques for calibrating cameras, even in on-line conditions (49-51).

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D. ColorImageProcessingApplications In recent years, color computer imaging technology has been extensively applied to numerous food-related applications, which can be broadly grouped into color evaluation, defect detection, and texture evaluation of different food products, including dairy, meat,fish, fruit, vegetables, and others(52). This variety of applications, however, presents challenging color image processing issues. These applications can be discussed under two broad image processing categories: color segmentation and image analysis and understanding.

1. Color Segmentation In processing color images, segmentation refers to isolating or separating a homogenous or desirable region of interest in the image. Removal of a background from an image is a simple example. Segmentation is also used to identify and quantify all sorts of defects, diseases, and other abnormalities. The segmentation problem in color image processing is more complex than in gray scale applications. A color imageis comparable to three gray level images having color information contained in three color components, e.g., red, green, and blue. Thus, segmentation becomes more time-consuming and involved for color images. Of course, the level of complexity depends significantly on a given application. The inherent random variability of quality attributes of raw materials (agricultural products) for food products further adds to the complexity of segmentation of color images of many food products. Thresholdingbasedonhistogram is usedforapplicationsthatseparate background from the object or separate two or three dissimilar contrasting regions in the image. One requirement is that there should be a good amount of color difference among the regionsto be segmented. Sometimes investigationis neces(54). sary to choose the appropriate color coordinate for performing segmentation To evaluate the color of French fries, for example, removing background information from the French fries was required. Finding the threshold based on histogram on the RGB color component was difficult and time-consuming. Using the HSI coordinate, a histogram was obtained on the I (intensity) component. Finding a threshold in the intensity image alone was easier than with the RGB image. Using the determined threshold, the background was isolated from the image. All backgroundpixelswerelabeled in intensityimageandsubsequentlymappedinto saturation and hue images. Thus, for color evaluation, the hue and saturation information of background pixels were not considered (54). Use of adaptive thresholding techniques for a histogram-based segmentation is also recommended for food images. They promise higher accuracy and robustness than a fixed (global) threshold. Adaptive thresholding techniques can adapt to changes in lighting and spectral characteristics of an object as well as the background. Therefore, they are well suited for real-world applications and

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most food quality evaluation applications. The description of different adaptive thresholdingtechniquesincludingothertraditionalimagesegmentationtechniques such as region growing, clustering, and region merging can be found in Refs. 43, 44, 54, and 55. With the recent advent of neural network technology and its associated advantage of being fault-tolerant intelligent, it has been used for unsupervised segmentation of color images (56-58). Unsupervised neural networks are best suited for real-world images. They do not need supervision or a teacher. as do supervised neural networks, in order to conduct segmentation. The self-organizing map (SOM) has been extensively studied for unsupervised image segmentation. This type of neural network can work for multidimensional data such as a color image having three-dimensional color information.It preserves the image’s topography and simultaneously will maintain its spatial relationships. Details of the architecture of SOM neural networks can be found in Ref. 58. Though applications of SOM networks or other neural networks for food image segmentation have not been reported extensively in the literature, the success of their applications on natural color images(59,60) and other multidimensional pattern recogniof neural network tion techniques (6 1 ) clearly reinforces the potential success technologies for image segmentation of food products.

2. ImageAnalysisandUnderstanding

In analyzing color images of food, the exact techniques for image analysis and understanding differ from applicationto application. For example, an image analof corn might ysis and understanding algorithm developed for color classification not work fully with high accuracy for potatoes or potato products. This provides additional challenges and requires investigation for developing successful applications. Selecting appropriate color coordinates for analyzing a given food color image is critical. An accurate answer to the question, “Which color coordinate do I choose?” can be obtained by experimentation only for a given application. For color evaluation of edible beans, Panigrahi et al. (63) evaluated both r-g-b (normalized RGB) as well as hue h-s-i (normalized HSI) coordinates. Both sets of coordinates provided accuracy up to 100% in classifying beans in three color groups (63). To identify and quantify fat in meat images, RGB color space was usedwitharectangularprismand Mahalano bois distance criteria (64). RGB color coordinates were used for locating citrus fruits for harvesting (65) and RG color space wasutilized along with Bayes’ decision theory for image partitioning and subsequent color classification of stone fruits (66). Recently, the potential of artificial intelligence technologies, such as neural networks and fuzzy logic, has also been explored for image classification and understanding purposes. Both neural network and fuzzy logic techniques are intelligent, adaptive, and fault-tolerant(57),and they complement each other. Neu-

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ral networks are a new paradigm of computing or information processing inspired by biologicalmodels.Forcolorclassification of Frenchfries,Panigrahiand Marsh (54) condensed bothhueandsaturationhistograminformationbytwo separate back-propagation neural networks.The condensed color information was thenfedasinput to another neuralnetwork,“RProp,”avariationofbackpropagation neural network. The maximum color classification accuracy obtained by this modular network was 96% for classifying a given French fry sample into three color groups (54). (In this case, neural networks were used in a modular format.) Similarly, another probabilistic neural network was used for color classification of French fry samples into three color groups: medium, light, and dark (67). A few multistructure neural network classifiers were used to classify four of accuracy of 95.9% varieties of pistachio nuts with an average classification (68). Detection of blood spot and dirt staining on eggs was performed with an accuracy of 85.6% and 80%, respectively, using neural networks (69). These are only a few successful applications of neural networks for color image classification; their applications are growing rapidly. A few potential neural networkarchitecturesareback-propagation,learningvectorquantization, radial-basis neural network, and recurrent neural network. Details about these neural network architectures can be found in Haykins (59). Similarly, fuzzy logic is another intelligent information processing mathematical paradigm for dealing with uncertainty, vagueness, and ambiguity. Fuzzy logic has been successfully used for real-world complex image classification and understanding (70). It was used to diagnose tomato disease (71) and to analyze and classify other biological and agricultural images (75). Extending its use to classify and evaluate food images is definitely very encouraging. Rough sets theory, similar to fuzzy logic technology, has also been used for defect detection and quality evaluation of edible beans based on their color. A knowledgeThe maximum classification accuracy achieved was 99.6% (72). of corn kernel propbased discrimination function was adopted for dissemination erties along with a learning vector quantization network resulting in a classification accuracy of 95-100% for yellow and white corn kernels (73). The complementary characteristicsof neural networks and fuzzy logic have created a new technique called “neuro-fuzzy” system. Neuro-fuzzy techniques in soybean seed with a maximum accuracy have been used to classify disease of 95% (74). Other applications of neural networks and fuzzy logic for image segmentation and classification can be found in Ref. (75).

VI.

THREE-DIMENSIONALCOMPUTERIMAGING

Most of the food materials are three-dimensional (3-D) objects, and hence for a complete and thorough evaluation of food quality, 3-D information is necessary. Therefore, there is an increasing need for extracting 3-D information, which will

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increase the capabilities of computer imaging systems similar to that of human vision. Though there are similarities between the 2-D and 3-D computer vision applications, there are also some differences in how the image is acquired and represented. 3-D imaging is an extension of 2-D imaging where an additional measurement “depth or range” provides the third dimension to the image. Figure 9 outlines different techniques that can be used to measure 3-D information about an object (77). These methods are broadly grouped as direct and indirect methods. In direct methods, the depthor range measurement is obtained directly. However, in indirect methods, 3-D measurements are obtained indirectly from 2-D image(s) (77).

A.

Overview of Direct 3-D Measurement Techniques

1.

Time of Fhght

A time-of-flightrangesensor as describedbyNitzan (77) includes “a signal transmitterandsignalreceiver.Thesignaltransmittersendsthesignal to the target object. The receiver consists of a collector that collects a part of the signal reflected by the target and other required electronics for measuring the round trip travel time of the returning signal. The two types of signal generally used are

Fig. 9 Differenttechniquesformeasuring3-Dinformation.(Adaptedwithpermission fromRef. 77. 0 1988 IEEE.)

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ultrasound and laser light.” Beacuse it is an active range sensor, the principle of Lambertion reflections governs its operations. Therefore, it may not function properly if the surface of the object being viewed at is highly specular (77). One type of ultrasonic range camera is made by Polaroid (76), which uses 53, and 50 kHz. Range finders ultrasonic signals at four frequencies: 60, 57, based on ultrasonic systems are generally not capable of being used for mediumto high-resolution applications. Still, for other applications, such as navigations, this system can be used to determine the locations of impediments (76). Generally, ultrasonic range sensors have relatively low resolution because of their inability in properlyfocusingacoustichltrasonicsignalascomparedtoalaser beam. Nevertheless, for applications requiring low resolution, they might be more cost effective than a laser-based range sensor (76,77). According to Nitzan (77), “time-of-flight laser range sensors generally use a scanning mirror to direct the transmitted laser beam to sweep across the target object with equal angular increments to obtain a dense range data consisting of N X M range elements called “rangels.” There are two schemes for measuring the time of flight between the object and the target in a time of flight laser range sensor: pulsed time delay and phase shift.” Figure I O depicts the configurations of both of these. In a pulsed time-delay system,a pulsed laser is utilized to deter-

Target Pomt

1

I\

Beam Transmitted

Transmitter Laser * Pulsed Time Delay * Modulated (Frequency Amplitude) or

+

t

Ltght

\Recewed Light Recewer Range Intensity

Scannmg Mirror Unit Reference Beam

3

Fig. 10 Configuration for pulsed time delay and phase shift laser range sensing systems. (Adapted with permission from Ref. 77. 0 IEEE 1988.)

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mine the range based on the measured time of flight of the transmitted signal (76,77). In a phase shift system, a frequency or amplitude modulated laser sends the signal. The measured phase shift between the transmitted and the received signal determines the range (77). Laser-based range sensors could provide better resolution than ultrasonic range sensors, but they are relatively costly (76,77). Moreover, slow measurements and ambiguity (when phase shift is greater than 360”) issues create additional problems for laser range sensors (77). Nevertheless, the rapid reductionin the cost of the laser systems, along with their increased capabilities, could eliminate some of these problems. 2.

Triangulation

Triangulation based on elementary geometry (Fig. 1 1 ) is defined by Nitzan (77) as: “Given the base lineof a triangle, i.e., the distance between twoof its vertices and the angles at these vertices, the range from one of the vertices to the third is computed as the corresponding triangle side.” A simple range-finding geometry as described by Jarvis (76) is presented in Fig. 12. According to Nitzan (77) “Triangulation techniques are subdivided into two schemes: structured light us-

Epipolar Plane

Projector (StructuredLtght)

Fig. 11 Illustrationofthetriangulationprocess.(Adaptedwithpermission 77. 0 1988 IEEE.)

from Ref.

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Baseline

Fig. 12 A simple range-finding geometry. (Adaptedwithpermission 0 1983 IEEE.)

from Ref. 76.

ing a projector of controlled light and a camera/detector(an active scheme) and stereo using ambient light and two cameras (a passive scheme).” The accuracy of depth measurement that can be obtained using this technique depends on the measurement accuracies of distances, positions and angles (79,117). Detailed analysis of triangulation systems is given by Case et al. (78). The mathematical concepts and relationships for using triangulation techniques to approximate 3-D data can be found in Faugeras (79). a. Srrucrured Lighting. Structured lighting refers to the process of controlling or structuring the light to form different patterns or structures on the used. The first and most basic object (78,81,83,84). Three common techniques are technique allows the light to fall on the object in the form of a spot. Then, an imaginary triangle is formed using light source, detector, and the point on the object (Fig. 13A) (78). This technique only allows the measurement of a single pointlspot for each scan. Thus, to generate a 2-D image of resolution pxq. a total number of (pxq) scan would be required (78). In the second technique, a light source is used to generate a single stipe of light which falls on the object and the image is acquired by a2-D detector or camera (Fig. 13B) (78,80,81). This arrangement allows the acquirement of an image using a fewer scans (compared to that required by the first technique) (78).

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Object

Detector

(A)

(B)

(C)

Fig. 13 Different structured lighting systems:(A) simple system,(B) light stripe system, (C) multiple stripe system. (Adapted with permission from Ref. 78. 0 Kluwer Academic

publishers.)

The width of light stripe defines the spatial resolution (84). If the object is moving, the vision system will needa few or no moving parts. However, the direction of travel should be perpendicular to the light stripe (78). The third technique uses multiple stripesof light simultaneously (Fig. 13C) (78,82) and is very similar to the second arrangement (Fig. 13B). One disadvantage with this approach is that multiple stripes can cause ambiguity during the process of image recognition. However, the use of several gray-coded light stripes might solve this problem (82,83). The gray-coded light stripe method provides computational advantages too. For example, if N ordinary light strips (N scans) were required to completely scan an object, only log, N gray-coded light stripes (log? N scans) would cover the entire object (81-83). Bayer and Kak(1 16) reported the integration of color with structured lighting. This method of color-encoded structured lighting shows a few benefits, i.e. increased speed and better accuracy. In this technique, they ( 1 16) used a single encoded grid of colored light stripes to obtain range information. Grid to grid alignment problems generally found in multiple stripe technique, were overcome with this method (1 16). However, problems were encountered in dealing with objects having deeply saturated colors. Thus, the applicability of this technique might be limited to applications where the objects are of neutral color (1 16). Many benefits are associated with structured lighting. Its inherent simplicity to acquire 3-D information. has madeit one of the most commonly used technique it. Objects with high Nevertheless, several difficulties are still associated with specular surface characteristics could provide incorrect or sometime very little range information (76,77,82,83). Though structured lighting technique sometimes could be slow in acquiring image information (77,82,83), recent developments in high speed processors and detectors might eliminate this problem. In cases where triangulation techniques are used with structured lighting, sometimes hidin acquiring quality den surface or edges of the object might cause problems images or processing acquired images (77,8 1,83,84).

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Fig. 14 A typical stereo configuration for 3-D imaging. (Adapted with permission from Ref. 117. 0 SME)

Structured lighting has been used for several agricultural applications, some of which dealt with production and animal agriculture. Structured lighting with two-constraint propagation was used to measure 3-D surface featureson potatoes (85). Structured lighting was also used to find stalk and calyx of apples during high-speed detection of blemishes on apples (86). Another 3-D measurement system using structured lighting was developed to determine the shape of soybean seed, its axial length, surface area, volume, particle density, compactness, and sphericity by Sakai and Yonekawa (87). In at the center their study. “A soybean sample was mounted on a needle located of a supporting table. A camera was placed over the sampleat a known distance. to the A vertical plane of light struck the sample at an oblique angle relative camera’s horizontal axis. A helium-neon laser light source was used, which emitted structured light vertically deflected by a polygon mirror at 10,000 rpm and was narrowed by a biconvex lens. The system’s performance was satisfactory” (87). However, more work is necessary to further develop the system.

b. Stereo Stereo is a popular 3-D imaging technique ( 1 17). This method is also called “stereo triangulation technique” (77,117).In this method, an imaginary triangle is formed with the objectas one of its vertex. The other two vertices of the triangle are generally meant for two cameras and are called “camera vertices.” Two static cameras can be used on the “camera vertices” to produce a stereo pair of images. Sometimes one camera can be used but it must be transported between the two vertices of camera locations. A typical stereo system is illustrated in Fig. 14 ( 1 17).

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Camera modeling and calibration are important procedures for 3-D information acquisition using stereo (79,117). According to Bachnak ( 1 17), “Camera calibration is the procedure for determining intrinsic and extrinsic parameters of the camera. Such parameters include the focal length of the camera, the scaling factor of the system, the transformation relationship between the 3-D scene or object, and the image plane.” Detailed description of camera calibration is mentioned by Faugeras (79). Extraction of depthor3-Dinformationusingstereoinvolvesdisparity, cameraparameters,andimagecorrespondence(76,88,117).Imagecorrespondence implies matching corresponding points in the stereo pair images (76). For determiningcorrespondencefromimages,Jarvis(76)emphasizedthat“there must be sufficient visual informationat the matching points to establish a unique pairing. Two basic problems arise with this process. The first occurs at parts where uniformity of intensity or color makes matching impossible. The second happens when the image of some part of the scene or object appearsin only one view of the stereo pair because of occlusion effects or limited field of view captured in the images.” According to Bachnak (1 17), “two regularly used approaches to matching or correspondence are area-based and feature-based matching. Area-based approaches result in reasonably accurate disparity maps, but they are sensitive to changes in contrast and depth. Feature-based methods, on the other hand, focus on easily distinguished properties i n the images such as lines, corners, etc. The result is normally an accurate sparse disparity map.” Further discussion on tackling correspondence problems are mentioned by Yakimovosky and Cunningham (88). Another critical step to be emphasized for using the stereo techniqueis the optimization of baseline or distance between two cameras (76,77,88,117) (Fig. 1 I). If the baseline is smaller than optimum, accuracy in 3-D measurement could be affected. The largerthe distance than optimum between cameras, on the other hand, could increase the accuracy of depth measurement. However, the problem of hiddedmissing surfaces could occur (76,77,117). A stereo system developed at NASA’s jet propulsion laboratory for guiding a robotic vehicle is described by Matthies and Anderson (89).

B. 3-D Microscopy Study of microstructure is one of the most fundamental ways of evaluating food quality because the macroscopic textural properties are, in fact, a manifestation of the microstructural arrangement of constituents of a complex food material. Stanley and Tung (90) defined microstructure as a complex organizationof chemical componentsundertheinfluence of externalandinternalphysicalforces. Foods having similar structures can be loosely grouped together as foods that

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have similar textures (90). Therefore, study of microstructure has been well established. However, past studies on food microstructure are mostly based on subjective, qualitative assessmentof 2-D micrographs. Such subjective evaluations cannot provide enough information to quantify the effects or establish interactions amongvariousparameters.Therefore,imageanalysis is oftenused to obtain quantitative information from micrographs (91). In conjunction with image analysis, microscopy can also be used for adaptive control of food fermentation and other biotechnology applications (92). Almost all instruments used to provide an enlarged view of food systems a light or can be used with image analysis, either through direct interface with electron microscope or by scanning outputs such as photographs or negatives. Image analysis can also help to understand mechanisms of complex processes that alter product characteristics. There are several ways to study the microstructure of food materials. The method chosen depends on factors such as the nature of the food, the microscopic information of interest, and the level of resolution required (92). Electron microscopy offers the advantage of high resolution, but sample preparation procedures such as sectioning, dehydration, and chemicalfixation are laborious and may lead to artifacts. Confocal laser scanning microscopy (CLSM) offers an alternative way to observe food structure with high resolution but without disturbing the internal structure. It is a powerful tool to penetrate a sample’s surface and to visualize thin optical sections. These thin optical sections can be used to study the layered 2-D microstructure, and a computer algorithm can also reassemble them into 3D images for 3-D image analysis of their microstructure. The basic principle exploited by the confocal microscope is that of defocus (93). When a conventionally imaged object is displaced from best focus, the image contrast decreases, but the spatially averaged intensity remains the same. In a confocal imaging system, however, the image of a defocused surface appears darker than if it were in focus. Thus, confocal optics can be said to of haveaxialresolution in additionto lateral resolution.Asaconsequence this property, it is possible to extract topographic information from a set of conof focal planes. In order to form a confocal focal images taken over a range image, the signal is recorded as the object is scanned relative to the image of the point source in a plane parallel to the focal plane. Multiple confocal image slices are obtained by repeating the process at various levels of object defocus. By focusing at different heights (along the z-axis) on the object, a 3-D topographical map of the object is obtained. The resolution in the z-direction (axial resolution Az) depends upon the numerical aperture (NA) of the lens, the degree to which the pinhole is open, and the wavelength of laser light. If the confocal pinhole is fully opened, the microscope becomes a conventional scanning light microscope with reduced lateral resolution and a larger depth of field in the zdirection.

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Oneprincipaladvantage of opticalsectioningoverserialsectioning is avoiding physical specimen distortion dueto cutting and having image alignment from the various image planes. Another advantage is depth resolution; while inferior to the lateral resolution in each plane by a factor of about 2-3, it is still useful for many applications (9). The minimum separation between observation planes is 0.05 pm (94), a difficult resolution to achieve using regular light microscopy. Maximum observation depths of 10-100 pm can be achieved, depending on a specimen’s opacity and absorption characteristics. The CLSM has been a proven technique for a number of biomedical applications. However, it is still in its infancy for food quality evaluation (3). Its application for studying food materials is expected to increase within next few years due to the ability of CLSM to: Penetrate deeply but noninvasively into the specimen Obtain large numbers of sequential, thin optical sections that may be reassembled by a computer to produce 3-D images or stereo pairs to threeorfourseparatechemicalcomponents Identifyandlocalizeup (depending on the number of laser lines available on the instrument) by using specific fluorochrome labeling techniques (95) In addition, specimen observations can be made within a plane both transverse to and along the optical axis, as compared with conventional light microscopy, which can only make images transverse to the optical axis. Sample preparation for CLSM involves staining the lipid or aqueous protein phase of cheese with fluorescent dyes and subsequent observation after laser excitation (97). However, CLSM is limited by the maximum possible magnification, which is about 400. Vodovotz et al. (96) provide details of CLSM functioning. Brooker (97) presented some figures of mozzarella cheese microstructure obtained using CLSM.Hassan et al.(98) used CLSM to observe coagulum formation resulting from milk acidification. Vodovotz et al. (96) and Blonk and van ( 1 00) Alast (90) reviewed many other CLSM applications. Ding and Gunasekaran have developed a 3-D image reconstruction procedure to build a 3-D network of fat globules in cheese. The 3-D view of fat globules in Cheddar cheese is presented in Fig. 15. Throughthisreconstruction,informationabouttheglobule volume and related properties were measured and related to cheese-making procedures, which were not otherwise possible (101).

Fig. 15 3-D microstructureevaluationof fat globulesinCheddarcheese.(A. B. C) Selccted 2-D layered images of full-fat, low-fat. and very-low-fat Cheddar cheeses, respectively (the width of each microscopic image is 77 pn). (D) Reconstructed 3-D view of fat globules in low-fat Cheddar cheese. (Adapted from Ref. 101.)

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A

B

C

Layer 16

D

c

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CLSM not only allows visualization of in situ 3-D microstructure, it also allows quantification of some important features. Because 3-D analysis provides comprehensive data from a sample much larger than those used for typical 2-D microscopy, 3-D data are more accurate and less dependent on sample location and viewing direction. However, the limitation of observation depth is a potential problem. Quantificationof 3-D image features obtainable with CLSM could serve as objective criteria for evaluating quality or the effectof a number of variables of interest in cheese making. Data-handling requirements of 3-D images are very extensive. For exam13megabytes of storage ple, 50 layers of a 512 X 512imagerequireabout space. Image processing isalso computationally intensive, and, depending on the computer’s CPU, it may take several days to completely process a single 3-D image. Image storage requirements may be minimized by compressing individual 2-D slices, for example, by the joint photographics expert group (JPEG) compression algorithm. There is still no standard image compression algorithm (9). Ding and Gunasekaran (100) developed a computer algorithm to reconstruct a 3-D network of fat globulesin cheese from sequential 2-D layered images obtained from a CLSM. A few 2-D slices and the reconstructed 3-D image of fat globules in cheese are presented in Fig. 15. Thus, the 3-D image processing technique has helped us, for the first time, to evaluate in situ 3-D characteristics in of a fat globule to understand the effect of process parameters and fat level cheese textural qualities. Due to the development of novel imageprocessingtechniquesandimof computers, 3-D microscopy is fast proved computational power and speed becoming the latest trend in microstructural analysis of foods.

VII.

NONVISIBLECOMPUTERIMAGING

Although the majorityof computer imaging technology uses the visible spectrum (380-700 nm), the nonvisible electromagnetic spectrum also has potential for (UV), use in computer imaging. These nonvisible bands include x-ray, ultraviolet near-infrared (NIR), and infrared (IR). Advances in semiconductor-based detector technology and declining component prices have triggered the integration of nonvisible imaging techniques for food quality evaluation.

A.

FluorescentImaging

Most food products or raw materials of food products can use fluorescent imin the next chapter. Formost cases aging. Principles of fluorescence are described of fluorescent imaging, the wavelengths used range from the far end ofUV (300

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nm) to the far end of VIS (700 nm). Intensified CCD cameras have been used for this typeof application. Because of the low amount of signals available, these intensified CCD cameraswork better than a conventionalCCD camera. Recently, however, the introduction of DSPs with conventional CCD cameras has made it possible to create low-light cameras for acquiring fluorescent images without using intensified CCD. These low-light cameras have the ability to vary the time 1/60 or 1 / 130 second to several minutes. of integration of image information from By integrating a weak fluorescent signal for a longer time, a quality fluorescent image is obtained. The introduction of BCCD has also generated another viable option for acquiring quality fluorescent and NIR images. The spectral sensitivityof a BCCD camera is significantly higher than that of intensified CCD and conventional CCD cameras, especially at the UV and NIR ends of the visible spectrum (19).

B.

NIR Imaging

NIR images can be very valuable for food quality evaluation. For imaging pur700-1 100 nm and poses, the NIR waveband can be divided into two groups: > 1100 nm. Because of the higher sensitivity of BCCD cameras in the lower NIR region, they can be used for NIR imaging of food products. Similarly, some monochrome CCD cameras have relatively high sensitivity in the lower NIR region. Although the sensitivity of monochrome CCDs in the 900- 1 100 nm zone is not as high as that of a BCCD, there is a big difference in cost. Thus, which camera one chooses to use depends on the application. Note that before using a monochrome CCD camera for NIR imaging, the IR filter in front of the CCD sensor head must be removed. It is also highly recommended that the sensitivity curve of the camera be obtained from the manufacturer to verify that the camera is appropriate for the application. NIR imaging can also be achieved by using a liquid crystal tunable filter. to a standard CCD detector to produce Tunable filters can be easily coupled digital images at any wavelength within 400- I 100 nm (102). It has no moving parts. Since it is capable of acquiring images at many wavelengths,it can be used to generate multispectral images (102). Note that the quality of the image still depends on the sensitivity of the CCD detector used. NIR images based on 700-1 100 nm can be used for detecting defects and formappingmoisture (970 nm)andprotein (1020 nm) in foodproducts. An example is detecting defects in peaches. A monochrome CCD camera witha band pass filter centered at 750 nm (with a bandwidth of 40 nm) produced the images. The images were further analyzed for placing a peach into one of eight classes based on different defects. The classification error based on NIR images was 3 1 % compared to 40% obtained with color images (103).

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The NIR spectrum (700-2500 nm) is sensitive to chemical constituents, e.g., protein, moisture, and oil of food and agricultural products. Although NIR spectroscopic techniques have been used for quality evaluationof food products, NIR imaging could provide additional spatial information that is not available from traditional spectroscopic signals. For example, NIR spectroscopy can be used to measure the overall protein, oil, or moisture content, whereas NIR images will show the distribution of such constituents within the food material. ThereIt is fore, NIR imaging may replace NIR spectroscopy for some applications. in conjunction with more likely that NIR imaging/visible imaging may be used visible/NIR spectroscopy. Park et al. (104) integrated multispectral imaging (using 542. 571. 641, 700, 726, 847 nm) with visible/NIR spectroscopy (417-965 nm) for inspection of poultry carcasses. NIR images above 1 100 nm can be obtained using indium-gallium-arsenide (1nGaAs)-based cameras available from Sensors Unlimited (Princeton, NJ). Area to cameras are sensitive to 900-1700 nm. and line-scan cameras are sensitive 800-2200 nm. Both cameras produce analog and digital output, including RS170. They can be operated at room temperature, thus eliminating the need for cooling (105). These capabilities show tremendous promise for integrating nonvisibleNIRtechnologyintoevaluationand analysis of food composition and constituents in a nondestructive manner. Most food constituents such as protein, oil,water,starch,sucrose,glucose.andotherchemicalsbasedonhydrogencarbon bonds have been evaluatedby spectroscopic methods. NlR imaging would provideadditional spatial informationthatspectroscopycannotprovide.With these capabilities, functional or compositional images of food products can be acquired. which can help quality evaluation and inspection and also provide information on the interaction of food components that could be valuable for product development and quality evaluation.

C.InfraredImaging Focal plane array thermal infrared cameras without liquid nitrogen cooling (using a sterling cycle-based cooler instead) are now available from commercial sources. Some of them are compact and easy to use and provide better spatial resolution and thermal sensitivity. They are sensitive to the thermal infrared band (3-5 p n ) and can capture images at 30 frames/s with 12-bit dynamic ranges. With emissivity and atmospheric correction capabilities, they can create thermal images of food products. 1R cameras can also measure temperatures from - I O to 1500°C. Thus, IR cameras promise another rapid and nondestructive technique for food quality evaluation. especially for characterizing thermal properties, thermal mapping, and moisture-related studies. 1R imaging was used to estimate the internal temperature of chicken meat after cooking (106).

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D. X-Ray Imaging X-rays are another component of the electromagnetic spectrum. They contain high energy and can be used for nondestructive imaging. Recently, the development of filmless and low-energy x-ray detectors has created expanded possibilities for x-ray imaging for food and agricultural applications. X-ray line-scan imaging was used to classify apples based on water core features (107). X-ray technology was used to predict grain yield (108). Although the integration of x-ray technology might find some obstacles for food quality evaluation from consumers, low-energy x-ray devices might gain acceptance in the future for nondestructive evaluation where other imaging techniques will not work.

VIII. ON-LINE OR MOVINGSCENEANALYSIS Most computer vision systems designed in the past were concerned primarily with static scenes. However, the perception of visual motion plays an important role in many emerging computer vision applications. Thus, computer vision systems to analyze dynamic scenes are being designed. Input to a dynamic or moving a changing world. scene analysis systemis a sequence of image frames taken from in motion. Each The camera used to acquire an image sequence may also be frame represents an image of the scene at a particular instant in time. Changes in a scene may be due to the motionof the camera, the motion of objects, illumination changes, or changes in an object’s structure, size, or shape. It is usually assumedthatchanges in a scene are due to camera and/or object motion. A system must detect changes, determine the motion characteristicsof the observer and the objects, characterize the motion using high-level abstraction, recover the structure of the objects, and recognize moving objects. Depending on the designof the imaging system, different image processing techniques are required. Recovering information from a mobile camera requires a different technique than one suitable for images from a stationary camera (30). In the food industry, the most common design is that of stationary camera and moving objects. Image input is a frame sequence represented by F(x,y,t), where x and y are the spatial coordinates in the frame representing the scene at time t. The value of function F represents the intensity of the pixel. In many applications, an entity, a feature or object, must be tracked over a sequence of frames. If there is only one entity in the sequence, the problem is easily solved. With many entities moving independently in a scene, tracking requires the use of constraints based on the nature of the objects and their motion. A number of real-time visual tracking algorithms are described in Eklund et al.

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(109). Due to inertia, however, the motion of a physical entity cannot change instantaneously. If a frame sequence is acquired at a rate such that no dramatic change takes place between two consecutive frames, then no abrupt change in motion can be observed for most physical objects (30). This has been the basis of most on-line applications currently available in the food industry. The important factor is then to set the image acquisition rate fast enough to minimize image by frame. Real-time image blur so the analysis of image data can take place frame processing boards and real-time processors are available to assist in on-line realtime computer vision applications ( 1 10). For a continuous stream of material flowing down a conveyor belt, a computer vision system can be designed using a line-scan camera for image acquisiof photosensitive sites. tion. A line-scan camera contains a one-dimensional array The line-scan camera is suitable for fairly fast moving object scenes.In addition to higher speed, line-scan cameras offer high resolution and the ability to handle infinitely long image scenes. A new breed of cameras, known as time delay and CCD image sensor technolintegrate (TDI) cameras, are line-scan cameras using ogy to gain an increase in speed or sensitivity of up to 100 times that of conven( 1 1 I ) . A 2-D image tional cameras while providing exceptional spatial resolution can be produced if there is relative motion between the camera and the object of interest. The columns of information from the line-scan camera are usually stored sequentially in a framestore allowing interpretation of returned data as a 2-D image. The author’s research team is currently evaluating such an on-line system to evaluate the qualityof shredded cheese. The run-length coding (binarizing an image in which each pixel is a 1 or 0) of the binary image is used to identify object locations in the scene (a string of Is represent an object’s presence). Syntactic pattern recognition technique was used in the image interpretation step. Use of strobe lighting is also an effective technique for acquiring on-line information from a moving scene.To obtain a complete imageof the scene under strobe lighting, the strobe firing must be synchronized with camera and image acquisition. Lack of synchronization will appear as partially light and/or partially or totally dark digitized images. The most straightforward strobe and image acquisition synchronization is where an object present is a signal typically generated by a photo eye or a proximity switch device. In this technique, the strobe light is fired immediately on an object’s arrival, so the amount of object placement uncertainty in the direction of travel is reduced significantly. However, this technique requires high-precision object sensors, a special television camera with the capability of scan inhibit, and an electronic circuit that synchronizes the various to avoid image blur during timing signals. Ni et al. ( 1 12) used a strobe light image acquisition to examine individual grain kernels. The kernels were traveling at a modest speed of 6.5 m/min.

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General requirements for on-line applications are throughput (speed), accuracy, consistency, durability, diversification, flexibility, and adaptability. Considerations of these conditions and constraints have to be given atall stages of system design and development. Speedof evaluation is perhaps the most striking requirement. For example, Tao et al. (8) estimated that an on-line apple grading system may have to examine at least 3600 fruit/min. They describeda computer vision system sorting 3.5 million fruit in an 8-hour day, which consisted of two separate lighting unitswith eight camerasand one processing and control console unit. This type of machine is being widely installed in the packing industry for sorting applies, citrus, peaches, tomatoes, and various other fruitsand vegetables.

IX. SUMMARYANDFUTURETRENDS Computer vision techniquesplay a significant rolein fulfilling the needs of rapid and nondestructive sensors in food quality evaluation. The increasing demand for real-time or high-speed food quality evaluationwill be met by recent developments in high-speed DSP integrated cameras and frame grabbers. Commercially available DSP chips (e.g., Texas Instrument’s C-40, C-44, and C-80; Intel’s I860) offer flexibility, expandability, and upgradability suitable for parallel processing of images. Image processing analysis operation is a better candidate for parallelism. Therefore, parallel processing can be integrated for high-speed and real-time applications using the DSP chip. At present, many commercial DSP integrated frame grabbers are available that can be configured for parallel processing ( 1 13). Dedicated high-speed processors also offer alternate solutions for high-speed and real-time imaging applications. Recentdevelopments in busarchitecture,such as compact PC1 (CPCl), high-speed networks for image transmission, and cost-effective storage devices will add to the increasing integrationof real-time imaging systems for food quality evaluation ( 1 14). The development of portable, cost-effective, miniature laser systems permits the implementation of structured lighting techniques for extracting 3-D informationforfoodproducts.Moreover,ongoingtechnologicaladvancements have made 3-D cameras available from commercial sources. With the capabilities of high-speed processors and cost-effective 3-D imaging software, food sectors will encounter increased 3-D computer imaging for automated quality evaluation and inspections. Significantgrowth in thetechnologicalcapabilities of software has occurred along with their user-friendliness and adaptability. One does not need to know high-level programming in order to developa specific application. Capabil-

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ities in commercial software to programin English-like macro languages would allow users to develop the application quickly and easily. of artificial intelligence technolThere will also be a progressive integration ogies such as neural networks, fuzzy logic, and genetic algorithms to harness their advantages of fault-tolerance, intelligence, and accuracy for food quality evaluation (1 15). The capabilities of implementing neural networks and fuzzy logic algorithms on hardware chips would also expand the integration of these intelligence technologies for real-time or high-speed food applications. Thus, computer vision technology bears a tremendous potential as a nondeIt is structive technique for a variety of food quality evaluation applications. obvious that this technology will play an important role in making our food products safe and of high quality.

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Delayed Light Emission and Fluorescence Sundaram Gunasekaran University of Wisconsin-Madison, Madison, Wisconsin Suranjan Panigrahi North Dakota State University, Fargo, North Dakota

1.

INTRODUCTION

Biochemical degradation of chlorophyll results in exposure of carotenoids, giving fruits and vegetables a characteristic maturity symptom of yellowing (1). Since our eyes perceive colors, subjectivevisual evaluation has traditionally been used to judge maturity and other quality attributes of fruits and vegetables. Recent progress in the development of new methods utilizing optical properties has provided several objective techniques for rapid, accurate, more uniform quality assessment. The discovery of delayed light emission (DLE) by Strehler and Arnold (2) has offered an alternative for evaluating quality of chlorophyll-containing plant materials.The strong dependenceof chlorophyll concentration and the duration and intensity of DLE have been applied in developing indices for different (3). Recent investigations (4) quality attributes of various fruits and vegetables show that if the variables that are known to affect the duration and intensity of DLE are carefully controlled, the efficiencyof sorting fruits and vegetables could be increased. Fluorescence is another phenomenon exhibited by food and biological materials, Le., some materials “fluoresce” when irradiated with light. George Stokes initially observed fluorescence in the early 1800s (5). Although this is one of the as a tool in oldest analytical methods, it has only recently gained importance biological sciences related to food technology (6). Today, several quality evalua99

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tion applications employ fluorescence. Though fluorescence is similar to DLE in terms of “light emitted,” the mechanisms are very different. By definition, DLE refers to light emitted “after” excitation lasting up to several seconds, whereas fluorescence ceases abruptly when the exciting energy source is removed (6). Also, unlike DLE, fluorescence can be artificially induced by adding some fluoto be sensitive, rescence activators. Nonetheless, both techniques have proven rapid, reliable, and reproducible. In this chapter, the nature and occurrence of DLE and fluorescence are presented. The effects of various environmental factors are considered. Practical of applications of DLE and fluorescence measurements for quality evaluation food and biomaterials are reviewed, and future research needs are outlined, along with the instrumentation aspects associated with developing a measurement system for automatic quality evaluation.

II. THE PHENOMENONOFDLE Strehler and Arnold ( 2 ) accidentally discovered the phenomenon of DLE while studying adenosine triphosphate (ATP) formation during photosynthesis usinga firefly luminescent system as an indicator. They observed that when green plants are irradiated, they give off light for a considerable period after illumination. This phenomenon of DLE was explainedas a reflection of certain early reactions in photosynthesis which, by virtue of their reversibility, are capable of releasing a portion of their stored chemical energy through a chemiluminescent mechanism-luminescence due to the energy liberated in a chemical reaction. Adding nonpolar solvents (e.g., ethanol and ether)in moderate concentrations or irradiating chlorophyll solutions with ultraviolet (UV) light destroys the luminescence a chemiluminescent mechanism of intact cells, which supports the theory that might be involved in DLE. However, the chemiluminescent mechanism involved in DLEwasthoughttodifferfrom themechanisminvolved in phosphorescence-luminescence that persists after removal of the exciting source due to storage of energy. DLE was believed to involve biochemical reactions that produce excited molecules and to be associated with an enzyme system. Phosphorescence, on the other hand, is purely a physical reemission of trapped light energy. Preliminary measurements of wavelength distribution of the emitted light indicated a close correspondence to the wavelength distribution of chlorophyll fluorescence. It wasshown,however, thattheluminescence of DLEismore closely related to photosynthesis than to fluorescence (2,7). The similarities between the luminescent reaction and photosynthesis include: (a) the nature of temperature dependence; (b) the rate at which the reactions are destroyed by UV light; (c) the range of saturation intensity; (d) chemical compounds that inhibit

Delayed Light Emission and Fluorescence

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the reactions; (e) suppression by carbon-dioxide; and (f) the drop in intensity produced by continuous illumination with time. Contrary to the interpretation of Strehler and Arnold (2) that the luminescence phenomenon is a consequence of the reversibility of some of the early enzymatic photosynthetic reactions, the early processes following light absorption were observed to be nonenzymatic in nature (8,9). Later investigations suggested an interpretation of the physical processes leading to DLE and photosynthesis in terms of semiconductor theory (10,l I). The emitted light was attributed to an electron transition between the first excited singlet state of chlorophyll and the ground state (9,12). However, the luminescenceof DLE is an extremely complex temperature-dependent process, suggesting that a multiprocess mechanism may be involved. Based on their temperature-dependent DLE studies on Chlorella, Scenedesmus, and spinach chloroplasts, Tollin et al. (9) showed that the early processes following light absorption are purely physical, whereas the later stages of emission are enzymatic in nature. Some investigators (13-15) have suggested that DLE is regulated by the rate of the electron transport reaction, and others ( 16- 18) have indicated that it is controlled by the rate of photophosphorylation (light-induced esterification of compounds with phosphoric acid). Typically, induction of delayed light would show a very fast increase to the initial level, then a slow increase to the peak, and a slow decrease toa steadystate level (2,7,19).

111.

FACTORSAFFECTINGDLE

All fruits, vegetables, and plant materials undergoing photosynthesis probably produce DLE. Major factors affecting the intensityof emitted light are:(a) wavelength of excitation, (b) excitation intensity, (c) excitation time and time after illumination, (d) dark periods, (e) sample thickness and area of excitation, ( f ) temperature, and (8) chlorophyll content of the plant material.

A.

Wavelength of Excitation

The DLE intensity produced from intact green peaches for a broad activation spectrum is shown on a relative scale in Fig. 1. Although the visible spectrum peak is observed at about 680 nm, the fruit is significantly excited over a range of about 625-725 nm. Significant DLE intensities were observed from oranges when excited by white light (tungsten lamp) in the 680-740 nm range with a peak at 710 nm. Experiments with tomatoes (20) andtea leaves andspinach chloroplasts (21) exhibited similar results of peak DLE intensity at about 700

700

Gunasekaran and Panigrahi

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600

500

400

800

Wavelength of Excitation (nm) Fig. 1 DLE activation spectrum for green peach measured 2 s after illumination. (Data from Ref. 24.)

nm. However, the wavelength dependence may vary slightly based on the measurement system used.

B. ExcitationIntensity In general, the intensityof DLE bears a direct relationship to the excitation intensity until saturation occurs. Itoh and Murata (7), however, observed an opposite trend with spinach chloroplasts. Additional illumination has little effect in prolevel (22-24). For ducing an increase in DLE intensity beyond the saturation oranges, for example, DLE intensity levels off after an excitation intensity of 2750 lx (Fig. 2 ) . This aspect is desirable for DLE measurements since small changes in excitation will not interfere with quantitative measurements of DLE, it provided the illumination is sufficiently above the saturation point. However, has been shown (7,24) that higher illumination intensity lowers the time required for saturation. Total excitation energy required to obtain maximum DLE is differto obtain high DLE intensity ent for each product. Optimal measuring criteria for various fruits are listed in Table 1.

C. Excitation

Time and Time After Illumination

After a certain duration of excitation, DLEmay be observed over varying periods. At room temperature, DLE is observable for 0.25 ms or less up to one hour (2,13). However, the intensity of DLE decreases with the time after excitation, which is known as the decay of DLE. Various investigators have shown several

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0

2000

4000

6000

Exciting Illurnination intensity(Ix)

Fig. 2 Effect of exciting illumination intensity on DLE from green oranges after different decay periods: A, 0.7 s; B, 1.0 s; C, 1.5 s; D, 2.0 s. (Data from Ref. 22.)

phases of DLE decay (9,13,14). The most rapid known has a half-life of about one ms and is popularly termed “millisecond delayed light emission” (19). Certain other systems have much longer half-decay times. The decay curves of Chlorella luminescence are shown in Fig. 3. The decay appears to be faster at lower temperatures than at higher temperatures. For a given decay period, maximum DLE intensity is reached within the first 2-4s. Longer excitation times gradually degrade DLE intensity. Therefore, it is advisable to provide short (about 2-4 s) but strong excitation (to assure saturation). Temperature has a profound effect

Table 1 OptimalMeasuringCriteria

for ObtainingHighDLEIntensitya

Excitation (min)

Temperature Illuminationperiod Product

Dark

Tomato Satsuma orange Persimmon Japanese apricot Banana Papaya Measured after 75-s delay. Source: Ref. 38.

10

>20 15

(1x1

Time (s)

(“C)

13-17 550 2750 2800 1-32

3-6 4-7 1-3

-

23-28

2

>20

>SO0

1

>IO

18-25 2750 >5500 15-22

1-2 2-4

-

Gunasekaran and Panigrahi

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a

'm

0.8

5 0.6

-E W

&

0.4

-

0.2

d

0 0

2

4

6

8

Time After Excitation (9)

Fig. 3 DLE decay curves from Chlorella at 28°C (A) and 6.5"C (B) (Data from Ref. 2 . )

on the shape of DLE decay. In general, the lower the temperature, the faster the decay of DLE intensity. Jacob et al. (24), however, observed an opposite trend of faster decayat higher temperature in immature oranges. The effect of temperature on DLE is discussed further in a following section.

D. DarkPeriods In order to eliminate the effect of previous illumination on DLE intensity, the product is kept in a dark chamber fora certain length of time between successive on the same product. illuminations when more than one measurement is made In some instances, the product may be subjected to dark periods after excitation but prior to DLE measurement. Based on the studies of DLE in tomatoes (20), Satsuma oranges (22), and tea leaves (21), definite relationships between the preconditioning dark periods and DLE have been established. Usually, short dark periods (<1 min) cause a reduction in DLE intensity while longer dark periods (about 10-15 min) bring about some recovery of DLE intensity. Regarding chlorophyllderived from spinach chloroplasts, the illumination after varying dark periods showed initial DLE peaks of varying levels depending on the duration of dark periods (7). The effect of a dark period before excitation on DLE intensity of yellow persimmons excited at 5500 Ix is shown in Fig. 4.

E. Sample Thickness and Area of Excitation Scattering and absorption of light limit the depth below the surface, which contributes to DLE. This may be considered a disadvantage in DLE applications to detect subsurface defects. Jacob et al. (24) found 2.5 mm as the limiting depth

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10

0

5

20

Dark Period (rnin)

Fig. 4 Effect of dark period before excitationon DLE intensity from yellow persimmons excited at 5500 lx as a function of decay periods: A, 0.9 s; B, 1 .0 s; C, 1 .5 s. (Data from Ref. 24.)

for DLE detectionin tomatoes. Chuma and Nakaji (20) obtained highDLE intenrind, the interior being scooped sity from tomato flesh 6 mm thick below the out. A direct relationship between the area of excitation and the intensity of emitted light has been observed (20,22,24). Figure 5 represents the linear relationship between the area of excitation and the DLE intensity of green tomatoes at different decay periods. Also, a larger area of excitation resulted in a faster DLE decay. Therefore,it is necessary to carefully define the areaof excitation for each product and for each quality attribute to obtain reproducible results. This would have a bearingon developing automatic grading systems for fruits and vegetables.

F. Temperature TemperatureeffectsonDLE,asobservedearlier,areextremelycomplex to (9,22,25). Chlorella luminescence shows a temperature dependence similar that of an enzyme-catalyzed reaction (Fig. 3). The amplitude and decay kinetics of DLE are temperature-dependent in the time range of seconds (3,8,9,15). DLE

Gunasekaran and Panigrahi

0

1

2 3 4 5 Illumination Area (cm’)

6

Fig. 5 DLE intensity vs. area of excitation in green tomatoes excited at 5500 Ix. (Data from Ref. 20.)

intensity is generally maximum in a narrow range of sample temperatures. However, the variation from maximum DLE intensity is dependent on the particular of DLE from green product. Figure 6 represents the temperature dependence tomatoes and bananas, respectively. Peak intensities are at 16 and 25°C for green tomatoes and green bananas, respectively.Also, higher temperatures havea more profound effect on DLE from tomatoes than from bananas. The temperature dependence of DLE, however, is not a major problem when agricultural products encounter relatively small temperature changes (24). Abbott and Massie (26) related temperature and duration of exposure to chilling temperaturesof cucumber fruit to DLE; Chuma et al. (22) observed an increase in DLE from orange peel up to 30°C and a sharp decline reaching almost zero level beyond 40°C; and Nakaji et al. (21) found a strong dependence of DLE on the temperature of tea leaves. Terzaghiet al. (27) reported that the temperatureof maximum DLE upon chilling was strongly correlated withlateral phase separation temperature but was about4°Cloweronaverage.Thephenomenonobserved in chilling-sensitive plants during chilling a minor component below freezingis lateral phase separation. These investigations indicate that large temperature changes have a definite effect on DLE, which can be used to predict certain quality attributes.

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.. .

3.5

I

n 10

15

25

20

30

35

Temperature ("C)

Fig. 6 Effect of temperature on DLE intensity for green tomatoes (A) and green bananas (B) after a decay period of 0.7 s. (Data from Refs. 20, 23.)

G. ChlorophyllContent DLE is probably produced by all vegetables, fruits, and plant materials undergoto the chlorophyll concentration of the ing photosynthesis and can be related product (24). This is perhaps the most important aspect of DLE from a practical viewpoint because the change in the level of chlorophyll content is a primary

0

20

40

60

80

100

Time (s)

Fig. 7 DLE intensity at different stages of maturity of lemons: A, Dark green: B, light green: C, silver, D, yellow. (Data from Ref. 24.)

Table 2 Empirical Relationships Developed for DLE from Various Products as a Measure of Chlorophyll Content or Peel Color Based on Chlorophyll Content

A

0 Q)

Correlation Product (variety)

Empirical relationship

+ 0.298

(W

Measurement conditions

Ref.

content (pg/

0.92

Illumination intensity = 5500 lx Illumination area = 9.07 cm? Excitation time = 4 s Decay period = 0.7 s Dark period = 10 min Product temperature = 13OC Source = 100 W tungsten lamp Excitation time = 5 s Dark period = 10 min Product temperature = 20°C Source = Xenon flash Illumination area = 4.425 cm2 Excitation time = 0.4 ms Decay period = 1.0 s Product temperature = 27°C Source = 80 W tungsten halogen lamp Illumination area = 78.5 cm? Excitation time = 3 s Dark period = 10 min Illumination intensity = 5500 Ix Illumination area = 7.07 cm? Excitation time = 4 s Decay period = 1.0 s Dark period = 10 min Product temperature = 18.5OC Illumination intensity = 5500 Ix Illumination area = 4.4 cm' Excitation time = 4 s Decay period = 0.7 s Dark period = 10 min

20

Dependent variable C-chlorophyll I00 cm')

Tomato (Japanese)

DLE = 0. I63 C

Tomato (Duke)

DLE = 0.0121 C 0." 13

+

C-total chlorophyll content (pg/g fresh weight)

0.86

Tea leaves (Japanese)

DLE = 0.0896 C + 0.318

C-chlorophyll content (pg/ 100 cm? leaf area on hack side of leaf)

0.86

Papaya (Hawaiian)

DLE

P-papaya maturity grade (P = 9 for mature green; P = 28 for 3/4 ripe)

0.92

Orange (Satsuma)

DLE = (0.235 C 0.276)"' + 0.512

Banana (monkey)

DLE

=

DLE intensity, V .

=

0018 P

+

1.054

DLE = 10.9 exp(-0.72 GPC)

C-chlorophyll content of peel (pg/100 g fresh weight)

GPC-grade

of peel color

4

21

29

D

22

C

a

P v)

0

FP 23

a a

D

a 'CI

P

1. (CI

z

5

Delayed Light Emission and Fluorescence

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indicator of maturity and sometimes quality of various fruits and vegetables. Jacob et al. (24) observedthat DLE related to apparent chlorophyll concentration in apples,apricots,bananas,nectarines, bell peppers,olives,onions,oranges, peaches, persimmons, plums, pomegranates, and tomatoes. Figure 7 depicts the relationship between DLE and four commercialcolor grades of lemon from dark green to yellow, representing decreasing levels of chlorophyll concentration in the lemon skin. Table 2 lists empirical equations relating DLE intensity and the chlorophyll content or peel color based on chlorophyll content obtained for various products. Since the measurement conditions have a pronounced effect on DLE intensity, these empirical relationships must be carefully interpreted and used.

IV.

QUALITYEVALUATIONBASEDON DLE

Typically, the process of ripening involves degradation of chlorophyll, which exposes carotenoids, giving a characteristic maturity symptom of yellowing ( I ) . Watada et al. (28) observed that chlorophyll content decreased steadilyfrom 13.4 0 to 46.7 pg/g to 0.3 pg/g fresh weight, and lycopene content increased from as tomatoes ripened from stage 1 to stage IO. Similar changes in pigment content of tomatoes and papayas were also observed as these fruits matured (2.29). Because of the change in pigment composition, fruits exhibit reduced absorbance i n the 670 nm region as they mature. Spectrophotometric methods take advantage of this optical characteristic in indicating maturity and other quality attributes of fruits and vegetables. Because of their simplicity, spectrophotometric methods have been widely used for maturity and quality evaluationof biological materials.However,DLEmeasurementshavethefollowingadvantagesover spectrophotometric methods (24):

1.

Illumination need not be spectrally well defined, which permits wider choice and simpler design of the exciting source. 2. Illumination and measurement are not simultaneous, i.e., illumination can be removed in time and position from measurement. 3. Beyond saturation level, additional illumination has little effect on increasing DLE intensity,so small changes in illumination will not interfere with quantitative measurement of DLE. On the other hand. DLE measurements provide no compensation for size. A large specimen emits more light than a smaller one of equivalent chlorophyll concentration. To reduce this effect, usually only a portion of each specimen is examined. The disadvantage of this is getting a result that does not characterize the maturity of the whole product. This can be overcome, however, by special

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measurement techniques such as taking the average of several readings on the same specimen at different places. Recent advances in automatic product quality evaluation have been made in the area of computer vision systems. Computer vision or image processing systems essentially duplicate conditions as the eye sees an object but are able to enhance or statistically evaluate some aspect of an image thatis not readily apparent in its original form. Image processing applications to date include detecting bruises in apples (30), stem and blossom ends in tomatoes (3 l), orientation and shape of bell peppers (32), and various quality factorsof corn, rice, and soybeans (33-37). However, almost all of these applications rely largely on the contrast between defective and undamaged regions of the image. Therefore, an image processing system may not be able to detect subtle color changes associated with various stages of ripening and maturity. Jacob et al. (24) were among thefirst to investigate the application of DLE to quality evaluation of fruits and vegetables. They observed significant differof lemon from dark green to ripe, ences among four commercial color grades representingvariouslevels of chlorophyllconcentration in thelemonskin (Fig. 7). Chuma and Nakaji (20) observed an almost linear relationship between the chlorophyll content of tomatoes and DLE intensity. Higher efficiency was reported in sorting green tomatoes than red ones. Since separating green tomatoes from breaker stage fruits is of greater importance, such measurement could be of commercial significance. Forbus et al. (4) classified tomatoes into three stages of maturity on the basis of visual color and evaluated for differences in DLE intensityandchlorophyllandcarotenoidpigmentcontents.Resultsshowthat DLE has a high potential for use in the automated sorting of fresh market tomatoes into categories of maturity. Jacob et al. (24) reported sorting tomatoes under field conditions based on DLE measurements. DLE characteristics as related to the quality of Satsuma oranges were also investigated (22). Sorting efficiencies of 55, 90, 84, 86, and 100% were reported based on DLE measurements for five color groups of dark green, light green, color groups yellowish green, yellow, and orange. Since identifying the latter are of more practical importance, the corresponding higher efficiencies indicate applicability on a commercial scale. The usefulness of DLE measurements for commercial color grading is further enhanced by the fact that surface treatments on oranges such as brushing and waxing have negligible effects on DLE (22). However, storage treatments seem to have a certain effect on DLE. Based on investigations at room temperature, cold storage (2"C), and dark storage, Chuma et al. (22) found a decline of DLE up to 336 hours, with the fastest decrease for room temperature-stored product. Measurements of DLE on bananas indicated that the intensity of emitted light decreased with ripening, and bananas just beyond optimum ripening had

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only a trace of DLE (23). The grade of peel color of bananas had been related to DLE intensity (Fig. 8), which indicates the possibility of using DLE measurements to determine the state of maturity of bananas. Other quality attributes of bananas such as sugar content and firmness were also related to DLE. As the sugar content of the flesh (S, Brix) of bananas increased from 1 to 20% during ripening, the intensity of emitted light (Y, volt) decreased almost linearly as:

Y

=

-0.773 (1) S

+ 3.26

Similarly, the relationship between the fruit firmness was expressed as:

Y(2)= 14 F

-

(F, N) and DLE intensity

3.50

DLE intensityalso has a significant correlation with firmness for other fruits such as apricots and papayas (38). DLE characteristics of the chlorophyllof green tea leaves werealso investigated to serve as a means of nondestructive estimation of the quality of tea and tobacco leaves (38). Each ingredient valueof leaf color (hue, value, and chrome) wasfound to correlate highlywith DLE intensity of fresh leaves. The linear relationship between the chlorophyll content of fresh leaves and DLE intensity could be used to estimate the quality of tea leaves. Abbott and Massie (26) and Abbott et al. (39) related the temperature and duration of exposure to chilling temperatures of cucumber and bell pepper fruit and coleus to DLE measured at nonchilling temperatures. They observed that the DLE measurement would help determine the mechanisms of chilling injury and the methods of ameliorating chilling injuryof cucumbers, bell peppers, and other chlorophyll-containing tissues (40). Besides chilling injury, DLE measurements can also be used to inspect fruits and vegetables for mechanical stress during

2

3

4

5

6

7

Color Grade

Fig. 8 Banana peel color grade vs. DLEintensity.(Data

from Ref. 23.)

Gunasekaran and Panigrahi

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harvesting (41) and injury from sunburn or air pollution before visible symptoms develop (42). Based on the effects of quarantine heat treatments on DLE measurements on papayas (43), Forbus and Chan (44) investigated the potential for DLE as a biochemical indicator of papaya heat treatment. The DLE system was not as sensitive to deleterious effects of heat on the ethylene-forming enzyme system. However, they concluded that DLE measurements provided a quantifiable, nondestructive means for measuring the primary deleterious effects of heat damage. Forbus and Senter(45) evaluated Saticoy cantaloupes at six stagesof maturity for differences in DLE, chlorophyll, and soluble solids. DLE varied significantly and predictably by stage of maturity, indicating its potential as a rapid, nondestructive method of measuring cantaloupe quality. Forbus et al.(29) developed an index for separating ripe and unripe papayas (Fig. 9). Working with papaya maturity ranks, they found that ranks 9 through 28 (mature green through three-quarters ripe4 days after harvest) couldbe sorted more accurately than ranks 1 through 9 (immature green 4 days after harvest). Half-ripe or riper papayas (maturity ranks21 through 28) are the most susceptible in to fruit fly infestation(43).Therefore,DLEmeasurementscangreatlyaid identifying and removing infestation-prone fruits and thus assure quality in large for DLE commercial shipments. Forbus and Chan (44) demonstrated the potential

1

0.9

I .b u) c

-

0.8

Q)

c m

0.7 Overhalf-ripe

K UI

.-

x

Under half-ripe

0

w 0.6

21

J

n 25

0.5

27

0.4 5

10

15

20

25

30

Relative Maturity Rank

Fig. 9 Regression of DLE intensity vs. papaya maturity (I' = 0.92). Nutnhers 9 through 27 on the tigure represent papaya maturity rank arranged in order of increasing tnaturity. (Data from Ref. 24.)

Delayed Light Emission and Fluorescence

113

as a rapid screening technique for detecting papayas ripe enough to be susceptible to fruit fly infestation. There was a high correlation ( r = -0.92) between DLE intensity and Hunter b values for freshly harvested papayas at seven stages of maturity. Recent reports (46-48) indicate a significant decrease in DLE intensity with increased maturity grade of different peaches. Similar results were observed for Japanese persimmons (49), plums(50),and muskmelons (51). DLE also corof canary melons (cv. Juan related highly ( r = 0.85) withthematurityindex Canary) (52). The maturity index was calculated based upon values for chlorophyll, yellow pigments, and soluble solids content.

V.

INSTRUMENTATION AND MEASUREMENTUNITS

There have been varying designs of DLE measurement units to evaluate fruit and vegetable quality (20,24,29,43). Jacob et al. (24) reported one of the earliest instrumentation systems for continuous DLE measurement and sorting fruits. Forbus et al. (53) developed an experimental DLE meter for horticultural crops. According to them, DLE measuring systems must incorporate the following components: (a) a light source for illuminating a sample to activate DLE, (b) a totally dark enclosure in which to isolate the sample for measuring DLE after illumination is removed, (c) a photomultiplier tube mounted inside the totally dark enclosure for detecting light emitted from the sample, (d) an amplifier for applying of emitted low-intensity signals, and (e) a recorder for recording the intensity light as a function of time. The basic components of instrumentation and measurement units for autoto most optical and/or matic quality evaluation based on DLE are very similar spectrophotometric units. A detailed description of various detector properties and specifics of associated electronics to measure photoluminescence and other optical radiations can be found in an excellent multivolume treatise on optical radiation measurements (54-56). However, an attempt is made here to broadly unit. The overall outline various components of a typical optical measurement measurement unit can be divided into four major systems to facilitate: sample illumination, viewing and sensing, signal processing, and sorting.

A.

IlluminationSystem

Incandescent lamps (tungsten filament and tungsten halogen), arc lamps, andfluorescent lamps have commonly been used as sources of radiation in optical instruments. Occasionally, low-power lasers have also been used as light sources in optical systems (57,58). Laser light is highly intense, coherent and directional, has a well-defined beam diameter, and is readily adaptable for optical use (57). However, its use may be limitedby its monochromatic nature. Primarily, a source

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and

Gunasekaran

should emit a substantial amount of radiation in the spectral regionof interest. For more general applications, light sources must have a wide band of illumination of sufficient intensity. However, because of the broad activation spectrum of DLE, the wavelength of illumination is not very critical for most measurement conditions where the product’s surface layer is the primary indicator of quality because DLE reaches saturation at some threshold illumination levels. DLE intensity is usually large in the range of 650-700 nm wavelengths. Readily available illumination sources such as fluorescent and incandescent lamps produce good output within this range. However, a very large increase in illumination is needed to penetrate enough to reach deep tissues containing chlorophyll (23). Glass color filters that absorb in certain wavelength regions and transmit in others are commonly used in optical instruments for wavelength control (5860). Using a prism or grating to disperse a light beam into a spectrum and using a narrow slit tolet only the desired wavelength band pass through can also isolate light within a certain wavelength band. Beam splitters and optic fibers also serve to control the radiation. Use of beam splitters in optical systems adds the convenience of having two light beams from a single source (57), which can help in making dual measurements and allow for a more compact system. Optic fibers provide easy access for illuminating and sensing of light over hard-to-reach and to a bare minimum. intricate areas. Also, light transmission losses are kept

B. SampleViewingandSensingSystem The sequence of operations in this system is singulation, conveying and orienting, and scanning and sensing. The singulation stageis incorporated into systems that of discrete objects is formed evaluate individual products. At this stage, a bulk in a noncontiguous single file. Even though there is no established theoretical basis for the study of the process of singulation, Henderson and Shawver (61) described singulation as a three-stage process. First, the objects received in bulk are spread into a disordered monolayer. Second, the monolayer is organized into rows. Third, the objects in the rows are separated to a specified spacing between them.Acompendium ofsingulationandassociateddevicespublishedby Shawver and Henderson (62) is an exhaustive reference on this subject. Conveying the singulated materials at the proper orientationis critical for an accurate evaluation of quality. Since most agricultural and biological products are irregularly shaped, orienting them properly is not easily achieved. An orienting and conveying device for sorting dried prunes developed by Burkhardt and Mrozek (63) is a good example. Gaffney (64) suggested incorporating techniques that measure the size and shape of objects into the design of a conveying and orienting unit. Another important aspect is the speed of conveying, which could affect the sorting efficiency.

Delayed Light Emission and Fluorescence

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Selecting a suitable sensor unit and associated electronics should be done judiciously with due regardto the system as a whole. The choice of photodetector should be based on certain important parameters: physical dimensions, sensitivity, speed of operation, and noise characteristics. Some popular optical detectors A photomultiplier arephotomultipliertubes,photoresistors,andphotodiodes. tube is a fast, efficient detector and is widely used in the near-UV, visible, and near-infraredregions.However,theyarerelativelylargeandrequireahighto their cost and somewhat difficult voltage power supply, which contributes mechanical design (64). For these reasons, useof solid-state sensors such as photoresistors and photodiodes has become common. A photoresistor is essentially a film of material whose resistanceis an inverse function of incident illumination. Photoresistors are inherently slow. Because their sensitivity is somewhat dependent upon past history, they are not entirely consistent. When photodiodes are used in reverse-biased mode, they respond very quickly and linearly over many decades of irradiance. a wide Generally,opticalsensorsshouldhaveaspectralresponseover wavelength band. They should also exhibit a flat response at the wavelength of interest rather than a peak response for stable output. The peak DLE emission a good being in the red region (650-700 nm) indicates that a phototube with response in the red region is the most suitable. Besides matching the spectral aspects of incident radiation, the selection of photodetectors should also consider geometric aspects (e.g., angle of incidence, beam cross section, density and uniformity of radiation, relative position of the sample) and temporal aspects (e.g., rise time of pulsed radiation as compared to rise time of the detector)of the input. An important difference between common optical systems and DLE measuring units is the provision of dark chambers in the latter. These dark chambers may be placed to provide a dark period between illumination and measurement. In the case of multiple measurements on the same sample, dark chambers can provide dark periods between subsequent illuminations. The length of the chamber and/or the speed of conveying the product should be carefully controlled to allow for a sufficient dark period resulting in optimal DLE.

C.SignalProcessing

System

Signal processing is an essential part of the measurement unit. Often the ability of the signal processing componentwill dictate the designof the rest of the system (39). In a broad sense, signal processing simply means transforming the photodetector response into a more useful form to aid in decision making. For this, it can or be as simple as direct reading of the electrical signal coming from the detector, it can be as involvedas requiring sophisticated mathematical analyses ofmeasurement data performed with the aid of large digital data processing facilities. In

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processing photoluminance or fluorescence data, simple manipulation of measured values from some meter or analog-to-digital converter is inadequate. In this case, the domainof data analysis may extend from the direct detector output to the digitization process and finally to the conversion of these quantities to information of analytical interest (65). For this reason, greater reliance on signal processing is a necessity. Typically, a microcomputer will be dedicated for this with specially developed software and hardware incorporated into the system. In general, this system provides overall control, calibration, and operation of the unit and helpsto display, record, and/or store the optical characteristics measured.

D. Sorting System At this stage, this is the actual classification according to the information gained from the signal processing system. This may be grouping fruits or vegetables into different grades or rejecting defective samples. Prompted by the microcomputer signal,a solenoid could actuate a deflecting vane (24)or an air gun (62,66). of A pneumatically operated sliding gate could also be used to direct the flow defective products (66,67). Sorting speed should match the conveying speed, and to cause mechanical damage to the theunitshouldbecarefullydesignednot objects being sorted.

VI.

FLUORESCENCE

According to Guibalt, “Fluorescence refers to the property of light (luminescence)emittedbymolecules of a particlewhentheyareexcitedbyphotons (light)’’ (6). The word “fluorescence” is derived from the mineral “fluorite” ( 5 ) . Fluorite generally glows in blue color under sunlight and the UV spectrum in the sunlight causes this. This process was observed by Sir George Stoke in early 1800s ( 5 ) and this phenomenon was named “fluorescence” (5). However, fluorescence phenomenon is not just observed under UV light, it can also be NIR spectrum of electromagnetic radiation observed under visible, x-ray, and ( 5 ) . The emission (luminescence) can also be observed not only in visible, but in NIR and infrared region( 5 ) . Thus, a general working definitionof fluorescence canbewritten as theproperty of anobject(particle) to emitphotonswith a wavelength (emission wavelength) higher than that at which the object was excited with (excitation wavelength) (5,6,104).It is to be noted that the fluorescence phenomenon stops abruptlyas soon as the excitation sourceis removed (5,6,104). For some materials, the emission (luminescence) continues after the removal of excitation source. This process is called “phosphorescence” (5). Guibalt (6) describes fluorescence as follows. Every molecule possesses a series of closely spaced energy levels and can go from a lower to higher energy

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t

\

\

Excited Trlplet State Energy by

W

T

First Exc SI -

I

c

v=4 v=3 v=2

I

i

s

so -

lnteratomlc Dstance Along Critical Coordinate

Ground Electronic State

Fig. 10 Schematic energy level diagram for a diatomic molecule. The potential energy level of a diatomic molecule are indicated with the various vibration levels represented as v = 0. I , 2, 3, and 4 on each curve. (Reprinted from Ref. (6). p. 3.)

level by the absorption of a discrete quantum of energy, e.g., light, equal in energy to the difference between the two energy states (Fig. IO). Only a few molecules are raised to a higher excited state and hence are capable of exhibiting luminescence. Between each main electronic state are the various vibration levels of the molecules. When a quantum of light impinges on a molecule, it is absorbed in about s, and a transition to a higher electronic state takes place. This absorption of radiation is highly specific, and radiationof a particular energy is absorbed only by a characteristic structure.The electron is raised to an upper excited singlet state, S I , S I , etc. These ground-to-singlet transitions are responsible for the visible- and UV-absorption spectra observed for molecules. The absorption transitions usually originate in the lowest vibrational level of the ground electronic (1 0-j s), some state. During the time the molecule spends in the excited state energy in excess of the lowest vibrational energy level is rapidly dissipated. The lowest vibrational level (v = 0) of the excited singlet state S is attained. If all the excess energy is not further dissipated by collisions with other molecules, the electron returns to the ground electronic state with the emission of energy. This energy emission is called fluorescence. Because some energy is lost in the a briefperiodbeforeemissionoccurs,theemittedenergy(fluorescence)has longer wavelength than the energy that was absorbed.

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When excited with a specific form of electromagnetic radiation (such as UV light, NIR light), a part of the radiatiodenergy is reflected, some portion is absorbed and the other portion is transmitted. The exact amount of reflection, to object. For some materials, absorption and transmission varies from object the absorbed radiationlenergy affects the atomic structure of the object (6).The absorbed energy is generally expressed in integral units called quanta (6).The quanta-energy relationship is expressed as: E = -hc

h where E = energy (J), h = Planck’sconstant (6.63X J . s), c = velocity of light = 3 X 10’ (m/s), and h = wavelength (m) (6). The energy of the photons in the visible spectrum is 146-607 KJ/mol and they cause electronic transition (6).The resulted fluorescence lasts for typically I O ns (6). The time for which the fluorescence phenomenon exists is called “fluorescence lifetime” (6,104). Fluorescence has two characteristic spectra-an excitation spectrum and an emission spectrum. The excitation spectrum refers to the relative efficiency of different wavelengths of exciting radiation to cause fluorescence. The shape of the excitation spectrum should be identical to the absorption spectrum and is independent of wavelengthof fluorescence measurement. The emission spectrum represents the fluorescence spectral data. It is the relative intensity of emitted radiation at various wavelengths (6).

A.

Types of Fluorescence

Fluorescence may be classified as either primary or secondary. Primary fluoresto the intrinsic or native fluorescence, also known as autofluorescence, refers cence characteristics of an object/product that fluoresces under UV or other electromagnetic radiation. Many plant products, botanical tissue components, animal tissues including meat and fish products, and other food products possess autoSecondary fluorescence, also known as influorescence characteristics (6,l 13). duced fluorescence, refers to the fluorescence characteristic of an object obtained byaddingspecificfluorescenceactivatorscalledfluorochromoorfluorescent stains or fluorophores. Some nonfluorescent objects show fluorescence characteristics after undergoing a chemical reaction or after the addition of chemical reagents (104,113). In some cases, fluorescence may be classified as (6): 1.

Stokes fluorescence-thereemission of lessenergeticphotons that is normally have a longer wavelength than absorbed photons, which observed in solutions.

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2. Anti-Stokes fluorescence-emission at shorter wavelengths than those of absorption, which occurs when thermal energy is added to an excited state or when a compound has many highly populated vibrational energy levels. It is often observed in dilute gases at high temperatures. 3. Resonancefluorescence-thereemission of photonspossessingthe same energy as the absorbed photons. It occurs in gases and crystals and is not observed in solutions. The relationship of fluorescence to the molar concentration of a material is described by:

F

=

ql,, ( I

-

e)-"k

(4)

where q = quantum efficiency, I,, = radiant incident power, a = molar absorptivity, b = path length of the sample, and c = molar concentration (6). The quantum efficiency or yieldq is furtherdefinedasthe ratio of the emitted to the absorbed number of quanta (6). The higher the values of q, the I,, could cause greater the fluorescenceof a compound. However, higher intensity photodecomposition of the sample(fluorophores),thusreducingfluorescence. Therefore, a source of moderate intensity is used (e.g., mercury or xenon lamp) (6). For very dilute solutions, Eq. (4) reduces to Beer's law:

F

=

KqIoabc

(5)

where K is a constant (6). The plot of fluorescence versus concentration should be linear at low con(6). Generally, a linear centrations and reach a maximum at higher concentrations response could be obtained until the concentration of the fluorescent species is large enough to absorb a significant amount of incident radiation. To obtain a linear response, the sample should absorb less than 5% of the incident radiation (6). Some food and biological products can be heterogeneous, and could contain bigger particles (104). These could scatter light. This light scattering is called Raleigh's scattering (104). The absorbance from this scattering is inversely proportional to the fourth power of excitation wavelength, h (A a l/h4), and can be identitied as background absorption (104).

B. AdvantagesandLimitations Fluorescence and phosphorescence are well suited as analytical tools for food quality evaluation becauseof their extreme sensitivity and specificity. Concentration of substances as low as I in 10"' can be detected. The main reason for this extreme sensitivity is that the emitted light is measured directly and can be increased or decreased by changing the intensity of the exciting radiation. The

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specificity of fluorescence is due to the fact that there are few components that fluoresce compared to those that absorb light energy, and two wavelengths are used in fluorometry compared to only one in spectrophotometry. Many biological and food products tend to have unique fluorescent characteristics. Two compounds that absorb radiation at the same wavelength will probably not emit at the same wavelength. The difference between excitation and emission peak could range from I O to 280 nm (6). There are, however, the following limitations of fluorescence as an analytical tool (6):

1.

It is strongly dependent on environmental and physicochemical factors, such as temperature, pH, ionic strength. and viscosity. 2. Photochemicaldecompositionordestructionofthefluorescentcompound providing a gradual decrease in fluorescence could also create a problem in fluorescence measurement. Methods used to avoid photochemical decomposition include using longest-wavelength radiation to to strike the excite the sample, not allowing the excitation radiation sample for a long time, and avoiding or minimizing exposure ofa photochemically unstable standard solution to sunlight or UV prior to the experiment/measurement process. 3. Quenching: the reduction of fluorescence by a competing deactivating process is known as quenching. It may be observed due to oxygen, impurities, temperature, and concentration. Dissolved oxygen, small amounts of iodide, and nitrogen are very effective quenchers. Impurities such as dichromate also interfere with fluorescence. Temperature has been found to reduce fluorescence by 1% (and up to 5 % ) per 1°C increase. High concentration of the fluorophore studied is known to decrease fluorescence intensity.

C.Detection

of Fluorescence

Fluorescence can be measured by either transmission or reflection techniques. The transmission technique is more subject to quenching effects than the reflection technique (6,104). Therefore, reflection measurements are more common. Reflected light can be detected by different instrunlents/systems such as spectrofluorometer, spectrophotometer, and imaging systems specially configured for fluorescence measurements. The basic components of any fluorescence measurement system are (6,104):

1.

A light source to generate UV radiation in the required excitation wavelength. 2. An excitation filter toallowonlytheexcitationwavelengthspecific

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radiation to interact with the sample. (In the cases where laser is used as the light source, a filter may not be necessary.) 3. A detector and other associated electronics including A/D converter. The detector will detect the emitted spectrum/fluorescence. The A/D converter and associated electronics will convert the emitted signal into equivalent digital form. 4. A host computer, which could host the detector assembly and store the digitizedfluorescenceinformationforstorageandadditionalprocessing. 5 . Absorbance filter to allowtheemittedsignal in theselectivewavelength range before the signal is digitized.

1. Illumination Sources Manybroadbandlightsources (e.g., xenon lamps, UVlight,mercurylamps, xenon-mercury lamps, and lasers) are used as illumination sources (6,104). High intensity discharge lamps such as mercury and xenon lamps are mostly used as illumination sources (6,104,105). Mercury lamps provide sharp spectral output and many times are used for calibration purposes along their expected lifetime in high- and low-pressure format. Xenon in average (105). They are also available lamps provide continuous spectral output, generally from 270-400 nm (UV zone) (104). Xenon gas is contained under high pressure in xenon lamps (104,105) and therefore should be handled carefully. Xenon and mercury combination-based (104). It is xenon-mercury arc lamps provide higher output than a xenon lamp always recommended to obtain detailed spectral output from the manufacturer before selecting a particular light source. Promising research is being carried out by various entities, and it is anticipated that UV light emitting diodes (LEDs) will be commercially available soon. of the unique propertiesof Another typeof illumination source is laser based. One lasers is their ability to emit high-energy coherent lightof a specific wavelength. of laser Depending on the required wavelength and cost, the appropriate type can be selected. 2. Detectors

Detection of fluorescent spectra can be performed using eithera photo-multiplier tube (PMT) or photodiodes/phototransistors. Though recent technological advancements have allowed the use of differin many fluorometers ent types of detectors, PMT-type detectors can be found (6,104). The PMT provides an output signal (in terms of current) that is proportional to the light received by PMT. However, it only provides an average signal without any spatial information in it (6,104). The exact type of photodiodes/phototransistorsselected for detecting fluo-

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rescent spectra will depend on the type of detecting material used in the detector (which is dependent on emission wavelength, active area of the detector, noise equivalent power, and detectivity) (106). For fluorescence (emission) measureGe (germaments in the visible and near-infrared range, generally Si (silicon), nium), and InGaAs(indium-gallium-aresenide) type detectors are used (107). For of light is low, new types of many fluorescence measurements where the level photo-receivers such as femtowatt receivers (New Focus, Inc., CA) can be very of food products, nanosecond phouseful. For fluorescence lifetime measurement todetectors (New Focus, Inc., CA) with typical rise time of 1 ns may be appropriate (108). To obtain fluorescent images, different types of cameras/computer imaging systems are used. A computer-based fluorescence imaging system differs from a standard computer imaging system mostly by the image acquisition system or camera. The camera oftenis incorporated with a microscope. The most commonly used camera for a fluorescence imaging system is an intensified charge coupled device (ICCD) video camera. The detectors on these cameras are cooled to provide high sensitivity and spatial resolution with video rate (30 frameds). Other cameras that are used for fluorescence imaging use cooled and UV-enhanced or back-illuminated CCD detectors (109). UV-enhanced or back-illuminated CCD provides enhanced sensitivity in the UV or visible band to acquire low-light fluorescence images ( I 09). Fluorescence images can also be obtained with digital signal processing (DSP) integrated CCD camera system. DSP circuitry allows the integration of information over a user-defined amountof time (generally from 0.01 s to several minutes) to obtain high-quality images. This type of system works well with applications where the amount of light (fluorescence emission) a is relatively high. Fluorescence imaging can provide more information than spectrofluorometer such as spatial distribution of intended fluorescence information. Thus, fluorescence imaging can be very useful for food quality measurements.

VII.APPLICATIONS

FOR FOOD QUALITYEVALUATION

Many food, agricultural, and biological materials possess autofluorescent characto fluoresce (i.e., secondary fluorescence) by teristics, and many can be made in a number adding appropriate fluorophores. These properties have been used of quality evaluation applications.

A.

AutofluorescenceTechniques

Separation of bran (pericap, aleurone, and fatty germ) from wheat flour (endosperm) is performed in many wheat-processing operations. Chemical analysis of

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ash along with the color check are conducted to measure the effectiveness of separation (1 IO). However,thismethod is verytediousandtime-consuming. Munck (1 IO) proposed a rapid methodin which the autofluorescence characteristics of different components of wheat seeds were used. He showed that the aleu350 nm wavelength light, rone autofluoresces at 420 nm (blue) when excited with and the pericarp fluoresces at 540 nm (yellow) when excited at 450 nm ( 1 IO). A similar method was usedto study the components of sorghum via fluorescence microscopy (68). Mycotoxins (toxic fungal metabolites) in food products have been linked with different forms of fatal and chronic illnesses for humans and animals (70). Theyhavebeenreportedtobefound in manyfoodproducts at any stage of production (70). Wood and Mann (70) have described the use of fluorescence Le., aflatoxins, citrinin, techniques to detect and quantify selected mycotoxins, ochratoxin A, and zearaleurone. Traditional detection methods for these mycotoxins have been either thin layer chromatography (TLC) or high-performance liquid chromatography (HPLC) (70). The fluorescence characteristics of aflatoxins (BI , B2,G1,andG2).citrinin,ochratoxin, andzearaleuroneare listed in Table 3 (70). Because of the tediousness of the process involved in TLC and HPLC, investigation is being carried out by one of the coauthors (Panigrahi) to develop a fast, nondestructive method for detection and/or quantification of mycotoxins. He is using a personal computer-based fiber optic spectroscope to investigate fluorescence characteristics of wheat samples infected with mycotoxins and to develop techniques to quantify the amount of mycotoxins present. Similarly, Lil1 ) studied the relationship between aflatoxin concentration and moislehoj et al. (7 ture at harvest and bright greenish-yellow fluorescenceof different corn hybrids. Panigrahi et al. (72) investigated the autofluorescence characteristics of edible beans for rapid, nondestructive evaluation. There are many types of edible beans: navy, pinto, red, black, etc. As with all other food and agricultural products, cracks or splitsin edible beans are undesirable andneed to be detected and/ or quantified. The conventional approach of crack detection in navy beans in-

Table 3 FluorescenceCharacteristics of Selected

Mycotoxins Toxins Aflatoxins Citrinin and ochratoxin A Zearalenone Source: Adapted from Ref. 70

Excitation wavelength

Emission wavelength

340-380 254

440 400-700

308

400-700

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volves dying them with a red dye and then counting the of visible numberdefects/ cracks. Use of red dye is justified because this enhances cracks and defects for easy identification. The problem with this approach is that dyed beans are not of generally used for human consumption. It was found that the endosperms the bean autofluoresce under UV light (72). The fluorescence imaging system (72) included a long-UV lamp for illumination. developed for crack identification CCD The autofluorescenceof the beans was imaged using a low light-sensitive camera. The acquired images were processed to quantify and assess the extent of defect. Figure 11 is a typical image of a bean showing the bright defective/ cracked region that autofluoresces. Autofluorescence has been used for quality characterization of fish meat, to separate fishbone from meat, to quantify lean in animal carcasses, to detect parasites in meat products, etc. (73-80). When excited at 340 nm, the fishbone of a number of fishes (sod, whiting, haddock, plaice, flounder, and sole, etc.) 360 nm. The fluoresces at 390 nm, but fish meat has a fluorescence peak at intensity of fluorescence from the bones is also much higher than that from the fish meat (Fig. 12) (81). These differences have been used to evaluate the quality of fish fillets. Commercial fishbone detectorsare available that employ autofluorescence. The fluorescence intensity from fishbone decreases with the thickness of the fish meat covering the bones. For example, when the flesh thickness of 0 to 2 mm, the relative fluorescence intensity from cod and plaice increased from the bones decreased almost linearly from 100% to about 10% (Fig. 13). Therefore, present technology is suitable for surface or near-surface detection, which

Fig. 11 Fluorescenceimage of an edible bean showing autofluorescing crackldefect region.

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100

75

50 25

0 350

450

400

500

Wavelength (nm)

Fig. 12 Fluorescence emission spectra of fishbone and fish meat when excited at 340 nm. (Ref. 81.) (Reprinted with permission from Jourrzul of Food Protecfion. 0 Interna-

tional Association for Food Protection.) is not a serious limitation for the tish tillet industry. In general, the fluorescence characteristics of fishbone are also valid for porcine, bovine, and chicken bones and for cartilage and connective tissues(76,77,82). Therefore, techniques similar to those used for fishbone detection may also be useful in meat processing. In muscle foods, extensive and complex lipid-protein interactions generally result from the peroxidationof unsaturated fatty acids. These interactions,in turn, contribute to undesirable chemical changes such as insolubilization of proteins and toughening of meat (83-85). The method commonly used to evaluate lipid oxidation in meats is to measure the thiobarbituaric acid (TBA) value and perox-

0

0.5

1

1.5

2

Thickness (mm)

Fig. 13 Decrease in relative fluorescence intensity from fishbones with flesh thickness. (Ref. 8 I .) (Reprinted with permission from Journal of Food P rorecrion. 0 International Association for Food Protection.)

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ide value (PV). TBS and PV values do not reliably relate to lipid oxidation in meats (86), particularly in freeze-dried meat (86). Nakhost and Karel (88) used fluorescence techniques to assess the effectiveness of antioxidantTBHQ (monot-butylhydroquinone), reduction of headspace oxygen, and compression on the of oxidized beef storage stability of freeze-dried meats. Fluorescence spectra showed maximum excitation and emission at 350 and 440 nm, respectively. The nonoxidized beef showed three peaks in the excitation spectrum at 308, 3 18, and 350 nm and a broad peak in the emission spectrum at about 475 nm. The use of in beef samples, antioxidant (100 ppm TBHQ) greatly decreased the fluorescence and the fluorescence spectra of “oxidized” beef showed maximum excitation of 350 nm and emission excitation at 440 nm (89). In the case of fruits and vegetables, as with DLE, trends in chlorophyll contentwerefollowedusingwhat is knownaschlorophyllfluorescence.For apples (90) and broccoli (91), fluorescence parameters were found suitable for evaluating quality and maturity. Thus, use of chlorophyll fluorescence may be suitable for maturity sorting of chlorophyll-containing fruits and vegetables on commercial packaging lines (90). Woolf and Lasing (92) studied avocado fruit skin fluorescence following hot water treatments and pretreatments used as possible postharvest disinfection techniques. They reported that chlorophyll fluorescence clearly reflected the effects of heat on the photosynthetic systems in avocado fruit but did not detect the alleviation of heat damage by pretreatments. Fluorescence spectra of fruit juices have also been proven useful quality evaluation criteria (93,94). Seiden et al. (93) correlated the fluorescence spectra of correctly with soluble solids in apple juice and two apple cultivars as a means to clasclassifying them. Similar fluorescence chemometrics were also suitable sify and predict quality and process parametersof white sugar solutions and thick juice samples of sugar beets.

B. InducedFluorescenceTechniques In induced (secondary) fluorescence methods, food products are made to fluoresce by adding fluorochromes or by producing specific fluorescent reaction products in the tissue or by applying fluorescent-labeled biological molecules (lectins antibodies) with specific binding affinity for the given food constituent. Table 4 lists commonly used fluorochromes for food applications. The useof secondary fluorescence characteristics has been reported for many applications including the use of calcoflour to detect glucans in malting barley, seed fixation system, use of fluoroscamine to detect sprouting, and morphological and microchemical analysis of cereals (95,110). Fulcher et al. (96) provided a comprehensive overview of fluorescence microscopy and its application to characterize nonstarchy carbohydrates and for routine analysis of products and processing conditions in industrial laboratories. Barnes and Fulcher (97) further reported about measuring fat

pplications

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Table 4 CommonlyUsedFluorochromesfor

Food Applications Fluorochrome Safranin Aniline blue Congo red Diphenylborinic acid Acid fuchsin Acridine orange Acriflavine Ruse Bengale Calcoflour white Auramine

Starch Cellwalls P-Glucans Flavonoids Storage protein Microbiology, bacteria E. coli

Live and dead yeast Fungal element in food Bacteria in food

Solrrcy: Refs. I I I , 112. (Adapted with permission.)

usinginducedfluorescence.According to theirstudy(97),whenNileRed (a fluorescence dye) was added, aqueous suspension of wheat germ showed strong fluorescence (Fig. 14). The absence of Nile Red, however showed almost no fluorescence (Fig. 14) (97). The defatted powdered wheat germ (with Nile Red) showed significant less fluorescence than normal wheat germ (Fig. 14) (97). This implied the effect of fat content in emitting fluorescence. This postulation was verified from the findings of Barnes and Fulcher (97) (Fig. 15). Figure 15 shows strong dependency of fluorescence intensity on fat content of wheat flour mill (measured by soxhlet extraction). Useof Nile Red for fluorescence measurement of lipid and oils can be atool for quality control vegetable oil (97). The different oils/lipids with dissolved Nile Red showed different emission wavelengths and fluorescence intensities (Table5 ) (97). Tryglycerides (with Nile Red) were found to have higher emission wavelengths than those of polar monoglycerides and unesterified fatty acids (with Nile Red) (97) (Table 5 ) . Fulcher et al. (69) examined the aleurone layer in barley seeds using fluorescence microscopy. Fluorescence has also been used with cytometry for rapid analysis of food microorganisms. The fundamentalprinciple of flowcytometry is asfollows: a stationeryoptical “The sample to be examined is allowedtoflowthrough sensing system in the form of continuous stream (using a flow cell). The optical sensing system consists of a light source (excitation source) and a detector. The of the detector detects the optical signal representing the specific characterization microorganism present in the sample” (98). Maximum flow rates up to a 0.5 mL/min and cell countratesupto 1O,OOO/s usingflowcytometryhavebeen reported by Pinder and Godfrey (98). Fluorescent labeling technique, along with flow cytometry, has shown potential for many structural and functional parame-

128

550

Gunasekaran and Panigrahi

450

Emission Wavelength (nm) Fig. 14 Fluorescence emission spectrum (arbitrary units) for an aqueous suspension of powdered wheat germ (A) and defatted wheat germ (B) with Nile Red and without Nile Red (C). (Ref. 97.) (Reprinted with permission.)

.-E ln c

3 .

c

C

0

2

4

6

8

10

Fat Content (%)

Fig. 15 Fluorescence intensity (arbitrary units) vs. fat content of mill streams from a commercial wheat flour mill. (Ref. 97.) (Reprinted with permission.)

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Delayed Light Emission and Fluorescence

Table 5 Fluorescence Characteristics of Nile Red Dissolved in Lipids and Commercial Vegetable Oils

Fluorescence Emission ensity' (nm) max.Lipidhegetable (nm) max. oil

Excitation

Mono-olein Oleic acid Triolein Monolinolein Linoleic acid Trilinolein Corn oil Olive oil Palm oil

508 522 SO4 526 520 507 505 SO6 515

600 596 567 600 600 589 519 58 1 59 1

48

65 27 1 46 63 121

200 20 1 160

Relative. arbitrary units. Source: Ref. 97. (Reprinted with permission.)

ters for food products. The structural and functional parameters are cell size, cell shape, DNA and RNA content, surface sugars, total proteins, enzymes activity, intracellular pH, DNA synthesis, etc. (98). Fluorescent flow cytometry has been used to identify bacteriaby using light scattering and nucleic acid staining. Identification of food pathogens including Salmonella and Campylobacter bacteria has been reported by immunofluorescent labeling (98). Other applications that have usedflowcytometryinclude analysis of fruit preparation, milk products, and brewing yeast (98,99). Shelf life predictionof salads and fruit juice hasalso been performed using flow cytometry (98). Fluorescence cytometry, fluorescence microscopy, and other fluorescence spectroscopic techniques have been presented in more detail in other standard sources (100,101).

VIII. SUMMARY DLE and fluorescence are valuable techniques for evaluatinga variety of quality factors.Allfruits,vegetables,andplantmaterialsundergoingphotosynthesis probably produce DLE. However, the intensity and duration of the emitted light vary widely, depending upon many factors. Because of the strong dependence of DLE on chlorophyll content, variationin DLE can be expected among different varieties of the same product. Similarly, not all materials exhibit fluorescence, and inducing fluorescence should be done carefully so as not to add agents that would render foods unsafe. Fluorescence is also strongly dependent on environ-

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mental and measurement conditions. Therefore, quality evaluation based on DLE and fluorescence measurements requires careful selection of measurement criteria. It is necessary to obtain precise measurement protocols for a particular product and/orqualityfactorunderasetcondition,andmeasurementconditions should be carefully validated to establish standard measuring criteria. Somemeasurementrequirements ofboth DLE-andfluorescence-based quality evaluation systems may not match the high throughput values of more traditional spectrophotometric or calorimetricunits. The operating speed of newer computer visioninspectionsystems is alsomuchhigher.However,DLEand in situations where product quality fluorescence measurements can play a key role in physiological charis not readily apparent but based on very subtle differences acteristics and in cases where accuracy rather than speedof operation is of prime consideration. Fluorescence measurements, for example, are very widely used for microscopic measurements that are not possible with other measurement techniques. New advances such as photoluminography, which is imaging of chlorophyll-containing products using DLE ( I 02,103). and fluorescence imaging will make DLE and fluorescence applications more commonplace. With continued research and innovation, quality evaluation systems based on DLE and fluorescence measurements will find wider applications.

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2. 3.

4. 5.

6. 7.

8.

9. IO. 1I.

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48. WR Forbus, GG Dull. Delayed light emission as an indicator of peach maturity. J Food Sci 55(6):1581-1584, 1990. 49. WR Forbus, JA Payne, SD Senter. Nondestructive evaluation of Japanese persimmon maturity by delayed light emission. J Food Sci 56(1):985-988, 1991. 50. FI Meredith, SD Senter, WR Forbus, JA Robertson, WR Okie. Postharvest quality and sensory attributes of “Byrongold” and “Rubysweet” plums. J Food Quality 15(3):199-209,1992. by delayed 51. WR Forbus, GG Dull, D Smittle. Measuring netted muskmelon maturity light emission. J Food Sci 56(1):981-984, 1991. 52. WR Forbus, GG Dull, DA Smittle. Nondestructive measurement of canary melon maturity by delayed light emission. J Food Quality 15(2): 119-127, 1992. 53. WR Forbus, Jr., GA Hardigree, JH Adams. Experimental delayed lights emission meter for horticultural crops. Agricultural Research Service Bulletin No. 41, 1985. 54. F Grum, RJ Bechever. Optical Radiation Measurements, Vol. I , Radiometry. New York:AcademicPress,1979. 55. F Grum, CJ Bartleson, eds. Optical Radiation Measurements, Vol. 2, Color Measurement. NewYork:AcademicPress,1980. 56. KS Mielenz, ed. Optical Radiation Measurements, Vol. 3, Measurement of Photoluminescence. New York: Academic Press, 1982. 57. S Gunasekaran, MR Paulsen, GC Shove. A laser optical method for detecting corn kernel defects. Trans Am SOC Agric Eng 29(1):294, 1986. 58. Z Sun, P Chen. Distributionof light transmitted through agricultural products. Am SOC Agric Eng Paper No. 83-3089, 1983. 59. GS Birth. Electromagnetic radiation: optical. In: Instrumentation and Measurement for Environmental Sciences. ZA Henry, ed. St. Joseph, MI: American Society of Agricultural Engineers, Supplementary Publication No. SP-0375, 1975. 60. DR Massie, KH Norris. A high-intensity spectrophotometer interfaced witha computer for food quality measurement. Trans Am SOC Agric Eng 18( I ) : 173, 1975. 61. JM Henderson, BM Shawver. Singulation-a problem in design. J Eng Ind 2:101, 1973. 62. BM Shawver. JM Henderson. A compendium of singulation and associated devices. Department of Mechanical Engineering Report, University of California, Davis, CA,1982. 63 TH Burkhardt, RF Mrozek. An orienting and conveying device for sorting dried prunes. Trans Am SOC Agric Eng 17(6):1173, 1974. 64 JJ Gaffney. Light reflectance of radishes as a basis for automatic grading. In: JJ Gaffney, ed. Quality Detectionin Foods, Vol. 5 . St. Joseph, MI: American Society of Agricultural Engineers, 1976, p 75. 65 MP Fogarty, CN Ho, IM Warner. Data handling in fluorescence spectrometry. In: KD Mielenz, ed. Optical Radiation Measurements, Vol. 3, Measurement of Photoluminescence. New York: Academic Press, 1982, p 249. 66 WF McClure, RP Rohrbach, LJ Kishmam, WE Ballinger. Design of a high-speed fiber optic blueberry sorter. Trans Am SOC Agric Eng 18(3):487, 1975. 67. AG Story, GSV Raghavan. Sorting potatoes from stones and soil clodsby infrared reflectance. Trans Am SOC Agric Eng 16(2):304, 1973. 68. CF Earp, CA Doherty,LW Rooney. Fluorescence microscopy of pericarp, aleurone

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layer,andendospermcellwallsofsorghumcultivars.CerealChem60(5):408410,1983. 69. RG Fulcher.GSelterfield,MMcCullyd,PWord.Observationsonthealeurone layer 11. Fluorescence microscopy of the Aleurone sub-aleurone junction as the emphasis on possible B 1,3 glucan deposits in barley. Aust J Biol Sci 25:23-34, 1972. 70. GM Wood, PJ Mann. Detection of selected mycotoxins in foods by fluorescence. In: Fluorescence Analysis in Foods. L Munck, ed. Longman Group, 1984, pp 158170. 71. EB Lillehoj, A Manwiller, TB Whitaker, MS Zuber. Hybrid difference in estimation of preharvest occurrence of bright greenish-yellow fluorescence and aflatoxin in corn. J Environ Quality 12(2):216-219, 1983. 72. S Panigrahi. H Jiao, J Lindsley. Computer vision methods for defect detection of edible beans. Am SOC Agric Eng Paper No. 97-3130, 1997. 73. SAa Jenson, S. Reenberg, L. Munck. Fluorescence analysis in fish and meat technology. In: Fluorescence Analysis in Foods. L Munck, ed. Longman Group, 1989, pp 182- 192. 74. SP Aubourg, I Medina. Quality differences assessment in canned sardine (Sardina pilchardus) by fluorescencedetection. J AgricFoodChem45(9):3617-3621, 1997. 75. JJ Swatland. Relationship between the back-scatter of polarized light and the fibreoptic detection of connective tissue fluorescence in beef. J Sci Food Agric 75( I): 45-49,1997. 76. HJ Swatland, CJ Findlay. On-line probe prediction of beef toughness, correlating sensory evaluation with fluorescence detection of connective tissue and dynamic analysis of overall toughness. Food Quality Preference 8(3):233-239, 1997. 77. HJ Swatland, C Warkup, A Cuthbertson. Testing a UV fluorescence probe for beef carcass connective tissue. Computers Electron Agric 9(3):255-267, 1993. 78. P Velinov, RG Cassens, ML Greaser, JD Fritz, ME Cassens. Evaluation of meat products by fluorescence microscopy. J Muscle Foods 2(1):57-63, 1991. 79. SP Aubourg. Effect of pH on fluorescence formation related to fish deterioration. Food Res Techno1 207(4):268-272, 1998. 80. SP Aubourg, CG Sotelo, R.Perez-Martin. Assessment of quality changes in frozen sardine (Sardirza pilchardus) by fluorescence detection. J Am Oil Chem SOC 75(5): 575-580,1998. 8 I . HH Huss, P Sigsgaard, SAa Jensen. Fluorescence of fish bones. J Food Prot 48(5): 393-396,1985. 82. SAn Jensen, L Munck, P Sigsgaard, HH Huss. Method for quality control of products from fish, cattle, swine and poultry. U.S. Patent 4,631,413 (1986). 83. JD Love, AM Pearson. Lipid oxidation in meat and meat products: a review. J Am Oil Chem SOC 48547-549, 1971. 84. Z Nakhost,MKarel.Measurementofoxidation-relatedchangesinproteinsof freeze-dried meats. J Food Sci 49: 1 171- 1 173, 1984. 85. Z Nakhost, M Karel. Effect of salt, tripolyphosphate, and tertiary butylhydroquinone on myoglobin-lipid oxidation indicatorsin freeze-dried meats. J Food Sci 50: 1748-1749,1985.

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86. SL Melton. Methodology for following lipid oxidationin muscle foods. Food Techno1 37:105-140,1983. 87. JR Chipault, JM Hawkins.Lipidoxidationinfreeze-driedmeats. J AgricFood Chem 19:495-499, 197 1. 88. Z Nakhost, M Karel. Fluorescence due to interactions of oxidizing lipids and proteins in meats. In: Fluorescence Analysis in Foods. L Munck, ed. Longman Group, 1989,pp193-206. 89. AR Kamarei, M Karel. Assessment of auto-oxidation in freeze-dried meats by a fluorescence assay. J Food Sci 49: I517-1520, 1524, 1984. 90. J Song, W Deng,RM Beaudry, PR Armstrong. Changesin chlorophyll fluorescence ofapplefruitduringmaturation.ripening,andsenescence.HortSci32(5):891896, 1997. 91. PMA Trivonen, JR DeEII. Differences in chlorophyll fluorescence and chlorophyll content of broccoli associated with maturity and sampling section. Postharvest Biol Technol14(1):61-64,1998. 92. AB Woolf, WA Lasing. Avocado fruit skin fluorescence following hot water treatments and pretreatments J Am Soc Hort Sci 12 1(1): 147- IS I , 1996. 93. P Seiden, R Bro, L Poll, L Munck. Exploring fluorescence spectra of apple juice and their connection to quality parameters by chemometrics J Agric Food Chem 44( 10):3202-3305, 1996. 94. L Norgaard. Classification and prediction of quality and process parameters of thick juice and beet sugar by fluorescence spectroscopy and chemometrics. Zucker Ind 120(1):970-981,1995. 9s. RG Fulcher. Fluorescence microscopy of cereals. Food Microstructure 75( I): 167175.1982. 96. RG Fulcher, PJ Wood, SH Yiu. Insights into food carbohydrates through fluorescence microscopy. Food Technol (Jan): 101- 106, 1984. 97. PJ Barnes, RG Fulcher. Fluorometric measurement of fats. In: L Munck, ed. Fluorescence Analysis in Foods. New York: John Wiley and Sons, Inc., 1989. of food micro98. AC Pinder, S Godfrey. Fluorescence cytometry for the rapid analysis organisms. In: Food Process Monitoring Systems. AC Pinder, G Godfrey, eds. New York: Blackie Academic and Professional, 1993. 99. L Jespersen. Use of fluorescence staining and flow cytometry for analysis of brewingyeasts (Snccharornyces cerevisiae). DissertationAbstractsInt57( I ) : 1 IO, 1996. 100. OS Wolfbeis, ed. Fluorescence SpectroscopyNew Methods and Applications. New

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New York: Plenum Publishing, 1993, p 48. JE Kaufman, ed. IES Lighting Hand Book. New York: Illuminating Engineering Society,1972. L Godfrey. Choosing a detector for your light sensing application. Laser Focus World (March):I09-119, 1995. K Kaufman. Detectors cover the spectrum of instrument applications. Laser Focus World. (Feb.):99-105, 1994. Product Catalog 1999-2000. New Focus, Inc., Santa Clara, CA, 1999, pp 56-62. GM Williams, MM Blooke. How to capture low light level images without intensitiers. Laser Focus World (September): 1995. L Munck. Practical experiences in development of fluorescence analyses in an applied food research laboratory. In: Fluorescence Analysis in Foods. L Munck, ed. New York: Longman Group/John Wiley and Sons, 1989, pp 3-29. RG Fulcher, DW Irving, A de Francisco. Fluorescence microscopy: applications in food analysis. In: Fluorescence Analysis in Foods. L Munck, ed. New York: Longman Group/John Wiley and Sons, 1989, pp 85-86. P Sigsgaard. Fluorescence microscopy techniques in the practice of food microbiology.In:FluorescenceAnalysisinFoods.LMunck,ed.NewYork:Longman GrouplJohn Wiley and Sons, 1989, pp 131-139. GG Guilbault. Principles of fluorescence spectroscopy in the array of food products. In: Fluorescence Analysis in Foods. L Munck, ed. New York: Longman Group/ John Wiley and Sons, 1989, pp 34-46.

X-Ray Imaging for Classifying Food Products Based on Internal Defects Ernest W. Tollner The University of Georgia, Athens, Georgia Muhammad Afzal Shahin Canadian Grain Commission, Winnipeg, Manitoba, Canada

1.

INTRODUCTION

Increased consumer awareness of product quality is making the marketing of food products, ranging from fresh produce to processed foods, very competitive in that maintaining high quality is essential to economic survival in the business. Products may be damaged during harvest and postharvest operations (external defects). Products may also have some internal defects ranging from foreign materials (e.g., metal fragments, bone fragments), watercore (e.g., apples), andvaria direct adverse effect ous neckrots (e.g., onions). These quality defects have on the market value of the products. Consumers desire consistent, high-quality products, thus, removal of defective units from the batch becomes absolutely necessaryforcustomersatisfaction-akeytosuccess in anybusiness.X-ray techniques are widely used commercially for detecting bone fragments and other foreign objects in processed foods. Commercial application of x-ray-based techniques for inspecting fresh foods will likely be realized in the near future. as a At present, visual inspection, though very subjective, is being used means to determine external food quality. Some researchers have also reported the use of computer vision systems, and these techniques are beginning to see commercial implementation. However, product classification based on internal quality is almost nonexistent commercially. Internal qualityis typically estimated a product lot viaa destrucby visual inspection of product randomly picked from 137

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tive test. A major limitation of this method, besides being destructive, is that internalqualityestimation is basedonrandomsamples.Henceproductswith internal defects may go undetected. In the case of fruits and vegetables, storage of products with internal defects may result in spoilage of surrounding healthy produce during storage. Due to a shortage of labor force willing to accept short-term employment during the harvest season, the fruit and vegetable industry is considering the use of mechanical harvesters as the obvious solution to overcome the problem. The use of mechanical harvesters, which may cause more bruise damage to the fruit, is anticipated to require a thorough inspection of individual fruits. Again, due to lack of experienced workers for food quality evaluation, the need for automatic sensing techniques has increased. It is also desirable that the technique is nonso that even internal defects can be destructive and can be performed on-line detected by examining each of the products being processed or shipped. Internal defects may cause downgrading or even rejection of the whole lot for fresh market-a big loss to the growers. For processors, foreign object detection by consumers can be economically devastating, resulting in the need to remove contaminated product. For packers, the storage cost of fruits with internal defect decreases their profit margin. Moreover, internally damaged units pose a threat of tissue breakdown during storage that can lead to the loss of the whole batch. Hence, detection of internal quality is essential in order to make a decision

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Fig. 1 Schematicdiagram of the overalldecisionsupport system (DSS) showingthe fruit supply decision based on quality and economic inputs. Fruit quality decision based on internal defects was of immediate interest.

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whether the product should be marketed immediately, stored for supply later on to maximize profit, used for making alternative products, or discarded. Automation of the quality evaluation process could generate significant economic benefits to growers, packers, and processors. Our overarching view of the decision support system for food systems is shown in Fig. 1. The component enclosed by the dotted line in Fig. I , concerned with internal quality, is the primary subject of this chapter. Various energy-based imaging techniques have been tried in the past for the detection of internal defects with varying degrees of success. The most appropriate image analysis technique that can relate image features to product quality is, in many cases, yet to be identified. An emphasis should be placed on the development of better image analysis and decision-making techniques for the widest range of inspection applications. The goal of this chapter is to emphasize x-ray techniques in the food industry and present some fairly promising case study results and review relevant applications.

II. FUNDAMENTALS OF X-RAYIMAGING A.

Image Acquisition

X-ray imaging is a radiographic method that provides a cross-sectional view of an object's interior. Specification of an x-ray system involves selection of an energy level, residence time or signal intensity, product orientation, and scan type (computer tomography, CT, or line scan, LS). A typical x-ray imaging system consists of an x-ray source, a detector array, and mechanical systems to rotate and move the components relative to the source and detectors (Fig. 2 ) . When xrays pass through a material, they are partly absorbed along the way by the test specimen. The intensity is reduced according to an absorption coefficient p(x,y), which depends on the local elemental composition and density of the test specimen.Acomputer is used toreconstructanimage of across-sectionalplane through an object. With x-ray CT imaging, it is possible to virtually slice open the test object, examine its internal features, and identify internal defects that may exist. The process of x-ray absorption is described by Beer's law given as:

where

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x-ray photon intensity striking the detectors (photons/s) initial x-ray photon intensity (photons/s) x-ray absorption coefficient (m") length of the projection transact through the test object (m)

1.Line scan voxei CT voxel

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Fig. 2 Three-dimensional schematic (a) of the x-ray imaging system showing two modes of operation: line scan (LS) and CT scan. In the LS mode, the object moves across stationary source-detector assembly. The LS voxel is indicated along with a corresponding LS pixel in (a). In the C T mode the source-detector assembly rotates around the stationary object as indicated in (b). Fig. (b) is an end view of Fig. (a), rotated the indicated degrees. The CT voxel is indicated, as is the corresponding pixel in (a).

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The measurement of the x-ray absorption coefficientp (or some parameter related to p) is of paramount interest in x-ray applications. The image is in effect a map of p. X-ray absorption in food products is partitioned into solids pg and water p wcomponents, as shown below:

where

vu, = x-ray absorption coefficient of water at the specified energy level (m") p, = x-ray absorption coefficient of the solids at the specified energy level pw= ps = pSgw= p,$\ =

(m") mass of water per unit volume of sample (kg/m3) dry bulk density of the sample (kg/m') density of water (assumed constant, kg/m3) particle density of dry solids (assumed constant, kg/m')

Note that p\+/pbg\\ is equivalent to the volumetric moisture content and p,/pSFIis equivalent to the volumetric solids content. In most applications involving food products, the water term dominates and the solids dry density term may be neglected. Thus, one may infer that CT and LS map water content of the product. on The x-ray absorption coefficients for the dry solids and water depend the energy level of the x-rays (1). Thus, in theory, one could scan a sample with x-rays of different energy. Knowing the x-ray absorption coefficient for each phase at each energy level, one could then solve for the water density and solids density. (Detailed discussions followin a later section.) X-rays with energy levels in the 120 kV or in the 5- 15 kV range are much more easily absorbed than higher range. Thus, absorption coefficients are inversely proportional to energy level. Therefore, lower energy levels are more appropriate for small product inspection. CT scanners operate at 120 kV or higher due to the need to penetrate large samples. The available energy level with most x-ray machinesis fixed. One would generally select the lowest usable energy level. X-ray spectral analyzers enable oneto quantify x-ray intensity as a function of x-ray energy level. In a study involving potential foreign objects in processed foods, Morita et al. (2), using 25 and 50 kV x-ray sources, observed noteworthy differences in the absorption of acrylic materials. This bodes well for foreign material identification and removal from processed foods. The two scanning modes for generating x-ray images, CT and LS, are schematically illustrated in Fig. 2. In the CT mode, a series of projections around the stationary test object is obtained to reconstruct the image. The reconstructed image is a quantitative map of p(x,y) at each point in the scanned plane. The CT voxel is acube(typically, 2 mm X 2 mm X 2 mm)definedbycollimation

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thickness and number of projections. CT image pixel is shown over a scaled 2 mm X 2 mm region at intensity corresponding to the average absorption value for the CT voxel. CT scans provide an accurate view of the interior of the object and work well for off-line research applications. Filtered back-projection using in case of an ideal inverse filter produces a good-quality, high-contrast image low noise projection data. Noisy projection data, however, result in poor quality image (low signal-to-noise ratio) as the inverse filter tends to enhance the highfrequency details (noise). CT scanning has become well established asan inspection, evaluation, and analysis tool in medical diagnosis and other research applications. As one may discern from the numbers of projections indicated in Fig. 2 required for a CT image, CT is primarily a research tool in nonmedical applications. However, modern CT scanners are now using electronic waveguides (as opposed to mechanically moving the x-ray source and detectors) to very rapidly collect projection data. By using hardware approaches (as opposed to software), image reconstruction can be very fast. Thus,CT may be a viable tool for selected inspection applications in the near future. In the LS mode, projection data are collected as the object moves between the x-ray source and detector hardware. The reconstructed image in the LS mode is a quantitative map of p(x,y) along numerous collinear paths. The LS voxel is a rectangle defined by the collimator resolution and depth of the test object. The LS pixel is displayed such that the typically 2 mm X 2 mm pixel dimension is shown at a brightness level correspondingto the average relativex-ray absorption of the LS voxel. Line scan imaging provides relatively less accurate image of the object interior (because of the averaging over the projection line). However, because of the speed of operation, the LS mode is more appropriate for on-line inspection applications than the CT mode. Luggage inspection at airports is one familiar example of line scan imaging applications. Line scanning offers the potential to automate the process of produce classification based on quality. Therefore, x-ray line scan imaging is the most suitable for on-line inspection. Line in potatoes scanners have been commercially used for hollow heart detection for nearly I O years (3). The x-ray line scanners are becoming commercialized particularly for foreign object detection in food products (4), and increased commercial use is expected in the near future. X-ray image intensifiers enable one, in effect, to magnify small defects. Approaches involving intensifiers are proving useful for inspecting small grains and nuts for insect larvae presence (5). This technology is currently a research tool. Additional details of x-ray absorption for food quality evaluation applications are described by Garrett and Lenker (6).

B. ProductOrientation Product orientation has a direct effect on the feature characteristics. Orientation relative to the scanned plane affects both shape and brightness of features. These

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features bear on the Gestalt approach to perceptual organization (7). For example, watercore in apples does not always occur as a simple “blob.” Instead, it occurs as a constellation of blobs and is probably a three-dimensional feature. X-ray imagesreducethree-dimensionalfeaturestotwo-dimensionalfeatures(unless one does exhaustive serial CT scans and constructs a three-dimensional representation).Furthercomplicatingtheissue,theexpertsourcesmakeobservations based on two-dimensional cuts, which are usually somewhat random. The experts loss of do not articulate the nature of the visualized “blob” relationships. This intelligence hinders the automation process. Because LS integrates along zones through the product, one can influence the degreeof absorption by causing muchof the featureto be containedby voxels. One may also orient the product such that some of the feature occursin as many voxels as possible. Preliminary studies are generally sufficient for this determination. For watercore and bruise determination in apples it was observed that scanning along the stem-calyx axis was clearly preferable to other orientations. The of the bruised tissue with stem-calyx orientation enables minimal interference sound tissue since most bruises occur on the fruit periphery. There may be an advantage to having some constancy in physiological features (e.g., the core of an apple usually shows as a star-shaped void when line scanning apples along thestem-calyxaxis). CT scanningplanesgenerallyprovidemostinformation when the feature of interest occupies as many pixels as possible. For example, the extent of watercore is readily observed by orienting the fruit such that the stem-calyx axis is normal to the scanned plane and positioned such that the scan plane intersects the maximum fruit diameter.

C. Image Noise The image analysis processis essentially one of extracting desired image features (if present) from the vast arrayof possible undesirable image features. The undesirable features arise from a variety of sources: the instrument, image artifacts, image features not related to the quality attribute at hand, and lack of fit of the decision engine algorithm and the distribution of the desired feature. Each proto decrease the cessing step in an image processing protocol must be designed into nondesirnondesirable features without transforming some desirable features able features. At this point we consider instrument-induced noise and image artifacts. Other noise sources will be considered in the discussion of feature extraction and decision engine selection. During the process of image acquisition, noise is introduced inadvertently to theactualsignal,whichmustberemoved or at least minimized to extract features related to the quality of the product. Instrument-induced noise is in addition to noise associated to unrelated features of interest in an ideal x-ray image. An x-ray image may be subject to instrument noise and interference from several sources, including electrical sensor noise, channel errors, spatial sampling and

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gray-level quantization noise, reconstruction errors, etc. The use of a window function such as a Hamming window (8) leads to a superior image reconstructed from projections. The window function deemphasizes high-frequency components, which mostly represent noise (9). The most common source of noise is counting statistics in the image detecof incident particles (photons, electrons, etc.). The tors due to a small number number of x-ray photons detected at each point in the projection set is subject to fluctuations due to counting statistics. The number of detected x-rays varies in a Gaussian or normal distribution whose standard deviation is the square root of the number counted (IO). The noise is, however, inversely proportional to the square root of the number detected. Counting100 x-rays per detector, on average, produces a noise whose standard deviation is 10%; while on average 10,000 xrays are needed to reduce the variation to 1 %. Noisy images may also occur due to instability in the sourceor detector during the time required to scan or digitize an image. The pattern of this noise may be quite different from the essentially Gaussian noise due to counting statistics. Modern systems are designed to give warnings when lower than desired x-ray photons are detected. Additional sources of noise are particularly important with CT imaging. Beam hardening describes the effect in which the lower energy “softer” x-rays in a sample. As the lower from a polychromatic source are preferentially absorbed energy x-rays are absorbed, the attenuation coefficient of the sample changes independent of any actual change in composition or density. Beam hardening is not a major problem in biological and medical applications. It can be a serious problem in industrial applications where the composition and density within samof each image feature changes according ples vary over a wide range. The contrast to where it lies within the object with respect to the source. Diffraction and scattering of x-rays also affect the quality of images in a way similar to that of beam hardening. Beam hardening and diffraction effects are minimized by avoiding metals and other high x-ray-absorbing materials.The avoidance of sharp boundaries in the scanned region controls these difficulties in CT applications. They are not major problems in LS applications. Appropriate preventive maintenance and frequent calibrations minimize these errors. A window function placed in the reconstruction algorithm minimizes artifacts due to quantization error and sensor noise (9). The process of image reconstruction from projections amplifies the effect of noisein CT images because the filtering process suppresses the low frequencies of errors in the and enhances the high frequencies. Another important source reconstruction of CT images is imprecise knowledge of the location of the center of rotation or variation in that center due to imperfect mechanical mechanisms ( 1 1). When the objectis not exactly in the center of rotation, circular or U-shaped artifacts appear in the reconstructed image. Usually, a large number of views and to provide enough enough detector positions along each projection set are used

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information for CT reconstruction. In the event that fewer projections in a set or fewer views are used, the image has more artifacts and poorer resolution. Shahin et al. ( 1 2) evaluated noise levels in a CT and LS application and discussed an approach for designing filtering masks for minimizing the impact of using of image noise on accuracyof feature extraction. The approach consisted correlation and variography for assessing image noise and noise patterns, where or from waves in the case of water patterns may arise from imaging artifacts transport. Filter mask type and size may then be proposed. Bracketing the proposed mask size while using a feature extraction approach may then be used to selectthemasksizebasedonthemaskassociatedwiththebestdiscrimination.

111.

X-RAYAPPLICATIONS IN THE FOOD INDUSTRY

X-rays have been usedin the food processing industry for several years. Schatzki et al. (1 3) demonstrated a technique for determining the density of lettuce heads al. (14) demonstratedthatx-raytransmission priortoharvesting.Dieneret through bruised apple tissue was less than the transmission through nonbruised tissue, although the typeof bruise was not clearly specified. X-ray machines have also been used for detecting hollow heart in potatoes (1 5) and split-pit defect in peaches ( 16). In food processing plants, x-ray-based technologies have success(17). Direct transmission fully detected stones, bones, metal, and other objects x-radiology can be useful for evaluating overall internal quality of several food products. However, the resulting information from these techniquesis unsatisfactory for analyzing specific point locations of a fruit because of inherent volume averaging effects. The U.S. Department of Agriculture (USDA) now uses linear array x-ray scanners for detecting food in baggage at some airports. X-ray technology hasalso been applied for detectionof watercore in apples (18). Whiledetectionwaspossible,thevariation of pathlengthorradiation a very short length at the edge of the fruit to a through the whole fruit, from maximum just outside the center, produced an irregularly exposed radiograph. of Theapplication of x-radiographyforquantifyingphysicalproperties fleshy fruits requires appropriate correlations between the physical property and x-ray absorption. Tollner et al. (19) present results for apples. Such correlations enable the useof x-ray CT and lineararray scanners to nondestructively quantify physical properties of food products. X-ray CT can effectively simulate linear array scanner action; thus, most discussion is focused on tomography. a X-ray CT imaging is a proven method for nondestructively evaluating cross section of an object. CT greatly reduces volume averaging compared to linear array scanner. Each point on a CT image represents a small volume in the

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scanned plane by the x-ray, while a point on a transmission x-ray image (film) represents a volume average of many volume elements between the x-ray source and the film or detector array. X-ray CT enables detailed evaluation of relative x-ray absorption coefficients over a defined plane within a fruit. One can also simulate direct transmission (line scan) x-radiography with a CT scannerby summing results in selected directions. Thus CT scanners are ideal for studying xray absorption properties of fruits. X-ray CT was used to image the interior region of Red Delicious apples (19). X-ray absorption properties of under varying moisture and density states apples with watercore disorder were different than apples without watercore disto the order. The increased absorption of x-rays in watercore apples was due increased water content of the tissue. CT image of watercore apple cross section clearly revealed the disorder and severity results from the image. The above studies were mainly oriented toward manual detection of various defects from x-ray images. Studies of this type are necessary to determine feasibility of defect detection and to learn the defect signature. X-ray-based defect detection can at the least provide a machine assist for graders and other quality control personnel. Once manual defect detection is possible, one may then cona decision sider automating the feature extraction process and subsequently using engine to classify the images based on the degree of defect.

IV. FEATUREEXTRACTION Feature extraction is the applicationof various image processing operations such as morphological operators, edge detecting with various edge detectors, thresholding, pattern matches, pixel counts, and pixel moments, etc. Operations are not necessarily in this order, nor do specific protocols include all operations. The development of the protocol for specific applications is as much an art as it is science. One in effect decides at the feature extraction juncture what is and is not feature noise. The automation process should begin with considerable input from those experienced in the physiology of the disorder or defect in question. This is especially true with issues related to moisture distribution and factors related to handling and processing, which may affect the distribution of moisture. One should be familiar with appropriate grade standards and extensionsto these standards, which are typically imposed by individual processors and packinghouse operators who happen to be the client or customer. One should have experience in manually extracting the feature of interest before attempting to do same with computerized feature extraction. Judicious selection of x-ray energy level and product orientation relative to the x-ray source is always appropriate. These decisions should be determined in the manual extraction phase.

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A. Watercore in Apples Shahinetal.(20)summarizedacasestudyinvolvingwatercore in apples. Watercore is a cosmopolitan disorder that occurs sporadically, prevalent in some years and absent in others. It occurs only later in the season in mature apples. In addition, watercore occurs onlyin susceptible cultivars, and generally the characteristic symptoms appear only when the fruit is attached to the tree. According is described as to the U.S. grade standard for apples, damage due to watercore “invisible watercore existing around the core and extending to watercore in the vascular bundles; or surrounding the vascular bundles when the affected areas surrounding three or more vascular bundles meetor coalesce; or existingin more than slight degree outside the circular area formed by the vascular bundles; or any externally visible watercore.” A normal fruit has 20-35% of the total tissue volume occupied by the intercellular air space. In apples with watercore this large air space is filled with a liquid. The liquid in the air spaces also increases the density of the fruit. Tollner et al. (19) reported that density of the whole apples increased with increasing severity of watercore disorder, however, the difference was not always statistically significant. These changes in fruit density and absorption characteristics with watercore have been employed in nondestructive detection of disorder. They (19) used x-ray CT to image the interior region of Red Deliciousapplesundervaryingmoistureanddensitystates.X-rayabsorption properties of apples with watercore were different from apples without watercore disorder. The increased absorption of x-rays in watercored apples was due to increased water contentof the tissue. CT images of watercore apple cross sections clearly revealed the disorder severity. Results from the image analysis correlated well with visual observations on the cut-open apples. Throop et al. (21) reported a classification standard based on natural features for apples to define watercore. Watercore can be thought of as groups of “watery” tissues, starting at the center of the fruit and growing outward. In an x-ray image, the regions corresponding to watercorewill appear as bright regions consisting of adjacent pixels. This a priori knowledge canbe of great importance to identify features indicative of watercore. The likelihood principle along with the laws of perceptual organization can be very useful in determining fruit quality from the image features. This implies that the area of these brighter regions or “blobs” might be a good measure of watercore severity. Based on this assumption, fruit area in the processed image was used as an indicator of watercorein apples (22,23). Since morphological operations are especially good for blob extraction, morphological opening was used to extract the area features from line scan images of red delicious apples. Morphological opening is a combination of erosion and dilation operations. Erosion operation eliminates the false features, whereas dilation operation makes sure that the true features remain intact. A structuring element approximately the size of the prefiltering mask is appropriate.

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Morphological image processing is a powerful tool for examining “bloblike” shapes and areasin images. McDonald and Chen(24) described some applications where morphological image processing was used for size distribution, shape discrimination, and texture analysis of agricultural commodities. Throop et al. (21) presented an algorithm to separate old and new bruised tissues from stored apples using NIR reflectance. Apple images were first low-pass filtered to removesmallsurfacecharacteristicsandthennormalized.Theshapes of the bruised areas were evaluated based on a shape factor. Morphological closing was used to eliminate unwanted isolated details. Recognition of agricultural objects by shape using morphological erosionof the absolute gradient of the x-ray image was described by Schatzki et al. (25). The ratio of the fruit areas measured before and after processing may be an important feature in order to eliminate the effect of fruit size. Variation in the fruit image intensities may be important due to the fact that watercored fruits are expected to show more variation in the fruit image as compared to good fruits. Therefore, standard deviationof the gray values withina fruit image may provide someinformationaboutwatercoreseverity.Otherapplicationsnodoubtmay cause other areal features to come to the fore. Typical results before and after prefiltering plus application of the morphological operator to apple line scan images are shown in Fig. 3. Image transforms are mainly usedto represent images in compressed form. This makes the transform coefficients potentially good indicators of fruit quality. The Karhunen-Loeve transform (KLT) is the optimum transform that minimizes the mean square error in the reconstructed image (26). The KLT yields a set of uncorrelated coefficients, but it is slow to compute. However, the discrete cosine transform (DCT), which is muchfaster to compute, closely approximates the information packing ability of the optimal KLT (27). The discrete wavelet transform (DWT) is another commonly used transform in image processing applications. The DWT generates coefficientsthat are local both in frequency and spatial (28). domains; a characteristic that the discrete Fourier transform (DFT) lacks Both DCT and DWT have been widely used in the general areas of image compression and feature extraction applications. 11 was hypothesized that textural features extractable by transforms such as those above would provide additional information to a decision engine. The choice of a particular transform i n a given application, however, depends on the computational resources available and the a1nount of error that can be tolerated. Information packing ability of DCT is superior to that of DFT and WHT. Although this is usually true for most natural images, the KLT, not the DCT, is the preferred transform in the information packing sense (29). However, because the KLT is data dependent, obtaining the KLTbasis images is a nontrivial computational task. For this reason, the KLT is seldom used in practice. Instead, DCT, whose basis images are fixed, is normally preferred because it is fast and closely

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I Fig. 3 Results of applyingthresholding and morphologicaloperatorto raw line scan of apples: (a) original line scan image of apples; (b) image following thresholding and morphological processing.

approximates theKLT. Ahmed et al.(30) first noticed that the KLT basis images closely resemble the DCT basis images. As the correlation between adjacent pixels approaches unity, the basis images become the same for both the KLT and DCT (31). The DCT has found widespread applications in speech and image processing for the purpose of data compression and feature extraction (32) and

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noise filtering (33). However, no application of DCT for area defect detection was found in cases of food products. Wavelets are a set of mathematical functions, called basis functions, that form a compact description of a signal. These basis functions can be used to of resolution. As such, wavelets compress represent signals at multiple levels images and recover information from even the murkiest images. Wavelets also isolate the location as well as the scale of features in a signal/image; thus, they can encode rapidly changing signals in compact form. Details on wavelet transforms are givenby Daubechies (34). Wavelet transform has been used widely for feature extraction purposes in a variety of applications. Agricultural applications include grading beef carcasses (35) and picking green peppers in a field of green leaves (36).

B. Bruises in Apples Quality defects such as surface bruising decrease market value of fresh fruits. of the fresh market fruit producTender fruit varietiesthat make up a large portion tion are especially susceptible to bruise damage during mechanical harvest and postharvest handling. According to Studman et al.(37), on the average more than 15% of apples do not qualify as “US extra fancy” due to bruise damage during orchard transport. As much as 29% of apples have been reported to have some bruising after harvest. In order to maintain fruit quality, bruised apples must be separated during the grading operation. At present, apple sortingis done through human visual inspection that is inconsistent, inefficient, and subjective. Additionally, bruises in dark-colored fruits such as Red Delicious apples are verydifficult to see, especially when the fruits are moving on the conveyors, thus further hinderingvisualinspection.Applesarethethirdlargestfruit crop i n theUnited States (38). Hence, substantial economic benefits could be achieved by timely redirection of bruised fruits for alternative processing, proper utilization of storagespaceforstoringonlygoodfruits,andreducinglaborcostsformanual sorting. Impact stress occurs when the productis dropped from a sufficient distance to cause injury. This type of injury is generally seen as bruising. With bruising the injury is restricted to the interior flesh of the tissue and in many products may be only initially detected as a water-soaked area after peeling. With time, in the exposure of the damaged cells to air in the intercellular spaces results thetypicalbrowning symptoms. The damaged tissue mayeventuallybecome desiccated, thus opening the possibility of x-ray imaging. X-ray imaging is a leading contender becauseof its abilityto detect both external and internal defects such as surface bruises and watercore (39,40). Apple defect classification from x-ray images warrants major emphasis. Shahin et ai. (41) found that edge detection in the zone just under the fruit skin correlated with bruise presence and

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severity. Figure 4 shows line scan images of apples prior to prefiltering (Fig. 4a) and after prefiltering and Roberts Edge operators were applied (Fig. 4b). Figure 4c shows the effect of substituting the median filter for the Gaussian filter. The Gaussian filter was ultimately chosen based on subsequent testing. The edge corresponding to the skin edge was removedby further image subtraction operations (results not shown).

C.Diseases

in Onions

Sweet onions are an important specialty crop grown in the Vidalia region of of onions,sweetonionslackpungencycomGeorgia.Unlikeothervarieties pounds (42). As a result, they are readily susceptible to disease inoculation and tend to have a shorter shelf life. Several decay-causing organisms can possibly (43,44). Mechanical injuries invade the onion bulbs at any time via soil or air occurring during spraying and harvest operations may damage the bulbs leading to infection. Quality defects such as internal decay, ring separation, and internal of onions. These physiological changes cause sprouting decrease the market value voids and/or other tissue change, which alters moisture distribution. X-ray imaging has shown promising results for detecting internal defects in fruit and vege(45), pests (39), watercore tables such as interior voids and foreign inclusions (20), and freeze damage (46). X-ray imaging and image processing techniques were employed (47) to identify poor quality onions. They emphasized theneed for better image analysis techniques for improved accuracy of classification. In light of the physiology of the onion bulb and physics of the line scan operation, edge detection techniques are expected to perform well for enhancing internal defect features in x-ray imin eggs (48) and bruise ages. Success of edge detectors for enhancing cracks damage in apples (49) provided a reasonable likelihood of success with disease detection in onions (50) sincethe feature of interest in each case appearsin images as a line or edge. Figure 5 shows the results of line scanning onions before (Fig. Sa)andafter(Fig.5b)prefilteringandfeatureextraction.Arealfeaturesplus various transform coefficients provide a suite of potential inputs to a decision engine for classification purposes.

D.MiscellaneousStudies Long-established feature extraction approaches for optical images (e.g., see Ref. 51) may be applied to x-ray images. X-ray image feature extraction research is of baby ongoing at several laboratories. Problems including the sex separation chicks (52), insects, bruises, rots and browningof apples (39), and other general agricultural products ( 5 3 ) are being researched. Techniques ranging from simple segmentation to sophisticated watershed delineation algorithms (54) are under

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Fig. 4 Line scanimage of apples (a) followed by application of Gaussianfilterplus Robert’s edge detection (b) and application of median filter plus Robert’s edge detection (c).The Gaussian filter proved superior to the median filter in later tests.

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Fig. 5 Line scan image of onions before and after Gaussian filtering and Roberts edge detection. (a) Original line scan image of onions: dark features indicate defects. (b) Processed image showing features of good and defective onions:bright arcdlines insidebulb boundary indicate defects.

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evaluation. The numberof laboratories for x-ray imaging research for food products continues to expand.

V.

DECISIONENGINES FORFEATURECLASSIFICATION

Pattern classificationis the taskof recognizing the class label w from the measurement vector v. This task is equivalent to establishing a many-to-one mapping x + d from the measurement space V into the decision space D, containing M discrete points, each of which represents one of the M classes. A common task in pattern classification is variable or feature selection. Typically, a single feature has little discriminating power. Hence, combining several features improves the chances of coming to a satisfactory result. But combining many features makes it more likely that the feature set as a whole has correlation and redundancy. In this situation, it is not very useful to evaluate the potential contributionof a single feature in isolation to solve the classification task. Rather, mutual dependencies among the features need to be considered. The decisive task is to determine the all features is already contribution of a single feature after a certain subset of used for classification. The answerto this problem is rank ordering of the features based on their additional contribution after a subset has already been selected (55). The quality of the many-to-one mapping among the features truly related to the defect affects the classification noise. For example a nonlinear many-to-one a linear mapping. mapping when needed would have less classification noise than Statistical methods, suchas Bayesian classifiers, can provide optimal classification, but their performance deteriorates when the input data fail to meet the assumptions of normality and equality of covariance matrices. Artificial intelligence approaches such as fuzzy logic and neural classifiers, on the other hand, entail less stringent assumptions about the statistical characteristics of the input to perform data. Thus, fuzzy logic and neural network classifiers are expected equally good or better than the Bayesian classifier.

A.

Bayesian Classifier

The Bayesian classifier is a probabilistic approach to recognition. Probability in patternrecognitionbecause of theranconsiderationsbecomeimportant clasdomness under which pattern classes normally are generated. The Bayesian sifier is optimal in the sense that, on average, it yields the lowest probability of committing classification error. The Bayesian classifier is difficult to apply in practice, especially if the number of representative patterns from each class is not large or if the underlying form of the probability density function is not well behaved. By far the most prevalent form assumed is the Gaussian probability

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density function. The closer this assumptionis to reality, the closer the Bayesian classifier approaches the minimum average loss in classification. The Bayesian classifier implements a decision function of the form: dj(x) = p(x/oj)P(oj)

j

=

1, 2, . . . , M

where a pattern x is assigned to class mi if di(x) > dj(x) for all j f i. P(wj) and p(x/oj) are the probability of occurrence and the probability density function of class w,, respectively. M is the number of classes the pattern can be assigned to. Assuming a Gaussian probability density function, the Bayes decision function is given by: d,(x) = In P(wj) - 0.5 lnlCjl - 0.5[(x - rn,)TC;’(x

-

mj)]

(4)

where mj and C, are the mean vector and covariance matrix, respectively. In addition to the normality assumption, the Bayes discriminant function assumes that the group covariance matrices are equal and the output changes linearly with the input variables. Outputs from typical discriminant analyses enable one to analyze the relative contribution of the various inputs. Inputs not contributing meaningful information need not be further considered, thus simplifying the model. Datasets for discriminant analyses are comprisedof the image features plusan expert classification using destructive techniques. Model development generally is performed using a subset of the data.The remainder of the data are used for model validation. The primary outputof interest is the classification of each sample product. Percent accuracy of the classification along with the percent of false positives are generally reported. Classification accuracy may range anywhere from random (e.g., number of categories divided by 100%;very poor) to 90% plus, considered to be very good. In cases where the expert standardis hand inspection, 90% accuracy is to assure considered excellent. Percent accuracy represents the system’s ability a good product, and false positives (100% accuracy) represent a tangible loss to the system in that a good product is rejected. The significant image features found to be significant contributors in an apple watercore study were blob area, mean grey value, and the tenth discrete depending cosine coefficient (20). Prefiltering may or may not be appropriate, on the feature of interest. Selection of the prefiltering mask can be completed once one has determined the significant image features contributing to meaningful classification. In the case of apples with watercore, Shahin et al. (20) justified the size of the structuring element used in the morphological processing based on the image noise statistics (20). Results from the watercore study are summarized in Table 1 . For the most part the classification was generally acceptable by modem standards. Results for the “moderate” watercore category were worse than either the “none” or the “severe” category. Some of the “moderate” cate-

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Table 1 Classification Results for Bayes Classifier Applied to Red Delicious Apples with Watercore

No/mild Moderate Severe Total predicted

False positives

Predicted class

actual Watercore No/mildclass

Moderate

Severe

10

4 0

52

m; 2 19

19

actual

(%)

(%I

58 14 63 1618 90

83 50 89

8

21

Source: Ref. 49.

gory error was thought to moderate category.

be related to the difficulty of hand classifying the

6. Fuzzy Logic Fuzzy logic is based on fuzzy set theory introduced by Zadeh (56). Fuzzy logic is a nonparametric decision engine that can encompass nonlinear relationships. Biological systems are complex in nature, and when idealized to develop representation by traditional models, they can distort to the extent where the models are not representative and have little use. Fuzzy logic methods present alternatives for modeling complex biological systems with a remarkably simpleway of drawing precise conclusions from vague, ambiguous, or imprecise information. The initial stage of fuzzy model development depends to some extent on intuition and the designer’s “feel” for the nature of the problem. Design of a fuzzy model does not require a well-defined mathematical model of the system to be modeled. It can start with a general idea of how the model should work. The idea may consistof defining input variables and their membership functions (fuzzification), a set of rules to define the model behavior under different conditions (fuzzy inferencing unit,FIU), and an output variable to translate fuzzy output of the FIU into a crisp value (defuzzification). Several fuzzy models have been developed to predict the response of biological systems. Among several applications, fuzzy logic has been used for predicting yield for prescription farming (57), estimating forest parameters from image data (58), analyzing plant structure(59), diagnosing tomato diseases(61), predicting peanut maturity (60), and sorting tomatoes(62) and apples (23) based on quality. The dataset for fuzzy classifier development consists of image features plus expert data. Fuzzy approaches do not enable a ready evaluation of significance

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of contribution by each parameter. Thus we suggest using Bayesian analyses on the model building dataset for purposes of determining the relevant parameters fortheFuzzyclassifierdevelopment.Fuzzyclassifierdevelopmentwhenthe number of inputs exceeds twois hindered by difficulties in tuning the membership functions for optimal performance. Shahinet al. (20) developed a fuzzy classifier for apple watercore using the three significant image features (area, mean grey value, tenth discrete cosine coefficient) identifiedin the Bayesian analyses above and found results very comparable to those presented in Table I .

C.NeuralNetworks Artificial neural networks (ANNs) provide an alternative way of modeling sysor ill-definedinput-outputrelationships.ANNclassifiers temswithunknown in a manner similar to humans. The have the capability of classifying patterns power of a neural network lies in its ability to model nonlinear relationships and in realits robustness to deal with noisy and incomplete data commonly found life situations. ANNs are capable of forming highly nonlinear decision boundaries and have an intrinsic power to generalize. Neural networks look for patterns in develop the ability to correctly training sets of data,learnthesepatterns,and classify new patterns. The multiple-layer feedforward network (MLFN) was originally described byRumelhart et al.(63). It is themostwidelyusednetworkarchitecturefor problems that can make use of supervised training (learning through examples). The MLFN can require longer training periods, but execution of the trained network takes relatively little time. Hence, real-time applications are best served by the MLFN model (64). Details of neural networks can be found in a number of publications (65-67). Neural networks have been successfully used for food quality evaluation and related applications. Boudolf (68) used the neural network approach to predict peanut maturity from nuclear magnetic resonance (NMR) data. Pate1 et al. (48) successfully developed a neural network for crack detection in eggs using computer vision. Elizondo et al. (69) developed neural a network model to predict flowering and physiological maturity dates of soybean. Das and Evans (70) used computer vision and neural networks to separate fertile and infertile eggs. Deck et al. (71) compared neural networks and statistical methods for sorting potatoes into various classes using machine vision. The dataset for theneural net classifier development is identical to that for the Bayesian analyses except that insteadof a model building set and a validation data set, the data are divided into model building, model testing, and validation datasets. The MLFN requires that a test set be accessed for limited testing in the model building phase. Using the same three image features used with the Bayesian and fuzzy classifier development discussed above, Shahin et al. (20) found

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Table 2 Classification Results for Neural Classifier with Four Hidden Nodes Predicted class

Watercore observed No/mild class Nolmild Moderate Severe Total predicted

False Total Accuracy positives observed‘ (%) (%I ”

Moderate

0

-3 3 0 58

Severe

- 3

14

2 13

58

95 57

5 38

18 19

90

‘Observed watercore seventy was assigned based on visual judgement of the cut fruit.

substantial improvement in classifier accuracy and false-positive scores (Table 2), especially in the moderate category. Similar studies with old bruises in apples (41) and diseases in onions (50) resulted in improved classification accuracy.

D. OtherClassifiers Bayesian, fuzzy, and neural net classifiers tend to represent classifier types in and themselves do not comprise the entire universe of classifiers. A feature extraction methodcalledthe“maximumrepresentationanddiscriminatingfeature” (MRDF)was reported(72) to classify good and bad pistachio with nuts accuracies be an advanced nonlinear implementaat or above90%. Their method appears to tion of the Bayesian method. They found that adding the quadratic component to the linear model increased accuracy, which may explain why a number of are studies (40,41,50) reported increased accuracy with neural nets. Neural nets known to represent nonlinear data fairly well.

VI. SUGGESTED PROTOCOL FOR CLASSIFIER DEVELOPMENT Development of a protocol for obtaining usable x-ray images and optimal extraction of usable features for defect identification has been the focus of this chapter. The following steps appear to provide a protocol for developing classifiers from x-ray images:

1. Select the x-ray energy level(s) depending on product size, density, and available equipment. 2. Run preliminary studies for optimizing orientation of product. Determine the characteristics of the features that correlate to defects (e.g.,

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blobs, lines, etc.). Determine the nature of features that do not correlate to defect. Select an image processing method that minimizes undesirable and enhances desirable features. Areal features,transformfeatures, and other common image processing features should be considered.Considerrevisitingequipmentselection if multipleenergies, image intensifiers, etc. would result in enhanced features. 3. Select some test materials, such as acrylic, and determine the structure of the correlation functions within the image. Determine if artifact removal by preprocessing would be beneficial. Determine if morphological processing is appropriate based on persistence of correlation. Size of mask may be estimated based on the noise present and correlation persistence. 100 or more typically) good and defect product at 4. Scan multiple (e.g., theselectedorientationandenergylevels.Runtheselectedfeature extraction processes. 5. Develop a Bayesian classifier for initial parameter evaluation, using a subset of the data for model building. Using the significant factors, of evaluate a range of filter masks that bracket predicted mask sizes step 3. 6. Train a neuralnetclassifier (or othernonlinearclassifier if desired) using the selected inputs from step 5 and evaluate using the validation data set. Evaluate the results in the light of expected accuracy, quality of expert data, and client expectation. To improve resultsmay one have to revisit step 1.

VII.

FUTURETRENDS

X-ray technology has been usedin the past in the food industry for foreign matter detection,such as screeningmeat/poultryproductsforextraneousmaterials. However, single energy x-ray technologyis incapable of identifying small defects and contaminants. Small defects and contaminants are of considerable interest (73) because of lack of availability of sufficient number of qualified inspectors. This is often because the variance in mass density between the defect/contaminant and the productand the product noise confound positive identification. Adding an additional scanning energy is being proposed to address these concerns. Dual energy scanning allows imaging based on molecular weight and mass density. One could use images at each energy level to further enhance features of interest. Simultaneous measurementof moisture and soil density has been done using dual energy level monochromatic sealed gamma sources for many years. Aylmore (74) developed a dual energy gamma CT system, which successfully detected plant moisture uptake and root growth. They developed an image or

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map of total absorption coefficients at each energy level.For each corresponding image pixel, they wrote Eq. (2) for each of two energy levels, knowing the dry soil and water absorption coefficients at each energy level. They simultaneously solved the equations for the volumetric moisture content and dry bulk density. The x-ray sources are polychromatic. This requires detectors that can detect x-rays at multiple wavelengths (i.e., energy levels, 140 kV vs. 70 kV) to enable effective results. Dual energy x-ray technology has been developed by Ergun and in medicalanalysis(76-79).The Mistretta(75).Ithasbeenusedeffectively major medical application is bone densitometry. Line scanners used for security purposes now use multiple x-ray energy level-based images for highlighting features of interest. They typically use one polychromatic source with detector arrays with 70 and 140 kV energy windows. in processedfoodsand Use ofthistechnologyfordetectingforeignobjects diseasesldefects in fresh foods is just beginning. One issue not researched as yet is that of the required residence time of product in the detector. The residence time would be the total time required for irradiation plus time required for image processing, feature extraction, classification decision, and action actuation. This cannot as yet be projected until working prototypes incorporating hardware processing for each step in the classification protocol are available for testing. The information gathered from this study could be useful in developing a decision support system such as the one shown in Fig. 1. The scheme shown in NIR sensors to Fig. 1 may be conceptually expanded to include optical and/or give external quality attributes. Needs of the client and general economic conditions must be fxtored into a more complete classifier. Current trends in labor availability and economics will likely continue to drive the industry towards more automated inspection and decision support systems.

REFERENCES JA Richards, FW Sears, MR Wehr,MWZemansky.ModernUniversityPhysics. Reading, MA: Addison-Wesley, 1960. 2. K Morita, Y Ogawa, CN Thai. Softx-ray imaging for detection of foreign materials in foods. Paper No. 986067.St. Joseph, MI: American Society of Agricultural Engineers (ASAE), 1998. 3 . M Lefebvre. Potato Operation: Automatic Detection of Potato Diseases. SPIE Proc. Optics in Agriculture, Forestry and Biological Processing. Boston: SPIE 1994, pp I.

2-8. 4. NK Gupta. X-ray system for on-line detection of foreign objects in food. Presented at the Food Processing Automation Conference IV, Chicago, IL, November 1995. 5. TF Schatzki. Personal communication, 1998.

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6. RE Garrett, DH Lenker. Selecting and sensingx and gamma rays. In: Quality Detection of Foods. St. Joseph, MI: American Society of Agricultural Engineers, 1976, pp 107-115. 7. EB Goldstein. Sensation and Perception. 4th ed. Pacific Grove, CA: Brooks/Cole Publishing Co., 1976. 8. RW Hamming. Digital Filters. Englewood Cliffs, NJ: Prentice Hall, 1977. 9. AC Kak, M Slaney. Computerized Tomographic Imaging. New York: The Institute of Electrical and Electronics Engineering, Inc., 1988. IO. IC Russ. The Image Processing Handbook, 2nd ed. Ann Arbor: CRC Press, Inc., 1995. 11. FL Barnes, SG Azavedo, HE Martz, GP Roberson, DJ Schneberk, MF Skeate. Geometric effects in tomographic reconstruction. Lawrence Livermore National Laboratory Rep. UCRL-ID- 10.5 130, 1990. 12. MA Shahin, EW Tollner, SE Prussia. Filter design for optimal feature extraction from X-ray images. Trans ASAE, 1998. 13. TF Schatzki, SC Witt, DE Wilkins,DH Lenker. Characterization of growing lettuce from density contours: I . Head Selection. Patterns Recog 13(5):333-340, 1981. to sort bruised 14. RG Diener, JP Mitchell, ML Rhoten. Using an X-ray image scan apples. Agric Eng 51(6):356-357, 361, 1971. 15. RE Nylund, JM Lutz. Separation of hollow heart potato tubers by means of size grading, specific gravity and X-ray examination. Am Potato J 27-214-222, 1950. 16. SV Bowers, RB Dodd, YJ Han. Nondestructive testing to determine internal quality of fruit. ASAE Technical Paper No. 88-6369. St. Joseph, MI: American Society of AgriculturalEngineers,1988. 17. GL Gerber, QA Holmes, R Calhan. Industrial machine vision with X-ray sensor for online food processing inspection. SME Paper MS85-1009. Dearborn, MI: Society of Manufacturing Engineers, 1985. 18. BL Upchurch, HA Affeldt, WR Hruschka, JA Throop. Optical detection of bruise and early frost damage on apples. ASAE Technical Paper No. 89-3013. St. Joseph, MI: American Society of Agricultural Engineers, 1989. SE Prussia.RelatingX-rayabsorptionto 19. EW Tollner, YCHung,BLUpchurch, density and water content in apples. Trans ASAE 35(6): 1921-1928, 1992. 20. MA Shahin, EW Tollner, HR Arabnia and MD Evans. Watercore featuresfor sorting red delicious apples: a statistical approach. Trans ASAE, 1998. 21. JAThroop, DJ Aneshansley,BLUpchurch.DetectingWatercoreinAppleswith Machine Vision. ASAE Paper No. 91-7536. St Joseph, MI: ASAE, 1991. 22. MA Shahin, EW Tollner. Detection of watercore in apples using X-ray linescans: Feature extraction and classification. Proc NRAES Conference on Sensors for NondestructivE Testing, Orlando, FL, February 18-21, 1997, pp 389-400. 23. MA Shahin, EW Tollner. Apple Classification based on watercore features using fuzzy logic. ASAE Paper No. 97-3077, St. Joseph, MI: ASAE, 1997. 24 T McDonald, Y Chen. Applicationof morphological image processingin agriculture Trans ASAE 33(4): 1345- 1352, 1990. 25. TF Schatzki, A Grossman, R Young. Recognitionof Agricultural Objects by Shape. IEEE Trans Pattern Anal Machine Intell 5(6):645-653, 1983. 26. WK Pratt. Digital Image Processing. New York: John Wiley and Sons, 1978.

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27. WKou.DigitalImageCompressionAlgorithmsandStandards.Norwell,MA: Kluwer Academic Publishers, 1995. 28. N Sweeney. Wavelet transforms represent signals in terms of both time and scale. Personal Eng 37-42, 1996. 29. HP Kramer, MV Mathews. A linear coding for transmitting a set of correlated signals. IRE Trans Inform Theory T-2:41-46, 1956. 30. N Ahmed, T Natarajan, KR Rao. Discrete Cosine Transforms. IEEE Trans Comp C-23~90-93, 1974. 31. RJ Clark. Transform Coding of Images. New York: Academic Press, 1985. 32. M Bi, SH Ong, YH Ang. Coefficient Grouping Method for Shape-Adaptive DCT. Electron Lett 32(3):201-202, 1996. 33. AH Lia, NH Yung. New feature-preserving filter algorithm based on a priori knowledge of pixel types. Optical Eng 35( 12):3508-3521, 1996. 34. I Daubechies. Wavelet bases. In: L Schumaker, ed. Recent Advances inWavelet Analysis. Boston: Academic Press, 1994, pp 237-257. 35 ND Kim, V Amin, D Wilson, G Rouse,S Upda. Texture analysis using multiresolutionanalysisforultrasoundtissuecharacterization. Rev Prog QuantNondestruct Eva1 2: 1351 - 1358, 1997. 36. C Davidson, D Rock. Wavelets and HONN: plx-perfect marriage. AI Expert (January): 31-35,1995. 37. CJStudman,GKBrown, EJTimm,NLSchultz,MJVreede.Bruisingonblush and non-blush sides in apple-to-apple impacts. Trans ASAE 40(6): 16551663,1997. 38. RSChilds,RAMilligan,GBGWhite,WC Stiles. Adynamicapproachtoapple orchard replacement. Cornell University Agricultural Economics Research 83-1 I . Ithaca,NY:CornellUniversity, 1983. 39. TF Schatzki, RP Haff, R Young, 1 Can, LC Lee, N Toyofuku. Defect detection i n apples by means of X-ray imaging. Trans ASAE 40(5): 1407- 141 5, 1997. 40. MA Shahin, EW Tollner, SE Prussia. Detecting defects in apples using X-ray imaging: Filter design and features enhancement considerations. TransSt. Joseph, MI: ASAE,1998. 41 MA Shahin, EW Tollner, RW McClendon, HR Arabnia. Apple classification based on surface bruises using image processing and neural networks. Manuscript submittedtoAI Applications, 1998. 42. BW Maw, YC Hung, EW Tollner. Some physical properties of sweet onions. ASAE Tech. paper no. 89-6007. St. Joseph, MI: ASAE, 1989. 43. DR Sumner. Soilborne pathogenic fungi, root diseases and bulb rots. In: BW Maw, ed. Georgia Onion Research Extension Report. Cooperative Extension Service, University of Georgia, College of Agriculture and Environmental Sciences, 1994. 44. RD Gitaitis. Bacteriology results 1993 onion trials. In:BW Maw, ed. Georgia Onion Research Extension Report. Cooperative Extension Service, University of Georgia, College of Agriculture and Environmental Sciences, 1994. 45. EW Tollner, HA Affeldt Jr, GK Brown, P Chen, N Galili, CG Haugh, A Notea, Y Sarig, T Schatzki,I Shmulevich, B Zion. Nondestructive detectionof interior voids, foreign inclusions and pests. Nondestructive technologies for quality evaluation of fruits andvegetables,Proceedings of theInternationalworkshopfunded by the

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United States-Israel binational agricultural research and development fund (BARD), Spokane, Washington. St. Joseph, MI: ASAE, 1993. BL Upchurch, HA Affeldt, DJ Aneshansley, GS Birth, RP Cavalieri, P Chen, WM Miller, Y Sarig, Z Schmilovitch, JA Throop, EW Tollner. Detection of internal disorders. Nondestructive technologies for quality evaluation of fruits and vegetables. Proceedings of the International Workshop Funded by the United States-Israel Binational Agricultural Research and Development Fund (BARD), Spokane, Washington. St. Joseph, MI: ASAE, 1993. EW Tollner, YC Hung, B Maw, DR Sumner, RD Gitaitis. Nondestructive testing for identifying poor quality onions. Proc SPIE Tech. Conf. 2345:392-402, 1995. VC Patel, RW McClendon, JW Goodrum. Crack detection in eggs using computer vision and neural networks. AI Appl 8(2):21-31, 1994. MA Shahin, EW Tollner, RW McClendon. AI classifiers for sorting apples based on watercore. Manuscript prepared for AI Applications, 1998. MA Shahin, EW Tollner, RD Gitaitis, DRSummer,BWMaw.Classification of onions based on internal defects using image processing and neural network techniques. Trans ASAE, 1999. AK Jain. Fundamentals of Digital Image Processing. Englewood Cliffs. NJ: Prentice-Hall,1989. Y Tao. Feature extraction and pattern recognition method for automated sex separation of baby chicks. Tech. Paper No. 983046. St. Joseph, MI: ASAE, 1998. DA Casasent, A Talukder, HW Lee. X-ray agricultural product inspection: Segmentation and classification. Proc SPIE 3205:46-55, 1997. A Talukder, D Casasent,HW Lee, PM Keagy, TF Schatzki. Modified binary watershed algorithm for segmentation of X-rayed agricultural products. Proc SPIE 3543: 1998. C McHenry. Multivariate Subset Selection. J Royal Stat Soc C 27(23):29 1-296, 1978. LA Zadeh. Fuzzy sets. Inform Control 8338-353. 1965. JR Ambuel, TS Colvin, DL Karlen. A fuzzy logic yield simulator for prescription farming. Transactions of the ASAE, Vo137(6): 1999-2009, 1994. FC Maselli, T de Filippis Conese, S Norcini. Estimationof forest parameters through fuzzy classification of TM data. IEEE TRANS. Geoscience Remote Sensing, Vol 33(1):77-83, 1995. W Simonton. Bayesian and fuzzy logic classification for plant structure analysis. ASAE Paper No. 93-3603, ASAE, 2950 Niles Rd, St Joseph, MI 49085-9659, 1993. MA Shahin. Fuzzy logic model for predicting peanut maturity. Submitted to Transactions of the ASAE, 1998. C-H Wu, S-W Hu. Fuzzy related computer vision for diagnosing tomato diseases. ASAE Paper No. 933033, ASAE, 2950 Niles Rd,St. Joseph, MI 49085-9659, 1993. N Pu. Fuzzy logic DSS for sorting tomatoes based on quality. Unpublished Master’s Thesis, The University of Georgia, Athens, 1994. DE Rumelhart, GE Hinton, JL McClelland, eds. A general framework for parallel distributedprocessing.In:ParallelDistributedProcessing-Explorationsinthe Microstructure of Cognition.Vol. 1: Foundations.Cambridge,MA:MITPress, 1987.

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64. TMaster.SignalandImageProcessingwithNeuralNetworks.NewYork:John Wiley and Sons, Inc., 1994. 65. JL McClelland, DE Rumelhart. Eds. Parallel Distributed Processing: explorations in the microstructure of cognition. Vol. I Foundations. Cambridge, MA: MIT Press, 1986. 66. R Hecht-Nielsen. Neurocomputing. New York: Addison-Wesley, 1990. 67. DF Cook, ML Wolfe. A back-propagation neural network to predict average air temperature. AI Appl 5( 1):40-46, 1991. 68. VA Boudolf 111. Predicting peanut maturity: an artificial neural network approach. Unpublished master’s thesis. Athens: The University of Georgia, 1996. 69. DA Elizondo, RW McClendon, G Hoogenboom. Neural network models for predicting flowering and physiological maturity of soybean. Trans ASAE 37(3):98 1 988,1994. 70. KC Das, MD Evans. Detecting fertility of hatching eggs using machine vision 11: Neural network classifiers. Trans ASAE 35(6):2035-2041, 1992. 71. S Deck,CTMorrow. PHHeinemann,andHJSommer 111. Neuralnetworksfor automated inspection of produce. ASAE Paper No. 92-3594.St. Joseph, MI: ASAE, 1992. 72. A Talukder, D Casasent, H-W Lee, PM Keagy, TF Schatzki. A new feature extraction method for classification of agricultural products from X-ray images. SPIE Proceedings for Photonics East, Boston, 1988. 73. USDA. Food Safety Research: Current Activities and Future Needs, Washington, DC, 1994, p 36. 74. LAG Aylmore. Use of computer-assisted tomography in studying water-movement around plant-roots. Adv Agron 49: 1-54, 1993. 75. DL Ergun, CA Mistretta. Single exposure dual energy computed radiography: improved detection and processing. Radiology 174:243-249, 1990. 76. MJ Bentum, RGJ Arendsen, CH Slump, CA Mistretta, WW Peppler and FE Zink. Design and realization of high speed single exposure dual energy image processing. Proceedings of the Fifth Annual IEEE Symposium on Computer-Based Medical SYStems Durham, North Carolina, 1992, pp 25-34. 77. DL Ergun,WW Peppler, JT Dobbins111, FE Zink, DG Kruger, F Kelcz, FJ de Bruijn, EB Nellers, Y Wang, RJ Althof, MGJ Wind. Dual-energy computed radiography: improvements in processing. Medical imaging: image processing. Proc SPIE 2167: 663-67 1, 1994. DG Johnston. Compari78. SA Beshyah, C Freemantle, E Thomas, B Page, M Murphy, son of measurements of body composition by total body potassium, bioimpedance analysis, and dual-energy x-ray absorptiometry in hypopituitary adults before and during growth hormone treatment. Am J Clin Nutr 61:1186-1194, 1995. 79. SA Jebb, GR Goldberg, G Jennings, M Ella. Dual-energy x-ray absorptiometry measurements of body composition: effects of depth and tissue thickness, including comparisons with direct analysis. Clin Sci 88:319-324, 1995.

5 Nuclear Magnetic Resonance Techniques and Their Application in Food Quality Analysis R. Roger Ruan and Paul

L. Chen

University of Minnesota, St. Paul, Minnesota

1.

INTRODUCTION

Laboratoryanalysis of foodqualityofteninvolvesextensivepreparation procedures,duringwhichfoodsareseverelydisturbed by size-reducing,deforming, or diluting steps. This is very unlike the way in which food consumersevaluatethequality of foodproducts (Le., foodsareconsumedwhile theirintegrity is generallyintact.).Onemaythereforeask:Arethecurrent chemical and physical methods for food quality measurement reliable? Naturin a completely ally it would be desirable to be able to analyze food quality nondestructiveandnoninvasiveway.Nuclearmagneticresonance(NMR) a task.NMR techniquesareamongthosemostcapableofperformingsuch techniques,includingNMRspectroscopyandNMRimaging,ormorefreto nondequentlytermedmagneticresonanceimaging(MRI),canbeused structivelyandnoninvasivelystudythechemicalandphysicalpropertiesand phenomena, anatomical structure, and dynamic processes of food materials and products. In this chapter we will introduce the basic principles and methodologies of proton NMR spectroscopy and imaging techniques and present some examples of practical applications of the techniques.

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BASIC PRINCIPLES OF NMR SPECTROSCOPY

A. NuclearMagneticResonance

N M R is a spectroscopic technique based on the principleof resonance. Certain nuclei (spin quantum number I # 0) are considered as spinning about an axis (Fig. 1). The spinning generates a small magnetic field, known as magnetic moment. Like a normal magnet bar, this magnetic moment has a north and a south pole, called a nuclear magnetic dipole. When the nucleus is placed in a static magnetic field, itwill interact with the field via its dipole, that is, the dipole will tend to align with the field much as a compass needle aligns with the earth’s magnetic field. In addition to this magnetic dipole, a second phenomenon occurs when the nucleus is placed in the static magnetic field. This canbe demonstrated by considering the spinning nucleus as a spinning top. When the top is spun, it of the earth’s will spin, but it begins to wobble when it slows down because gravitational field. This wobbling or angular spinning is called precession. Similarly, the spinning nucleus in a static magneticfield will precess about the static of the nuclear dipole is called Larmor magnetic field. The frequency of precession frequency (1) defined by the M o r equation:

o = ?Bo

(1)

where o is the frequency of precession,Bo is the strength of the static magnetic field, andy is termed the geomagnetic ratio and has a precise value characteristic of each nuclear species. For example, a proton has a y of 42.6 MHz/T. If the static field Bo is 1 T, the o is 42.6 MHz. If Bo = 4.7 T, 61is -200 MHz. However, the orientationsof magnetic dipolesin a static magnetic field are different depending on their magnetic quantum number, that is, some nuclei align

Fig. 1 Magnetic moment (p)precesses about the bulk magnetic field Bo with an angle 8.

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themselves in either the same direction or the opposite direction as the field. In the classic view described above, the alignment of the dipoles is determined by whether the nuclei precession is in a clockwise or counter-clockwise direction. A nucleus that has its spin aligned with the field will have a lower energy and more stable than when it has its spin aligned in the direction opposite to the field (Fig. 2 ) . The energy difference BE is proportional to the strength of the static field and the population difference:

and

and N,,,,, represent the where R is the Planck's constant divided by 27c, Nupper population of nuclei in the upper and lower energy levels, respectively, k is the Boltzmann constant, and T is absolute temperature. Equation (3) is also called Boltzmann's law. which describes the thermal equilibrium of the nuclear spins. One NMR experimentinvolvesinducingtransitionbetweentheneighboring energy levels by absorption or emission of a photon with the requisite energy. This requisite energy is applied in the form of a rotating magnetic field B , or radiofrequency (RF) pulse, which exactly matches the Larmor frequency and has an energy equalto the AE, causing resonant absorption or emission of the

<

-112 ........................

Aligned anti-parallel

Zen, field

2:w ti

1

........................

Aligned parallel with B,

Fig. 2 NuclearZeemansplitting quantum number I/:.

of energy levels in a magneticfield

for spins with

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energy by the nuclei. This resonance effect is therefore termed nuclear magnetic resonance.

B. VectorDiagrams of Magnetization The magnitude of the magnetization caused by the RF pulse can be visualized through the vector model (Fig. 3). For protons with I = %I/?, the magnetic quantum number m I takes two values, + I / ? and --I/?, that is, the spins align in either the same or opposite direction with Bo. Thenumber of spins pointing in the same direction as Bo is greater than that pointingin the opposite directionas B,)because the former alignment represents a low-energy or stable state. The individual spins precess about the Bo at the Larmor frequency. The net magnetization of the spin system is M. The length of the vector M corresponds to the difference in population between the two energy states. We can put this vector model in a laboratory coordinate with a set of axes as shown in Fig. 4. The applied magnetic field Bo is traditionally parallel to the z-axis, and the net magnetization, M,, also points in the z-axis direction. If the spin system remains in the Bo field long enough, the number of spins oriented with the field reaches an equilibrium value. This value is termed Mo and is the largest possible value of magnetization. Because the net magnetization is precessing so fast, visualizing its motion is difficult. One solution to this problem is to use a rotating frame to replace the stationary frame. The condition is that the rotating frame rotates about the z-axis at exactly the Larmor frequency. Therefore, within the rotating frame, the net

M

Fig. 3 Vector modelof magnetization of protons in the presenceof a permanent field Bo.

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z

Fig. 4 Magnetizationvectorsin a stationarylaboratorycoordinatesystem.

magnetization does not appear to be precessing and is thus easier to follow. The rotating frame uses x’, y‘, and z’ to label the three axes as shown in Fig. 5. Visualization of interactions between applied RF field B , (in a direction perpendicular to Bll)and spin magnetization becomes much easier with the rotating frame. Figure 6 shows a series of situations when different RF pulses are applied to the spin system placed in Bo field. In the equilibrium state (a), the net magnetization along the z’-axis has its maximum value, M, = MI].The net magnetization vector can be flipped by an angle a from the z’-axis if individual x‘-y‘ nuclei absorbed RF energy and shifted to the higher energy state (b). An

Fig. 5 Netmagnetizationin a rotatingframe.

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y = o z' %= "0

t

Fig. 6 Interactions between RF pulses andnet magnetization.

plane component is introduced by the RF pulse with a value of M,, = MI, cos a, while z'-axis net magnetization is M, = M,, sina. It can be seen that flipping M, and increases Mxy.The net the net magnetization from the z'-axis reduces magnetization can be flipped to any angle by application of a suitable RF pulse. The 90", 180", and 270" RF pulses in the x'-direction, for example, will rotate the net magnetization to the ??'-axis, z'-axis, and $-axis, respectively. After the application of an RF pulse, the net magnetization will return to its equilibrium state. During the return, the net magnetization along the $-axis and on the x'-y' plane changes with time, in the form of recovery or decay. It is a process where the excited spins release their energy and go back to the stable state or a process in which the excited spins interact with each other and lose their phase coherence. These processes are termed relaxation processes, which will be discussed next. The ability to flip the net magnetization through RF pulses allows us to manipulate magnetization so that the characteristicsof the nuclei can be observed and detected. Sometimes, a sequence of RF pulses is used to generate the magnein this chapter. tization signal. We will discuss the topic later

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RelaxationProcesses

After excitation, the spins return to their equilibrium states or equilibrium population distribution during the periodof free procession. The majority upward transition population, that is, the originally lower level population, returnsits to equilibriumbylosingenergy in theform ofan RF waveviavariousradiationless transition processes called "relaxation processes."The RF wave signal is characterized by the Larmor frequency of the nucleus and can be received and recorded by the NMR instrument (RF-receiving antenna). of a The magnitude of M, and M,, versus the time after the application 90" pulse is plotted in Fig. 7. The magnetization along the z-axis, M,, indicates a recovery or growth of the signal, which was first flipped to the y-axis direction with M, = 0 by the 90" pulse. The recovery of M, is in exponential form and eventually equilibrates to Mo. The x-y plane magnetization, M,,, is at its maximum value (M,,) immediately after the 90" pulse, after which it decays to 0. The recorded signal of recovery or decay is characteristic of the relaxation processes. There are two kinds of relaxation processes: spin-lattice (or longitudinal) relaxation and spin-spin (or transverse) relaxation. The time constants describe these exponential relaxation processes and are known as relaxation times.

"_

Mo <

:

nz

. .

Fig. 7 Relaxation of NMR signals after a 90" RF pulse.

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The spin-lattice relaxation time is designated by T I , and the spin-spin relaxation time is designated by T2. Relaxation time is a function of the spin species and the chemical and physical environments surrounding the spins. In other words, of the chemical and the relaxation time constants are a fundamental property physical environment. Therefore, analysis of T I and T2 of a sample will allow the study of the chemical and physical properties of the sample. A long T I or T? indicates a slow relaxation; a short T , or Tz value indicates a rapid relaxation. The relaxation time constant should not be confused with relaxation rate. The relationship between relaxation rate R and the time constant is simple and takes the form of

Rl

=

1

-

T, where I takes the value of 1 and 2 for spin-lattice relaxation and spin-spin relaxation, respectively. Figure 7 also shows that M, reaches its equilibrium after approximately five Tls, suggesting that the time to reach complete relaxation should be greater than five times the spin-lattice relaxation time constant. This is important in NMR experiments, for which complete relaxation is often required.

1. Spin-LatticeRelaxation, T, The lattice here means the environment surrounding the nucleus, including the remainder of that molecule and other solute and solvent molecules. The energy of the resonant nucleus is transferred to the various components of the lattice as additional rotational, transnational, or vibrational energy until thermal equilibrium is reached between the nuclear spin and the lattice. The more the lattice components rotate at the resonance frequency, the more efficient or faster the spin-lattice relaxation. In other words, there is a distribution of rotation frequenTI. Because cies in a sample of molecules. Only the Larmor frequency affects of the Larmor frequency is proportional to B,,, T I will also vary as a function magnetic field strength. In general, T I is inversely proportional to the number of molecular motions at the Larmor frequency.

2. Spin-SpinRelaxation, T2 Chemical exchange or mutual exchange of spin states by two neighboring spins is the typical form of spin-spin relaxation, although the sources contributing to spin-lattice relaxation also lead to spin-spin relaxation. Unlike spin-lattice relaxation, spin-spin relaxationis an adiabatic process, and the redistribution of energy among thc spins doesnot change the numberof nuclei i n the higher energy level.

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3. RelaxationMechanisms Excited spins may reach equilibrium state with their environment (lattice) in varito equilibrium: ous ways. The following sources can cause a spin system to come Dipole-dipole coupling caused by magnetic moments of other nuclei Paratnagnetic relaxation caused by magnetic moments of unpaired elcctrons Anistropic electronic shielding due to angular variations i n the electronic shielding of the stationary B,, magnetic tield Electric quadrupole relaxation causcdby electric quadruple moment of the nucleus being irradiated Spin-rotation relaxation as a result of molecular magnetic monuments

for many Dipole-dipolecoupling is thedominantrelaxationmechanism spin systems and involves neighboring spins (magnetic dipoles). This interaction is referred to as dipole-dipole coupling because the motionof one spin is coupled into the motion of the other. Each of these spins is affected not only by the B,, tield but also by the magnetic moment of the other; the latter is dependent on the magnetic moments of the two spins and the distance and relative orientation between the two spins. For more details about rclaxation mechanisms, the reader is referred to the texts of James and McDonald (2), Field (3). and Farrar (4).

D. RelaxationTimesandMolecularMobility Both T , and Tz are related to molecular mobility in a system. T2 is proportional to the rate of rotational motion of thc molecules. As a general rule,Tz is inversely proportional to the molecular weight. A larger T2 generally means greater mobility. As we mentioned earlier, spin-spin relaxation is affected by a chemical exchange process. which is especially true in biological systems like foods. Chemicalexchangetakesplacebetweendifferentsites of differentmobilities.For example, protonso f water exchangewith protons ofmacromolecules. and protons of less mobile water of more mobile water molecules exchange with protons molecules. etc. The rate of exchange has great influence on the T? value. If the exchange is very fast, the differencein T? of the two exchanging sitesis averaged. On the other hand, if the exchange rate is low, a broad distribution of T2 and even two separate Tz sites can be expected. The spin-lattice relaxation process is slightly different, although in normal situations a larger T i also means greater mobility. For very fast motion, T i approximates T?. If the motion is extremely slow (e&, at very low temperature or whenthe system is very rigid), T , increases with decreasing mobility whereas T2 continues to decrease. Therefore, rigid or solid-like materials could havea very short T? but a very long T i . We will discuss

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this later in this chapter. It appears that T2 is a more convenient indicator of molecular mobility of a system. While the proton intensity is related to the concentration of proton-containing compounds in foods, relaxation times provide extremely useful information about the motional properties of food systems. Many researchers have succeeded in relating relaxation times to mobility of water, physical state of food of foods, etc. polymers, physiological state of plant origin foods, temperature This presents a great opportunity for us to probe the quality and stability of food products.

E. Measurement of Relaxation Times Measurement of T, and T2can be accomplished through various pulse sequences. The signals, normally the intensities, are recorded after one or more pulses are applied to the spins. The timingof each step during the pulse sequenceis of great importance to the correct data acquisition and meaningful analysis and explanation of the data.

1. Measurement of T, Because magnetization along the z-axis is difficult to detect, T, measurement is impossible by using a single 90" RF pulse. Instead, the common approach is to use an inversion-recovery pulse sequence to generate the detectable signal. In RF pulse is followed by a 90" the inversion-recovery pulse sequence, a 180" RF pulse. The 180" pulse inverts the magnetization to the -z-direction while maintaining a magnitude -Mo. If the 90" pulse is applied immediately after the 180" pulse, the detected magnetization, M,,, will be equal to Mo. If we apply the 90" pulse at some time T after the 180" pulse, the net magnetization has a chance to decay somewhat due to the T , relaxation. Thus, the detected signal along the y-axis will be slightly smaller than M,,. If this process is repeated with longer T values, the delay times between the 180" and 90" pulses, the relationship between the TI relaxation and time can be established through the following equation: M,(z) = M(,(1

-

2e-T"'l)

(5)

Therefore T , canbedetermined by runningmultiple 180"- T-90"sequences to equilibrium is with varying T. A plot of M, at various times during return shown in Fig. 8. 2. Measurement of T2

Tz describes the decay of M,, and is readily detectable. A 90" RF pulse flips the magnetizationalongthey-axiswithamagnitude of M,, = M,). M,, starts to

175

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....................................................................................

w

Time

"0

r...................................................................................

Fig. 8 Change in netmagnetizationalongz-axisobservedduring a 180"-t-90° pulse sequence experiment. The recovery curve is in exponential form, from which T, canbe determined.

shrink following the application of the 90' pulse in exponential form as mentioned earlier (Fig. 7). The decay curve is normally termed free induction decay or FID and can be described by the following equation:

M,,

=

M,,e -'IT?

Therefore, I /Tz is the rate constant of the decay of a magnetization caused by a 90" RF pulse.However,unexpectedresultsareoftenobtainedwhenasingle 90" pulse experiment is performed. After the application of a 90" pulse, the net magnetization decays more rapidly thanEq. (6) predicts. The spin-spin relaxation time obtained from such an experiment is much less than the T2 defined by Eq. (6) and is termed apparent T2 orTZ This has something to do with the magnetic field inhomogeneity, diffusion, etc. in different If themagnetic field is notperfectlyhomogeneous,nuclei of the field and hence parts of the sample experience slightly different values in rotate at slightlydifferentfrequencies.Thiscanbeeasilydemonstrated Fig. 9, which shows spins in three regions (I, 11, and 111) of the sample in the magnetic field. In the case where the magnetic field is perfectly homogeneous (Fig. 9A), all spins in the three parts of the sample are flipped to the x-y plane along the y-axis as shown at time 0. The magnetization vectors M,, in the three to MI).Because the magnetic field parts of the sample are the same and equal

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RcgonII RcgonI

Time0 +y-

X

Time A +y+.y+y+-y ... ....

TlmcB

.

....

Regon111

TotalNct

$-.$X

X

... ....

. . . ..

(A)

... .....

y T : y

RcgonI

y"

X

.....

RegonIII

$-.

X

X

.+y+.Y+>........ +y

.....

Region11

+y

TotalNeI

T

X

:

X

y

........

....

...

(B)

Fig. 9 Decay of x-y plane magnetization M,, in perfect ( A ) and imperfect (B)magnetic field.

is perfectly homogeneous, all spins are rotating at the same frequency and pointof therelaxationprocess,although ing in thesamedirectionatanytime the net magnetization vectors shrink at later times because of T? relaxation. In suchconditions i t is said that all the spinsare in phase orhavemaintained phase coherence. In this condition, the net magnetization of the entire spin system at anygiventime is equal t o the net magnetization of anyspins in the system and can be described by the true Tz. On the other hand. when the magin the netic field is imperfect(Fig. 9B), thex-yplanemagnetizationvectors to M,, afterthe 90" pulse,shrink by thesame threeparts,initiallyequal in slightlydifferentdirections at amountdue to the Tz relaxation.butpoint times greater than 0 because the spins i n the three parts of the sample are rotating at different frequencies. This condition is termed dephase. and the spins in thethreeparts aresaid to losetheirphasecoherence.Becausethevectors do not point in the same direction, the total net magnetization vector is shorter thanthe total fortheperfectmagnetsystemthesametimeafterthe initial pulse. The characteristics of such relaxation in an inhomogeneous magnetic field is determined by TT, which includes contribution from natural molecular interactions (T?) and magnetic field inhomogeneity-related component (T2,,J:

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However,because it isimpossibletodetermine T?,,,withasingle 90" pulse, 90" pulse have been many methods involving additional pulses after the initial is to rephrase developed for measurement of true T?. The common approach or refocus the spin magnetization some time after the initial pulse. Spin-echo pulsesequence ( 5 ) is acommonmethodforT2measurement.Thespin-echo sequence consists of two pulses, a 90" and a 180" pulse, in the form of 90"7-180" (Fig. 10). Figure 1 I illustrates the principle of spin-echo method. When the spins are placed in the stationary magnetic field Bo, all the spins align with the B,,-direction or z-direction (Fig. 1 IA). After the application of a 90" pulse, to M,, (Fig. all the spins are aligned along the v-axis, giving a nonzero value 1 le). M,, commences to decay because of T? relaxation and inhomogeneity in themagnetic field. The field inhomogeneitycausestheindividualspins to in alossofphasecoherence,as precess in differentfrequencies,resulting in the x'-y' plane, as some spins move discussed earlier, and they will fan out faster (f) and some slower (s) (Fig. 1 IC). If after a time T a 180" pulse is applied, the spins will be flipped 180" about the x'-axis, as shown in Fig. 11D. Because theindividualspinsare still precessingatthesameindividualfrequenciesas before, the spins will all be in phase again at time 2~ with the orientation in the -v'-direction (Fig. 1 1 E). This is called refocusing or rephrasing. The continuing processing of the spins causes the spins again to lose their phase coherence (Fig. 1 IF). The rephrasing of the spins causes a free induction signal to build to a maximum (i.e., refocus) at time 2 T. This signal is termed spin-echo or SE ampli-

90"RF

180"RF

Pulse

Pulse

Fig. 10 The spin-echoexperiment (9O"-t-18O0) induces a peak alnplitude or "echo" at time 2 t .

"-c

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90" pulse

Y' X'

03) 180" pulse

Dephase

X'

(C>

Rephase

X'

(E> Fig. 11

X'

(F>

A simple spin-echo experiment: 90"-r-180".

tude. However, the dephasing process occurs not only because of the field inhomogeneity but also involves decay due to the natural transverse relaxation with a time constant T2.If the 180" pulses are applied at different times, decay in SE amplitude will be observed, characterizedby T2. Therefore,T2can be determined by carrying out a separate pulse sequence for each value of t and detect the SE amplitude at time 2 2 . However, the spin-echo method is limited in its range of applicability because of the effect of molecular diffusion. The precise refocusing of all the spin magnetic moments is dependent upon each nucleus remaining in a constant magnetic field during the timeof refocusing experiment ( 2 ~ )If. diffusion causes nuclei to move from one part of an inhomogeneous field to another, the echo amplitude is reduced.

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Fig. 12 CPMG experiment (90"-z- I80O-3~-180°-5r-l80"-. . .) produces a train of echoes at time 2 2 , 4 ~62. . . . . The envelope ofthe multiple echo amplitudes reflects thc transverse relaxation or decay, from which Tz canbe measured.

Carr and Purcell (6) proposed a variation of the simple Hahln's spin-echo method, which reduces drastically the effect of diffusion. Their method involves a 90" pulse at time t = 0 followed by a series of 180" pulses at times t, 3'1, 5t, . . . , which refocus the individual spins to form echoes at times 2t, 4.t. 6t. . . . However, the inaccuracy in the 180" pulse width can cause some errors in the as the spin-echo magnitude, which can be cumulative, becoming more serious number of 180" pulses in the Can-Purcell sequence increases. The Carr-Purcell sequence was modified by Meiboom and Gill to eliminate the errors. The CarrPurcell-Meiboorn-Gill or CPMG pulse sequence is the most common method for Tz measurement. It uses the same pulse sequence as the Cam-Purcell technique, but the 180" pulses are applied along the positive y'-axis. Thus, all of the subsequent refocusing is along the y'-axis, and a l l of the echoes are positive (Fig. 12). The drawback of the CPMG experiment is that it takes a long time and is unable to detect spins that relax so fast that their signal ceases to exist before CPMG begins data acquisition. Therefore,the one-pulse (90") experiment is still useful in obtaining information about the fast decay typically solid component. to magnetic For solid samples, T2 is so short that the apparent relaxation due field inhomogeneityI/T?,,, is unimportant,and 1/TT = 1 /Tz + I/T,,,, 1/T2, or Tz J: T?:.

F. MRI Methodology MRI is an extension of NMR. It provides additional spatial information regarding spins.The first publishedMRimage is credited to PaulLauterbur (7) in the

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early 1970s. The image was produced using projection-construction computer algorithms borrowed from computed tomography (CT) scanning. This method is often referred to as backprojection reconstruction imaging. Because this method requires a large number of "snapshots" at many angles covering an arc of at least 180" to obtain a cross-sectional image, modern MRI techniques have chosen other more efficient methods. However, the key idea, which will be discussed later, is still to superimpose magnetic field gradients onto the mainmagnetic field, except that modern MRI techniques not only make the resonance frequency a function of the spatial origin of the signal but also relate phase to the spatial origin of the signal and expand the magnetic field gradients for many other uses in the MRI image acquisition. The MRI system is designed to excite and receive signals from a single point, a line, a plane, or a three-dimensional volume in a sequential manner. The point and line scanning methods are inefficient and less useful and have been superseded by two- and three-dimensional methods, which are more efficient. The two- and three-dimensional methods apply linear field gradients to provide spatial information.

1. Magnetic Field Gradient and Frequency Encoding From Eq. (1) we know that the Larmor frequency of a spin is proportional to the magnetic field that it is experiencing, If three tubes of water are placed in the same field, there will be only one frequency and one peak in the NMR spectrum (Fig. 13). If a linear field gradient, G, is superimposed on the main magnetic field, Bo, water tubes in different horizontal locations will experience different field strength because the field strength varies with horizontal location in this case. Therefore, there will be two frequencies, and two peaks will appear in the NMR spectrum. The amplitude of the signal is proportional to the number of water tubes or spin density. Because Larmor frequency is proportional to the field strength [Eq. ( l ) ] and field strength is a functionof position, we can establish a relationship between frequency and spatial position of the spins by an equation similar to Eq. (1):

w

=

r(Bo

+ xG,)

= Wo

+~xG,

(8)

or

where x is the position in x-axis direction and G, is the field gradient applied in the x-axis direction. Here we say the frequency is spatially encoded. Field gradients can be appliedin all three directions. The symbols for a magnetic field gradient i n the y- and z-directions are G, and G,. Similarly we have

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S I

I

S

Fig. 13 Application of a field gradient can spatially encode the signals from different spatial positions. The three black spots represent three tubes of water in the sample.

Equation (8) forms the basisfor all magnetic resonance imaging methods. The backprojection reconstruction imaging methodwill be presented in thenext section to illustrate how the MR image is produced.

2. ProjectionReconstructionImaging This method is of great historical importance since the first MR image was pro(7). Backprojection reconstruction is an extension duced by Lauterbur in this way of the frequency encoding discussed above. A one-dimensional linear field gradient is applied at many different angles covering an arc of at least 180" and up to 359". An NMR spectrum is recorded at each angle (Fig. 14A). Once a set of data are recorded, the data are backprojected through space using a computer program, and an image is reconstructed (Fig. 14B).

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Fig. 14 Principle of backprojection reconstruction imaging.(A) A one-dimensional field gradient is applied at slightly different angles covering an arcof I 80°, and NMR spectrum is recorded. (B) A cross-sectional image is reconstructed by backprojecting the signals through space.

3. FourierTransformImaging Fourier transform (FT) imaging is currently the most widely usedMRI technique. in 1974 by The first two-dimensionalFouriertransformimagewasproposed Kumer et al. (8). In FT imaging, both amplitude information and phase information are acquired to spatially encode the signal. The amplitude information (frequency encoding) is obtained by using the originalfield gradient, G,, from which x can be calculated. Phase encoding is achieved by applying a phase encoding gradient just before the originalfield gradient is turned on, and at the right angle to the original gradient, normally in y-direction, denoted by G , or Go. The phase encoding gradient is raised from zero in a series of small successive steps (time duration t). Phase angle Cp is given by:

Cp

= 'Iy

1:

G,(t)dt

(12)

Due to the application of G, for a period oft, a point at a given y position is off resonance by an amount of AW = -yyG,, and in time t the magnetization vectorrotatesat an angle Cp = A u t = -yyG,t. @ canbedeterminedthrough -y, G), and t, we will be able to experiment (9). Knowing the phase change, G, will contain both the calculate y. Data collected after the original gradient n is the frequency and phase encoded information, and the number of the data set sampling points. If we increment G , and repeat the procedure m times, we

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will get a data set containing tn rows and n columns. By using Fourier transform, we can produce an t n X tz image.

4. SliceSelection Data for production of MR images are collected from spins of a plane through an object. This plane will have certain thickness and is therefore termed a slice. An MRI instrument is capable of selecting a slice of certain spatial location and thickness. Slice selectionis of great importance to signal-to-noise ratio and image resolution. Slice selectionis achieved by applying a one-dimensional, linear magnetic field gradient during the period that the RF pulse is applied. A 90" pulse applied in conjunction with a magnetic field gradient will rotate spins, which are look located in a slice or plane through the object. To picture what this would to examine like if we had a cube of small net magnetization vectors, we need of frequencies. the frequency content of a 90" pulse. A 90" pulse contains a band This can be seen by employing the convolution theorem. The frequency content of a square 90" pulseis shaped as a sinc pulse. The amplitude of thesinc function is largest at the frequencyof the RF, which was turnedon and off. This frequency will be rotated by 90", while other smaller and greater frequencies will be rotated by lesser angles. The application of this 90" pulse with a magnetic field gradient in the xdirection will rotate some of the spins in a plane perpendicular to the x-axis by 90". The word "some" was used because some of the frequencies have a B , less than that required for a 90" rotation. As a consequence, the selected spins do not actually constitute a slice.

5. Pulse Sequences The basic procedure for productionof a two-dimensional MR image can be summarized into five stages:

I. 2. 3. 4.

Excitethespins of selectedslice Apply a phase encoding gradient for a fixed time Apply a frequency encoding or read gradient and collect 12 data points Increment the value of phase encoding gradient and repeat steps I to 3 t n times 5. Perform two-dimensional Fourier transform on the data to produce an n z X n image

Therefore, pulses are applied in a sequential order. A schematic diagram of a typical MRI pulse sequence is shown in Fig. 15. Other MRI pulse sequences provide additional information. These include the three-dimensional imaging sequences, multislice imaging sequences. oblique imaging, spin-echo imaging, fast imaging sequences, gradient-recalled smallflip-

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RF

Slice Selection Gradient

Phase Encoding gradient

Frequency Encoding or Read Gradient

Signal Detection

Fig. 15 A two-dimensional MR imagingsequence.

angle imaging, echo planar and hybrid imaging, etc. Due to limited space, these techniques will not be discussed in this chapter.

6. Imaging of Relaxation Time, Chemical Shift, and Flow Velocity In addition to the spin density, other NMR parameters such as relaxation time, in MRI. chemical shift (IO), and flow velocity can also be used as contrast agents To use these additional parameters to construct MR images, some modifications to the basic MRI sequence have to be made. These additional contrast agents are strongly associated with the mobilityof protons, which is governed by the physical and chemical environments in which the protons are embedded. Therefore, imaging of these parameters will provide valuable spatial information about the physical and chemical characteristics of the samples. Many have used T, mapping to determine distribution of temperature and mobility in food systems. The commonly used T, imaging methods are inversion recovery and spin-echo imaging sequences, both considered to be time consumTI by ing. While the multipoint inversion-recovery method, which determines fitting a recovery curve to a series of measurements on a pixel-by-pixel basis, is

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the most accurate, its typical measurement time is more than a half hour since The it requires a long repetition time (TR) and varying inversion delay times. spin-echo method involves a standard spin-echo sequence with two TR values and takes less time but still requires minutes for data acquisition. A fast T, imaging pulse sequence is being developed in our laboratory. The basic idea is to eliminate the waiting periods within conventional imaging sequences by employing RF excitation pulses with flip angles considerably less than 90", e.g., 10". The RFpulse is applied in the presence of a gradient affecting only a certain slice of spins out of the three-dimensional object. Applying phase encoding and read-out gradients in the two directions achieves two-dimensional spatial discrimination, respectively.The signal is detected in the formof gradient echo generated by the reversal of the read gradient. To map T I , a series of images are acquired during magnetization relaxation following a single inversion pulse, almostwithoutdelaybetweentwoadjacentimages.This is thekeypoint to TI image. Currently, this TI imaging achieve very short acquisition time for a TI mapping techniques and can technique is 20-100 times faster than previous be used for dynamic food processes during which temperature changes rapidly.

111.

APPLICATIONS OF NMR AND MRI TECHNIQUES IN FOOD QUALITY ANALYSIS

The intensity and motional properties of protons are two of the most important NMR properties from which a number of analytical methods have been developed. The abundant presence of protons in foods makes them excellent image of contrast agents, enabling nondestructive observation of the internal structure food products. The initial signal intensity (before decay or relaxation) is directly proportional to the number of nuclei present. Therefore, it is possible to measure the moisture and fat contents of a food product by measuring its proton signal intensity. Through MRI, mapping of moisture and fat in food products can be achieved. Similarly, the relaxation time constants, T I and Tz, can be related to molecular mobility of food products, as discussed earlier, and canalso be mapped out using MRI. Previous research has demonstrated that variations in relaxation is interaction between liquid and times arise from many reasons. Among those solids in the system. The interaction between liquid and solids is governed primarilybymoisture content, composition, particle size, structure, temperature, and physical state of the system. Therefore, relaxation times can be used to probe many aspects of food systems.

A.

InternalStructure of Food Products

MRI has sinceits invention mainly been used to noninvasively visualize the internal structure of the human body for diagnosis purposes. In recent years MRI has

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Fig. 16 Multislice MR images of fresh orange.

been increasingly applied to food systems. MRI is now capable of producing two- and three-dimensional images, as shown in Figures 16-1 8. Figure 16 shows In addition, the darker regions repreorange segments that are clearly identified. sent the seeds, which had weaker signals probably due to less moisture content and/or a more rigid structure. This also suggestsMRI thatcan be used to examine external objects such as plastic, wood, and metal pieces that may be accidentally 17 shows that cooking weakened the signal mixed with food products. Figure

Raw

Cooked

Fig. 17 MR images of eggs. The brighter the image, the stronger the signal.

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Fig. 18 Three-dimensional MR image of corn on the cob.

intensity from egg, which may be attributed to the tighter structure and stronger 18 demonwater binding due to heat denaturalization of egg proteins. Figure strates the possibility of using MRI to acquire three-dimensional images, although the procedure for getting a three-dimensional image is very complicated.

B. Ripeness and Bruise of Fresh Plant Foods MRI has proven to be an excellent tool for illustrating the differencesin N M R properties of fruits and vegetables in different development stages. Ishida et al. (11) studied the distribution and relaxation times of water in ripe tomato using MRI. They found that waterwith a long relaxation time was preferentially accumulated in seeds and seed envelopes in immature green fruit. The total amount of water increased in mature red fruit, where water with a long relaxation time was localizedin the outer wallsof the pericarp and water with a shorter relaxation time was distributed throughout all tissues except for seeds and seed envelopes. MRI was able to distinguish the physiological variations among different types of tissues and the physiological changes during maturation of tomato fruit. MRI has been used to evaluate the internal quality factors of fruits and vegetables (12-16). These quality factors include bruises, dry regions, worm damage, stage of maturity, and presence of voids, seeds, and pits. Wang et al. (14) used the MRI method to obtain images of water core and its distributionin Red Delicious apples. The uneven distributionof mobile water and itsTI and T2 shown in the MR images was attributed to the variations in internal structures of fruits, including petal bundle, endocarp, outer limit of carpel, carpel dorsal bundle, receptacle cortex, receptacle pith and seeds, and to the difference in NMR properties between the normal and water core-affected tissues. The MR images indicated that water core occurred primarily in an area 20 mm from the center

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Bruised

Soft

Fig. 19 h4R images of unripe, ripe, and bruised strawberries.

of an affected fruit, and the area most affected was between 5 and 10 mm from 19 also shows that the bruised tissue had the center toward the stem end. Figure stronger signal intensity, probably due to increased water mobility in the bruised tissue. Figure 20 shows two MR images of kiwifruits, one unripe (left) and the other ripe (right). These images are T2-weighted. Inwords, other they have partial information on the mobility of water because of the way the imaging was conducted. In this type of MR image, the high intensity is attributed to both the amount and mobilityof protons in the sample. In the unripe,firm fruit (left), the

Raw Fig. 20 M R images of unripe and ripe kiwifruit.

Mature

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core has a very low intensity, suggesting that this part of tissue is very firm and low in moisture content. In contrast, the tissue surrounding the core appears very bright, indicating a soft texture with alot of moisture. The signal intensity of the As the fruit ripens, the outer flesh layer is between the core and middle layer. signal intensity in the fleshy portion increased dramatically while the intensity in the core remained low, forming a sharp contrast between the fleshy and core tissues. The three images shown in Figure 19 are cross sections of strawberries. The effect of ripening on the NMR signal intensity of strawberry is similar to that in the kiwifruit.

C.

Distribution of Moisture and Fat: Sensory and Microbiological Concerns

Water and fat have a profound impact on the sensory quality of food products. Equally important in this matter is the distribution of water and fat throughout the food product. Distribution of water and fat in foods may be uneven due to improper processing, handling, and redistribution of water and fat during proin variations in texture and chemicessing, storage, and transport. This may result cal reactions, resultingin inhomogeneous product quality.In addition, since water is essential to the microbiological activitiesin food products, uneven distribution of water may cause deterioration in regions having higher moisture contents. MRI is anidealtoolforinvestigating the distribution of water and fat. However, it is a challenging task to separate signals of water protons from those of fat protons since protons in both the water and fat molecules contribute to the NMR signal in a conventional proton imaging sequence. Differentiation between moisture and fat is essential to the acquisition of MR image of protons from watermoleculesonlyandfromfatonly.(Theterms“water-only’’and“fatonly” will be used here to refer to MR images of protons from water molecules only and from fat molecules only, respectively.) In medical applications, numerous techniques have been proposed to generate moisture-only and fat-only imof proton signal while supages. Basically, these techniques promote one type pressing the other type of proton signal. The principle of these techniques lies in the difference in relaxation times and/or resonance frequencies between water and fat protons. The protons in water and fat molecules are found in different physical and chemical environments and, correspondingly, have slightly different resonance frequencies. The difference in resonance frequency between water and fat, also termed chemical shift, is about 3.5 ppm or about 700 Hz in a 4.7 T magnetic field. Their relaxation times usually are not the same. Most current moisture and fat separation techniques in MRI take advantage of the differences in resonance frequency or relaxation times and can be classified into chemical shift select-

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ive (CHESS) presaturation, frequency-selective excitation, Dixon's method, and relaxation-based technique. The CHESS is widely used in clinical area for its ease to implement and reasonable accuracy. Rosen et al. (17) used CHESS presaturation technique to image lipid and water in a human forearm. Haase et al. (18) imaged water and lipid in a human hand and presented the 'Hspectra obtainedby selective elimination of H 2 0 and CH2. Keller et al. (19) suggested multisection fat/water imaging with CHESS presaturation, and obtained water-only and fat-only images of a right knee, a of the upper abdomen spine and a head. Semelkaal.et(20) acquired water images by presaturation of fat to increase the depiction of abnormalities and minimize loss of anatomic detail around the edges of structures surrounded by fat. Mao et al. (21) obtained water images by suppressing fat with an improved selective presaturation pulse. The improved pulse has a broad and flat pass band and a in medical studies, we have sharp transition band. In addition to applications used CHESS sequence to acquire water-only and fat-only images (Fig. 21) for cheddar cheese blocks(22) with reasonable good suppression quality, as shown in Fig. 22. In the CHESS imaging sequence, the first soft 90"RF pulse is frequency selective with carrier frequency set at the unwanted spin resonance frequency. It flips the unwanted spin magnetization into the transverse plane, and subsequently the spoiling gradient dephases the transverse magnetization. This presatu-

(4

(b)

Fig. 21 MR images of cheese: (a) water-only and (b) fat-only images.

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-Water Saturated

-Fat Saturated

-No 20 22 24

Saturation 18 10 1612

14

8

6

4

2

ppm

Fig. 22 NMR spectra of cheese. Bottom: No suppression was applied. Middle: Fat was suppressed. Top: Water was suppressed.

ration leaves the spin system in a state where no net magnetization of unwanted component is left while the wanted component remains entirely unaffectedin the form of longitudinal magnetization. A conventional spin-echo imaging sequence is then applied to obtain the desired proton image.

D. State ofWater and Food Product Shelf Life It has been pointed out several times that water is of great importance to the quality and stability of food products. Studies of properties of water in foods have a long and distinguished history. Before the recognition of water activity, a,$, the moisture content of a given product was thought to be the critical factor In thelate1950s, in thecontrol of microbialgrowthandchemicalreactions. microbiologist W. J. Scott (23) suggested the water activity concept, believing that it could provide a better and more reliable measure of the “availability” of by manyfoodandbiological water.Sincethen, a,? has beenwidelyaccepted scientists and industries and has been used as a primary guideline for the safely and quality control of food, biological, and pharmaceutical products (24-26). Today. a,, is still a treasure for many industries and is used extensively to model product shelf life. In recent years, the theoretical and practical limitations of 11., have been voiced by many food scientists and technologists (27-29). The measurement of a,of a food system is based on the assumption that the food system is in its equilibrium state, i.e., the measured partial vapor pressure above the food system is presumed the same as that of water within the food system. This assumption may hold true for infinitely dilute systems, where diffusion rates of water mole-

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cules are high compared to the time scale of the thermodynamic measurement. Unfortunately, most food systems usually are nonequilibrium systems and, during processing and storage, are maintained or brought intoa state of thermodynamic instability, a state perhaps better designated as “pseudo-stable’’ since the unstable state may last longer than the normal lifetime of the product. Therefore, a concept rootedin equilibrium thermodynamics, such as a,, cannot fully represent in most food systems and should only be used to describe systems that exist equilibrium with their aqueous environments (e.g., water vapor) (28). Some researchers have begun to use relative vapor pressure (RVP) instead of a, because a, is in fact indicative of the rate at which water migrates out of the system. Another defect of the aH.concept is that when different solutes are used to lower the a, of a system, the response of the system in terms of the reaction rates or stability is different. This suggests that measurement of a, is insensitive to the solute-solute and solute-water interactions, whichin fact have a profound impact on the reaction kinetics of the system. The “availability” of water isa manifestation of how “freely” water molecules can participate in reactions or how easily water molecules diffuse to the reaction sites to participate in the reactions. The availability of water can be reduced by adding certain chemical compounds, such as sugars (30,31). The reduced availability of water has been attributed to the increased viscosity of the systems (3 1-34) or increased hydrogen bonding between water and sugar molecules (35). Many researchers have found that the mobilityof water, as measured by N M R and other techniques, is related to the availability of water in complex systems (31,351-41). The higher the mobilityof water, the higher the availability of water. Very mobile water molecules take a long time to reach their equilibrium state, or relax very slowly, thus having a small relaxation rate( R , or R?) or long relaxation time (T, or T2, TI= 1/R, and TI = 1/R2). Figure 23 shows that added sucrose suppresses the spin-spin relaxation time constant of sucrose solutions. N M R has been proven to be one of the most successful techniques to measure the mobility of water, hence the availability of water (31,35-41).

1. Discrete and Continuum Models for Analysis of Relaxation Times Interpretation of N M R relaxation measurements has been model dependent. Because of the complex or heterogeneous characteristicsof food systems, many use multiexponential models to analyze theN M R relaxation data. Mostof these models predicted relaxation decay data based on specific model assumptions, e.g., a certain small integral number of discrete exponential decay components of differit may not produce ent mobility (41,42). This does simplify the analysis, but satisfactory solutions tovery complex, heterogeneous systems because the factors affecting spin-lattice and spin-spin relaxation behaviors are not yet well under-

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1700

1

1300

v

F

900

500

100 0

3010

20

40

50

60

Sugar concentration (Ye)

Fig. 23 Spin-spin relaxation time constant (TI) decreases as sugar content increases in a sugar (sucrose) solution.

stood. In a heterogeneous system, spins exist in a wide variety of different environments, giving rise to a spectrum of relaxation times. In addition, chemical and diffusive exchange, an important factor affecting spin-spin relaxation proa variety of T2 values, assuming there is a wide cess, would also give rise to range of exchange rates within the heterogeneous system (43-45). Therefore, the measured relaxation decay is a sum of contributions from all spins, which have sampled many different environments, or exchange with other spins at different rates during the course of NMR experiment (46). It is thus reasonable to assume that a continuum of relaxation times would arise from a continuum of different exchange rates and different environments in which spins exist. Some researchers in various have used the continuum models to follow the relaxation behavior systems, although they focused on the state of water instead of that of biopolyal. (44) claimed that the mers that interacted with water (44,46-51). Lillford et continuous distribution of relaxation times is a better representation of the information content of the relaxation experiments. The continuum approach seeks a continuous distribution of relaxation times and effectively adjusts a continuous variable number of degrees of freedom to the minimum value necessary for a given data set. The CONTIN computer program of Provencher (52,53) has been used by researchers to process noisy data

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including NMR relaxationdecaydata (S4,SS). It canproducerelaxationtime (e.g., Tz) spectra that could be regarded as a probability distribution of spins at various relaxation times. One of the advantages of the continuum approach is that it conforms to the continuum natureof food systems. Furthermore, additional information may be obtained from the continuum models. There are often several a larger number of and/or broader peaks on a T2 spectrum. A spectrum with peaks would be expected for the heterogeneous samples but not for the more homogeneoussamples.Therefore,thenumberanddegree of variation of the peaks could be used to indicate the homogeneity of the sample under analysis. Proton relaxation is normally in exponential form, and the relaxation time constants can be determined from the decay curves.For a 90” pulse, the resultant free induction decay (FID) curve can be expressed as

where A is the amplitude at delay time t and AI, is the amplitude at equilibrium. Usually, for heterogeneous systems like wheat flour dough. a multicomponent model (56) is used:

Equations ( 13) and (14) work well with simple systems. Both models have been developed based on several assumptions, such as some predetermined number of discrete components in the decay curve (41). WhittallandMackay (47) proposedamethodcallednonnegativeleastsquares (NNLS), which uses a continuum approach to analyze this type of data. The general integral equation describing multiexponential relaxation is

where y(t,) is the observed amplitude measured at time t, and S(T) is the unknown amplitude of the spectral component at relaxation time T, which could be T, or T?. The limits a and b are chosen to contain the values of T expected for the physical system being analyzed. A linear inverse method has been developedto solve this equation. Assuming a large number of known relaxation times T, solved from the corresponding amplitudes Sj. it is assumed that the spectrum is a sum of M delta functions with unknown areas S, at known relaxation times T,:

NMR in Food Quality Analysis

J=

I

Substitution of Eq. (15) into Eq. (14) results in:

This is an equation of linear systems, whose general form

is:

,=I

where A,, is a matrix with elements exp(t,/TJ), and M is set large enough SO as not to bias the solution into small number of relaxation times. Because of the noise contamination, Eq. 18 cannot be solved exactly. A non-negative least-squares algorithm has been developed for this kind of relaxation time analysis. in which extra constrains are incorporated into the matrix A of Eq. 18 to alter the discrete character of the basic least-squares solution. A general form is to minimize

for fixed p > 0. a trade-off parameter. H, is a matrix representing K additional constrains. and f k is the corresponding vectorof right-hand side values. The leastsquares solution is obtained when p = 0 (47). CONTIN is a Fortran program for inverting noisy linear operator equations. This program uses the NNLS method but, even more, is a general-purpose constrained regularization method, which finds the simplest solution that is consistent with prior knowledge and the experimental data. CONTIN has been proved as a favorable approach compared with conventional NNLS and linear programming approaches (57) or the Pade-Laplace method (54). The degree of successfulness of this program is dependent on the number of temporal data points, the time range of the measured data, and the signal-to-noise ratio (5435).

2. Mono-Exponential Decay Proton relaxation is normally in exponential form, and the relaxation time con1I.E that stants can be determined from the decay curves. We know from Sec. T I can be determined by running an inversion recovery or saturation recovery experiment, and T2 by a 90" pulse or a CPMG pulse experiment. Take the 90"

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t Fig. 24 Freeinductiondecay (FID) curve.

pulse experiment, for example; the resultant FID curve is normally represented by:

In A(t) = InA.

-

1 -

t

T2 where A(t) is the amplitude at timet and A. is the amplitude at equilibrium. The plots of A versus t and In A versus t are shown in Figs. 24 and 25.

3. Multiexponential Decay Figure 25 is a straight line as expected from Eq. (20). However, in many cases, the plot of In A(t) versus t is not a straight line, as shownin Figure 26, suggesting

t Fig. 25 Semi-log plot of mono-exponentialFIDcurve.

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Fig. 26 Semi-log plot of multiexponential FID curve.

that there are two or more groups of water molecules that relaxat different rates. The relaxation curve cannot be described by using the mono-exponential model [Eq. 141. Instead, the multicomponent model described by Eq. (15) must be folin isolated amplitudesand relaxation time constants. lowed. This model can result Figure 27 shows a three-component model.

Fig. 27 Schematic diagram of a three-component model for multiexponential behavior of proton relaxation.

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45

Fig. 28 Continuousdistribution of spin-spin relaxation times determinedbythe 90" pulse experiment as a function of moisture content. Peaks on eachcurve at moisture contents of 12, 18, and 23% are labeled as PI and P2 from left to right. Above moisture content of 23%, the single peak on the curve is labeled as P2.

4. Continuum Model The example given here is from our study of state of water in wheat flour dough. 90" Dough samples of different moisture were prepared and measured using the and CPMG pulse sequences. Analysis of the data obtained from the 90" pulse and CPMG experiments using the CONTIN package resulted in spectra (continuous distribution) of T2 (Figs. 28,29). The x-, y-, and z-axes are T2value, amplitude, T2 spectra computed and moisture content, respectively. Figure 28 shows the from data obtained from the90" pulse experiment.At moisture contentsof 12% to 28%, two peaks (PI and P2) appear on each spectrum. Water molecules covered by these two peaks can be regarded as two groups having distinctly different 1 to 66 ps, proton mobility. Because theT2values of these two groups range from signals falling into these two groups can be regarded as from the solids (proteins and carbohydrates) and/or water molecules very close to the solids. Below moisture content of 23%, the increase in the area of the second peak and decreasein average T2of individual peaks could be attributed to the increased available binding sites of the swollen flour substrates as a result of addition of water (58). The disappearance of the first peak at moisture above 23% may be due to the same

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1

45%

O.OEMO\ loo

403

,~, 6579

-c

..--,

Fig. 29 Continuous distribution of spin-spin relaxation time determined by the CPMG experiment as a function of moisture content. At and above moisture content of 23%, peaks on each curve are labeled as P3, P4, and P5 from left to right.

reasons explained earlier. The increasein both peak area and T2 above moisture content of 23% suggests that at 23% moisture level, all the water-binding sites two on the flour solids have been hydrated, and the additional water would be or three layers away from these binding sites and therefore exchange and relax more slowly. The CPMG experiment was intendedto detect proton signals having relatively longer spin-spin relaxation time than the one-pulse experiment. The analysis of the data obtained from the CPMG experiments indicated that at moisture content below 18%, no signal was detected, suggesting the dry flour had little mobile water. At and above the 18% level, there were one to three peaks (P3, P4, and P5) on the spectra (Fig. 29). T2values shown in Fig. 29 range from lo2 to IO5 ps, suggesting that the detected signals were from water molecules with relatively high mobility. The number and size of peaks increased and the mean T2values shifted to the right (increasing T2value), with moisture content increasing up to 28%. The appearance of new peaks suggests that new physical and of addition of chemical environments were formed within the system as a result

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water to the system. This coincides with the beginning of dough formation at the moisture level above 23%. It is noticed that the moisture content of the dough samples affects the shape of the spectra. A broader distribution could indicate a greater variation in the chemical and physical properties of the system. Calculationof the coefficient of variation (c.v.) of individual peaks would thus provide information about the homogeneity of the systems under analysis. The following equations were used to compute the coefficients of variation of spin-spin relaxation times (59):

SD c.v.% = y x 100 X

where T2 and S are spin-spin relaxation time and amplitude, respectively, SD is = T 2 S i / CSi istheweightedaverage of T2. standarddeviation of T2, and The results are shown in Fig. 30. Figure 30 shows that the coefficientof variation of T2s in the range of 580 ps (the two peaks shownin Fig. 28) remained almost constant as the moisture content was increased, suggesting that the environments with which the solid-

I

+Pl

+P2

" P 3

+P4

+PS

X

t

10

20

30

40

50

Moisture content (YO)

Fig. 30 Coefficient of variation of spin-spin relaxation times determined using the continuum model (see Figs. 28 and 29 for labels of PI, P2, P3, P4, and P5).

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like and tightly “bound” water protons were associated did not change very much while the moisture content increased substantially. The coefficients of variation of those longer T?s (the three peaks shown in of 40%, which Fig. 29) showa gradual and slow increase below moisture content of may be a result of gradual formation of the bicontinuous network structure dough and uneven distribution of water among the flour constituents, as discussed earlier. Upon reaching a moisture contentof 40%, which is more than the normal dough moisture level, the coefficients of variation rose sharply, suggesting that theexcesswater mayhavecreatedveryinhomogeneousdoughmorphology, which should be further investigated.

E. Measurement of Glass Transition Temperature Glass transition, a term in polymer science to describe a process in which a rubbery material, if cooled below a critical temperature, turns into a glassy material (60,61), has made frequent appearances in food science and technology publications in recent years (62-73). Food scientists and technologists are fascinated by the idea that foods are essentially polymers and can therefore be treated like synthetic polymer systems in various analyses. Connections between the glass transition and structural and textural characteristics, and chemical and microbial activity of foods have been made (65,67-69,74). Unlike low molecular weight substances that can exist in three states of aggregates-solids,liquid,andgas-polymersexistonly in solidandliquid states because they will decompose before they are vaporized, suggesting that to break free polymers never reach a mobility high enough for the molecules even if heated to a very high temperature. In low molecular weight crystalline solid substances, individual molecules sit at their respective positions with a little vibrational motion but without translational or Brownian motions. When the temperature increases, more and more kinetic energy is added to the system and the individual molecules move vibrationally and more and more vigorously. When the vibrational motion increases sufficiently to cross the energy barrier that holds the individual molecules in the of their fixed positions, equilibrium positions, the molecules start moving out activating Brownian motion. Eventually, upon further heating, the molecules difis fuse all over randomly, and the well-defined molecular arrangement pattern lost. By now, the substance is melted and is in a liquid state. What happens when a high molecular weight amorphous or partially crysall talline polymer is heated gradually? A high molecular weight polymer has the characteristics of a low molecular weight substance at room temperature, but when heated the difference between the polymer and the low molecular weight substance can be seen. A high molecular weight polymer has a number of chain segments. When we increase the temperature, some segments within the long

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chain of the polymer molecule are first mobilized before the whole molecule starts moving. Further heating causes the entire molecule to move and become liquid. From this discussion we can differentiate two types of motions: internal or segmental motion and external or molecular motion. Both motions can be regarded as Brownian. At a certain temperature, both motions are frozen; at higher temperatures both motions can be activated. These two situations correspond to the solid state and liquid states, respectively. What happens if the polymer is placed at a temperature where only the segmental motion is activated while the molecular motion is frozen? The activated segments correspond to the liquid state, while the molecule as a whole, with the mobility frozen, corresponds to the solid state. This state, which is indeed a mixture of liquid and solid, is called the rubbery state. On further heating the polymer in the rubbery state becomes a highly viscous liquid and starts flowing; this state is called the viscofluid state, the transition taking place at the flow temperature Tf (Fig. 31). Polymers can have two types of motions: segmental and molecular. How can we relate these motions to the NMR properties? Can we determine the Tg by detecting the motional characteristicsof the polymer? Does waterplay a role here? NMR measuresprotonrelaxationcharacteristics, Asdiscussedearlier, which can be related to the mobility of the proton-containing molecules. In a liquid state, a long relaxation time constant indicates high mobility and increases with increasing temperature. When the system is moving toward a solid state, for instance, due to suppressing of temperature, the relaxation processes behave somewhat peculiarly. We have mentioned that the spin-lattice relaxation is a process of energy release by the excited spins to the lattice (the entire molecular

0Motion

not activated

Motion activated

Fig. 31 A schematicpresentation of therelationship between states of materialand motional characteristics.

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system). To facilitate an efficient energy release by the excited spins, the lattice In the solid state, the number of has to oscillate at the resonant frequency a,,. molecules oscillating or rotating at the resonant frequency olIis very small, suggesting a very inefficient spin-lattice relaxation. Therefore, the dynamic contribution to the T I decay is very small, causing TI to become very long again. On the is very short due to the decrease in static contribuother hand,in the solid state, Tz tion to transverse-component dephasing. The dynamic motion of spins (or molecules) can also be characterized by a correlation time z,, which is, roughly speaking, the minimum time required for the nuclear magnetic moment to rotate one radian (I/? 7t of a complete circle). In general, T, for a nonviscous liquid is very short. With water, for instance, T, is about 10"' s. On theotherhand, T, forsolids is verylong,about s. Within Z~ and the more quickly a perlimits, the slower the motion, the longer will be Z, and relaxation time turbed spin system will relax. The relationships between constants can be described as follows:

where K = 3y2/160d h?yJ/r?is a constant that includes a number of nuclear parameters and constants. These equations have been proven very useful for the understanding of a single relaxation process dominant system. Figure32 is a plot of Z( versus relaxation time constants. A plot of relaxation time constants versus reciprocal of absolute temperature is generally in the form shown in Figure 33. It can be seen that curves in Figures 32 and 33 share the same shapes. This is because that, within limits, Z, is related to temperature. Their relationship can be described by 7, = T , , , ~ ~ . ~ < I ' L ' '

(25)

where E:,,, is the activation energy for rotational motion, k the Boltzmann conz~,,a constant. We should also be aware stant, T the absolute temperature, and of o0q..This is especially the that the relaxation time constants are a function case of T , . Figure 34 shows that the corresponding T, and magnitude of T I minimum shift when OIlis varied. From Eq. (23). we know that T I minimum occurs when c o , ) ~=~ 0.616. We have

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Fig. 32 A generalized schematic diagram showing the relationships between relaxation time constants (TI and TJ and correlation time (q).

Fig. 33 NMR relaxation time constants (TI and Tz) as a function of reciprocal absolute temperature ( 1 IT).

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10000 +T1

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'

.

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"""'

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'

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Fig. 34 Theoretical curves of correlationtime T, versusrelaxationtime constants and Tz)at different resonance frequencies.

where fll is the frequency of the NMR equipment related to the static magnetic field Bo, i.e.,

BecauseformostcommercialNMRanalyzers, fo is in therange of 10-600 MHz, o,] is in the range of 60-3800 MHz. Therefore, for T , minimum to occur, z, (= 0.616/o11)has to be in the range of 10""-10~* s. Another model for calculation of z,, the Debye-Stockes theory, relates T, to molecular size (with radius of a), medium viscosity 11,and absolute temperature T:

zc = 4na3q/3kT

(28)

where k is theBoltzmannconstant.Thismodelpredictsthatcorrelationtime increases with larger molecules, viscous solutions, and low temperature. In other words, within certain limits, the relaxation time constants for large molecules, viscous solutions, and low temperature systems are small. On a qualitative basis, Eq. (28) predicts a linear dependence of T, on T / q for some liquids (75). However,thesemodelsare not alwaysapplicable to complexsystems. Many synthetic polymers, which have more than one group on the main chain andprobablymorethanonecorrelationtime,undergomultiplerelaxation

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processes (76). Themotion of eachgroup is differentandmaybecomethe dominant process in a certain temperature range. This multiple relaxation proT, minima. Each minimum is related to the cess is characterized by multiple motional behavior of a specific group and sometimes, if the chemical structure of the polymer is known, can be assigned to a specific group. However, with increasing molecular weight or length, relaxation processes become more complicated, and the distinguished minima may merge due to the overlapping of T, minimum motions contributed by individual groups to form a single, broad (77). For most food systems, there are so many different compounds in the systems that overlapping of motions experienced by individual compounds may occur over a range of temperatures. The dominant relaxation processmay produce a single T, minimum where the segmental motion of the dominant compound is activated. Figures 35 and 36 show the dependenceof T, on the temperature for amorphous maltodextrin of dextrose equivalent (DE) of 5 and 25 with 25% moisture, respectively. A single, broadT, minimum is observed for DE 5 over the temperature range tested, while the cure for DE 25 exhibits an “L” shape. Figure 37 is a plot of T2 versus temperature. T? changes very little when temperature is lowered to a critical point from which the solid begins its “rigid lattice behavior.” The onset of rigid lattice behavior is characterized by T2 = z,, which is about IO” s, a value found for many solid materials. The midpoint

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400

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NMR in Food Quality Analysis 1000 loo0

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207

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1 0 0 ;: 100

10 150

200

250

300

350

400

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300

400

Temperature (K) Fig. 37 Spin-spinrelaxationtimeconstantsasafunctionofabsolutetemperature maltodextrin samples (DE = 5 ; moisture content = 25%).

for

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of the T2 transition on the T2-temperature curves is often considered related to the glass transition process. We used differential scanning calorimetry (DSC 7, The Perkin-Elmer Corporation, 20 mg of sample size, S"C/min of heating rate, midpoint T, calculated using the system built-in computer program) to measure the T, of the material and found that the transition temperature determined from the TI-temperature and T2-temperature curves are very close to the DSC determined T,. This further suggests that the transition phenomena observed with NMR relaxation experiments are indeed associated with the glass transition process. We will provide more examples later to demonstrate the use of NMR in determination of the glass transition process in various food systems. Glass transition is largely affected by the composition of the system of interests.Amongthecompositionalfactors,moisturecontentandmolecular T, (69,78,79). weight of the macromolecules are the major players governing Water as a plasticizer is capable of mobilizing the solid matrix and increasing the mobility of the system. Large molecules have less mobility, hence larger 'cc, and require larger input of thermal energy to mobilize the structure. Water is a plasticizer that can penetrate into the polymer matrix and establish polar attractive forces between it and the chain segments. These attractive forces reduce the cohesiveforcesbetweenthepolymerchainsandincreasethesegmentalmobility, thereby reducing the T, value. Figure 38 shows a shift of minimum point on the T,-temperature curves to lower temperature as moisture content was increased in the maltodextrin samples. Similarly, the T2-temperature curves (Fig.39) show

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300

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Fig. 38 Effect of moisture content on spin-lattice relaxation time constantTI for maltodextrin (DE = 5).

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a shift of the transition point to the lower temperature as the moisture content of the samples increased.

F. MRI MAPPING OF GLASS TRANSITION TEMPERATURE Many foods and biological materials are heterogeneous. Such heterogeneity may be caused by the incompatibility of ingredients, poor mixing, or relocation of the ingredients during processing and storage. For example, the crust on the surface of many baked food products such as a cake is due to the excess heating and loss of moisture during baking. Therefore, there will be an uneven distribution a variable of the physical structure and moisture content within the cake, and distribution of T, is expected. For baked goods, an uneven distribution could mean that the textural properties of the products are not uniform. For other products where chemical and microbiological activities are key deteriorating factors that are strongly influenced by T,, an uneven distribution of T, could be a major challenge to the safety control, which is usually based on a single average parameter, e.g., T,. A localized low T, can put the corresponding spot in a condition well above the T,, allowing chemical and microbiological activities to take place at this very spot. Currently there is no study on the measurement of T, distribution reported in the literature. The conventional techniques do not have the capabilityof provid-

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ing spatial information about the thermal dynamic or dielectric properties of the materials. On the other hand, MRI can generate spatial information about the nuclear relaxation characteristicsof the material, which can be related to the glass transition process as discussed earlier in this chapter. Therefore, if we can estabL = 1,2) encoded with lish a relationship between relaxation time constants (TL, the spatial information and temperature (T), i.e., TL(X, Y) = f(T)

(29)

we will be able to produce a “Tgmap.” To construct aT, map of a sample, we need to obtain a series of TL maps the TIminima or Tz transition of the sample at different temperatures, from which points and corresponding temperatures for each pixel are computed using a curvefitting program, after which a map of temperatures corresponding to T the I minima or T2 transition points, that is, a Tgmap, is constructed. This procedure is demonstrated in Fig. 40A and B. Figure 40A shows that, as temperature increases, TIvalues of individual pixels changed. The two samples with different moisture contents responded to temperature differently. The analysis of the average TIvalue of each image as a functionof temperature revealed that the minimum T I value was found between -5 and 6°C for the sample with25% moisture content and between 13 and 23°C for the sample with 18% moisture content. The temperatures corresponding to the T I minimum values agree well with the Tg determined by the pulsed NMR and DSC methods.

M C = 25%

M C = 25%

MC 1Wo

M C = 18%

47°C30°C-5°C 23°C13°C 6°C

Fig. 40 (A) Tlmapsof maltodextrin (DE 5 ) samples differing in moisture content(MC) obtained at different temperatures (darker = high T, value). (B) Tg map of maltodextnn (DE 5) samples calculated and constructed based on TImaps shown in (B) (darker = higher temperature).

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This procedure demonstrates that two samples placed side by side can be measured simultaneously. This is a great advantage of the MRI mapping technique for simultaneous determination of T,s of multicomponent foods.

IV.

FUTURE TRENDS

NMR and MRI have found use in other areas not mentioned specifically above. of NMR is widely used for rapid determination of moisture and fat contents oilseeds. Use of MRI to measure rheology of food products has also been reported. Attempts were also made to combine MRI and mathematical modeling techniques to study simultaneous heat and mass transfer. Efforts are needed to address the relationships between NMR properties and sensory quality attributesin food systems so that NMR can be used for rapid, nondestructive, and noninvasive evaluation of food products. There are also a need to develop affordable NMR hardware and more sophisticated analysis software.

GLOSSARY 180"pulse: RF pulse designed to rotate the net magnetization vector 180" from the static magnetic field. 90" pulse: RF pulse designed to rotate the net magnetization vector 90" from the static magnetic field. B,,: Conventional symbol for the main magnetic field in MRI system. Carr-Purcell (CP) sequence: Sequence of a 90" RF pulse followed by a repeated 180" RF pulse to produce a train of spin-echos. Carr-Purcell-Meiboom-Gill (CPMG) sequence: Modified CP sequence with 90" phase shift in the rotating frame of reference between the 90" pulse and the subsequent 180" pulse to reduce accumulating effects of imperfections in the 180" pulses. Correlation time: The minimum time required for the molecule to rotate one radian. Echo: A form of magnetic resonance signal from the refocusing of transverse magnetization. Equilibrium: Themagneticstate of anobjectthat is fullymagnetized by a static magnetic field. Fourier transform imaging: A mathematical procedure used in MR that converts a time-domain signal into a frequency- or spatial-domain signal or image. It is analogous to the way that our ear distinguishes or separates out separate sounds or frequencies from noise we hear. Our eyes do not

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work this way. If we see a mixture of blue and yellow we see the color green, not the original blue and yellow. Free induction decay (FID): A form of magnetic resonance signal from the decay of transverse magnetization. Frequency encoding: Use of a magnetic field gradient to produce a range of frequencies along the MR signalto provide information on spatial position. Gradient: Amount and direction of the rate of change in space of some quantity such as magnetic field strength. Larmor frequency ( 0 ) : The frequency at whichmagneticresonancecanbe excited. Lattice: Environmentssurroundinganucleus or spin. Magnetic moment: Measure of the magnetic properties of an object or particle that causes it to align with the static magnetic field. Magnetization: A vector quantity measuring the net magnetic moment of nuclear spins per volume of a sample. NMR signal: Electromagnetic signal i n RF range produced by the precession of the transverse magnetization of nuclear spins. Precession: A rotational motion of a spinning body about an axis of a vector whose origin is fixed at the origin. Pulse sequence: A series of RF pulses and/or magnetic field gradients applied to a spin system to produce a signal representative of some property of the spin system. Radiofrequency (RF): Electromagnetic radiation lower in energy than infrared. RF is in the range of 10 to 100 MHz. Relaxation rates and time constants: After excitation, the spins tend to return to their equilibrium state at certain rate. This rate is called relaxation rate. The reciprocal of relaxation rate is relaxation time. There are two relaxation processes: spin-lattice or longitudinalrelaxation. I t is the return of longitudinal magnetization to its equilibrium value after excitation through exchange of energy between the spins and the lattice with a characteristic time constant termed spin-lattice relaxation time T I .The second is called the spin-spin or transverse relaxation process. in which the transverse component of magnetization vector, which is at right angles to the static magloss of transnetic field. decays towards zero. The characteristic time for verse magnetization to zero is termed spin-spin relaxation time T:. Spin or nuclearspin: The intrinsic magnetic momentum of an elementary particle such as a nucleus responsible for the magnetic moment. A fundamental property of matter responsible for MRl and NMR. Spin-echo: Reappearance of an MRIsignalaftertheFIDhasdisappeared. It is the result of the effective reversal of the dephasing of the nuclear spins.

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50.

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Levine, L Slade, eds. Water Relationships in Foods. New York: Plenum Press, 1990: 405-452. HK Leung, JA Magnuson, BL Bruinsma. Pulsed nuclear magnetic resonance study of water mobility in flour doughs. J Food Sci 44(5):1408-141 I , 1979. JR Zimmerman, WE Brittin. Nuclear magnetic resonance studies in multiple phase systems: lifetime of a water molecule in an adsorbing phase on silica gel. J Phys Chem61:1328-1333,1957. PJ Lillford, AH Clark, DV Jones. Distribution of water in heterogeneous foods and model systems. In: SP Rowland, ed. Water in Polymers. 1980:177-195. PS Belton, BP Hills. The effect of diffusive exchange in heterogeneous systems on NMR line shapes and relaxation processes. Molec Phys 61:999-1018, 1987. RM Kroeker, RMHenkelman.AnalysisofbiologicalNMRrelaxationdatawith continuous distribution of relaxation times. J Magn Reson 69:218-235, 1986. KP Whittall, ALMacKay.QuantitativeinterpretationofNMRrelaxationdata. J Magn Reson 84: 134-152, 1989. RS Menon, PS Allen. Application of continuous relaxation time distribution to the fitting of data from model systems and excised tissue. J Magn Reson 86:214-227, 1991. CD Araujo, AL MacKay, JRT Hailey, KP Whittall. H Le. Proton magnetic resonance techniques for characterizationof water in wood: application to white spruce. Wood Sci Techno1 26(2):101-113, 1992. C Tellier, F Mariette, J Guillement, P Marchal. Evolution of water proton nuclear magnetic relaxation during milk coagulation and syneresis: structural implications. J Agric Food Chem 4l( 12):2259-2266, 1993. CH Newcomb, SJ Graham, MJ Bronskill. Effects of nonlinear signal detection on NMR relaxation time analysis. J Magn Reson 90:279-289, 1990. SW Porvencher. A constrained regularization method for inverting data represented by linear algebraic or integral equations. Comput Phys Commun 27:2 13-227, 1982. SW Provencher. CONTIN: a general purpose constrained regularization program for inverting noisy linear algebraic and integral equations. Comput Phys Comrnun 27:229-242,1982. C Labadie, JH Lee, G Betek, CSJ Springer. Relaxograph imaging. J Magn Reson 105:99-112,1994. JH Lee. Magnetic Resonance Studies of Tissue 23 Na and 'H20signals. State University of New York, 1993. R Ruan, PL Chen. Water in FoodandBiologicalMaterials:ANuclearMagnetic Resonance Approach. Lancaster, PA: Technomic Publishing Inc, 1998. K Overloop,L Van Gerven. NMR relaxation in adsorbedwater. J MagnReson 100(2):303-315,1992. W Bushuk, V K Mehrotra. Studies of water bindingby differential thermal analysis. 11. Dough studies using the melting mode. Cereal Chem 54(2):320-325, 1977. JL Devore. Probability and Statistics for Engineering. Monterey, CA:' BrookslCole Publishing Co., 1982. LH Sperling. Introduction to Physical Polymer Science, 1986. VR Gowariker, NV Viswanathan, J Screedhar. Polymer Science.New York: Halsted Press,1986.

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62. HC Troy, PF Sharp.aandbLactose in somemilkproducts. J Dairy Sci 13:140157,1930. 63. R Katz, TP Labuza. Effect of water activity on the sensory crispness and mechanical deformation of snack food products. J Food Sci 46:403-409, 1981. 64. GW White, SH Cakebread. The glass state in certain sugar-containing food products. J Food Technol 1:73-82, 1966. 65. YH Roos, M Karel, JL Kokini. Glass transitions in low moisture and frozen foods: Effects on shelf life and quality. Food Technol SO( I1):95-108, 1996. 66. YH Roos. Effect of moisture on the thermal behavior of strawberries studied using differential scanning calorimetry. J Food Sci 52:146-149, 1987. 67. H Lavine, L Slade. Apolymer physicochemical approach to the study of commercial starch hydrolysis products (SHPs). Carbohydr Polym 6:213-244, 1986. 68. H Lavine, L Slade. A food polymer science approach to the study ofcryostabilization technology. Conm Agric Food Chem 1.3 15-396, 1989. 69. L Slade, H Lavine. Glass transitions and water-food structure interactions.Adv Food Nutr Res 38:103-269. 1995. 70.JMFlink.Structureandstructuretransitions in driedcarbohydratesmaterials. In: M Pelega, EB Bagley, eds. Physical Properties of Foods. Westport. CT: AVI Publishing Co., Inc., 1983:473-521. 7 I . JMV Blanshard, F Franks. Ice crystallization and its control in frozen food systems. In: JMV Blanshard, P Lillford, eds. Food Structure and Behavior. Orlando, FL: Academic Press, Inc., 1987:51-65. 72.HLavine, L Slade.Collapsephenomena-aunifyingconceptforinterpretingthe behavior of low moisture foods. In: JMV Blanshard, JR Mitchell. eds. Food Structure-Its creation and Evaluation. London: Butterworths, 1988: 149180. 73. H Lavine. L Slade. Influence ofthe glassy and rubbery states on the thermal, mechanical, and structural properties of doughs and baked products. In: H Faridi. JM Faubion, eds. Dough rheology and baked products texture.New York: AVI, 1990: 157330.

74. HY Roos. M Karel. Applying state diagrams to food processing and development. Food Technol 4 3 l2):66, 68-71, 107, 1991. 75.TLJames.NuclearMagneticResonance in Biochemistry:NewYork:Academic Press,Inc.,1975. 76. H Pfeifer.Nuclearmagneticresonance andrelaxationofmoleculesadsorbed 011 solids. In: p Diehl. R Ksfeld, eds. NMR: Basic Principles and Progress. New York: Springer-Verlag, 1 9 7 2 5 - 153. 77. WP Slichter. NMR studies of multiple relaxations in polymers. J Poly111 Sci 14:3348, 1966. 78. HY Roes, M Karel. Water and molecular weight effects on glass transitionsin amorphous carbohydrates and carbohydrate solutions. J Food Sci 56: 1676- 1681, 1991. 79. YH Roes, M Karel, JL Kokin. Glass transitions in low moisture and frozen foods: effect on shelf life and quality. Food Technol SO( I I ):95- 108. 1996.

Ultrasonics John Coupland The Pennsylvania State University, University Park, Pennsylvania David Julian McClements University of Massachusetts, Amherst, Massachusetts

1.

INTRODUCTION

Sound waves are transmitted through materials as perturbations in their physical structure. Hence, it is often possible to relate the ultrasonic properties of a material to useful information about its macroscopic and microscopic composition and structure. This chapter introduces the physics of high-frequency sound and principles of ultrasonic measurementof food properties. Applicationsto real food materials (solutions, polymers, dispersions,and muscle and plant foods) are then discussed. Finally, someof the many possible untapped applications of ultrasonic sensors are introduced. The absorption or speed of various types of radiation is characteristic of the properties of the material through which it passes. This is most commonly exploited with electromagnetic radiationin the well-known formsof spectroscopy used in the nondestructive evaluation of foods (e.g., infrared, ultraviolet, visible) ( I ) ; however, mechanical waves may also be used. Mechanical spectroscopy is most widely known at the low frequencies used in small deformation rheological measurements, but higher frequencies are also valuable, importantly ultrasonics (-20 kHz to 100 MHz). Ultrasonic spectroscopy shares two common features with all spectroscopic of no value to a working food techniques. First, the actual measurements are scientist in their own right. Their practical importance arises from correlations or processing pabetween the spectroscopic measurement and practical quality rameters. The relationships are most often empirical but can also be analytical. 217

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However it is established, the relationship between how a consumer and a spectrometer “see” a material is likely to be weak. An analytical basis for any technique is therefore preferable because it clearly identifies the relationship between material properties and instrumental readings. Second, different regions of the spectrum are sensitive to different physical and chemical structures present. In general, a higher frequency “sees” faster events, which most typically occur at smaller scales. Mechanical waves are a series of mechanical disturbances that propagate as stresses and strains in the physical bonds of the material. The speed and efficiency of the transmission is sensitive to the nature of the bonds and masses of the molecules present and therefore to composition. There are two distinct types of ultrasonic waves; the most commonly used in food nondestructive evaluation (NDE) arelongitudinalwaves(Fig. la). In this case,thedeformations of the material occur in the direction of transmission of the wave. In the second case, a shearing action, causing shear waves, the wave passes through the material with deformations normal to the movementof the wave front (Fig. lb). Combinations of shearing and longitudinal propagation are also possible. Shear waves are very strongly attenuated in fluids, and because they cannot propagate far, they are very rarely used to characterize food materials (typically largely liquid). It is important to distinguish between the low-powered ultrasound used for NDE of materials and the high-powered ultrasound used for homogenization, welding, cell disruption, etc. In sensing applications, the deformations caused by the passing wave are small-ideally within the elastic limit of the material and

Direction of Propagation

Fig. 1 Diagrammaticillustration of themodes of vibration in (a) longitudinaland (b) shear waves.

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hence nondestructive. The large energy levels used in high-powered ultrasound cause small transient air bubbles to form in the material (i.e., cavitation), which implode causing large shearing forces and disrupting the material. Ultrasonic testing reveals certain material properties not readily available by other techniques (e.g., fast kinetics, microstructure of optically opaque materials-see below). However, their important value in food NDE is that they can be readily made in optically opaque materials (e.g., meat, milk, chocolate) and through the walls of pipes, containers, and many packaging materials. The most significant practical limitation of ultrasound is that it is highly attenuated by gas cells in a sample. In practice that means it is difficult to transmit high-frequency ultrasound (> 20 kHz) through many real foods (e.g., most fruit, dough, some cheese). Additionally, ultrasonic measurements are quick and easy to make, they can be easily automated for on-line use as part of a process control system, and they are easily made to a good degree of precision. An ultrasonic wave passing through a material can be expressed in terms of its velocity and attenuation. Concisely, this relationshipis given by a complex wavenumber, k = o / c i a , where c is the ultrasonic velocity, o is the angular frequency (= 2 x 0 , f is the frequency, i = 4 - I , and a is the attenuation coefficient.* The wavenumberis related to material properties via the following equation ( I ) :

+

where E is the adiabatic elastic modulus of the material,' which is equivalent to C,p/C, for a gas or K for a fluid, p is the density, C, and C, are specific heats at constant pressure and volume, respectively, and K is the bulk modulus. When a beam of ultrasound passes through a bulk solid, there is some shearing at the beam edges and K is replaced by K + 4/3G, where G is the shear modulus. This relationship gives longitudinal ultrasonic velocity measurements some sensitivity to material shear properties, but as typically K >> G, they are hard to measure. All the material parameters in this equation are complex, with thereal and imaginary parts containing the storage and loss information of the wave, respectively. In many cases it is possible to neglect the imaginary component and rewrite Eq. ( I ) in the more widely known form (K is the adiabatic compressibility = K"):

* The attenuatlon coefficient of a material can be expressed as Nepers or decibels per meter, Np.m

I

or dB.m", respectively. where 1 Np = 8.686 dB. ' This should be distinguishcd from the isothermal elastic modulus normally measured in static loading experiments when heat generated has time to dissipate.

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The compression of fluids by a sound wave causes changes in the physical alignment of the molecules, and sound energyis lost to heat via conduction from hot (compressed) to cold (rarefied) areas and by the friction of one molecule against another. These mechanisms are knownas thermal and viscous dissipation losses, respectively, and their contribution to measured attenuation is given by classical scattering theory (1):

where a,is the classical attenuation coefficient, y = C,/C,, 6 is the thermal conductivity, and q is the viscosity. In systems whereit is applicable, classical theory can be usedto measure any of these useful physicochemical properties. However, often the measured attenuation is higher thanthat predicted classically due to the scattering of sound by small particles (see Sec. 1II.D) or the presence of certain chemical equilibria. Additional energy can be lostif there are chemical equilibria present whose position is affected by the ultrasonic wave. These additional (nonclassical) losses can be measured and have been applied to the measurement of fast chemical a material at equilibkinetics (2). When the compression wave passes through rium, the change in temperature and pressure displaces the balance of reactants and products. The equilibrium seeks to reestablish itself in the new conditions, and its capacity to do so depends on both the speed of the reaction and the frequency of the sound. At low frequencies the temperature-pressure gradients are so slight that the reaction remains in equilibrium and at high frequencies the reaction cannot proceed fast enough to respond to the rapid fluctuations. Under these conditions there is little excess sound absorption, but at intermediate frequencies thereactionposition is constantlyshiftingandthere is alargeabsorbance peak and a corresponding relaxation in velocity. By measuring the energy loss (attenuation) as a function of frequency over the relaxation process, the rate constant, k,, of the reaction is given by: 1

2nfC= - " 2 k , d s z where Kc is the equilibrium constant,f, is the center frequency of the relaxation, T is the relaxation time, and C is the concentration. The rate constant can then be calculated froma plot of relaxation time against the square root of concentration if the equilibrium constant can be calculatedby macroscopic methods. This method is appropriate for reaction times of the order 10-5-10"" s. Slower reactions can also be followed if the reactants have a different velocity than the products, although other methods such as optical spectroscopy are generally preferred.

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II. METHODS A varietyof experimental designs have been developed to measure the ultrasonic properties of food materials (2,3). All share the common elements of an electrical signal generator, which is used to generate vibrations in an ultrasonic transducer, and another transducer (or the same one after a time interval) to reconvert the acoustic energy back to an electrical signal, which is then digitized for analysis.

A. Pitch-and-CatchMethod Perhaps the simplest and most widely used implementation of these elements is the “pitch-and-catch’’ or “sing-around” pulsed sound method (3-5). The two major variations of this device are (a) pulse-echo-the sound is reflected from a metal plate and detected at the original transducer (Fig. 2b)”and (b) through transmission-the sound from one transducer is detected by a second (Fig. 2a). If the ultrasonic properties of the material are reasonably frequency independent (nondispersive), velocity can be calculated from the time taken for the pulse to travel the known pathlength (from a water calibration) and attenuation from the (1). If required, the frequency depenlogarithmic decrease in energy with distance

Precise Pathlength

I

I

Imprecise Pathlength

2:

1

Fig. 2 Diagram of some typical methods of making ultrasonic measurements: (a) and (c) are pulse echo methods using a single transducer to produce and detect the acoustic signal; (b) and (d)are through transmission methods usingone transducer to produce and another to detect the signal. (a) and (b) are methods using a fluid cell which can be precisely calibrated; (c) and (d)are measurements on irregularly shaped objects whereit may be impossible to precisely know the pathlength.

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dence can be calculated by using a finite number of cycles of pure-frequency A/C voltage to excite the transducer and measuring the propertiesof the singlea frequencysoundgenerated (i.e., toneburst).Byrepeatingthemethodwith range of frequencies, it is possible to measure a full spectrum. Alternatively, an electrical spike can be used to excite a broad-band transducer, which generates a narrow pulse of sound containing a range of frequency components. Using a fast Fourier transformation to compare the frequency content of the signal before and after transmission through the material, it is possible to measure a region of the spectrum around the center frequency of the transducer from a single pulse (5). More precise measurements can be made using an interferometer ( 1 ) or a fixed pathlength resonator (6,7). Both methods set up a standing (continuous) as a function of pathlength wave in the sample cell and measure the intensity (interferometer) or frequency (resonator). The received intensity varies as the pathlength or frequency is continuously altered, forming nodes and antinodes at the detector. From the intensity variation the velocity and attenuation may be calculated.Thesemethodsaretypicallymoreprecisebutslowerthanpulse methods. In all cases the parallelism of the cell, the reflectance at all interfaces, and the energy loss due to beam spreading must be considered. Good temperature control is also essentialin precise ultrasonic experiments; +O.l"C should be consideredaminimum.Alternatively,thetemperaturecanbemeasuredsimultaneously with the ultrasonic signal using either a thermocouple or a second measurement of a water-filled cell in close thermal contact.It is important to use a fast enough data capture system (oscilloscopeor analog-to-digital card) to retrieve all the information in the signal. A good rule of thumbis that the capture rate should be five times the highest frequency component. Postcapture data processing, such as averaging and Fourier-domain smoothing, are frequently used to improve the signal-to-noise ratio. The methods set out above are very precise but only suitable for liquid foods or solids with appropriate dimensions. Many foods are solid with irregular shapes or are too large for easy containment in a sample cell. In these cases it is rarelypossible to make veryprecisemeasurements or in some cases even measure absolute valuesof velocity and attenuation. However, useful information can often be obtained from the relative position of signal features or low-precision measurements. Measurements can be made using variations of the pitch-andcatch/reflectometer method described above; a single transducer is held against the sample (Fig. 2c) or a pair is clamped around it (Fig. 2d). The pathlength can be measured usinga micrometer or calipers. Signal qualityis frequently improved by coating the material under investigation with an ultrasonic coupling fluid (either water or a proprietary gel) to eliminate an air gap that would otherwise

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attenuate the signal. If transmission measurements are impossible, another approach is to measure the reflectance coefficient (proportion of normally incident energy reflected) at an interface and use it to calculate the ultrasonic properties of the materials from the following equation:

1 into where R,? is thereflectioncoefficient of a wave passing from material material 2 and z is the acoustic impedance of the material (=cp) ( I ) . Reflectance measurements have also been used to measure the surface smoothness of food materials (8).

B. Imaging Most people are familiar with the use of ultrasonic imaging techniques from their medical applications, for example, in prenatal care. These same principles have been applied extensively to foods to provide information on the spatial heteroa transducer geneity of food components. Acoustic images can be generated from fixed to a robot arm that is placed in a tank filled with a suitable couplant (e.g., water) with the material under investigation. Operating in either pulse-echo mode or through transmission mode (Fig. 2 ) , the transducer records echoes from the front surface, back surface, and internal structures in the material from a series of X-Y positions. Each of the recorded waveforms is known as an “A-scan.’’ A set of A-scans can be used to generate an image by assigning a color to either the magnitude of the signal at either a fixed time (B-scan) or a selected feature of the signal (e.g., second echo magnitude, time between successive echoes) (Cscan). The B-scan represents a slice through the material, whilea C-scan is more useful for identifying the changing properties of a component. Both imaging approaches discard a large quantity of the information in each A-scan and should be used critically. A diagrammatic illustration of image acquisition is shown in Fig. 3. In many cases, most especially large (e.g., whole carcasses and animals) and water-sensitive materials, it is not appropriate to use the scanning tank approach described above. Medical imaging techniques developed forin vivo measurements on human patients (9) have proved useful in these cases-particularly for muscle foods. By using very high-frequency ultrasoundit is possible to achieve resolution approaching optical microscopy. Acoustic microscopy has been used on occasion with foods and other “soft” materials, for example, the detection of sealworms and bones in cod fillets (10).

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A-scans

(time of second marked echo). Good data recovery

B-scan (amplitude in window) Some data loss

Fig. 3 Diagram showing how an ultrasonic image is generated using a one-dimensional image of a model solid with indentations cut in the back wall. The instrument generates a series of A-scans at different X positions ( I , 2, and 3) then generates an image based on the signal amplitude at either a set time in the A-scan (B-scan image) or of a selected feature (C-scan image). Both B- and C-scans are data-reduction methods that may give misleading results.

111.

APPLICATIONS

A.

Simple Solutions

1. Binary Mixtures One of the most successful groupof applications of ultrasound in food characterization is the determination of the composition of binary mixtures. The ultrasonic velocity in ideal mixtures can be calculated as a volume-weighted sum of the in Eq. (2). Nonideal behavior is an density and adiabatic compressibility used indication of association or segregation of components of the mixture and is difficult to predict a priori. So, a more practical approach to concentration determination is to prepare a standard calibration curve and use this for similar unknown samples. Some typical velocity-concentration curves for common food materials are shown in Fig. 4. Ultrasonic velocity measurements have been usedto measure the solids concentration in fruit juices (1 1)and can easily be used to measure the concentration of most two-component mixtures [e.g.. salt in brine (12), alcoholin spirits (13), solids in skimmed milk (14)].

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"

0

1"

5 10 15 20 Concentration (weight%)

25

Fig. 4 Concentration dependenceof velocity for a variety of solutes at 20°C;(0)sodium chloride, (0)glucose, and(W) ethanol. (All data from Ref.73 or measured by the authors.)

By making velocity measurements of concentration as a function of time and position using an ultrasonic velocity-based imaging device, it has been possible to measure the diffusion coefficient of sucrose in xanthan solutions (15).

2. Ternary Mixtures For many simple solutes (e.g., salts and sugars) thereis little temperature dependence in the concentration incrementof velocity, butin other cases (especially fats and alcohols) the temperature increment is negative while that of water is positive (T < 76°C). In the latter cases, at a critical temperature the speed of sound in the solute is identical to that of the solvent, and velocity is independent of the solutioncomposition.Thisproperty is veryuseful in concentrationmeasurements. Consider solutes 1 and 2 , the former sharing a critical point T, with the solvent; then cTC= f($z) and cT+TC= ($,, @).By developing two calibration curves at the two measurement temperatures, it is possible to measure the composition to measure alcohol of a three-component system. This approach has been used and solids in wine (13), fat and solidsin milk (14), and fat and proteinin fish (16). In the absenceof a critical point, orif temperature is not variable, multicomponent mixtures require additional nonultrasonic measurements for complete characterization; for example, Anton-Paar (Graz, Austria) have developed a nondestructive method based on simultaneous velocity and density measurements.

B. Lipids 1. Liquid Oils Food lipids are a mixture of various types of triacyl glycerols along with minor components including cholesterol, mono- and diacyl glycerols, and vitamins. The

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velocity of an oil is the volume-weighted sum of the velocities of the component in quality fatty acids (17). Oilused for deep-fat frying progressively declines (18) throughuse as it partiallyoxidizesandpolymerizes.LaceyandPayne showed that ultrasonic velocity (at 2.25 MHz) in corn oil increased from 1444.8 of deteriorato 145 1.1 m.s" with frying time and correlates with other measures tion but was insufficiently sensitive to detect product defects. An empirical relationship has been developed for ultrasonic velocityin oils as a function of refractive index, density. and iodine value (19).

2. MeltingBehavior The pure chemical components of food oils have a wide range of melting points in the range commonly encountered during food processing, use, and storage. As they are a mixture, the combinationof colligative properties and mutual solubility means the observed melting behavior of food oil occurs over a wide temperature range (20). The solids content of fatty foods is related to their perceived quality (e.g., gloss in chocolate, stabilityof emulsions, textureof butter). Ultrasound can be used to measure the volume fraction of solid fat mixed with liquid oil as the velocity of sound is much less in liquids than in solids. A typical melting profile for a sample of chocolate is shown in Fig. 5. The solid fat (SF) content can be calculated as:

"_ cf ct

1200 LO 10 20 30 40 50 60 Temperature (OC)

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where c is the measured ultrasonic velocity and c, (c,) the velocity in pure solid fat (liquid oil) extrapolated to the measurement temperature. This equation was developed from Eq. (2) by assuming solid fats and liquid oils have similar density and behave ideally as a mixture (21). Ultrasonic measurements give very similar data to conventional methods such as NMR and DSC (22). This technique has been applied to the measurement of solid fat in adipose tissue (21) and oil-inwater emulsions (22).

C. Polymers Polymers and their aggregates play an important role in determining the stability and textural characteristics of many foods, and there have been several attempts of polymer to use ultrasonic measurements to characterize the bulk properties networks and the structure of isolated molecules in solution. The attenuation of sound by a hydrocolbid solution is due to classical, scattering, and relaxational (fast physicochemical reaction) losses. By measuring the attenuation overa wide frequency range, it is possible to some extentto separate these effects and ascribe changes in attenuation to molecular and scattering events. Unfortunately, the relaxations occur over a very wide frequency range and several instruments are required to capture an entire spectrum. In oneof the most complete studies, Choi and coworkers (23) measured the spectrum of bovine serum albumin from 0.11600 MHz at pH 1.5-13.2 using five techniques to cover the entire range. They noted excess absorption at acid and alkali pH due to carboxyl and amino group proton exchange reactions and structural fluctuationsin the molecule. Other studies using single or narrow frequency ranges for attenuation measurements are less able to define which molecular events are causing the measured changes but have had some empirical success. Audebrand and coworkers (24) studied the gelation of alginate and amylose by ultrasonic spectroscopy. While velocity was unchanged during gelation, attenuation increased in a manner similar to the real part of complex viscosity (G') and, in the caseof amylose, turbidity. The time axis of the functions was different to different molecular profor the three assays consistent with their sensitivity cesses. Attenuation was shown to become more dependant on gelation at higher frequencies (100 > 80 > 50 MHz). Small changesin 5 MHz velocity (-2 m.s") were observed at temperatures close, but not identical, to the gel point of gellan measured by a mechanical test (25). Measurements of velocity and attenuation of a-amylase at lower wavelengths (2 MHz) were used (26) to measure the action on starch. The attenuation of the material decreased linearly with the number of bonds broken, and this was ascribed to the (unmeasured) change in viscosity; measured velocity was unaffected by enzymatic action. Coagulation of casein micelles to form a self-supporting network is a crucial stage in cheese manufacture. After a period of reaction, the cut-time, the coagulum is cut and excess water allowed to drain out. The cut-point is often

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defined by the expertise of the cheese maker but can be defined as the time at which the viscosity of the material sharply increases. Ay and Gunasekaran (27) showed that the ultrasonic attenuation coefficient a , at a frequency of 1 MHz, of milk decreases at a decreasing rate with coagulation and the turning point of a polynomial equation fitted to the measured data (daldt = 0) provided a similar time to the accepted rheological method. Velocity showed no significant change during coagulation, although there wasa very large variation in reported data (of the orderof ? I O m.s”) (28). In another studyof protein aggregation, the attenuation coefficient of solutions of broad bean legumin proteins reached a maximum at pH values near the isoelectric point ( - 9 , probably because the proteins formed aggregates that scattered the sound (29). This approach was also used to study the effects of dextran on limiting the isoelectric precipitation of the same protein (30). In summary, it seems that high-frequency attenuation is most sensitive to the state of food polymers and hencemany of their bulk properties. The velocity of sound in polymer solutions is largely frequency independent and relatively insensitive to aggregation.Velocitychangeshavebeenexploited to a much greater degree in measurements of individual molecular compressibility via Eq. (2). The compressibility of a molecule in solution is measured as the change in solution compressibility on adding one molecule of solute to pure solvent; in practice this is achievedby extrapolating measurements made in a series of dilute solutions. [It may also be possible to make measurements at higher concentrations, therefore requiring lower precision, if the scattering of sound is accounted for (31).] The compressibility of a molecule in aqueous solution depends on (a) its intrinsic compressibility and (b) the compressibility of the associated water molecules and is therefore very sensitiveto the hydration of polymers in solution. The intrinsic compressibility of the protein (believed to be due to a “cavity” in the molecular structure) is less than the surrounding water, while the bound surface water is less compressible than the bulk (32). This model gained support from a molecular dynamics simulation (33), which further suggested that the intrinsiccompressibility of the polymer (i.e., the “cavity”) was identical for the two globular proteins studied (superoxide dismutase and lysozyme). If this observation is generally true for globular proteins, then ultrasonic measurements can be used to directly measure their hydration state. The surface hydrationof a protein is largely a measure of surface hydrophobicity; an important parameter governing the functionality of food proteins (34). It is therefore unsurprising that compressibility correlates with protease susceptiof unfolding (35) and that there is a bility, foaming capacity, and free energy measurable change in compressibility on thermal or guanidine hydrochlorideinduced denaturation (32). However, empirical attempts to predict the compressof its constituent amino acids have met with ibility of a protein from the properties only limited success (36), suggesting that we are a long way from understanding mechanistically which properties of a protein are measured by compressibility.

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D. Dispersed Systems Many foods are dispersed systems, importantly emulsions (e.g., mayonnaise, soft of colloidal drinks),foams (e.g., beer,carbonateddrinks),andcombinations structures (e.g., bread dough, ice cream). When waves pass through the material inhomogeneities, there is an interaction known as scattering, which is dependant on the physicochemical properties of the two phases as well as their size, shape, concentration,spatialdistribution,andthefrequencyoftheultrasoundused. Some of the ultrasound is directed out of its path and so is not detected, and some is lost as heat as the scattering is not completely efficient. Consequently, the measured ultrasonic frequency spectra contain a relaxation, which can be related back to the physical properties of the material under evaluation. In certain cases it is possible to understand the acoustic lossesin terms of various scattering theories, but if this is not possible, empirical relationships may frequently be developed.

1. Emulsions Scattering of sound by emulsion droplets is relatively easy to solve analytically as the particles are spherical, and their typical size (-pm) is much less than the wavelength of the ultrasound (-mm), so the long-wavelength approximations to scattering theory are applicable (37). Under these conditions, sound is scattered by emulsion droplets by two important mechanisms:

I.

Thermalscattering: The particleandsurroundingmediumarecompressed to different extents by the wave, and the resulting temperature difference causes a heat flux. Thermally scattered energy radiates in all directions around the particle (Fig. 6). 2. Viscous scattering: The particle oscillates in the pressure gradient because it has a different density than the continuous phase (Fig.6). This oscillation leads to the generation of a secondary wave by the particle that has a cosine dependence on angle.In addition, the particle oscillation is damped by the viscosity of the surrounding liquid and some of the ultrasound is lost as heat. Neither mechanism is completely efficient, and there is significant energy loss. Using relatively few assumptions, it is possible to develop an analytical expression for scattering from a single particle. The effect of finite concentrations of particles can then be accounted for using scattering theory (38) or a core-shell a function of particle model (39) to calculate the bulk ultrasonic properties as of the component size distribution and concentration and the physical properties* phases.

* Viscoslty. density, thermal conductivity, thermal expansion coefficient, specific heat, and ultrasonic velocity and attenuation.

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Monopolar scattering _". "....___

Dipolar scattering

Droplet Fig. 6 Diagram illustrating the scattering of ultrasonic waves by emulsion droplets. The main mechanisms are droplet pulsation due to differences in thermal properties with the continuous phase and oscillation due to density differences. These mechanisms scatter monopolar and dipolar waves respectively.

The bulk properties of common food are well documented in the literature (40), so, for given ingredients, it is possible to predict the ultrasonic properties of any size/concentration emulsion. Typical results for a model food emulsion are shown in Fig. 7. At all frequencies the velocity and attenuation are dependent on concentrathe spectra upor down),but over a critical tion (changing the concentration moves range of frequencies there is also dependence on particle size. Therefore, using n

-".

t

1460

0.03 a, 0

w. 0

,E 1450

0.02 C

0.01

.-0

5 C

0.00

B a

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 log df

Fig. 7 Velocity and attenuation ofa 10%corn oil in water emulsion ( his the wavelength of the sound, other symbols are defined in the text). (Calculated as described in Ref. 38 using data from Ref. 40.)

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high- or low-frequency measurementsit is possible to measure droplet concentration and use this value to measure size from measurements in the central region. It would of course be preferable to record the entire spectrum and solve for size and concentration simultaneously. Particle size measurements using either velocity or attenuation agree with laser diffraction scattering measurements in pmin concentratedemulsions(volumefraction, Q < 0.5) sizedfoodemulsions (41,42). This is a particularly important application of ultrasonic NDE, as the information obtained cannot be readily measured by other methods. Commercial instruments based on these principles are available from various suppliers. An interesting extension of this method arose from one of its limitations. When the scattered waves from one particle interact with those from another (multiple scattering) less energy is lost. This occurs when the average particle separation decreases to a critical level, either at high concentrations when the measured attenuation increases less rapidly than the simple theory would predict or in flocculated emulsions. Detection of flocculation in emulsions is particularly of physical deteriorationof a product. important, as it is frequently the initial stage McClements (43) was able to detect flocculated emulsions with this method before they began to visibly cream. When the particles are charged, there is additional attenuation due to ionic “friction” between the moving particle and its counterions, which generates an A full soluA/C voltage that can be measured alongside the acoustic attenuation. tion of the viscous, thermal and electroacoustic scattering losses allows calculation of particle size and surface charge (c-potential) (44). This method showed good agreement with previously published values for casein micelles in skim milk (45). Ultrasonic imaging has been widely used to study the creaming of food emulsions under gravity. In its simplest form this method merely relates the vea known pathlength as a function of time locity for the sound to pass through and position to the volume fraction (46), but by measuring the scattering effects it should also be possible to determine size separation under gravity (47).

2. Foams The concentration, size, and growth of air cells in bread, fruit, dairy products, beers, and wines are vital to the perceived quality of these products. Ultrasound is very sensitive to dispersed gases and would seem an ideal investigative tool, but in practice the attenuation due to the resonant scattering of the bubbles is so strong that transmission measurements are not possible at high frequencies (>O. 1 MHz). Measurement of the surface reflection coefficient is a more practical apa proach to capturing the important frequency dependence of scattering from bubbly liquid, and the potential of this method has been demonstrated for some whipped food materials (48).

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E. Muscle Foods Ultrasound has been used for a number of years to measure the fat content of various live animals and carcasses. Such “whole animal” studies are beyond the scope of this work, but the principle of the measurement is similar to the unknown pathlength pulse-echo method described above (Fig. 2d). A transducer is held against the back of the animal and a pulse of sound fired through the surface layer and an echo recorded from the fat/muscle interface. The fat thickness corre(49-51). lates with overall fat content and other carcass and meat properties Alternatively, pattern recognition techniques developed for medical imaging devices may be used (52,53). Another imaging method that has seen some success in imaging muscle foods is elastography(54). In this method, an A-scan is recorded before and after the material is slightly compressed by the transducer. Pressing the transducer into the material causes the material to be deformed, the softer materials more than the harder, and the relative movements of the peaks can be tracked by crosscorrelation techniques. In this method isitpossible to get imaging across a plane in the material based on Young’s modulus. Ophir and coworkers(54) have used this approach to distinguish between fibrous muscle and perimysial tissue and to visualize a healed traumatic injury in beef samples. By calibrating the analysis with material of known properties, it is possible to make absolute measurements of Young’s modulus. A typical elastography image of a meat sample is shown in Fig. 8, in which the light bands represent bands of collagen and the dark areas fibrous muscle.

Fig. 8 Elastographic image of beef muscle; differentiation is based on the elastic modulus of the material. The white areas represent connective tissue and the dark myofibrillar muscle. (Image kindly donated by Rhonda Miller, Texas A&M University.)

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Muscle tissue can be considered a combination of a protein solution and fat, and the speed of sound usually lies somewhere between the values for oil and water (- 1400- 1500 ms”). The fat concentration can therefore be estimated fairly accurately for meat ( 5 5 ) and fish (56) using the methods set out above for it is possible to measure simple solutions. Using the “critical-point approach,” fat, protein, and moisture in fatty fish tissue (1 6). Attenuation is a less reliable measure of muscle composition, as it is very sensitive to microstructure. For example, there isa very large increase in the attenuation coefficient of fish tissue after a freeze-thaw cycle (D J. McClements, unpublished data), probably because small air bubbles forced outof solution by the freezing processdo not completely redissolve. This approachis limited, as it is difficult to make accurate velocity measurements of real meat cuts in a processing environment. One solution to this problem is to consider the frequency-domain energy distribution of a transmitted broadband pulse of ultrasound. Despite requiring less information about the measurea strong relationship (r’ = 0.89) ment system (pathlength not considered), there is between the number of frequency-domain peaks and fat content of beef muscle (57); weaker correlations were observed for other signal features. The same frequency-domain approach showed some correlation with the sensory perception as a predictive tool of juiciness, flavor, and texture but were too weak for use

(58). F. Plant Foods The ripening and deterioration of vegetable products is associated with changes in chemical composition and mechanical properties that might reasonably be expected to change the ultrasonic properties of the material. However, the correlations between ultrasonic and quality parameters are often weak for fruits and vegetables because the theoretical link between acoustic propagation and strucof ture is poorly understood. The acoustic properties are an unknown function vegetable material properties including size concentration and distribution of air cells, the cytoplasmic composition, the mechanical properties of the cell walls, and the intercellular bonds, while the perceived quality (e.g.,flavor, crunchiness) is probably a very different function of physicochemical structure. Methods that more closely mimic the consumers use of a food (e.g., compressional tests, GC analysis of volatiles) are likely to correlate better with perceived quality, but because these are inevitably destructive, acoustic methods have been frequently considered. A good example of the limitation of acoustic measurements was seen in recent (59) measurementsof the velocity, attenuation(37 kHz), and other properties of carrots cooked for different times (0-15 rnin). Measured velocity decreased linearly with strain at failure, Young’s modulus, solids content, and density, butthecorrelationswereweak (r = -0.69,-0.62,-0.46,and-0.29

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respectively), while attenuation showed still weaker positive and negative correlations with the same parameters. Practically, vegetables are difficult to measure because not only are they irregular in shape and variable in composition, but they also frequently contain intercellular air cells, which scatter ultrasound and cause unmeasurably high attenuation at high (->20 kHz) frequencies (60). Adequate transmission measurements are possibleat lower frequencies, butthis is not ideal as there is less spatial resolution and beam spreading and wave-guide effects can reduce the precision. in the Despite these limitations, many researchers have reported some success ultrasonic NDE of plant foods. Cheng and Haugh (61) identified whole potatoes with hollow heartas they absorbed more low frequency (0-75 kHz) sound energy than the healthy samples. The very-low frequency sonic resonance (<1 kHz) of whole apples decreased monotonically during the accelerated ripening process in a similar manner to texture measured bya puncture test (62). Self and coworkers used low-frequency ultrasonicsto follow ripening in banana (60) and avocado (63) and to determine the elastic modulus of apple tissue (64).

G. Miscellaneous One of the very few concerted uses of shear wave ultrasound in food systems was made by Lee and coworkers (65), who made measurements on cheese and dough (0.5-1 MHz) and comparedthe results with conventional oscillatory rheological measurements. Both devices showed similar trends in complex rheological properties with composition, but because the devices operated over different frequency ranges, quantitative comparisons could not be made. The success of thisstudy is surprising, because ultrasonic waves of this frequency would be expected to be very highly attenuated in a gas-containing mixture such as dough and shear waves would not be expected to propagate in fluid media. The time of flight for a 1.25 MHz ultrasonic pulse across a pipe was used to detect fouling at the pipe surface (66). The sensor could measurefilms between 0.5 and 6 mm thick, the lower limit being set by the wavelength of the ultrasound and the upper limit by the acoustic impedance of the materials. When implemented in a pilot-scale UHT milk plant, results were unaffected by flow rate (0I O L/min). The crispness of several types of cookies and crackers as measured by a sensory panel and fracture testing correlated with ultrasonic velocity (67). Ultrasonic absorption has been used to measure the texture of wafer sheets (68). There have been some attempts at using ultrasonic imaging to detect the growth of spoilage organisms in packaged milk (69) and other liquid foods (70). Good images could be recorded through plastic and metal packaging materials (paper contains air cells that strongly attenuate the sound) (70), and the speckle density could be correlated to conventional plate counts. However, only very

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large numbers of bacteria could be detected by this method, and a preincubation of many of period was required. It was suggested that the proteolytic activity the bacteria caused the changes detected by this method, which may limit any application.

IV. FUTURETRENDS A survey of the literature in this field reveals more than 40 years of ultrasonic (71,72). This methods of foodcharacterizationbasedonultrasonicprinciples concerted effort has produced remarkably few practical applications, most importantly on-line concentration meters, carcass evaluation tools, and, most recently, emulsion particle-sizing instruments. This developmental effort must be better focused to meet actual rather than perceived needsin the food industry. In developing a method it is important to remember the strengths and weaknesses of a given technique as summarized for ultrasonics in this work. It is also worth remembering that a new method will not be adopted based on its novelty alone; it must provide information that is not readily attainable by existing technology or be in some way more practical (e.g., cheaper, easier to use). Additionally, food a measurement device must maintain is an intrinsically variable material, and sensitivity to the important variable (e.g., crunchiness in apples) while ignoring changes in other variables (e.g., variety, size, insect damage). Food-processing plants are rarely static, single-product operations, and the sensor must be flexible enough to account for this. For example, a juice-bottling plant may switch bea day, and tween several product types over different lines over the course of any analytical controls must be equally easy to recalibrate or their potential for improved quality and production times will be lost in the inconvenience of operation.Bearingthesepoints in mind,wewouldliketosuggestsomeareaswe believeultrasound-basedinstrumentationcouldbeused in thenondestructive evaluation of foods:

Polymer characterization:Very high precision resonator cells and digital signal generators are now becoming available at moderate cost, and their of their hydration and application to food materials will reveal details small molecule binding that cannot be readily obtained by other methods. Guided wavetechnology: An ultrasonic wave can be angled using a wedge to trap most of the energy within a container or pipe wall. By adjusting the angleof incidence and the frequencyof the wave, element movement is excited along the length of the pipe (in phase vibrations) and at right angles toit (out of plane vibrations). Variationof the proportions of these vibrations changes the sensitivity of the technique to pipe and contents properties, respectively. Proper application of this method can be used

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to probe many tens of meters of pipe from a single measurement and can be used to detect corrosion, fouling, weld defects, and properties of the material contained (J. Rose, unpublished). Particlecharacterization: Ultrasonicspectroscopyhasbecomeanaccepted technique for the characterization of model and real emulsions. However, many of the important functional propertiesof food emulsions occur when they stop acting as isolated liquid spheres and instead aggregate,gel,orcrystallize.Newdevelopments in scatteringtheorywill uniquely allow ultrasonic sensors to investigate these events. Surface acoustic waves:Using printed circuit technology, it is possible to A/C makeminiatureultrasonictransducersthat,whenexcitedbyan pulse, transmit sound waves across the surfaceof the imprinted material. in pulseThe waves can be detected by a similar transducer (or used echo mode) and the speed of the wave measured. The speedof the wave is proportional to the distance between the transducers and is therefore sensitive to temperature (via the thermal expansion coefficient) and shear stress on the material. Modifications of this device are also sensitive to the chemical environment into which the sensor is placed and can be of trace chemicals. The used to detect pH, composition, and the presence great advantage of these devices is that they are small (< 1 n m ) , cheap to wireless interrogation. If a enough to be disposable, and amenable chip was attached to the packaging of an individual food item, it could be used to sound an alarm when decay reaches a specified level or in conjunction with an intelligent oven to determine if the food is cooked (V. Varadan, unpublished). Advanced imaging technologies: Medical imaging ultrasound is an established diagnostic tool that could be easily used to detect defects and contamination in food materials. Typical food-processing operations require much faster throughput thanis (thankfully) acceptable in a doctor’s office, so the large amount of information obtained must be reduced to a binary decision variable by computer-based data analysissoftware similar to that used in other machine vision applications. On-line imaging could be used to detect glass in opaque foods, insect contamination i n meat products, and mechanical damage to packaging materials. Shear wave studies: Shear wave velocity is much more sensitive t o the perceived texture of food than the more commonly applied transverse waves and is limited by difficulties making adequate measurements in fluids. Development of an accurate measurement method would effectively provide a noninvasive, on-line viscometer.

I n this chapter, useful information about foodstuffs that can be extracted from their interactions with high-frequency sound has been presented. New devel-

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opments in theory, instrumentation, and application are constantly emerging in the physical sciences literature, and it is the application of these fundamental ideas to food materials that will lead to the improvements in food quality and safety that would result from good nondestructive evaluation.

GLOSSARY Attenuation coefficient (a):The loss of acoustic energy per unit distance. Couplant: Ultrasound is strongly attenuated in air and a liquid couplant (often water of a proprietary gel) is needed to facilitate the transmission between the transducer and sample. Delay line: Apiece of material(frequentlyPlexiglasorquartz)designed to separate the large signal from the transducers initial ring from later measurable echoes. Dispersed system: A material that is inhomogeneous supramolecular scale but homogeneous in bulk. Includes emulsions and foams. Elastography: An imaging system based on the change in detected signal on bulk deformation. Emulsion: Aliquid-in-liquiddispersedsystem. Foam: Agas-in-liquiddispersedsystem. Frequency: The reciprocal of thetimerequiredforawave to complete on cycle. The frequencyof ultrasonic waves usedin food analysisis commonly in the 10hs" region. Imaging: Use of the spatial dependence of an ultrasonic signal to develop an image of internal structure. Impedance: A measure of the capacity of a material to transmit sound. Commonly measured as the product of density and ultrasonic velocity. Resonator: An ultrasonic measurement system where the properties of interest are measured from the constructive and destructive interference of a continuous wave with itself. Scattering: The process by which the direction of transmission of ultrasonic energy is changed by interaction with microscopic inhomogeneities. Transducer: Mechanical device to convert electrical to mechanical (ultrasonic) energy or vice versa. Ultrasound: Mechanical vibrations similar to sound but at frequencies far beyond the range o f human hearing (>20 kHz) Velocity (c): The speed of sound in the direction of transmission or detection. Wavelength: The distance between points of equal amplitude on a wave. Ultrasonic waves used in food characterization typically have a wavelength of the order of a millimeter.

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REFERENCES I . J Blitz. Ultrasonics: Methods and Applications. London: Butterworths, 1971. 2. MJW Povey, DJ McClements. Ultrasonics in food engineering. Part 1: introduction and experimental methods. J Food Eng 8:217-245, 1988. 3. EPPapadakis.TheMeasurementofUltrasonicVelocity.In: RN Thurston,AD Pierce, eds. Ultrasonic Measurement Methods. Vol. XIX Physical Acoustics. San Diego: Academic Press, 1990:s 1- 106. 4. EP Papadakis. The Measurement of Ultrasonic Attenuation. In: RN Thurston, AD Pierce, eds. Ultrasonic Measurement Methods. Vol. XIX Physical Acoustics. San Diego:AcademicPress1990:107-155. 5. DJ McClements, P Fairley. Frequency scanning ultrasonic pulse echo reflectometer. Ultrasonics30:403-405,1991. 6. F Eggers, T Funck. Ultrasonic measurements with milliliter liquid samples in the 0.5-100 MHz range. Rev Sci Instrum 44:969-977, 1973. 7. AP Sarvazyan. Development of methods of precise ultrasonic measurements in small volumes of liquids. Ultrasonics 20: 15 1 - 154, 1982. 8. N Sarkar, RR Wolfe. Potential of ultrasonic measurements in food quality evaluation. Trans ASAE 26:624-629, 1983. 9. KK Shung. Basic principles of ultrasound tissue characterization. In: SE Freeman, ed. Noninvasive Techniquesin Biology and Medicine. San Francisco: San Francisco Press Inc, 1990:205-217. 10. H Hafsteinsson, SSH Rizvi. A review of the sealworm problem: biology, implications and solutions. J Food Prot 50:70-84, 1987. 1 1 . NI Contreras, P Fairley,DJ McClements, MJW Povey. Analysis of thesugar content of fruit juices and drinks using ultrasonic velocity measurements. Int J Food Sci Techno1 27515-529, 1992. 12. BB Owen, PL Kronick. Standard partial molal compressibilities by ultrasonics. 11. Sodium and potassium chlorides and promides from 0-30°C. J Phys Chem 65:8487, 1961. 13. WC Winder, DJ Aulik, AC Rice. An ultrasonic method for direct and simultaneous determination of alcohol and extract content of wines. Am J Enol Vint 2 1 :1 - I 1. 1970. 14. JW Fitzgerald, GR Ringo, WC Winder. An ultrasonic method for measurement of solids non-fat and butterfat in liquid milk. J Dairy Sci 44:1165, 1961. 15. TK Basaran, JN Coupland, DJ McClements. Monitoring molecular diffusion of sucrose in xanthan solutions using ultrasonic velocity measurements. J Food Sci (submitted). 16. R Ghaedian, JN Coupland, EA Decker, DJ McClements. Ultrasonic determination of fish composition. J Food Sci (in press). 17. DJ McClements. MJW Povey. Ultrasonic velocity measurements in some liquid triglycerides and vegetable oils. J Am Oil Chem SOC 65: 1787-1789, 1988. 18. RE Lacey,FA Payne. Ultrasonic velocityin used corn oil as a measure of oil quality. Trans ASAE 37:1583-1589, 1994. 19. TH Gouw. JC Vlugter. Physical properties of triglycerides 111: ultrasonic sound velocity. Fette Siefen Anstrichmittel 3:159-164, 1967.

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cc Casmir, 20. PJ Lawler, PS Dimick. Crystallization and polymorphism of fats. In: DB Min,eds.FoodLipids:Chemistry,Nutrition,andBiotechnology.NewYork: Marcel Dekker, 1998: 229-250. 21. CA Miles, GAJ Fursey, RCD Jones. Ultrasonic estimation of solid/liquid ratios in fats, oils, and adipose tissue. J Sci Food Agric 36:218-228, 1985. 22. DJ McClements, MJW Povey. Comparison of pulsed NMR and ultrasonic velocity measurements for determining solid fat contents. Int J Food Sci Technol 23:159170,1988. 23. PK Choi, JR Bae, K Takagi. Ultrasonic spectroscopy in bovine serum albuminsohtions. J Acoust Soc Am 87:874-881, 1997. JR Emery. Investigationof gelation phenom24. M. Audebrand, JL Doublier, D Durand, ena of some polysaccharides by ultrasonic spectroscopy. Food Hydrocoll 9:195203,1995. 25. Y Tanaka, M Sakurai, K Nakamura. Ultrasonic velocities in aqueous gelIan S O ~ U tions.FoodHydrocoll7:407-415,1993. 26. MJW Povey, AJ Rosenthal. Technical note: ultrasonic detection of the degradation of starch by a-amylose. J Food Technol 19: 1 15-1 19, 1984. 27. C Ay, S Gunasekaran. Ultrasonic attention measurements for estimating milk clotting time. Trans ASAE 37:857-862, 1994. 28. S Gunasekaran, C Ay. Milk coagulation cut-time determination using ultrasonics. J FoodProcEng19:63-73,1996. 29. G Pavlovskaya, DJ McClements, MJW Povey. Ultrasonic investigation of aqueous solutions of a globular protein. Food Hydrocoll 6:253-262, 1992. 30. G Pavlovskaya, DJ McClements, MJW Povey. Preliminary study of the influence of dextran on the precipitation of leguminin from aqueous salt solutions. Int J Food Sci Technol 27:629-635, 1992. 31. VJ Pinfield, MJW Povey. Thermal scattering must be accounted for in the determination of adiabatic compressibility. J Phys Chem B 101: 1 1 IO- I 1 12, 1997. 32. Y Tamura, K gekko. Compactness of thermally and chemically denatured ribonuclease A as revealed by volume and compressibility. Biochemistry 34: 1878- 1884, 1995. in solvated 33. E Paci,MMarchi.Intrinsiccompressibilityandvolumecompression proteins by moleculardynamicssimulationathighpressure.ProcNatlAcadSci USA 9311 1609-11614, 1996. 34. S Nakai. Structure-function relationships in food proteins with an emphasis on the importance of protein hydrophobicity. J Agric Food Chem 31:676-683, 1983. 35. K Gekko, K Yamagami. Flexibility of food proteinsas revealed by compressibility. J Agric Food Chem 3957-62, 1991. 36 MM Gromiha, PK Ponnuswamy. Relationship between amino acid properties and protein compressibility. J Theor Biol 165:87-100, 1993. 37. DJ McClements, JN Coupland. Theory of droplet size distribution measurements in emulsions using ultrasonic spectroscopy. Colloids Surfaces A 1 17: I61- 170, 1996. in emulsions. 38. DJ McClements. Principles of ultrasonic droplet size determination Langmuir123454-3461,1996. 39. Y Fukumoto, T Izuyama. Thermal attenuation and dispersion of soundin a periodic emulsion. Phys Rev A 46:4905-4921, 1992.

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40. JN Coupland, DJMcClements.Physicalpropertiesofliquidedibleoils. J AmOil Chem Soc 74:1559-1564, 1997. 41. JN Coupland, DJ McClements. Droplet size determination in food emulsions: comparison of ultrasonic and light scattering methods. J Food Eng (in press). 42. DJ McClements, MJW Povey, M Jury, E Betsanis. Ultrasonic characterization of a food emulsion. Ultrasonics 28:266-272, 1990. 43. DJMcClements.Ultrasonicdeterminationofdepletionflocculationinoil-in-water emulsions containing a non-ionic surfactant. Colloids Surfaces A 9095-35, 1994. 44. RW O’Brien, DW Cannon, WN Rowlands. Electroacoustic determination of particle size and zeta potential. J Colloid Int Sci 173:406-418, 1995. 45.TWade,JKBeattie, WN Rowlands, MA Augustin.Electroacousticdetermination of size and zeta potential of casein micelles in skim milk. J Dairy Res 63:387-404. 1996. 46. PA Gunning, DJ Hibberd, AMHowe, MM Robbins.Useofultrasoundtomonitor gravitational separation in emulsions. J Soc Dairy Techno1 42:70-77, 1989. 47. VJ Pinfield, E Dickinson, MJW Povey. Modeling of concentration profiles and ultrasonic velocity profiles in a creaming emulsion: importance of scattering effects. J Colloid Int Sci 166363-374, 1994. 48. PF Fairley, DJMcClements,MJWPovey.Ultrasoniccharacterizationofsome aerated foodstuffs. Proc Inst Acoust 13%-70, 1991. 49.JCAlliston, AJ Kempster, MGOwen,MEllis.Anevaluationofthreeultrasonic machines for predicting the body composition of live pigs of the same breed, sex, and live weight. Anim Prod 35:165, 1982. SO. SD Chen. CY Huang, HJ Chen. Prediction of carcass quality in live pigs by ultrasonic measuring of backfat thickness and loin eye area. 11. TaiwanSugar41:24, 1994. 5 I . SDChen,CYHuang, HJ Chen.Predictionofcarcassqualityinlivepigs by ultrasonic measuring of backfat thickness and loin eye area.1. Taiwan Sugar 40:26. 1993. 52.RKolb, G Nilter.Digitizedultrasonicimagesonlivepigsforpredictionofmeat proportion in the belly. Zuchtungskunde 65:297, 1993. 53. JR Brethrour.Estimatingmarblingscoreinliveanimalsusingpatternrecognition and neural network procedures. J Anim Sci 72: 1425- 1432, 1994. 54. J Ophir, RK Miller, H Ponnekanti, I Cespedes. AD Whittaker. Elastography of beef muscle. Meat Sci 36:239-250, 1994. 55. B Park,ADWhittaker, RK Miller, DS Hale.Predictingintramuscularfat in beef longissimus muscle from speed of sound. J Anim Sci 72: 109-1 16, 1994. 56. R Ghaedian. EA Decker. DJ McClcments. Use of ultrasound t o determine cod fillet composition. J Food Sci 62:500, 1997. 57. B Park, AD, Whittaker, RK Miller,DEBray.Measuringintramuscularfat in beef with ultrasonic frequency analysis. J Anim Sci 72:l 17-125. 1994. 58. B Park,ADWhittaker, RK Miller, DS Hale.Ultrasonicspectralanalysisforbeef sensory attributes. J Food Sci 59:697-701. 724, 1994. 59. M Nielsen, HJ Martens.Lowfrequencyultrasonicsfortexturemeasurcments in cooked carrots (Dalruts c o w f a L.). J Food SCI62: I 167- 1 170, 1 175. 1997. 60. GK Self. MJW Povey, H Wainright. What do ultrasound measurements in fruit and

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vegetables tell you?In: MJW Povey, DJ McClements. eds. Developments in Acoustics and Ultrasonics. Bristol: Institute of Physics Publishing, 1992: 129-164. Y Cheng, CG Haugh. Detecting hollow heart in potatoes using ultrasound. Trans ASAE 37:217-222, 1994. LA Liljedahl. JA Abbott. Changes in sonic resonance of delicious and golden delicious apples undergoing accelerated ripening. Trans ASAE 37:907-912, 1994. GK Self, E Ordozgotti, MJW Povey, H Wainwright. Ultrasonic evaluation of ripening avocado flesh. Postharvest Biol Technol 4: 11 1-1 16. 1994. GK Self, G Chan, MJW Povey, H Wainwright. Ultrasonic nondestructive evaluation of the maturity and qualityof fresh fruit and vegetables. In: Ultrasonic International '91 ConferenceProceedings.Oxford:Butterworth-Heinemann,1991. HO Lee, H Luan,DG Daut. Use ofan ultrasonic technique to evaluate the rheological properties of cheese and dough. J Food Eng 16:27-150, 1992. PM Withers. Ultrasonic, acoustic and optical techniques for the non-invasive detection of fouling in food processing equipment. Trends Food Sci Technol 7:293-298, 1996. MJW Povey, CA Harden. An application of the ultrasonic pulse echo technique to the measurement of crispness of biscuits. J Food Technol 16: 167- 172, 198 1. G Joudiekene. A Petranskas, 0 Reidl, E Mohr. Possibilities of application of ultrasonics to determining the texture of wafer sheets. Ernahrung 14:507-51 I , 1990. R Ahvenainen, G Wirtanen, M Manninen. Ultrasound imaging-a non-destructive method for monitoring the microbiological quality of aseptically packed milk products. Lebenstn Wiss Technol 22:382-386, 1989. R Ahvenainen. G Wirtanen, M Manninen. Ultrasound penetration through different packagingmaterials-anon-destructivemethodforqualitycontrolofpackaged UHT milk. Lebensm Wiss Technol 22:268-272, 1989. DJ McClements. Ultrasonic characterization of foods and drinks: principles, methods, and applications. Crit Rev Food Sci Nutr 37: 1-46, 1997. MJW Povey. Rapid determination of food material properties. In: MJW Povey, TJ Mason, eds. Ultrasoundin Food Processing. London: Blackie Academic and Professional, 1998: 30-65. KH Hellwege. Molecular acoustics. In: KH Hellwege, AM Hellwege, e&. Land&Bornstein, New Series. Berlin: Springer-Verlag, 1967.

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7 Firmness-Measurement Methods Yen-Con Hung, Stan Prussia, and Gabriel 0.1. Ezeike The University of Georgia, Griffin, Georgia

1.

INTRODUCTION

A.

DefiningTexture

Texture is a basic physical property of foods. Consumers evaluate food texture as the sum total of kinesthetic sensations derived from eating a food. Perception of texture during eating encompasses the mouthfeel, the masticatory properties, the residual properties, and the sound (1). Other definitions of texture include (a) the physical properties of foodstuffs related to mouthfeel or eating quality (2), (b) one of the three primary sensory properties of foods that relate entirely (or in addition to the other primary properties) to the sense of touch or feel (3), and (c) overall physical properties perceivedby the eyes, fingers, or the mouth during mastication (4). The primary parameters describing texture have been reported and adhesiveby Szczesniak (5) as hardness, cohesiveness, viscosity, springiness, ness.

B.

Importance of TexturelFirmnessEvaluation

Food texture is understood to be the sensory manifestation of kinesthetic properties (6) and depends on particle size, shape, structure, and rheological parameters ( 5 ) . The quality and acceptability of any food are influenced considerably by textural and structural properties normally associated with it. In relation to food quality, the importance of texture can be best demonstrated by food product advertisements. Some words that have been used to describe desirable textural quality include crisp, crunchy, tender, juicy, creamy, firm, spongy, and smooth. Conversely, some descriptors that have been usedto characterize undesirable textural quality include soggy, sticky, gummy, soft, fibrous, tough. and dry. 243

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Texture is also one of the key factors in determining harvest date and can of foods (7). For example, Washalso be used for determining the market grades (8) requirethatRedDeliciousapplesatthetime of ingtonStateStandards shipping have firmness values of at least 53 N (12 Ibf) as determined by the Magness-Taylor (MT) puncture test. Texture has also been used as an indicator to crackers) also for freshness of seafood (9). Processed foods (from hotdogs need to maintain certain well-defined textural quality to meet consumer expectations. In manufactured foods, texture has also been used as an indicator for the consistency of products. as measuring Bourne (10) summarized texture-measurement instruments either force, distance, time, energy, multiple units, or chemical composition. An overview was also provided by Giese (1 1) on different food texture-measuring methods. However, there are more textural parameters than can possibly be covered in detail in one chapter. Firmness is a popular term for hardness ( 5 ) and has been described as the force necessary to attain a given deformation. Firmness has also been used as an indication of quality as well as a parameter for sorting operations. For the remainder of this chapter, the discussion is focused on firmness.

C.Overview

of Firmness-EvaluationMethods

There is a recognized need for development of mechanical devices to separate (1 2 ) grouped firmness-sensing methods objects based on firmness. Chen and Sun as (a) destructive firmness tests, (b) nondestructive methods, and (c) nondestructive measurement of secondary properties. According to Voisey and Larmond (13), the validity of an instrumental measurement of texture should be based on it is important to examhow well it predicts sensory perception of the food. Thus, ine how firmness determined instrumentally (destructive and nondestructive) reon the product lates to sensory perception of food texture. Firmness also depends being evaluated. For example, penetrometer readings showedlittle difference between the firmness of apple and muskmelon, even though substantial differences existed in sensory perceptions. Harker et al. (14) found that the relationship between instrumental and sensory perception of firmness was curvilinear and concluded that further work is needed to fully understand the fundamental psychophysical basis of human perception of texture. They also suggested that sensory perception of texture is more sensitive than instrumental measurements when soft fruit are being examined. The measurement of food texture falls broadly into two categories: instrumental and sensory methods (Fig. 1). The specific principles employed in many of the instrumental methods are nearly as varied as the products. The methods can be broadly divided into destructive and nondestructive methods, and the principles range from mechanical to acoustic, resonance, laser air-puff, nuclear mag-

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~

Sensory (Subjective, methodolog , psychology, physlologyr

Instrumental (Objective)

Destructive (Llmited sample size)

Nondestructive (On-line, more samples)

I

Acoustic

1 1 Resonance I I NMR 1 1 Other I Sensor Fusion

Human

I Judgment I

Fig. 1 Classification of texture-assessmentmethods.

netic resonance (NMR), etc.Dull ( I 5) defined the term “nondestructive” to indicate that the sample (of any physical form) can be analyzed to obtain meaningful of data by some means in such a way that the physical and chemical attributes the sample are not altered. This means that the sample, after testing, can be subjected to subsequent uses or activities such as food manufacture, handling, storage, or direct consumption without loss. After considering the various approaches used for categorizing firmness, we have decided on two broad categories: methods having mechanical contact and those having no mechanical contact with the food.

II. MECHANICALCONTACTWITHPRODUCT A.

DestructiveFirmnessTests

The standard measure of food firmness is conducted with a penetrometer developed by Magness and Taylor (16). It is a mechanically driven probe used to penetrate the food products with the maximum required penetration force giving an indication of firmness. In addition to the Magness-Taylor penetrometer, Studman and Yuwana ( 1 7) presented a new firmness-measurement method based on the moment necessary to rotate a blade attached to a spindle after it has pushed

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into the fruit. After the blade has been pushed into the fruit to the desired depth, the fruit is slowly rotated manually about the axis of the spindle. When the fruit tissue fails, the maximum angle of rotation is read and the value is used to determine firmness. The resistance to the moment is assumed to be due entirely to the crushing of the cells by compression and shear. This device requires less than IS seconds to complete and record each measurement, is able to take measurements at specified depths, and is able to sense a wide range of fruit firmness by using different sizes of blades for firm (smaller blade) and soft (larger blade) fruits. There is no need to clean the device between tests, and no need to remove the fruit’s skin. Another modified force-deformation method was investigated by Murillo et al. ( I 8) using peaches. A cylindrical plug measuring 1.5-2.0 cm long was cut from each peach 2-3 mm below the skin surface on the blush side using a 2-3 cm (internal diameter) cork borer. Using the Instron Universal Testing machine with a load cell of 50 kg at cross head speed of 20 mm/min. the specimens were cut axially through the plug with a 1.36 mm diameter wire probe, and a steadystate force was noted to represent firmness. The Magness-Taylor methodhas a key advantage of many years of history that have led to its use as a standard against which other methods are compared in order to determinetheiracceptability.However,the main disadvantage of methods based on this principle is that the probe test is destructive to the product being measured and only provides test results from a sample that is used to represent the lot. Therefore, this method cannot beused to measure firmness of every item going through a packing or processing line.

B. NondestructiveForceMethods Destructive tests for firmness continue mostly because suitable sensors are not available for nondestructively measuring firmness of all items in a lot. Consequently. considerable effort has been made for finding a nondestructive method. The specific principles employed in many of the instrumental methods are nearly as varied as the products. 1. Based on Human Touch

As indicated before. manual separation/sortingis the only practical method available now for firmness sorting of foods. Hung et al. (19) used 100 untrained consumers to rank five peaches with different firmnesson a hedonic scale from most firm, very firm, firm, less firm, to least firm by touching the peaches. A total of 160 freshly harvested peaches werefirst ranked by an expert judge into five firmby 100 untrained nessgroups with 32 peachespergroupandthenevaluated

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consumers by touching (no squeezing). The untrained consumers were instructed to rank a set of five peaches randomly placed on a tray from most firm to least firm. The ranking of the expert judges matchedwell with those of the consumers (19). Perhaps the human touch represents the best sensation of firmness, being nondestructive and utilized at the final point at which consumers may accept or reject a product based on the combinationof appearance and force (by touching). However, it is subjective, labor intensive, and slow.In addition, it is good mainly for products that have thin peels and those with uniform internal structure.

2. Based onForce-Deformation Mechanical force-deformation is the conventional means of evaluating the firmness of fruits and vegetables.The instruments measure either force or deformation or both (dynamic force-deformation). For nondestructive firmness evaluation, the force applied needs to be at a level that results in no damage to the product. Mehlschau et al. (20) measured the maturity of pears nondestructively using two 19 mm diameter steel balls to apply a constant force against the opposite sides of the fruit. A portable device developed by Timm et al. (21) employed two parallel surfaces to slightly compress the fruit to measure the firmness of sweet and tart cherries, blueberries, and strawberries. The device was mounted on an aluminum frame on which a stepping motor (incremented 0.1 mm per pulse at a constant rate of 8.85 m/s) was used to control speed. A computer was used to control the stepping motor and to acquire data from a load cell. it Mizrach et al. (22) developed a “mechanical thumb” device and used to measure force-deformation of tomatoes and oranges. The method is based on creating a slight elastic deformation of the fruit peel using a spring-loaded pin 3 mm in diameter. Two models of this device were tested, one operated in a go-no-go” mode in which a fruit peel deformed as the pin penetrated into the fruit and caused the movement of a pivot arm against a spring adjustedby a load presetscrew. An adjustable small movement (e.g.. 0.2 mm) of the pivot arm is adactivates a microswitch, thus preventing damage to the fruit. The spring justed to a certain cut-off stiffness value that is equivalent to the stiffness of a fruit of desiredfirmness.Fruit is consideredfirmwhentherecordedforce is greater than the predetermined spring load. The other mode of the device allows continuous deformation of the peel with a pin connected to a flat spring to which a strain gage is bonded. The deflection of the flat spring under load indicated fruitfirmness,withfirmerfruitsshowinghigherdeflection of thespringand higher output voltage from the strain gage. The two sensing devices described above were placed on a sorting line and tested with oranges and tomato fruits “

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with good results. The on-line system was implemented onan endless conveyor, which moved the fruits towards the sensing device attached to a pivot arm that was free to move up or down depending on the fruit size. Takao and Ohmori (23) implemented a similar concept an foroff-line computer-automated system using either deformation for a preset force or maximum on kiwifruit and melon. force for a preset deformation. Tests were conducted They used different compressing plunger shapes and diameters to accommodate different fruit shapes and diameters, respectively. The plunger speed was set at 50 mm/min and the plunger diameter ranged from 8 to 12 mm. to nondestructively Botta (24) used a unique force-deformation concept measure the firmness (and resilience) of Atlantic cod fillets, which could also be adapted for fruits and vegetables. A portable instrument was developed to apply a force of 100 mN at 20 mm/s to just touch the surface of the product placed on a rigid flat plane, using a 2.5 cm diameter probe.The force was then increased Di) the prodto 4.9 N, and the probe was made to depress (deformation distance, uct for one second. The force was then removed and the product was allowed D,, was measured. A texture to rebound for one second, and the rebound distance, (firmness and resilience) index (D,/D,) was then calculated. Davie et al. (25) used a digital micrometer for nondestructive measurement of kiwifruit firmness. A 5 mm diameter hemisphere probe was attached near the end of a horizontal lever such that it could depress a fruit placed under it. The mass of the lever and its assembly on the product side are counterbalanced using two weights (0.55 kg for coarse adjustment and 0.031 kg for fine adjustment) attached to metal rectangular channel made to slide along the part of the lever on the opposite side of the cross bar. A digital micrometer was mounted between the probe and the central pivot (micrometer spindle was 217 mm from the central pivot and 63 mm from the probe), with its tip touching the top surface of the lever. The resting forceof the probe on the fruit surfacewas adjusted to a slightly positive value using the coarse andfine weights. After zeroingof the micrometer, a mass of 0.1 kg was applied on the probe manually and the deformation was (SC) was calcurecorded every 5 seconds for 30 seconds. The softness coefficient lated as the slope of deformation versus natural logarithm of time. Tests with 28 fruits were correlated with Effegi penetrometer firmness (f) using the following equation:

where k, and k2 were coefficients.In a second prototype, they (25) used a digital micrometer fitted with a motorized system to raise or lower the probe ( 5 mm diameter hemisphere). After contacting the fruit surface, the micrometer was preset to zero and a mass of 0.1 kg was applied. A computer and data logger were

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used to acquire a burst of three micrometer deformation readings per second for 5 seconds. Dawson (26) described a nondestructive firmness tester for kiwifruit based on the displacementof a small mass pushed against the fruit by a spring. A device for the measurement of berry firmness was reported by Armstrong et al. (27). A 230 mm diameter turntable that had 25 spherical indentations was driven by a stepper motor with precise 0.13 mm/step. A fruit wasplaced into each indenture that had a small hole in the center by which a vacuum was applied to ensure the to the stepper motor had a plate by berry did not move. A load cell connected which compression was applied to the fruit and the load cell signal after amplification was digitally convertedby a data acquisition system. All 25 measurements could be completed in one minute. All of the above-discussed methods directly measure the force and the corresponding deformation. However, Abbott and Massie (28) developed a system to apply compressive force dynamically to apple and used the dynamic forcedeformation data in the range of 40-440 Hz to obtain a predictor of firmness. A 1.25 cm diameter force transducer was statically loaded against the apple with 8.8 N. A two-channel dynamic signal analyzer generateda sine wave sweep(40440 Hz), which drove an electrodynamic vibrator through an amplifier. A signal analyzerthencapturedthetimedomainsignalsfrombothforcetransducers attached to an accelerometer, and the frequency response (ratio of output (force) to input (accelerometer)) was used to calculate dynamic force/deformation from each test. The main advantage of force-deformation methods is that results give a direct measure of product firmness. However, most direct contact methods have limited speed and require mechanical contact with the product, thus increasing the chances of cross-contamination of the bulk.

3. Based on Impact Two general concepts havebeen adopted by different researchers, who have measured the firmness of fruits and vegetables by the impact phenomenon. In the first mode, the fruit is allowed to fall through space and impact a rigid surface, often fitted with a force transducer. In the second mode, an impacter is made to strike the product. Variations of these two concepts have also been explored by also found that the impact of a fruit some researchers. Many researchers have on a rigid surface can be closely modeled by the impact of an elastic sphere and that the firmness of a fruit could be obtained based on the impact force response. Delwiche et al. (29) sorted peaches and pears into hard, firm, and soft categories by analyzing the force from the fruit impacting a plate supported by force a rigid steel transducers. The impact force was measured when fruit impacted plate measuring 50 mm X 50 mm X 9 m m to which a piezoelectric force trans-

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ducer was attached. The transducer output was sampled at 1 1.62 kHz by a spectrum analyzer. Technological advances have enabled greater ease in acquiring, analyzing, and modeling of impact data required for improved sorting systems. Lichtensteiger et al.(30)investigatedimpactresponseforsortingtomatoesandother to controlthe sphericalviscoelasticobjects by incorporatingavacuumpump release of a sample onto an aluminum plate (3.75 cm in diameter and 0.32 cm thick) glued to the top of the force transducer to increase the impact area and to lower the natural frequency from 70 to 10.5 kHz. Rigney et al. (31) developed an experimental machine to orient peaches for on-line quality measurement as an aid to more consistent results where a precise presentation of the fruit is needed. Also, to overcome the variability aris(32) developed ing from the influenceof angle and location of impact, Ozer et al. a method based on multiple impacts. In their extensive study of nondestructive firmness measurement of cantaloupes, Ozer et al. (32) used a drop test from a fixed height of 2 cm to an impact surface. The impact response data of the fruit was captured by a force transducer at a sampling rate of 10 kHz. The same fruits were subjected to four multiple impact tests starting at the groundspot and moving along the equator to locate two other points, and the fourth was at the blossom end.Foreachimpact,theimpactparameter, FT, wascalculated to represent firmness:

where P, is the amplitude of the impact response (V), T,, is the starting time, and T,, is the end time of the impact, s. Theconcept of multipleimpacts on thesamefruitwasautomated by on afruitfirmnessgraderforkiwifruit(Softsort SchaareandMcGlone(33) grader) based on four impact stages on specially designed concave steps as the into the receiving fruit tumbled down an inclined chute. Unsorted fruit was fed conveyor, which moved the fruits through a single lane over inspection rollers where an operator removed fruit with external defects. The whole (unblemished) fruit thentumbleddownaninclinedchutecomprisingfourseparateconcave steps, each vibration-isolated (with sheets of rubber foam) from the grader frame. Each concave step served as an impact sensor formed by folding an aluminum sheet over a heavy steel base.A piezoelectric film sensor was glued to the underside of the aluminum step to sense the flexure of the aluminum during fruit impacts. The fruits were released at intervals to ensure that only one fruit was on the sensor at one time. The voltage producedby the piezoelectric film as the fruit impacts on a sensor is amplified and processed by a microcontroller attached to each of the four sensors. The four microcontrollers transmit firmness measurements to a dedicated microcontroller, which runs the grader.

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Bajema and Hyde (34) provided a guide for determining the suitability of an impact-based nondestructive method for fruits and vegetables. They developed an instrumented pendulum (3 m high) to study impact phenomena. The device was equipped to record force and contact area profiles during impact at the rate of I O kHz onto a flat rigid anvil. The anvil was fitted with a force transducer and with a contact areasensor. Two precisely spaced infrared beams (beam interrupted by the sample) allowed approach and rebound velocities to be measured. An accelerometer attached to the trailing end of the product measured deceleration during impact and free vibrations during rebound. The impact profile data were sampled at I O kHz per channel. Using the setup, they developed schemes for impact sensitivity for fruits and vegetables. Operating procedures for single impacts, constant height multiple impacts (for determining bruise resistance), and (to determine bruise threshold height) were increasing height multiple impact discussed. Chen et al. (35) used an impact-testing system fitted with an accelerometer and electromagnet and a computer-based data acquisition system to measure the firmness of pears. Based on the theoryof elasticity, they determined that the time (t) to reach peak force was given by DIV

t =

1.47

where D is maximum deformation and V is the relative velocity assuming negligible gravitational effect during impact. The peak acceleration (A) of the impactor was expressed as

(3) of approach

A = Flm, where F is the peak impact force and m, is the mass of the impactor. The impact force F depends on impact velocity V, mass of the impactor, mass of the fruit, fruit modulus of elasticity, Poison’s ratio of fruit (0.49), radii of curvature of fruit (35 mm), and impactor (9.5 mm). The firmness of the tested product was defined as the ratio A h . The impact method can be high speed. and some studies have demonstrated good firmness sorting results; e.g., Chen et al. (35) achieved good sorting ability on a prototype packing line by measuring the deceleration of a low mass impactor. However, a major problem associated with the measurement of firmness by the impact principle is the uncertainty of the angle and location of the impact on the fruit surface, especially when such surfaces are irregular.

4. Based on Impact-Rebound In the preceding section, it was shown how some researchers have used the reit, to directly measure firmness sponse of a product to an impact either on, or from of the product. A unique variantof this principle utilizes the characteristic trajec-

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tory information generated when a product is allowed to rebound following an impact. This principle of impact-rebound has shown some promise for firmness evaluation. A rotating steel drum method based on impact-rebound has been used for (36). Inthesestudies,oranges separatingsoftorangesfromundamagedones moved in lanes created by 0.3 cm thick, 5 cm high dividers on a belt conveyor inclined at 15" to the vertical. At the top of the conveyor, the fruits were allowed to descend in a freefall on to a rotating (30-130 rpm) steel drum and bounce of different firmness into a horizontal conveyor padded with soft rubber. Oranges grades assumed different trajectories, and the horizontal bounce position on the conveyor provided a means of separating unwholesome or cull fruit from the (37) bulk. A patented design of this concept for separating clods from onions using either forward- or backward-bouncing modes was developed. The separation efficiency (E) was determined as the product of recovery and rejection as given below:

where 0, is the number of onions in product exit, 0, the number of onions rejected, C, the number of clods in reject exit, andC , the number of clods remaining with product. They also used a similar system to separate clods from potatoes (38). Although the impact-rebound method has the advantage of high capacity, it is unsuitable for products having a wide variation in shape. In addition, it is unsuitable for delicate products.

5. BasedonVibrationalCharacteristics The vibrational characteristics of fruits and vegetables depend on their elastic modulus (firmness), mass, and geometry.A form of impact-rebound is generated when fruits are placed on a vibrating surface, and this principle has been studied as a means of sensing firmness. In a review of nondestructive firmness measurement, Chen and Sun (12) found that prototype equipment had been built by Bower and Rohrbach (39) for sorting blueberries and grapes by low-frequency vibration (200 Hz). The fruit was placed in an L-shaped trough, one side of which was vibrated at a fixed frequency and amplitude of 200 Hz and 0.3 mm, respectively. Firm fruit bounced outof the vibrating trough and intoa collecting trough, while softer fruit was conveyed along the trough by the vibration. The problem of mechanical contact between the fruit and sensor appears to have beenaddressed in a recentreport (40) on a uniquecommercialfruit firmness sorter for apples, nectarines, and kiwifruit. The machine is comprised of the fruit of a conveying system, which allows nondestructive physical contact

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and a sensor finger while the fruit was “on-the-go” at speeds of 2.5-7.5 fruits/ s per lane. An electrodynamic shaker wasused to vibrationally excite the bottom part of the fruit. The root mean square (RMS) of the input signal was measured in the shaker head while the RMS of the output signal (fruit response) was picked up with a miniature accelerometer attached to the sensor finger contacting the top of the fruit. A firmness index (PFT) was defined as PFT = X,/(X,,

-

X,)

where X,, and X, were the input and output RMS signals. Larger PFT indicated firmer fruits, because softer fruits tended to attenuate the input vibrational energy, while firm fruits pass a larger portion of the input vibrational energy. Vibrational methods have the advantage of firmness evaluations based on a response from the whole item. However, they can be limited by the influence of geometrical differences, such as size. There is also therisk of stimulating whole-fruit softening damage, depending on the regime of vibrational frequency and amplitude employed. Also, a technical problem that needs to be solved is theabilitytomaintaingoodmechanicalcontactbetweentheproductandthe sensor, since this is necessary to faithfully transfer the product response signal to the sensor.

6. Based on Soft Touch Difficulties and the destructive nature of the MT method of firmness measurement have led researchers to develop nondestructive methods for replacing the technique. Perry (41) designed and tested a nondestructive firmness laboratory device to releasecompressedair(62mPa) forpeaches. A solenoidvalvewasused the fruit being through an air hose into two pressure cells on opposite sides of tested. The pressure cell was made with a transparent Plexiglas cylinder (6.4 cm a silicone rubber diaphragm at internal diameter, 0.32 cm wall thickness) with the opposite end of the cylinder for holding the fruit and preventing air leakage. Adialindicatorwaspositionedinsidethecylinderwithitsstemprotruding through the diaphragm and resting lightly on the fruit surface. Test data were acquired by releasing compressed air into the cells for a specified time interval and recording the deformation (indicator of firmness) of the fruit as indicated by the dial gages. Also, Thai (42) developed a novel nondestructive firmness detector, which used a soft foam transducer/tactile sensor (STTS) to evaluate apples, peaches, and tomatoes. The fruit was dropped onto theSTTS sensor (thickness 1.27-6.35 mm) from a height of 5 cm. The change in electrical resistance at contact was measured by putting a known and fixed resistorin series with it. Signal collection (acquisition rate of 8 kHz) was triggered by an near-infrared (NIR) beam gener-

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ated by light-emitting diodes placed about 6.35 mm above the sensor. Firmness was determined by the change in electrical resistance. The main advantage of the soft touch methodis that it is intrinsically nondestructive. However, it is slow and difficult to integrate on-line, and in addition, different product size may affect accuracy.

C. NondestructiveMethodsBasedonSecondary Properties Themeasurement of secondaryproperties (not force-deformation)greatlyincreases the number of possible approaches. Most of the research has been on resonant frequencies, acoustic response, and ultrasonic methods. A major advantage of firmness methods based on secondary properties is that they evaluate the whole product. Some applications can also utilize remote sensing with no physical contact with the product. However, general limitations include the fact that some of the properties may not phenomenally relate to firmness, and i n some methods product size may affect the readings. Also, in some applications the cost of equipment may be high. The challenge with the firmness-sensing methods using secondary properties is either to develop devices that are independently accurate or to develop methods having very high correlations with the results obtained with force-deformation relationships of the product that are important to buyers and end users. Also, the speed of methods that require mechanical contact or signal processing A technical complication should be addressed for on-line sorting applications. of NMR is the need to avoid metallic objects near the vicinity of the sensor.

1. Based on Acoustic Response Muramatsu et al. (43) used acoustic vibration t o measure kiwifruit firmness using a pulsed sound, one-half wavelength at1 KHz. The sound generatedby a function synthesizer was transmitted to the lower surface of the fruit by a 3 cm diameter speaker. A small contact microphone was attached to the opposite surface of the fruit. To facilitate sound transfer, a small amount of clay (or athin sheet of rubber, I mm thick) was placed directly between the fruit and speaker. The transmitted wave was monitored with a storage oscilloscope and displayed together with the imposed wave. The extent of wave displacement was usedto estimate the elapsed time for sound transmission through the fruit, which was proportional to firmness. The elapsed time for sound transmission through the fruit increased as fruit became softer. Sugiyalna et al. (44) developed a portable firmness tester for melons based on the acoustic response of the fruit to an impulse. They used a mechanical impulse generator-a computer equipped with an analog-to-digital(A/D) converter

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to measure sound transmission velocity using two microphones placed on the melon surface 16 mm apart. The sound signals from the two microphones were amplified (gain of 50) and transmittedto the A/D converterat sampling frequency of 70 kHz per channel. To ensure high values of signal-to-noise ratio, a long impact rod isolated noise due to friction between the spring and the barrel of the impacter was used. The rod and barrel were made with resin (polyacetal), which reduced the amount of noise generated when the microphones were placed next to the impact point. The average time difference between the two sound signals (t,l) from the microphones was determined;

where id is the number of data points, t, ( = 14.2 ps) is the sampling period (70 kHz sampling/channel), and 0.5 was added t o account for the difference in sampling time between the two channels caused by the multiplexer of the A/D converter. Sound transmission velocity (V,l) was expressed as;

Vd = d/t, where d is the distance between the microphones. The average of five measurements of Vd from different locations on each fruit was determined and used as the nondestructive firmness.

2. Based on Sonic and Ultrasonic Technique Finney (45-47) demonstrated that subjecting peaches to a random-noise generator or a beat-frequency sine-wave generator and that measuring the amplitude of vibration of the fruit can provide data for sensing the fruit firmness. Amplitude measurements on the blush and nonblush cheeks of each fruit were made over a frequency range of 250 to 10 kHz. The second and third natural resonances (fn=2,1r3) correlated well with fruit MT firmness penetrometer tests. Abbott and Liljedahl (48) conducted sonic measurements on apple fruits to determine their firmness. Intact apples were mounted horizontallyusing floral to an electromagnetic vibrator. An clay on a small aluminum pedestal attached electronic signal equivalentto a 5 to 2 kHz sinusoidal scan from a signal generator was fed to a power amplifier to drive the vibrator’s electromagnet. The resulting pulse caused input vibrations of constant acceleration within the imposed frequency domain, which was measured with an accelerometer weighing 0.5 g held against the opposite sideof the fruit with an open cell polyurethane sponge under an arm. The amplitude and frequency spectrum contentof the accelerometer signal was extracted by FFT with a resolution of 5 Hz. Apple stiffness coefficients defined as f’m and f’m’’3 (m = mass and f = frequency, calculated from either the second or third resonances f2 and fJ were used to indicate firmness. They

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found that frequencies of resonant modes 2 and 3 are closely correlated (r = 0.95) and seldom varied by more than 3%. Firmness was also evaluated by the ultrasonic technique at an appropriate frequency range and power level (49) to evaluate different fruit-tissue specimens (50,51) and whole melon and avocado (49,52). Numerous researchers investigating fruit firmness by frequency analysis have used the stiffness coefficient (f’m or f’ m2’3)to represent the firmness. However, Galili et al. (52) developed a new frequency-related firmness parameter, F,, which is the centroid of the frequency response. Mathematically, F, was expressed as

where f, and H, are the local values of frequency and amplitude of the signal, respectively, and n is the total number of data points. The meritof this parameter is that it does not depend on the specific resonant frequency, but uses the entire spectrum of frequency response. Mizrach et al. (50) measured nondestructive firmness of mango fruit by A high-power, lowmeans of ultrasonic probes in contact with the fruit peel. frequency ultrasonic pulser-receiver, a pairof 50 kHz ultrasonic transducers, and a microcomputer and data acquisition system were used to generate the signal and to collect data. The ultrasonic head with a transmitter-receiver system allowed transmission and reception of ultrasonic signals, which passed through the peel and the fruit tissue next to the peel. Exponential-type plexiglass beam-focusing elements were used to reduce the beam diameter of each transducer on the fruit. (A, dB/ They found that firmness (FR, N) can be calculated from attenuation mm) as FR = 48.21A2 - 408.71A

+ 878.16

(10)

3. Other Methods Recently, nondestructive texture measurement of kiwifruit, pear, and peaches by (LDV) hasbeenreported (53). Individualfruitwas laserdopplervibrometer placed on a vibration generator, and a small amount of clay was applied directly between the fruit and vibrator stageto ensure integrity of vibration transmission. The vibrator generated inputs to the fruit in the frequency range of 5-2000 Hz at 0.5 Hz increments. A laser beam reflected from the fruit surface to the sensor head was used to measure the vibration at the upper surface of the fruit. A fast to identify the phase shift between Fourier transform (FFT) analyzer was used

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the input and output of the vibrational signal. A phase shift at either 1200 or 1600 Hz was used to indicate firmness. Also, Muramatsu et al. (54)used LDV to measure the firmness of kiwifruit and apples. They found that using the accelerFFT ation output as reference signal gave more accurate results than using a output signal as in their earlier study. Some research has been done relating firmness to NMR data of fruit. NMR methods are based on a processof magnetic relaxation following external excitation of a fruit. The signal induced in the magnetic coil decays exponentially with time. The relaxation data could be expressed in two forms, butmore the important mechanism is known as transverse relaxation (55). In a recent review article, Clark et al. (56) gave an overview of magnetic resonance imaging (MRI), including NMR applications in postharvest handling and quality evaluation of fruits and vegetables. Kim et al. (55) investigated the feasibility of using magnetic resonance imaging to determine the firmness of tomato. The principle of these techniques is based on many proton-containing species, in particular hydrogen atoms in fruits and vegetables, which are capable of absorbing radio frequency energy.In general, fruits were subjectedto magnetic to decay exporesonance, and the signal induced in the receiver coil was allowed nentially with respect to time. The relaxation allowed the spins to return to their equilibrium levels, releasing their surplus energy into the surroundings. The spectral data can be analyzed by two relaxation mechanisms. Longitudinal relaxation (spin lattice relaxation) was expressed by (55) as

where, MY is the initial amount of longitudinal magnetization, M(t), is the magnetic moment at a certain time t, and T , is the spin-lattice time constant. Transverse relaxation (spin-spin relaxation) is given by

where Mf is the initial amount of transverse magnetization, M(t), is magnetic (55) moment at a certain time t, and T2 is spin-spin time constant. Kim et al. measured the Ti and T2time constants usinga 0.6 T machine and bird cage coil. The terms l/Tz and In (Tz) were used to represent the fruit firmness. Another unique method for fruit firmness measurement involves the useof delayed light emission (DLE). In a review by Gunasekaran (57) it was reported of apricots and that DLE has been used successfully to measure the firmness of the method include papayas (58). However, many factors influencing accuracy wavelength of excitation, excitation intensity, time during and after excitation, length of dark period during which DLE decays, sample size and surface characteristics, temperature, and chlorophyll content. Also, NIR (near-infrared) spec-

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troscopy in the range of 380-1650 nm has been used to nondestructively estimate the firmness of apples (59).

D. FactorsAffectingFirmnessMeasurement 1.

Temperature

Often researchers simply conduct firmness tests at prevailing room temperature conditions or, at best, stipulate the temperature at which the measurements should be made. However, published data show that the temperature of fruit has a significant effect on the measured firmness. Bourne (10) proposed that temperature effect can be characterized by a firmness-temperature coefficient (KIT, %/“C):

where, f , and f2 are firmness at T, and T2, the lowest and highest temperatures, respectively. Jeffery and Banks (60) adopted this concept and obtained equations to evaluate the effect of temperature on the firmness of kiwifruit measured destructively at 0 and 20°C. They also found that fruit origins had no effect on the relations between KITand f , or f2, suggesting that within thescope of experimental to be used for fruits from a variety of error, the models may be robust enough sources. In addition,temperaturesbelowzeromayyieldfirmnessvalues that differ significantly from those measured at higher temperatures. Jackman and rigidity of refrigerated Stanley (61) found that the increased shear strength and tomatoes may be dueto a chilling injury mechanism in which the cell wall structure binds together firmly.

2. Composition and Structure Variation of mechanical properties within a fruit, generally determined with tissue samples, may also be observed due to internal variation of the fruit tissue. to measure The importance of thevariationmaydependonthemethodused et al. (30) found that “shelleffect” may have firmness.sinceLichtensteiger relative significance on a measurement. In the case where the core/inner layers are softer than the relatively thin external shell, a sudden change in the slope of the force-time curve occurred shortly after contact with the object. Lichtensteiger et al. (30), in their impact force analysis study on tomatoes and blueberries from drop height of 100 mm, found that the skin layer of the fruit may create what is generally known as a “shell effect.” They confirmed that this effect is more prominent when the internal structure is softer than a relatively thin external shell and less prominent when the internal elements are stiffer than the shell.

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The shell effect was also found by Ezeike and Otten (62) in cotyledonous seeds where an air layer separates the outer cotyledon from the seed. Abbott and Lu (63) found that the stress, strain, energy at failure, and Young’s modulus of apples varied with orientation at point of measurement and location within the fruit. Young’s modulus was higher in radial samples than in tangential and vertical samples. They also found that fruit variety was a contributing factor. For example,DeliciousapplesshowedgreatervariabilitythanGoldenandRome apples. Similar findings were reported by Maness and Brusewitz (64), who mea8 sured peach firmness using an Effegi penetrometer equipped with a standard mm diameter probe, on samples taken from inner, middle, and outer regions of the mesocarp at four angular positions around each peach half. Inner mesocarp was firmer than outer mesocarp. Variations were also found between middle and outer regions of the mesocarp and were cultivar dependent. Firmness was found to decrease longitudinally from the stem endto the blossom end and latitudinally from the suture to the cheeks. Weexpect that firmnessmethodsbased on sensingtheresponse of the outermost layers of fruit may be subject to the shell effect if the fruit has a structure with distinct outer and inner tissues. On the other hand, firmness methods that excite the whole fruit (e.g., acoustic methods) are less subject to the shell effect.

3. OtherFactors In their study of effects of mass and drop heighton kiwifruit firmness by impact, (a measure of impact McGlone et al. (65) developed equations for dwell time duration) and peak force and found that the firmness parameter Cz (= f&, f,, is the peak force and t, the dwell time) was independent of mass of the fruit. They also found that fruit was not damaged even with repeated impacts at 50 mm. Chen and De Baerdemaeker (66) conducted studies with apples and Litchtensteiger et al. (30) with spherical viscoelastic objects and tomatoes to optimize the parameters involved in impact firmness testing. These parameters also affect the performance of different firmness methods, which include impactor mass, fruit mass, internaUexternal properties, maturity/damping, calculation method, initial contact velocity, flat contact surface, elasticity of material, and stiffness factor.

111.

NO MECHANICALCONTACTWITHPRODUCT

A.

Concept

A nondestructive, noncontact method for measuring the firmness of food and other products was developed at the Universityof Georgia by Prussia et al. (67). The laser air-puff food firmness detector uses a brief puff of compressed air to

260

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deform the product surface about one millimeter. A laser displacement sensor provides a rapid and accurate measurement of the deformation. At a fixed air of a firm product is less than for a soft pressure, the maximum deformation product. When changing to a different type of product with a different firmness range, the pressure is easily adjusted to the level needed to causea deformation slightly less than necessary to cause damage. For example, a pressure setting of 70 kPa is common for soft peaches, while 400 kPa is needed for evaluating of apples.

B. Original Design Extensive tests with the first prototype verified the validity of the laser air-puff in its accuracy and repeatability for a wide concept and provided confidence range of fruits, vegetables,and other products (68). Compressed air was supplied through a pressure regulator to a tank located directly over pilot a actuated valve with an attached 9.5 mm diameter nozzle (Fig. 2). A laser displacement sensor was mounted at a 30" angle to the nozzle and aimed at the top of the product that was supported under the nozzle in a specially designed holder with vertical adjustment. A timer circuit controlled the duration of the puff (valve opening) after a switch activated the circuit. The amount of deformation caused by the compressed air was measured using a laser displacement sensor and recorded with a digital oscilloscope. A second pressure regulator on the main air supply assured that consistent pressure was delivered to the pilot side of the pilot actuated valve (ensuring repeatable speed of opening). A large tank (8 L) was used to minimize the drop in pressure while the valve was open for 200 ms to produce the puff of air. The laser displacement sensor (Keyence model LB 041) was mounted 40 mm from the product surface (stand off distance). The laser beam was aimed at the center of the deformation when the product was 2 cm from the nozzle. Vertical adjustment was provided for the product holder to assure that the top surface was at the set point regardless of variations in product size. Spherical objects were supported by three cups about1 cm in diameter and with a radiusof curvature slightly larger than the object. The cups were supported on a ball-and-socket joint that allows movement of the cups for accommodating products of irregular shape. Several stepswere required to obtaina firmness measurement. Product was placed in the holder, positioned under the nozzle, and the height was adjusted until a voltmeter connected to the output of the laser displacement sensor was zero (indicating the nozzle-product distance was2 cm). The pressure in the tank was then set by adjusting the pressure regulator until the desired pressure was reached as indicated by a precise digital pressure sensor (1.7 kPa resolution). The switch for the solenoid valve was then pressed when the digital pressure sensor indicated the desired value.

Firmness-Measurement Methods

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Fig. 2 The original laser air-puff food firmness detector.

Output voltage from the laser displacement sensor was 5 5 V, corresponding to the displacement 2of5 mm. The typical voltage trace on a storage oscilloscope rapidly increased as the puff of air caused the product to deform, decreased slightly after reaching a maximum, and then rapidly decreased when the puff ended. Peak deformation was found by subtracting the final displacement from the maximum (the final displacement accounted for any movement in the holder that occurred after the initial value). Mean deformation values had to be estimated 4 mV noise representing from the oscilloscope trace, which typically had about a displacement of 4 pm and occasional spikes. Figure 3 shows that plots of maximum displacement versus tank pressure for measurements made on a rubber ball were extremely linear (R2= 0.997).

Hung et al.

262

3000 A

E 3 2500 * C

t

y = 6.51ffiX - 295.78

2000 0

5m 1500 6 5 1000

-E i!

500

o f 0

100

200

I

,

300

400

500

Tank Pressure (kPa) Fig. 3 Effect of puffof air pressure on maximum displacement of a rubber ball.

Plots obtained for fruits and vegetables were also linear. Repeatable results over a wide range of products and conditions indicated that the concept and design was successful. to holdthemainstructure, A 1 m X 2 mtableorbenchwasrequired oscilloscope, laser control box, electronics box, and volt meter. Although it was it was somewhat difficult at 90 kg and with possible to move the equipment, several electrical connections. Due to the large size and complicated operating procedures of thefirst prototype, theunit was redesigned to facilitate easier operation.

C. Improved Design Figure 4 shows the improved prototype design of the laser air-puff unit, which is about the size of a tower computer. The componentsof the system are similar to the original prototype except that they are smaller in size and with electronic interfaces. A notebook computer is used for controlling a pressure regulator and a solenoid valve, for acquiring data from the laser displacement sensor, for anain a spreadsheet format. The software is lyzing the data, and for storing data a visual programming package called LabView from National Instruments. A PCMCIA card interfaces the computer with the hardware. The fruit sample holder shown in Fig. 4 is a new design that uses three balls to support the product rather than three cups as in the first prototype. The

Methods

Firrnness-Measurement

263

Fig. 4 The improved prototypeof the laser air-puffuses anotebook computer to control the measurements and to analyze and store the data.

cups had allowed some movement, which was only partially compensated by for calculating maximum displacement from the final rather than the initial displaceof movements. The purpose of using the balls is to allow a repeatable amount ment rather than trying to eliminate all movement of the product. The advantages of the laser air-puff approach include no contactwith the product, high speed, and good accuracy. However, it is limited by its cost (about $lO,OOO) and the need for an improved sample holder.

IV. TOOLSFOREVALUATINGFIRMNESSMEASUREMENT Nondestructive methods for detecting firmness are typically evaluated by comparing results with the destructive MT test. For over 75 years the MT test has been the standard for measuring firmness. One of two plungers (7.9 or 1 1.1 mm in diameter) with a specific convex tip is inserted into the product to a depth 7.9 of mm. The maximum penetration force is the value that represents the firmness. Results should specify the plunger to be used and the maximum force (not pressure).

Hung et al.

264

Comparing a nondestructive measurement of firmness with the results of the destructiveMT test has obvious difficulties. First, different physical phenomena occur during a nondestructive, viscoelastic deformation comparedto the destructive rupturing of tissues and cells for the MT penetrometer test. Comparing results when different physical properties are evaluated is somewhat like comparing apples with oranges. For example, McGlone et (69) al. observed some anomalies in firmness of kiwifruit measured nondestructively by impact force analysis of I O mm, during medium to long periods of cool storage from a drop height compared to firmness determined by penetrometer method. They found that the softening curve of kiwifruit stored at refrigerated temperatures showed an unexpected plateau and/or rise during medium to long periods of cool storage, but not during room temperature softening (between 15 and 25°C). The anomalous behavior was attributed to the fact that impact force response was closely correlated with whole fruit stiffness, a fruit property that is different from the flesh rupture force measured by the penetrometer. Whole fruit stiffness is usually measured by parallel plate compression and can remain constant for long periods, or even increase slightly, while the flesh rupture force measured by MT continues to decrease. A second difficulty is that the MT test is a measurement method that has about the same correlation with established engineering properties as other instruments. Thus, comparison of the instrument with the MT results in even lower correlations.

A.

Modulus of Elasticity

To avoid the problems mentioned above, one solution is to use an engineering property that can be calculated from different firmness-sensing methods. Fortunately, modulus of elasticity (E) can be used as such a standard. The American Society of Agricultural Engineers (70) gave the following expression for compression test of food materials of convex shape:

where

EASAE = modulus of elasticity, MPa R , = maximum radius of the tested spot, m R; = minimum radius of the tested spot, m d F D p

diameter of the indenter, m force applied, N deformation caused by the force = Poisson's ratio

=

= =

F, m

Firmness-Measurement Methods

265

The value for E found withthe ASAE procedure can be checked with other methods such as the removal of a specimen of known shape, which allows direct calculation of E from cross-sectional area, force, and deformation data. A similar equation for E was developed by Fan (68) for the laser air-puff method as

where ELASER= modulus of elasticity, MPa p = Poisson’s ratio P = pressure in air tank, MPa V = maximum voltage reading from laser sensor,

mV

Extremely high correlations (R2 = 0.92) were obtained between values of EAsAE and ELarrrfor peaches as shown in Fig. 5. The linear regression of the data also had a slope closeto 1.0 (1.08), and the intercept was reasonable(0.241 MPa).

2.5

2

1.5

y 1 . 0 8 ~+ 0.241

1

R z = 0.Q2lS

0

0.5

1

1.5

2

2.5

EASAE (MPa) Fig. 5 Correlation of modulus of elasticity calculated from the laser air-puff firmnesssensing data (ELASER) versus modulus of elasticity calculated from the deformation data using a spherical indenter (EASAE). (unpublished data)

266

Hung et ai.

A similar study with apples by Fan (68) also had a high correlation between the two methods for calculating E (R’ = 0.91). New Zealand researchers (65) also had extremely high correlations (R? = 0.94) for kiwifruit between E values calculated from parallel plate compression and values for E calculated from an equation using drop height and dwell time when the fruit impacted their SoftSense instrument. The New Zealand researchers (65) expressed the whole fruit stiffness (E) based on the parallel plate method (ASAE Standard S368.1) with the Instron Universal Testing machine as

where F is the compression force, r is the radius of curvature of fruit, and D is the sample deformation corresponding to the force. Also, their equation for Softsense (EJ stiffness calculated from Herz contact theory was

where mis the fruit mass, is r the radius of curvature of the impacted fruit surface, td is impact dwell time, h is the drop height, and p is the Poisson ratio (0.49 being considered appropriate for a hard fruit).

6. RegressionAnalysis Resultsfrom a nondestructive test areusuallycomparedwiththe MT test or MT or other tests on another method by regression analysis. Results from the products with a range of firmness values are typically plottedon the independent axis and values from the new method on the dependent axis.A correlation coefficient is used to judge the performanceof the new method. For biological products such as fruit, a correlation coefficient above 0.70 is considered good. However, correlation coefficients are affected by the number of data points, the range of the data, and the distribution of data over its range. A computer spread sheet program was developed for simulating correlations between the outa hypothetical new put of a hypothetical standard instrument and the output of instrument (71). The initial simulation had 100 data points uniformly distributed with a range of 12 N (4-16 N) on both the x and y axes. Each data point was given a random variation on each axis with a normal distribution having a mean atthepointandastandarddeviation of 1 N. Linear regression analyses gave differentresultsforslope,intercept,andcorrelationcoefficienteachtimethe simulation was run (much like comparisons of real instruments).

Firmness-Measurement Methods

267

Typical values for R' increased from 0.9 to 0.95 when the range of the to 16 N (4-20 uniformly distributed points was increased from the original 12 N). The typical values for R? decreased from 0.9 to about 0.8 when the ranges in the data points were determined by using 100 normally distributed points with a certain mean ( I O N) and standard deviation (23 N) (Fig. 6). There was very little change in R' values (typically 0.8) when the number of normally distributed data points was increased from 100 to 200. These results demonstratethat values to very firm fruit in a test. The for R' can be increased by including very soft range used for evaluating an instrument should be the same as the range that is important to the user. Regression analyses do provide a useful tool for making general comparisons between two firmness-measurement methods. However, rather than summary statistics, commercial operations such as sorting fruit at packing houses

0

20 2 18 4 16

614

012

10

Output of Standard Instrument (N) Fig. 6 Simulatedcomparisonofoutput of a newinstrumentwiththatofastandard instrumentusing 100 normallydistributedpoints(mean = 10 N; standarddeviation = "3 N). The precision of both the standard and the new instrument was also simulated by adjusting the position of each point in x- and y-directions according to normal distribution (mean = original value; standard deviation = 2 1 N). The boxes show calculations for hit, miss, false alarm, and correct acceptance rates for an arbitrary cut-off value of 6 N.

268

Hung et al.

depend on accurate differentiation among individual items with similar firmness values.

C.DiscriminateAnalysis The following description of discriminate analysis shows how individual items are evaluated for a quality characteristic. A firmness-sorting operationat a packinghouse has the purposeof removing items that are softer or firmerthan a specified level. There are only four possible outcomes for such an operation with either accept or reject decisions (72): Hit Miss False alarm Correct acceptance

Removing a defective item (too soft Allowing a defective item to pass Removing of a good item Allowing a good item to pass

or too firm)

Rates are calculated by dividing the four outcomes by the maximum number of hit rate is the itemsavailable in thecategorybeingsorted.Forexample,the number of defective items correctly removed dividedby the total number of defective items in the lot. The miss rate can be obtained as the complement of the hit rate or calculated by dividing the number of misses by the total number of defective items. Similarly, the false alarm rate is calculated by dividing the number of good items discarded by the total number of good items in the lot. Again, correct acceptance rate is the complement of the false alarm rate or the number of good items allowed to pass divided by the total number of good items. For a sorting operation the miss and false alarm rates are more important than the hit and correct acceptance rates. The miss rate indicates the percentage firm), while the false alarm of items in the final pack that are too soft (or too rate represents the percentage of good product that could have been sold at full price but was discarded. The calculations shownin Fig. 6 are examples of the simulated comparison of the output of a new instrument with thatof an existing (standard) method with an arbitrary firmness cut-off value of 6 N set for soft fruit. The comparison was made by plotting 100 normally distributed points (mean = 10 N; standard deviation = kc3 N). The precision of both the standard instrument and the new instrument was also simulated by adjusting the position of each point in both the x and y directions according to a normal distribution (mean at original point with a standard deviation of -C I N). The I O points to the left of the vertical line at 6 N represent soft fruit used for calculating the hit and miss rates. The 6 points below the horizontal line at 6 N and to the left of the vertical line represent hits. The hit rate of 60% was calculated by dividing the number of hits, 6, by the total number of soft fruit, IO. The miss rate of 40% is calculated by dividing the 4

Firmness-Measurement Methods

269

misses by the total number possible, 10. The number of false alarms was determined by counting the points to the right of the vertical line and below the horizontal line. The false alarm rate of 3.3% was calculated by dividing 3 by the 90 goodfruit (100 lessthe 10 softones).Similarly,thecorrectacceptancerate (96.7%) was calculated by dividing the number of pointsto the right of the vertical line and above the horizontal line, 87, by the total number of points to the right of the vertical line than, 90. The R’ value of 0.81 1 shown in Fig. 6 is reasonably high compared to or other biological products. Similar values many correlations involving food were obtained each time the simulation was run. However, the miss rates ranged 2 to from 10 to 30% for the same runs, while the false alarm rates ranged from 8%, indicating that high R2 values do not assure low miss and false alarm rates. The high miss rates indicate that poor sorting results can occur even when an instrument has high values of R’ compared with a standard, which also has vari2 1.0 N; Fig. 6). ability (such as MT-simulated with a standard deviation of Thus, it is understandable why a packinghouse manager is more interested in the number of soft fruit in a shipment than the R2 values from a comparison of two instruments. Discriminate analysis provides practical results for evaluating the performanceof a firmness-measurement method. However,it is still necessary to have a standard of comparison to determine the miss and false alarm rates.

V.

PERFORMANCEEVALUATIONOFDIFFERENT METHODS

Consumers often base their choice of product on their perception of the food quality assessed by their senseof smell, touch, and sight. Therefore, the relationship of objective (nondestructive) methods of quality evaluation need to also of this closely mirror subjective evaluations made by the human. In the remainder section. different methods usedby researchers recently to measure the firmness of fruits and vegetables are summarized. The information is presented in Tables I 3 showing the principle involved for each method reported and the typeof sensor system used to record the response. In addition, the products evaluated, measurement conditions, and performance factorsof each method are presented as well as the stageof development of the technique, along with the sourceof the reference.

A.

DestructiveFirmnessTestingMethods

Results of firmness measurements with the MT and Effe-Gi tests were shown by Abbott et al. (73) not to be entirely interchangeable, even though the probes and indicated force ranges were essentially the same. Different sizes and shapes of

Table 1 Firmness Measurement by Force-Deformation Methods (with and without mechanical contact) Principle Force deformation

Measurement Deformation Plunger

Product Tomato Tomato

Measurement condition Test 4 - 5 T 92-99% RH Test ambient

Oranges

Deformation Parallel plate

Moment

Deformation Puncture force Crushing strength

Kiwifruit

Test 0°C

Kiwifruit

Store room temp. and/or low pressure

Melon Blueberry

Cherries Blueberry Strawberry Store 2 1 "C. 57% RH Test room Apple temp. Cut specimen Pear Apple Kiwifruit

Laser air-puff Displacement and pressure

Store 2°C Test 2°C Store 12°C Test room temp. Store 0°C. 95% RH. Test 24'C

Peach Apple

Store 1°C Test room temp. Test room temp. Test room temp.

h)

Performance of device R' = 0.903 (device vs. penetrometer) cv. 0.17-0.28 Accuracy 100% (separation red from green tomatoes)

Status

Ref.

Research

83

Research, on-line prototype ( I 0 fruitds: 3.5 fruitds)

22

Accuracy 86% (device vs manual): cv. = 0.016-0.043 R' = 0.99 (device vs. Effegi penetrometer) R2 = 0.93 (device vs. fruit pressure tester value)

Research

25

Research and commercial

23

r

Commercial

27

Total misclassification rate of 30%

Research

21

cv = 0.03 to 0.133 (parallel plate)

Research

74

Research

17

Commercial prototype Patent research prototype

7 68

=

0.98 (devise vs. berry condition)

-l 0

cv. = 0.70 to 1.88 (puncture) R' = 0.98 (crushing strength vs. soluble solids) R2 = 0.86 (crushing strength vs. Effegi penetrometer) R' = 0.15 R 2 = 0.91 (modulus from laser v s . modulus from spherical indenter)

RH = relative humidity (5%); Store = stored at indicated temperatudcondition: Test = temperature during test: temp. = temperature.

I

e a

6

E

Methods

Firmness-Measurement

271

the two instruments as well as spring characteristics were thoughtto be responsible for the differences observed. In addition, operators reacted differently to the machines in anticipation of sudden failure. Lack of sphericity or roundness of in alignment of the fruit, the tester, and most fruits also presented difficulties compression surface. Oftenthe variability in measured results occurred by removing the skinto unrepeatable depths during sample preparation, since subcutaneous cells are smaller than those nearer the fruit center. Penetrometers in general are unable to assess flesh properties as a function of depth, and this becomes a problem for fruitsin which internal changes can occur, for example, columella softening in kiwifruit or central softening in mangoes.

B. NondestructiveForceMethods Methods for measuring the relationships between the deformation resulting from an applied force give a direct measure of firmness because the slope of a forcedeformation curve can be used to calculate the modulus of elasticity or stiffness of the product (Table 1). Mehlschau et al. (20) found a curvilinear relationship between firmness values andMT firmness. Typical characteristic curves for apple and pear were analyzedby nonlinear rheological models by Wan et al. (74) using apple and pear. Although the sorting rate was high (four fruitds), more accurate results were obtained for softer fruits than firmer ones. This is considered a major limitation of this system according to the study of fruit classification with nondestructive firmness index based on either weighted grade purity or contamination by Peleg (40). Ozer et ai.(32) found that for each impact, the impact parameter correlated well with flesh firmness by penetrometer (r = 0.86) and elastic modulus (r = 0.94). They also found that the same fruits can be subjected to four multiple impact tests without causing damage. Tissue damage after multiple drops at 40 mm height was evaluated to confirm that the test was nondestructive. They concluded that combination of multiple impact with a simple average gave better results than using multiple regression. They also claimed that random multiple impacts eliminate the need to specifically orient the fruit on a conveyor system and is fast for real time sorting (0.2 s). However, it is only suitable for fruits that do not bruise easily to ensure minimal damage during testing. A common difficulty for all the firmness-measurement methods based on nondestructive force is the need for mechanical contact between the product and the sensor. Mechanical devices can limit the speed of operation and can limit reliability. Speed is also limited in some of the approaches by the need to complete complicated analyses on the signal generated. There is also a potential problem of cross-contamination from one product to another when touched by the same probe.

Table 2 Firmness Measurement by Methods Based on Impact and Impact Rebound

h)

4

Princiole Impact of fruit on plate

Measurement Force Piezoelectric force sensor

Product Kiwifruit

Peach

Multiple impact of fruit on surface

Impact rebound by low-mass object on fruit

Measurement condition

Performance of device

Store 4°C Test 20°C Drop height 10-50 mm Test 23°C Drop height 50 mm

R? > 0.7 (device vs. Effegi penetrometer)

Research

65

r = 0.73-0.89 (device vs. Effegi) cv = 0.057-0.062 R' = 0.47-0.83 (device vs. penetrometer) Reject 89 2 3% of soft fruit (device) Reject 96 2 1% of soft fruit (human) r = 0.86 (device vs. Penetrometer) r = 0.94 (device vs. parallel plate) Accuracy = 7 8 4 9 % (device vs. Instron) Variability = 1.55% (device) S.D. = 12.9% (device) S.D. = 15.3% (Instron) R? = 0.78-0.84 (device vs. MT) No damage (20 g impactor) 32% damage for 59 g impactor at 2 cm

Research

84

Research

33

Research

32

Research

85

Research

86

Research

87

Kiwifruit

Test room temp.

Cantaloupe melons

Ambient Drop height 2 crn

Peaches

Test room temp. (20"C, 85% RH) Test 20°C Drop height 100 mm

Acceleration (mass impactor)

Cherry

Force Piezoelectric transducer

Pears

Store 0°C Test 2OoC, Drop height 2-4 cm Impact mass 10-50g

Status

Ref.

h,

z

3 u)

2 'u

00 00

m

m

o \ D

Firmness-Measurement Methods

!z I .

m

e,

*

3 E Q

273

274

Hung et at.

Acoustic resonance

Sound velocity Microphone

Melon

Test room temp. 2 mics, 6 cm apart

Store 2°C Test 20°C 2 rnic. 1.5 cm apan Kiwifruit Store 5°C Test 2 0 T Ethylene-treated Store 7 2 2°C. 4 0 4 0 % RH Peach Test 20°C I rnic Store 1°C. 96% RH Apple Test 20°C Tomato Store and test 20°C. I mic Store 1°C Apples Test 1°C I mic Store 20°C. 65% RH. Test 20°C Refleaion spectra IOOO-1650 nm Apple

Vibration Microphone Vibration Microphone

NIR spectroscopy

NMR (85.5 MHZ)

Microwave (0.2-20 GHz) Laser doppler

Reflectance spectra (visible light and PbS detectors) Signal decay (relaxation) Dielectric constant ( E ' ) and loss factor (el') Surface vibration Doppler laser

Avocado

Test rmm temp

Tomato Peach

Test r w m temp Test 23 f I T

Kiwifruit Store 5°C Test 20°C Store 10°C Peach Test 20°C Store 10°C Pea Test 20°C

r = 0.94 (transmission velocity vs. E) r = 0.87 (transmission velocity vs. force) r = 0.79 (transmission velocity vs. E) r = 0.89 (transmission velocity vs. MT)

research

98

Research

87

Trend i n ultrasonic elapsed time agreed with trend in penetrometer tests (60"probe)

Research 43

R'

Research 93

=

0 64-0.74 (device vs. Effigi)

n -'

!

(D

u)

'p u)

23 (D

R' = 0 84 (device vs. spherical probe) R: = 0 84 (device vs. parallel plate) R' = 0 7 6 (device vs E) R2 = 0.27 (device vs MT) r r

= =

0.75 (device vs. MT) 0 90 (device vs stiffness factor. f' m"')

Research 99

Research

100

n

u)

Research 59

Research

R:

Research

= 0 81-0.92 (device v 5 penetrometer. 30" cone probe)

=

!2

= 0 98 (oil/water resonance peak ratio vs. fruit dry weight) R' = 0 85 ( I IT2 spin-spin time constant vs MT) R' = 0.57-0.80 (device vs MT)

R'

3

101

Research 55 Research 102

53

Store = Stored at indicated temperature/condition; Test = temperature during test; Freq = frequency; mic = microphone; temp. = temperature: NMR = nuclear magnetic resonance; PMR = proton magnetic resonance; NIR = near-infrared

h)

-I

vl

276

Hung et al.

C. MethodsBasedonSecondaryProperties A number of methods basedon indirect measurement of fruit firmness have been developed and investigatedby several researchers. These methods, most of which are experimental, are based on the secondary properties of fruits, which could be calibrated with a direct firmness-sensing method such as the MT test. Consumer studies by Hung et al. (19) showed that an expert’s ranking correlated well with the consumer panel ranking (r= 0.92) and that firmness among all five firmness groups were significantly different. Sorting accuracies of the expert’s ranking comparedto the consumer results obtainedby discriminate analysis were 76, 73, 74, 66, and 87% for most firm, very firm, firm, less firm, and least firm, respectively. This indicates that although human firmness sorting may be subjective, it is still a reliable method. Abbott (75) found that sonic measurements of Delicious apples correlated not as significantly with other destructive measurements but correlations were good as the ripeness scores obtained by the USDA inspectors. Mechanical impulse excitation and piezoelectric sensors were used by Galili et al. (52)to nondestructively measure the firmnessof avocado fruits. They foundthat the correlation coefficient, r, betweentheacousticparametersandthemaximumpenetration force ranged from 0.66 to 0.8 1. Kiwifruit, peach, and pea firmness was investigated by Muramatsu et al. (53, using a laser doppler vibrometer(LDV), with which there was no mechanical contact with the product. They found that the phase shift between the received and emitted wave by the fruit gave good correlations (R’ 5 0.9) with fruit firmness. of deMuramatsu et al. (53) also reported that the LDV technique was capable tecting citrus fruits afflicted with internal disorders. Using NMR, fairly good correlations(r = 0.7 I ) were obtained by Stroshine et al. (76) for cherries between elasticity and T? time as measured in a magnetic resonance process analyzer. Kim et al. ( 5 5 ) found the signal decay rate from the magnetic resonance was highly correlated with the firmness of tomatoes. Laser vision imagery employing an He-Ne laser at 632.8 nm wavelength and two diode laser modules at 670 and 685 nm as light sources were used by Lee et al. (77) to nondestructively measure the firmness of Golden Delicious, Fuji, and Rome apples. They compared the firmness values with those obtained destructively by the MT method(withoutpeeling theappleskin).Factorsthatinfluencedthe firmness measurements included light type (monochromeor color), skin removal, laser optical power, and laser wavelength. The performance data for these and other instrumental firmness measuring methods are summarized in Table 3.

D. NondestructiveMethodswith No MechanicalContact Several direct comparisons of the laser air-puff deformations with MT measurements have been made for peaches yielding R? values of 0.79 (78), 0.75 (7), and

Methods

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277

Table 4 Sorting Performance of Laser Air-Puff Detector and Magness-Taylor Test

Comoared to Packinghouse Manager and USDA Inspector Magness-Taylor detector air-puff Packinghouse manager

Laser Predicted soft

Predicted firm

Confirmed 21% 79% (miss) soft (hit) Confirmed (correct 73% 27% firmacceptance) (false (false firmacceptance) alarm) Magness-Taylor detector air-puff

ceptance) (false

Packinghouse Predicted soft manager Confirmed soft Confirmed

Predicted firm

75% (hit)

25% (miss)

15%

85% (correct

alarm)

Laser Predicted firm

USDA inspector

Predicted soft

Confirmed soft Confirmed firm

100%

0% (miss)

(hit) (hit) 37% &

63% (correct

alaI?Tl)

USDA inspector Confirmed soft Confirmed

Predicted soft

Predicted firm

100%

0% (miss)

26% (false alarm)

74% (correct acceptance)

0.86 (19). Correlations between modulus of elasticity values calculated by the ASAE Standard for spherical indenter and those calculated from similar equations for the laser air-puff were even higher for both apples (R’ = 0.91) and peaches (R? = 0.92). Correlations with rubber balls have ranged from R? = 0.94 to 0.97 (7). Table 4 demonstrates results from the laser air-puff andMT methods conipared with measurements by a packinghouse manager and a USDA inspector (78). When the packinghouse manager was considered as the standard, the laser air-puff had a slightly smaller miss rate (21%) than the MT (25%). However. the false alarm rate for the laser air-puff (27%) was higher than the MT (IS%). When compared with the USDA inspector, both the laser air-puff and the MT had a zero miss rate. Again, the laser air-puff false alarm rate was higher (37%) than the MT (26%).

VI.

APPLICATIONS

Nondestructive textural quality evaluations are made over a wide range of applications. Produce growers manually feel products as they matureto help determine

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harvest date (7). Workers who harvest products also use tactual information to evaluate maturity. Sorters on packing lines manually remove items that are too soft or too fitm. Government inspectors use tactual input to verify that grade standards are satisfied. Buyers judge quality by feeling the product. Consumers predict eating quality of products based on firmness when purchasing products. Mouthfeel judged by the consumer is the final evaluation of firmness. A primary application for a nondestructive firmness sensor is to replace subjective human evaluations with a quantitative measurement. Objective measurements of fruit firmness would enable precise descriptionsof the condition of shipments as they move from the grower to the final consumer. Firmness data from a calibrated instrument fora sample of product from a shipment would help resolve differences in opinion about the condition of the product. A relatively small sample size is collected from shipments for firmness evaluations when a destructive test is used because all of the product examined must be discarded after the test. When usinga nondestructive instrument a much larger sample size can be measured and returned to the containers. The increase in sample size provides a stronger inference for the actual condition of the lot (shipment). Researchers will benefit in a similar way as the private sector from equipment for making nondestructive firmness measurementson samples from storage of productare studies.Whenusingdestructivemeasurements,largeamounts A nonneeded so that small samples can be removed periodically for evaluation. to be returned to the study. destructive measurement allows the product tested Periodically following changes in the firmness of each item in the study also has the advantage of providing the opportunities of modeling changes to each item rather than modeling average changes. The ultimate goal of nondestructive firmness sensing is detection of firmness on fruit and vegetable packing lines. On-line firmness sorting of each item flowing through a packinghouse would improve the consistency of shipments and reduce the amount of good product discarded. Removal of the few overripe of decay spreading to good (soft) items in a container reduces the possibility items. The end result is higher economic returns not only for the shipper but for each business in the marketing chain. Finally, consumers benefit from improved firmness sorting by having product at the desired firmness at the point of purchase and when consumed.

VII. SUMMARYAND FUTURETRENDS The traditional approach of evaluating a new nondestructive instrument with the existing destructive instrument (Magness-Taylor) could be limiting the opportunity to identify one or more new approaches with the potential for improving

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overall food distribution systems. Another approach would be to evaluate a new instrument by using economic comparisons of two handling systems: (a) destructive measurements of firmness on a small sample to make decisions on the acceptance or rejection of a shipment compared with (b) nondestructively measuring to mechanically remove the firmness of each item before packing a shipment items that are too soft or too firm. Over the past two decades, a number of methods and devices have been used by different researchers to investigate the firmness of foods. The methods are often based on the product being tested, intended application (bench-top, field, on-line), and mode of measurement, Le., destructive or nondestructive measurement. There is also an enormous range of food materials (natural and processed foods), each behaving differently under different conditions. This makes study the of food firmness and specifically the development of standardized nomenclature of terminology difficult because each product requires a specific procedure and units of expression. to be nonhomogeneous and Indeed, most biological products are known anisotropic. Abbot and Lu (63) conducted compression tests on three cultivars of apple using an Instron Universal Testing machine and evaluated the effects of ripeness, specimen orientation, and location within the apple on failure stress, strain, and energy, and apparent modulus of elasticity (Young’s modulus). They found that mechanical properties were significantly influenced by specimen orientation, latitude (location from stemto calyx), and depth (from skinto core). Interactions among these factors were also found to be significant. Another problem associated with the study of food firmness is that it often requires a reference or standard material. Should such a standard exist, it should be structureless (have no atoms), isotropic (properties are independent of direction), and ideal (does notexist in reality).However, it shouldbepossibleto develop a realistic (if not perfect) standard product that could be purchased to enable calibration of nondestructive firmness detectors such as done for other instruments (color tiles for calibrating colorimeters). Ideally, firmness standards would have shapes that are similar to the products tested. One application of a standard for firmness is to make sure an instrument reads the same after a test as it did before the test. A standard could also be used for evaluating the performance of a new instrument compared to an existing one. A commercial application is to calibrate instruments at both shipping and receiving points.In all applia predeterminedfirmnessthatcouldbe cationsthestandardwouldhave reproduced or measuredbyindependentmethodssuchasusingdestructive methods. In addition, a distinction should be made between surface firmness and gross firmness. Surface firmness refers to the measurement made when firmness is determined based on the analysis of an input signal to, and response of, essentially the cellsof the product closeto the surfaceof the food. Examplesof surface

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firmness include puncture and laser air-puff. On the other hand, gross firmness is determined when the whole food product receives the input signal and responds with a measurable output response. Examples of gross firmness would be results obtained from acoustic, NMR, and NIR measurements. in this section indicate continuing The many recent dates for work cited interest in new approaches for measuring product firmness. However, there are difficulties with all the attempts described above in all three general approaches. of the need to extrapolate Destructive methods have the inherent disadvantage to represent the firmness of the batch or shipment. the results from a sample Methods that apply a controlled force to the product often have limited capacity due to speed restrictions from inertial responses of moving masses. Methods that measure a secondary property suffer from difficulties in relating the results to the firmness property. The Washington Fruit Research Commission conducted evaluations of several nondestructive firmness-measuring devices in 1994. The devices included an impact system in which a rod strikes the fruit with the force of the impact converted to a firmness reading, measuring sound resonance when the fruit is tapped by a plastic hammer, and by ultrasound (79). The results of all the devices had very weak relationships with readings from Magness-Taylor tests. Thus, a need is evident for a new solution to the problem of measuring firmness. However, Warner (80) contended that the fruit industry is searching for firmness-sorting devices that need not be compared with the Magness-Taylor method, which has its limitations, but devices that are themselves capable of demarcating soft from firm fruit on an absolute basis. To improve the accuracy and dependability of on-line firmness measurements, a concept of sensor fusion, that is, combining two or more sensing methods (8 1 ) attempted to automate fruit sorting is currently being investigated. Ozer et al. by fusion of data acquired from selected sensors. The information obtained was used to classify cantaloupe fruits into four maturity stages. Also, Zhang et al. (82) presented a method based on fuzzy modeling of the overall quality of a product based on a setof quality factors, F (which included firmness), and another p. Because each individset of grading factors,G, to obtain a single grading factor ual method for firmness evaluation hasits own limitations, sensor fusion can take the advantage of several sensing methods based on different sensing principles to obtain the best prediction/measurement of firmness. We believe that Sensor fusion (Fig. 1 ) is the correct direction for future firmness sensing.

REFERENCES I.

AC Mackey, MM Hard. MV Zaehringer. Measuring textural characteristics of fresh fruitsandvegetables-apples,carrots.andcantaloupes.TechnicalBulletin 123, Agricultural Experiment Station. Oregon State University, Corvallis. OR, 1973.

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46. EE Finney. Random vibration techniques for non-destructive evaluation of peach firmness. J Agric Eng Res 16(1):81-87, 1971. 47. EE Finney. Vibrational techniques for testing fruit firmness. J Texture Stud 3(3): 263-283,1972. 48. JA Abbott, LA Liljedahl. Relationship of sonic resonant frequency to compression testsandMagness-Taylorfirmness of applesduringrefrigeratedstorage.Trans ASAE 37(4):121 1-1215, 1994. 49. NGalili,AMizrach, G Rosenhouse.Ultrasonictesting ofwholefruit fornondestructivequalityevaluation.PaperNo.93-6026.StJoseph,MI:ASAE, 1993. 50. A Mizrach,U Flitsanov, Y Fuchs. An ultrasonic nondestructive method for measuring maturity of mango fruit. Trans ASAE 40(4): 1107-1 11 1, 1997. 51. Y Cheng. Non-destructive quality evaluation of fruits and vegetables using ultrasound.Ph.D.dissertation,VirginiaPolytechnicInstituteandStateUniversity, Blacksburg,VA,1992. 52. N Galili, I Shmulevich, N Benichou. Acoustic testing of avocadofor fruit ripeness evaluation. Trans ASAE 41(2):399-407, 1998. 53. N Muramatsu, N Sakurai, N Wada, R Yamamoto, T Takahara, T Ogata,K Tanaka, T Asakura, Y Ishikawa-Takano, DJ Nevins. Evaluation of fruit tissue texture and internal disorders by laser doppler detection. Postharvest Biol Technol 15:83-88, 1999. 54. N Muramatsu, N Sakurai, N Wada, R Yamamoto, T Takahara, T Ogata, K Tanaka, TAsakura, Y Ishikawa-Takano, DJ Nevins.Remotesensingoffruittextural changeswith a laserdopplervibrometer. J AmSOC Hort Sci125(1):120-127, 1999. 5 5 . S Kim, MJ McCarthy, P Chen. Feasibility of tomato quality grading and sorting using magnetic resonance. Paper No. 94-6519. St Joseph, MI: ASAE, 1994. 56. CJ Clark, PD Hockings, DC Joyce, RA Mazucco. Application of magnetic resonance imaging to pre- and post-harvest studies of fruits and vegetables. Postharvest Biol Technol 11: 1-21, 1997. 57. S Gunasekaran. Delayed light emission as a means of quality evaluation of fruits and vegetables. Crit Rev Food Sci Nutr 29(1):19-34, 1990. 58. Y Chuma, K Nakaji. Delayed light emission as a means of color sorting of plant products. In:P Linko, Y Malkki, J Olkku, eds. Food Process Engineering, Vol 1. London: Applied Science Publishers, 1979:3 14. 59. J Lammertyn, B Nicolai. K Ooms, V De Smedt, J De Baerdemaeker. Nondestructive measurement of acidity, soluble solids and firmness of JonaGold apples using NIR-spectroscopy. Trans ASAE 41(4): 1089-1094, 1998. 60. P Jeffery, NH Banks. Firmness-temperature coefficient of kiwifruit. NZ J Crop Hort Sci 22:97-101, 1994. 61. RL Jackman, DW Stanley. Failure mechanisms of tomato pericarp tissue suggested by large and small deformation tests. J Text Stud 23:475-489, 1992. 62. GO1 Ezeike. L Otten. Two-compartment model for drying Egusi (melon) seeds. Can Agricu Eng 33( 1):73-78, 1991. 63. JA Abbott, R Lu. Anisotropic mechanical properties of apples. Trans ASAE 39(4): 1451-1459,1996. 64 NO Maness,GH Brusewitz. Performance of an instrument designed for, and evalua-

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Linear Viscoelastic Methods M. Mehmet Ak Istanbul Technical University, Maslak, Istanbul, Turkey Sundaram Gunasekaran University of Wisconsin-Madison, Madison, Wisconsin

1.

INTRODUCTION

Food quality evaluation by consumers is a dynamic and complex process, which varies from person to person and also depends on the food and situation (1 -3). Nevertheless, food quality has long been investigated by sensory and instrumental means, often to establish a correlation between results from the two methods (43). Since food quality is a multidimensional and complicated subject, it is generally studied in the context of subcategories such as appearance, flavor, texture, nutrition, safety, convenience,and stability (3,4). Hence, instrumental quality evaluation within a subcategory (e.g., texture) can be expected to provide useful but partial information towards better understanding of the overall food quality ( 1 -3). Historical developments making texture one of the key attributes of food quality have been discussedin a recent articleby Szczesniak, one of the pioneers in the field of food texture(6). The influenceof textural attributeson the consumers’ perception of food quality changes depending upon the product (4,6,7). The textural attributesof foods are usually estimated by trained sensory panelists and/ or by a variety of instrumental methods (4,5,8-10). During sensory analysis of food texture, samples are often disintegrated (e.g., by mastication or pressing) in order to perceive textural attributes. Accordingly, the most widely used instrumental texture evaluation method [i.e., texture profile analysis (TPA)] is devised to simulate the action of the jaw (4). Therefore, it is also a destructive test. In a 287

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typical TPA test a sample is subjected to two consecutive compressions (“double bites”) that are usually beyond the point of fracture or gross failure of foods. to determine macroscopic textural attributes of Such studies have been useful foods. Food texture evaluation by destructive methods (e.g., TPA) and correlation research of instrumental data with sensory results continues to be an activeof area (11). Destructive techniques are often not sufficiently sensitive to elucidate the structure-property relationships in food materials. For instance, physicochemical reactions taking place in fruits and vegetables during maturation lead to several as an indicator of changes, including softening of the tissue, and can be used product quality (ripeness). Puncture tests or uniaxial compression tests are traditionallyused to evaluate fruit softening.Theselarge-deformation,destructive tests provide data related to failure properties rather than microstructural changes at the cellular level. Such large deformation tests to assess small-scale changes might be considered inappropriate, if only because of their relative insensitivity (12). However, small deformation tests, generally involving strains of less than 3%, permit evaluation of microstructural changes at the cellular level and facilitate characterizing time-dependent or viscoelastic properties thatall plant materials exhibit ( 13- 15). Accordingly, much of recent research has been directed towards understanding of structure-property relationships in foods. In these studies, the aim is to measure properties during and after structure formation without perturbing the process. Thus, the introduction of dynamic oscillatory method has been animportantstep,whichenabledsimultaneousmonitoring of viscousand elastic responses of foods in a nondestructive manner. Ross-Murphy (16) pointed out that NMR spectroscopy enables structural informationof foods to be obtained at the molecular level (over distances of 0.5-5 nm), whereas mechanical spectroscopy enables foods to be probed over supramolecular distances ( 1 pm to 10 mm). Rapid improvements in capability of rheological instruments, measurement and analysis methods, and continuous need to control and/or modify material in an impressive amount of properties for high-quality products have resulted publications on viscoelastic properties of foods. These studies have been very useful in revealing the microscopic textural attributes of foods. This chapter is comprised of two main sections. The first section is about viscoelastic behavior, where the general characteristics of viscoelasticity, the utility of mechanical models in describing viscoelastic behavior in transient and dynamic tests, and the features of small amplitude oscillatory shear (SAOS) tests are presented. In the second part, applications of the transient and dynamic tests are presented. Here, the examples are selected to show the diverse utility of the linear viscoelastic tests, particularly of SAOS,in studying viscoelastic properties affecting quality of different food systems.

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II.

VISCOELASTICITY

A.

IdealElasticandViscousBehaviors

An ideal solid material will respond to an applied load by deformingfinitely and recovering that deformation upon removal of the load. Such a response is called a direct “elastic.” Ideal elastic materials obey Hooke’s law, which describes and strain (y) via a proportionality constant proportionality between the stress(0) called modulus (G), i.e., 0=

Gy

It should be noted thatG is more accurately called shear modulus (or modulus of rigidity) because it is obtained via a shear test. However, similar relationa symbol E is used to ships hold good for tension and compression tests, and represent Young’s modulus obtained via these tests. Since shear is the favored in this mode of deformation in linear viscoelastic studies, we limit our discussion chapter only to shear tests unless stated otherwise. For a perfectly elastic solid E and G are related as E = 3G (1 7). An ideal fluid will deform and continue to deform as long as the load is applied. The material will not recover from its deformation when the load is removed. This responseis called “viscous”. Theflow of simple viscous materials is described by Newton’s law, which constitutesa direct proportionality between i.e., the shear stress and the shear rate

(v),

The proportionality constant q is called the shear viscosity. From energy of energy expended considerations, elastic behavior represents complete recovery during deformation, whereas viscous flow represents complete loss of energy as all the energy supplied during deformation is eventually dissipated as heat.

B. TimeFactor in Viscoelasticity An important feature of viscoelastic materials, in contrast to the ideal materials, is that their response depends on the time-scale of observation. A viscoelastic material may behave more like viscous liquid with some elastic effects, or like on how it is studied. Generally, elastic solid with some viscous effects, depending the faster the deformation, the closer the response is to being elastic, and the slower the deformation, the closer the response is to being viscous. Such behavior results from the molecular structure of the material: stretching intermolecular bonds, which causes an elastic response, is very quick; the motion of molecules past one another, which causes a viscous response, takes much more time ( 1 8). The time scaleof experiments should be considered in relation to the “characteristic time,” denoted by h, of materials (e.g., relaxation time).The character-

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istic time of the materials varies widely-infinite for ideal elastic solids and zero for ideal viscous liquids. A dimensionless number, Deborah number (De), is defined to relate h with the time-scale of observation (t) (19,20). D, = hlt A high Deborah number (De >> 1) corresponds to solid-like behavior (i.e., relaxation not observed), whereas a low Deborah number (D, << I ) corresponds to a liquid-like response (i.e., fast relaxation observed). The viscoelastic behavior (i.e., finite relaxation observed) will dominate when De is on the order of I . From the definition of De, a material can exhibit solid-like behavior due either to the long characteristic time orto rapid deformation processes (e.g., high10”’ speed testing). For instance, water has practically zero relaxation time, s, and consequently is perceived as liquidin most cases (20). However, water can respond as a “solid” material if the time of testingis very short (or equivalentlyif the frequency of testingis very high). During production, handling, and consumption, food materials are subjected to a wide variety of deformation processes, in Fig. 1, the range of shear each with a different time scale. As can be seen rates, or time scales, in various processes (not all food-related) differs greatly. In general,however,foodsarecomplexsystemsand do notpossess a single relaxation time but rather a broad range of relaxation times (21). Therefore, in a given process of finite duration, foods are more likelyto behave as viscoelastic materials. The significance of viscoelastic behavior and associated rheological properties in extrusion processes, for example, is discussed in several articles in Kokini et al. (22). At this stage,it is interesting to mention that rheologists have recently been deprived of a delightful and mythical example often usedto emphasize the effect of the time scale of observation on the material response. To illustrate liquidto an observation like behavior of a solid material, rheologists sometimes referred that windowpanes of old cathedrals are thicker at the bottom than at the top. The explanation was that the glasses of these ancient windowpanes slowly flowed at ambient temperatures under the influence of gravity. Given that the time scale as a plausible explanation of flow was several hundred years, this was regarded (23,24). However, earlier, Ernsberger (25) pointed out that at ordinary temperatures, glasses of commercially useful compositions are rigid solids and observation of thicker ancient windowpanes cannot be related to flow due to gravity. Plumb (26) pointed out that the correct explanation for thicker windowpanes at the bottom liesin the glass processingof the ancient times. Recently, the explanation based on sagging of glass due to gravity was disproved by Zanotto (27), who calculated that it would require more than the age of the universe for a typical window glass to flow appreciably so that the bottom would be thicker. Viscoelastic materials are characterized by various phenomena observable under different conditions. Some of these are (28):

-

astic

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291 TIME (s)

SHEAR RATE (SI)

mm m

I

N

I

Fig. 1 Shear rate ranges (and time scale of operation) for various food and industrial processes. A: Sedimentation of fine particles in a suspending liquid (spices in salad dressing, medicines, paints); B: leveling due to surface tension (frosting, paints, printing inks); C: draining under gravity (vats, small food containers, painting, and coating); D: extrusion (snackandpetfoods,cereals,pasta,polymers); E: calendaring(doughsheeting); F: chewing and swallowing (foods); G: dip coating (paints, confectionery); H: mixing and stirring (food processing); J: pipeflow(foodprocessing,blood flow); K: spraying and brushing (spray-drying, painting, fuel atomization);L: rubbing (applicationof creams and lotions to theskin); M: high-speedcoating(paper); N: lubrication(bearings,gasoline engines.)

If the stress is held constant, the strain increases with time (creep). If the strain is held constant, the stress decreases with time (relaxation). Effective stiffness depends on the rate of application of the load. If cyclic loading is applied, hysteresis (or phase lag) occurs, leading to dissipation of mechanical energy. Acoustic waves experience attenuation. Rebound of an object following an impact is less than 100%. Frictional resistance occurs during rolling.

a

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The viscoelastic response in a single physical entity can be manifest in many ways, some of which are conceptualized in terms of viscoelastic functions. A thorough understanding of viscoelasticity entails a simultaneous appreciation of all these manifestations and their analytical representations. Each viscoelastic function, if known over the full range of time or frequency, contains complete information regarding the linear behavior of a material. Nevertheless, each viscoelastic function emphasizes a different aspect of the behavior. It is interesting to note that viscoelasticity is the basis for the informal nondestructive testing of ripeness or maturity of some foods by tapping. For to judge centuries, people have been tapping foods like melons and coconuts their ripeness from the sound. This is an example of free decay of vibration following an impulse, a viscoelastic phenomenon (28).

C.

Viscoelastic Behavior in Transient Tests

Ideal elastic and ideal viscous behaviors present two extreme responses of materials to external stresses. As the terms imply, these are only applicable for “ideal” materials. Real materials, however, exhibit a wide array of responses between ideal viscous and elastic behaviors. Most materials exhibit both viscous and elastic properties simultaneously and, therefore, are characterizedas viscoelastic. Almost all foods belong to this group. Ideal elastic and ideal viscous behavior is represented by a spring and a 2). Thus,a dashpot(a fluid-filled, piston-cylindersystem),respectively(Fig. spring by itself can adequately represent a Hookean deformation, and the dashpot, the Newtonian flow. Since viscoelastic materials have both viscous and elastic properties, they are simply represented by a combination of spring(s) and dashpot(s). The series combination of a spring and a dashpot is called a Maxwell model (Fig. 2). In this, both the spring and dashpot experience the same total stress ((3) but share the total strain (y). (3

=

ch=

(3d

(4)

y = 1: + yd The subscripts s and d represent spring and dashpot, respectively. From the above, the following Maxwell equation is obtained:

where, T = q,,/G.; and qd = q. One of the inadequacies of the Maxwell model is that, for a constant stress (i.e., d d d t = 0) it reduces to Newtonian behavior, which is generally not observed with viscoelastic materials in a creep test. How-

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293

Output nseFunction Input Model Mechanical Element@) Spring Hookean

YC

Newtonian Dashpot

Maxwell Relaxation Stress

u = ooexp(-fflR)

:Lh_

Kelvin

Y =ro[l-ex~(-ff~~)l

Fig. 2 Some simple mechanical elements used to model viscoelastic behavior of foods and a set of their input-output relations.

ever, the Maxwell model is particularly useful in stress relaxation experiments, for which it yields:

where, T~ is calledtherelaxationtime.Stressrelaxationresultsareoftenexpressed in terms of a material function called the shear relaxation modulus, G(t):

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The Maxwell model with a single exponential term is generally not sufficient to describe relaxation behavior of foods. Therefore, it is often modified to include multiple exponential terms, each with a different relaxation time, representing an array of Maxwell elements in parallel (29). As can be seen in Eq. (7), the Maxwell model predicts a zero stress at very long times>> (t T ~ ) .That means the model cannot represent a truly viscoelastic solid, which would have residual stress after relaxation, as observed in many food products (30). To account for the residual stress, the Maxwell model is modified by adding a constant term (31). The parallel combination of a spring and a dashpot is called a Kelvin (or Voigt or Kelvin-Voigt) model (Fig. 2).In this, both the spring and dashpot experience the same total strain (y) but share the applied stress (0). d =

y

=

os+ dd =

yd

(9) (10)

From the above, the following Kelvin equation is obtained: d =

Gy

+ qj

(1 1)

where G = G, and qd = q. One of the inadequacies of the Kelvin model is that, = 0) it reduces to Hookean behavior, implying a for a constant strain (dyldt constant stress at a constant strain, which is generally not observed with viscoelastic materialsin a stress relaxationtest. However, theKelvin model is of particular value in creep experiments, for which it yields:

where T~ is the retardation time. Creep results are often expressed in terms of a material function called the shear creep compliance, J(t):

The Kelvin model can be generalized by combining many elements in series, each with a different retardation time (29). As can be seen in Eq. (12), the Kelvin model predicts an asymptotically reached strain (y(J at very long times (t >> 7,). That means the model cannot represent viscoelastic liquids showing continual flow. A viscous term is then included in the Kelvin model to account for flow at long times (32). The Maxwell and Kelvin elements are the simplest of many mechanical models. Many combinationsof several springs and dashpots (and other elements) are used to fully describe the viscoelastic nature ofreal materials (33). For exam-

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ple, the Burgers model, another common model used to represent rheological behavior of food materials, is a series combination of the Maxwell and Kelvin elements. The advantage of the mechanical models is that the elements of a model can be related to individual parts of the structural network of the material being studied. Fundamental rheological parameters obtained from mechanical models provide information about the dependence of food structure on composition, interaction among constituents, and processes(34). For example, Dienerand Heldman (35) used a viscous Maxwell-Bingham rheological model to evaluate the mechanical structure of butter. The viscoelastic parameters relaxation time, zR,and retardation time, zD, are experimentally determined,and the viscous and elastic parametersq,, and G,, respectively, are computed (7, = zD = qd/G,). The relaxation modulus, G(t), and 5 l/J(t). The limiting case of G = creep compliance, J(t), are related as G(t) 1 /J is applicable for a solid material following Hooke's law (17,18). Because the time response of viscoelastic materials (y vs. t in creep or o vs. t in relaxation) is nonlinear, it is difficult to discern the linear range from experimental data at one strain (or stress) level. In order to determine the linear range, then, multiple experiments must be performed at different constant strain (or stress) levels. The linear range of the isochronal-the plot of stress against strain at a specific time (in case of stress re1axation)"will indicate the extent of strain level over which the material response can be considered linear. The process of obtaining isochronal plots is illustrated in Fig. 3. The data obtained (y, and y2) arerepresented in Fig. 3A andB.From at twoconstantstrains

C

Fig. 3 Determininglinearviscoelasticrange from a set of stressrelaxationdata. (A) Data obtained at an applied strain of y,; (B) data obtained at y2; (C) isochronals plotted using data (a, b, c, and d) from A and B at times t, and tz.

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these, data points (for, e.g., a, b, c, d in Fig. 3) are gathered at different times o versus y plot is constructedfor (e.g., t,and t2). Thenthecorresponding each of the times at which the stress response is measured (Fig. 3C). The strain by dotted value at which the isochronal beginsto deviate from linearity (indicated line in Fig. 3C) is the upper limitof the linear viscoelastic region for the material. Thehatchedregion in Fig.3Cindicatesthenonlinearrangeofthematerial studied. Performing experiments within the linear range is helpful because the data can be analyzed using relatively simple linear viscoelastic models discussed previously. Within the linear range, the material functions (modulus and compliance) are independent of the strain or stress level used in the experiment. Conversely, once the modulus G(t) is known, the stress o(t) for any strain y and time t is = o(t)/y. Most polymers exhibit linearity known through the relationship G(t) at low stressesor strains (e.g., y z 0.5%) (36). In the case of food materials, strain levels less than 3% are considered acceptable to assure linearity(12). However, it is important to examine the linearity over the entire range of experimental condiso that the entire data set can be analyzed tions and select the smallest strain level uniformly.

D. ViscoelasticBehavior in DynamicTests Although it is possible to conduct small-amplitude oscillatory tests i n tension and compression, and there are commercial rheometers for such configurations (37,38),thesheardeformationhasbeen thedominantmode of deformation. Hence, the abbreviation SAOS, for small-amplitude oscillatory shear, is commonly used to represent dynamic viscoelastic tests. Concurrent with the developSAOS methodhasgainedmuchpopularity ment of modernrheometers,the among food researchers. The SAOS test is based on applying a sinusoidal strain and measuring the resulting stress. Of course, one can apply sinusoidal stress and measure the resulting strain. Either method,in principle, should produce the same material propertiesprovidedthattheimposeddeformation is withinthelinearviscoelastic region. Let us consider Fig. 4, where a thin disk of a Hookean solid is subjected to a sinusoidal shear strain r(t) between parallel plates of the instrument: r(t) = yo sin(ot)

(14)

where yo is the shear strain amplitude, o is the angular frequency, and t is the time. Inserting Eq. (14) into Eq. ( I ) , the constitutive equation for a Hookean solid, for y will give the resultant stress o (t), which is also sinusoidal: o(t) = oosin(mt)

(15)

297

Linear Viscoelastic Methods

Hookean Solid T

................... ....__ .__. .......

."....... ........ ................ ...... . q ; o f""

/=+

I

, ot

1 Stress output

Fig. 4 Illustration of parallel plate geometry in a dynamic rheometer (top) and typical stress-strain data for a Hookean solid (bottom). Stress and strain are in phase.

where o0is the shear stress amplitude. Therefore, for Hookean solids the resultant stress wave is exactly in phasewith the input strain wave. This means that when the strain is at maximum, so is the resultant stress, as shown in Fig. 4. Let us now apply the same strain input [Eq.(14)], this time using a concentric cylinder configuration (Fig. 5 ) , to a Newtonian liquid whose constitutive equation is given, in simple form, by Eq. (2). Taking the time derivative of the input strain function [Eq. (14)] and inserting the result for p in Q. (2) yields: o(t) = qmy, cos(0t)

(16)

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Newtonian Liquid

T

Fig. 5 Illustration of concentric cylinder geometry in a dynamic rheometer (top) and typical stress-strain data for a Newtonian liquid (bottom). Stress lags the applied strain by an angle 6 (=7c/2).

As shown in Figure 5 , the stress response of a Newtonian liquid is exactly 90" (or x12 radians) out of phase with the strain input. A viscoelasticmaterialwithbothsolid-likeandliquid-likeproperties should then exhibit an intermediate behavior (Fig. 6). The stress response of a linear viscoelastic material to a sinusoidal strain input is given as (39): o(t) = yoG'(o) sin(ot)

+ yoG"(o) cos(ot)

(17)

where two frequency-dependent functions,G' and G", represent the shear elastic (storage) modulus and the shear viscous (loss) modulus, respectively. The term in Eq. 17 having sin (cot) is in phase with the strain input, and the term having cos(ot) is 90" out of phase with the strain input. Relative magnitudes of these

299

Linear Viscoelastic Methods

Viscoelastic Material

T

'

Stress output

Fig. 6 Typical stress-strain data obtained in a dynamic rheometerfor a viscoelastic material. Stress lags the applied strain byan angle 6, (0 < 6 < d 2 ) .

terms will determine the degree of viscoelastic behavior. Therefore, we can rewrite the stress response of a linear viscoelastic material in a format similar to the strain input as: o(t) =

(so

sin(wt

+ 6)

(18)

where 6 is the phase angle between stress and strain. The phase angle 6 varies between 0 and 90" (or between 0 and 7cI2 radians), depending upon the relative magnitudes of the two terms in Eq. (17). Expanding the stress equation [Eq.(18)] using trigonometric relations (40), we get:

o(t) = ((so c o d ) sin(wt)

+ (o0cos(wt) sin6) (19)

By comparing Eqs. 17 and 19 we obtain:

and

G' is a measure of the energy stored and subsequently released per cycle of deformation per unit volume. It is the property that relates to the molecular events of elastic nature. G is a measure of the energy dissipated as heat per cycle of deformation per unit volume. G" is the property that relatesto the molecular events of viscous nature.

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From the last two equations, we obtain another commonly used dynamic viscoelastic property, the loss tangent (tan 6):

Hence, tan 6 denotes relative effectsof viscous and elastic componentsin a viscoelastic behavior. Recall that for a Hookean solid 6 = 0; hence in-phase shear storage modulus is G’ = o&, and out-of-phase shear loss modulus is G” = 0, as expected 6 = d 2 ; hence G’ = and required. On the other hand, for a Newtonian liquid 0 and G” = o,,/y,,, as expected and required. Another way to present results of dynamic measurements is to use complex modulus G*, the magnitudeof which is related to G’ and G” through the following equations (39):

Structurally, these terms can be explained in terms of the ability of the components to store (G’) or lose (G”) energy per test cycle. Thus tan 6 can be viewed as the ratio of energy lost to energy stored per cycle and would be expected to decrease in samples with more and stronger elastic components but would be higher for more viscous samples. Similarly, complex viscosity (q*) can also be defined in terms of real (q’) and imaginary (q”) parts of viscosity.

where,

The quantities G’, G” and q’, q” enable the rheological characterization of a viscoelastic Inaterial on the basis of SAOS measurements. For instance, for a Maxwell liquid the appropriate formulas may be derived using the above SAOS relations as (18):

tic

Linear

301

Thus, tan 6 = I/or,enables calculating the relaxation time 'tR from the SAOS data of G' and G". Furthermore, as o + 0, the dynamic viscosity q' +

q. Thus, theviscosity q occurring in the Maxwell model may be determined from the dynamic results (18). Often different publications denote the various viscoelastic parameters by slightly different names and with different symbols and units. It is desirable to follow a standard set of nomenclature such as the one adopted by the Society of Rheology (Table I).

1. Linear Range in Dynamic Tests The linear viscoelastic region, where stress and strain are proportional and the theory described in the previous section is applicable, can be quite different depending on the material and experimental conditions.

Table 1 Society of RheologyNomenclature for LinearViscoelasticity in Simple

Shear parameter Rheological Shear strain Shear modulus (modulus of rigidity) Shear relaxation modulus Shear compliance Shear creep compliance Equilibrium shear compliance Steady-state shcar compliancc Complex viscosity Dynamic viscosity Out-of-phase component of q* Complex shear modulus Shear storage modulus Shear loss modulus Complex shear compliance Shear storage compliance Shear loss compliance

SI units -

Pa Pa Pa ' Pa- ' Pa- I Pa- I Pa . s Pa . s Pa s Pa

Pa Pa

Pa- '

Pa I Pa- '

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Generally speaking, an investigation employing the SAOS method begins with the determination of strainor stress limit for which the linear viscoelasticity theory is applicable. For this, a strain or stress sweep test must be performed unless, of course, this informationis already available in the literature. However, before using the published values of linear limits, it is important to make sure in the published that the test material and test conditions match closely those report. A strain sweep test is conducted at a constant frequency (e.g., 1 Hz) by increasing the amplitude of the imposed strain. This test is schematically illustrated in Fig. 7. Once the linear viscoelasticlimit is determined, one can proceed

oKj

'r\A

0 05

0.01

0

0 O ' K ; J

-

!

-0.01

-0.05

-0.02

0

3.14 Time (s)

0

6.28

3.14 Time (s)

6.28

3.14 Time (s)

0

6.28

STRAIN AMPLITUDE INCREASES

A G',G

"*

4 -

, 0

k4

~

LINEAR VISCOELASTIC REGION

,

.*

>'

b NONLINEAR VISCOELASTIC REGION

STRAIN AMPLITUDE

Fig. 7 Typical strain sweep test is conducted by applying increasing strain amplitudes (top) and the resultant moduli (bottom) values are examined for their departurefrom being horizontal.

tic

Linear

Methods

303

withfurtherexperiments (e.g., frequency-sweep)performedatstrains (or stresses) smaller thanthe limit. It is highly recommended to repeat the strain sweep test at extremes of experimental variables, for there are data indicating that the linear viscoelastic region can vary with test frequency, temperature, and sample age (42.43). Variation of the linear viscoelastic region with test temperature is shown for mozzarella cheese in Fig. 8.

2. Features of SAOS Method Recent investigations on food rheology often use dynamic mechanical measurements (e.g., SAOS) to study viscoelastic behavior of foods. The small amplitude dynamic measurements are important and useful for many reasons.

I.

It is a nondestructive technique enabling measurements to be made while preserving specimen structure. This allows researchersto relate dynamic rheological parameters to the molecular structure of a mate-

Shear Strain Fig. 8 Linear viscoelastic region for mozzarella cheese as a function of cheese temperature. (Data from Ref. 43.)

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Ak and Gunasekaran

2.

3.

4.

5.

6.

7.

8.

rial. These tests provide very a sensitive meansof studying the molecular motions that give rise to the phenomenon like glass transition (44). This is discussed in detail later in Sec. 111. It allows selective probing of molecular events by choosing a proper frequency range since,in a dynamic mechanical experiment,the stress response is dominated by relaxation processes with time constants near I/o(45). The linear viscoelastic region can be determined easily in dynamic testing by changing the amplitudeof the input strainor stress function (46). Two quantities,usually G’ and G”, aremeasuredsimultaneously, which gives a wayto check on both experimental error and applicability of time-temperature superposition (46). When applicable, data from oscillatory tests can be utilized in timetemperature superposition technique to expand the frequency range, whichotherwisewouldbeinaccessibleexperimentally. The timetemperature superposition technique is based on the assumption that a certain change of temperature changes the rate of all relaxation and retardation processes by the same factor (47). The resulting graph of the superposition procedure is called the master curve. Subramanian and Gunasekaran (2 1) presented master curves for mozzarella cheeses of differing composition and age. The superposition technique can also be used with transient data from linear viscoelastic creep and relaxation tests. However, dynamic measurements are easier to perform with current rheometers than with transientstep strain and stress experiments. Winter (45) stated that it might actually be more accurate to determine relaxation modulus G(t) from G’ and G” data than to measure it directly in a step-strain experiment. Knowledge of dynamic properties such asG’ and G” allows computation of all other linear viscoelastic properties as well as the material behavior in other types of deformations such as tension (29,39). Much effort has been, and continues to be, expended in developing computer algorithms to perform such transformations accurately and fast. Modern commercial rheometers often come with softwares that perform some of these transformation functions. Molecular weight distribution of a material critically affects its propin its performance. Dynamic erties and thus plays an important role rheological data can be usedto determine the molecular weight distribution of materials (48-50). It is generally faster to perform oscillatory tests than to perform other line:lr Viscoelasticexperimentssuchas creeD andrelaxation.This,

astic

Linear

Methods

305

however, depends on the number and range of frequencies required (46). 9. Suddenchange ofthedisplacement(stressrelaxationtest) or load (creep test) is not required in oscillatory shear experiments (51). 10. Since it is afrequencydomainratherthanatimedomaintest,the amplitude of the deformation yo and the time scale l / w can be independently varied (5 I). These features of SAOS method are widely used to investigate various phenomena in different kinds of food systems as illustratedin the following section.

111.

APPLICATIONS

A. Transient Tests In terms of experimentalmethodology,compressiontesting is anexpedient it provides technique to distinguishgoodandbadsamples.Butbeingstatic, limited information on the mechanism(s)of the process(es) involved, understanding of which is needed for the development of newer products and/or textures. Transienttestsareinherentlyusefulforevaluatingmaterialstructureand/or structure development during processing. This has been the primary application of transient tests. Nonetheless, there have been many investigations to develop empirical correlations between the transient test parameters (relaxation time. retardation time, equilibrium modulus, etc.) and some food quality/texture attributes. Transient tests have been very popular among food researchers because of their simple test configuration. Stress relaxation tests can be readily performed using universal testing machines (e.g., Instron, Texture Analyzer). Creep tests can be performed by means of a loading device with a provision to continuously monitor (and record) the sample deformation. However, some practical problems exist. In the case of stress relaxation tests, it is virtually impossible to impose an instantaneous strain, andin the case of creep tests, dueto continually changing sample cross-sectional area, the applied stressis often not a constant, as generally required by the models used for data analysis. Evageliou et al. (52) described developing a new fat mimetic by carefully a evaluating the structure of the material by creep tests. A model combining Maxwell element in series with one or more Voigt elements is used to describe a s protein time dependency of the linear creep compliance. Biopolymers such (from egg, milk. gelatin), intact and modified starch, and/or gums (e.g.. carrageenan, alginate, pectin) are used to provide structure to fat mimetics. Since no

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biopolymer by itself can provide the required structure and a “plastic” flow, mixtures are used(e.g., gelatin and maltodextrin)in an attempt to impart “plasticity.” However, if incompatible polymers are mixed, phase separation leads to an unacceptable product (52). Ingel systems,networksareformedbyphysicalcross-linkswith finite lifetimes, which will stretch and eventually relax when under mechanical stress, thus allowing the system to flow. Creep curves of full-fat products suchas butter and margarine exhibit lower initial strain (about two orders of magnitude) than those of biopolymer networks (37,52,53).The difference is related to permanency of cross-links. The fat lattice is formed by the short-range van der Waals forces of attraction between glyceride crystals as opposed to the long junction zonesof helical strands in a biopolymer network. Comparing the creep curves of margarine and gelatin gels, the fat crystals were reported to flow more rapidly (larger compliance) than did the mimetic made of gelatin triple helix. Much of the creep was observed to occur beyond the instantaneous deformation. The instantaneous compliance is associated with of a network, and the higher proportion of the the initial elastic deformation total rearrangement in gelatin gels reflects the elastic character of gelatin helical associations as opposed to the plasticity of a liquid/solid fat body (54). Sakurai and Nevins (55) recommended use of stress relaxation measurements to study fruit and vegetables since physiconlechanical parameters derived through application of the Maxwell model were related to time-dependent biochemical and microstructural changes associated with softening (56). The minimum stress relaxation time for tomatoes changed with ripening as a function of (56) suggested that softening and position within the fruit. Sakurai and Nevins stress relaxation tests might be able to distinguish differences in fruit tissue due to ripening that are not observable by means of large deformation tests. Creep tests modeled as per the Burgers model (series combination of the MaxwellandKelvinmodels)mayprovidemoreinformationthanthesimple stress-relaxation model (Maxwell). Creep tests allow prediction of elastic, viscoelastic, and viscous flow characteristics separately. The creep test is perhaps the most appropriate means to determine the elastic parameter (instantaneous compliance) of a material (57). Model parameters may be associated with discrete components or constituents of the material being tested (14,15,58). This facilitates of the physicomechanigreater understanding of the microstructural determinants cal behavior or texture exhibited (1 2). A six-element model is found to best define in terms of physicomechanical the viscoelastic behavior of tomato pericarp tissue interpretation of the tissue softening mechanisms (12). The instantaneous elastic properties of tissue are attributed to the combination of cell turgor pressure and primary cell wall strength as dictatedby cellulose. The retarded viscoelastic properties are related to the independent changesin hemicelluloses and polyuronides. The steady-state viscous flow properties are related to higher cell wall fluidity

Linear Viscoelastic Methods

307

arising from a molecular weight downshift in cell wall and/or middle lamellar polymers and from exosmosis. The loss of turgor, breakdown of polyuronides, and overall increase in cell wall fluidity were each found to contribute about 2530% to the apparent softening of tissue. The downshift in molecular weight size distribution of hemicelluloses was presumed to contribute 10-15% to the tissue softening during ripening. Some recent transient viscoelastic investigations on raisins indicate that the raisin skin is the primary elastic element exhibiting relaxation times longer than those for theflesh, which contributes to the viscous character (59,60). Campanella and Peleg (6 1 ) investigated stress relaxation properties of processed American cheese in conjunction with mechanical models (three-element solid model) to simulate the interaction of the cheese with tongue or fingers. Though they made many oversimplified assumptions, they demonstrated that the sensory system performance could be analyzedin mechanical terms. Purkayastha et al. (62) fitted the creep curves of Cheddar cheese and potato flesh by a fourof the two materialsis clearly parameter model. The general rheological behavior expressed in terms of the model constants. Ma et al. (63) studied the viscoelastic properties of cheeses using creep and dynamic oscillatory tests. The creep test data are used to identify the differences in viscoelastic properties of cheeses due to fat reduction and addition of lecithin. It is shown by creep recovery measureof granular ments on the reduced-fat processed cheese that adding a small amount of the soylecithin or hydrogenatedsoylecithinimprovestexturalproperties cheese without affecting sensory acceptance scores (64). Kuo et al. (65) used a six-element Kelvin model to describe a modified squeeze flowtest data to determine cheese meltability. Accordingly, the following equation is used to represent the model for the cheese flow data:

where J(t) is the total creep compliance at time t, J,, is the instantaneous rigidity compliance, J , and J2 are the retarded compliances, T~ and zz are the retardation times, and vv is the Newtonian viscosity. Thevaluesoftheviscoelasticparameterscalculatedfor bothfull-and reduced-fat cheeses are given in Table 2 . The reduced-fat cheese had a lower instantaneous compliance (J,,) than the full-fat cheese. This suggeststhat a reduction in fat resulted in an increase in the elastic (or solid-like) character of the cheese. The instantaneous compliance may be related to the undisturbed protein (q,)of reduced-fat cheese network structure (63). The higher Newtonian viscosity suggested a greater resistance to flow at longer time. Newtonian viscosity may be attributed to the breakdown of protein network (66). Thus, the reduced-fat cheese could be considered to retain more of its solid-like viscoelastic structure than the full-fat cheese.

ters

308

Ak and Gunasekaran

Table 2 Six-ElementKelvinModelParameters of Regular-FatandReduced-Fat Cheddar Cheeses Subjected to a Creep Test for Meltability Evaluation

Cheddar cheese"

Six-element Kelvin model Jo( 1 /kPa)b J , (1 /kPa)' Jz( 1/kPa)'

0. I 4x 0.27" 0.55' 3.35" 28.04' 137.9'

2, (S)d

T?(s)d

qv(kPa.s)'

*Differentletters (x,y) In eachrowindicatesignificantdifference values. Instantaneous compliance. Retarded compliance. Retardation time. Newton~an viscosity. Source: DatafromRef. 65.

0.1 1" 0. 19x 0.46x 3.25"

23.89" 149.6" ( p

< 0.05) betweenthemean

A typical creep curve corresponding to the six-element model used by Kuo et ai. (65) is shown in Fig. 9. The curve has three segments corresponding to the Hookean, KelvinNoigt, and viscous elements. The retarded compliances (J, and J2) represent the principal components of the viscoelastic behavior of Cheddar cheeses. This reflected a high degree of retarded KelvinNoigt-type deformation

h

%

v

v

.........

0.9 "= :,-:

........ ..~"."."."._..

Wl")

J,+J,

...........................................

" ' . , 50

100

I

Jo

150

Time (s)

Fig. 9 Six-elementKelvinmodelusedtocharacterizecheesemeltability.(Datafrom Ref. 65.)

astic

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in Cheddar cheese under external loading. The instantaneous slope of the creep curve is calculated by taking the first derivative of Eq. (31) at time zero. This instantaneous slope is defined as the viscoelasticity index (VEI), which is computed as follows:

Cheese meltability determined by anothertest (67) and VEI correlated well, indicating the applicability of the creep test as a practical means for describing cheese meltability. Stickiness is a technologically important textural property that may cause major processing problems in baking and confectionery industries. Recently,Hoseney and Smewing (68) defined stickiness as the force of adhesion that results when two surfaces are contacted with each other. In the same article, they also discussed problems associated with measuring stickiness and described various instrumental methods used to measure food stickiness. Stickiness of wheat dough has been determined by the standard peel test, and the transient and dynamic rheological tests (69). The stress relaxation gradients were observed to be greater for sticky doughs than nonsticky doughs. Moreover, the similarity of stress relaxation gradients to the peel-rate plots is taken to indicate that adhesion is mainly a function of the bulk viscoelastic properties of the dough (69).

B. Dynamic Tests 1. Gelation and Properties of Dairy Products One commonuse of the SAOS technique is to measure linear viscoelastic properties of materials during gelation and aging (or curing). Here, we consider examples of milk gels formed by rennet or acid addition, and other dairy products (e.g., yogurt, cheese). Studies on gel development usually employ concentric cylinder system, whereas those on mature gels and cheese use parallel plate or cone and plate configuration. The cone and plate system is similar to theparallelplate configuration (Fig. 4) except that the top plate is replaced with a cone of small angle. The SAOS method has been used to follow gelation kinetics of various proteins and polysaccharides. In these studies, the technique is used to monitor the change in the dynamic moduli G’ and G” with time, and in some cases with temperature, at some selected frequency (usually 1 Hz). In a gelling system we usually observe sudden increases in G’ and G” after an initial lag period. This is often followed by a gradual decrease in rate of increase of G’ and G”, which eventually levels off in most cases. Moreover, once the mature (or fully cured) gel is obtained, it is common practice to make further measurements such as (a)

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strain sweep (G’ and G” vs. y) to determine the linear viscoelastic region, (b) frequency sweep (G’ and G” vs. O ) to determine the elastic character of the gel, and (c) temperature sweep (G‘ and G” vs. T) to evaluate thermal characteristics. Oneimmediateconcernariseswhen we want to determineviscoelastic to elastic gel. This is whether properties of a system going from viscous milk the applied strain or stress amplitude affects the aggregation process. Dejmek (70) measured G’ and G” during rennet coagulation of milk. When the applied shear strain was high (i.e.,‘yo = 0.2), the resultingG* from the continuous oscillationmodewassignificantlygreaterthan that from the intermittent oscillation mode. Apparently, continuous oscillation affects the aggregation process. Dejmek also reported that the coagulation process was not affected by continuous shear strains below 0.05 at 1 Hz (70). To avoid disturbing the skim milk gel network, Zoon et al. (71) started oscillatory shear measurements at 1 rad/s (-0.16 Hz) only after a weak gel formed (i.e. G’ = 2 Pa). Dejmek (70) reported that the linear viscoelastic strain limit for mature rennet gels is 0.05. The corresponding limit for rennet-induced skim milk gels is reported to be 0.03 (70). Paulsson et al. (72) applied strain amplitude of 0.02 of at 1 Hz to stay in the linear viscoelastic region while monitoring formation heat-induced P-lactoglobulin gels. Most studies on dairy gels seem to use strain amplitudes less than 0.05 and a frequency of I Hz. A typical curve obtained during gelation (or curing) is schematically illustrated in Fig. 10. The beginning of time axis (t = 0) usually refers to the addition

0

Time

~

F

Fig. 10 Illustration of typical dynamic moduli data (G’ and G”) as a function of time during gelation of milk showing the initial lag period and the subsequent gel development period.

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of rennet or acid to the milk. There is generally a lag period before dynamic properties attain values greater than the minimum torque of the rheometer. After the initial lag period, both G’ and G” increase with a rate that depends on the experimental conditions. Whether a dynamic property reach a plateau value or not also depends upon experimental conditions such as the temperature at which the gel is formed and aged (73). Bohlin et al. (74) designed and developed a dynamic testing instrument with coaxial cylinder geometry and used it to monitor coagulation of milk under conditions similar to thosein cheesemaking. They investigated effectsof calcium on coagulation of chlorideandrennetconcentrationsalongwithtemperature milk. Their data show that higher concentrations of rennet and calcium chloride and elevated temperatures result in an earlier start of coagulation and a faster gelation rate, leading to stiffer gels (i.e., higher G* values). The start of coagulation is identitied as the time at which G’ and G” began to deviate from zero (see Fig. IO). Nakamura and Niki (75) studied the influence of calcium concentration on rheological properties of casein micelles during gelation. They concludedthat of changing calcium concentration affects gelation rate but not the mechanism gelation. Several quantitative coagulation parameters may be obtained from gel development curves. For instance, Dejmek (70) presenteda method based on threeto derive coagulation parameters such as parameter Scott Blair-Burnett model of incipient gelation from the time constant of gel build-up and time constant gel development curves. Zoon et al. (73) obtained clotting time as the time from rennet addition to the formation of visible clots; it generally decreased with increasing gelation temperature. On the other hand, van Hekken and Strange (76) determined coagulation time as the time from rennet addition to the point where et al. (77) conducted G’ exceeded G”, or tan 6 became less than one. Guinee SAOS tests to investigate the effect of heat treatments of milk before ultrafiltration on the coagulation characteristics of retentates with different protein levels. They also used the Scott Blair-Burnett model to extract several coagulation parameters, which enabled them to compare the effects quantitatively. SAOS measurements are also used to characterize behavior of dairy products (e.g., yogurt, cheese, whey gels) as well toasevaluate effectsof many factors on viscoelastic properties of these products. Skriver (78) conducted SAOS tests on stirred yogurt to assess the effect of technological parameters such as culture type, fermentation temperature, and dry matter content on rheological characteristics of the product. Based on dynamic shear data, stirred yogurt is characterized as a weak gel. The main features of weak gels are given by Ross-Murphy (79) as G’ > G”. both G‘ and G” are largely independent of frequency, and the linear viscoelastic strain limit is small (y < 0.05). Moreover, Skriver (78) found that exo-polysaccharide produced by the ropy culture did not contribute to the dyin contrast to the viscometry namic gel stiffnessof stirred yogurt. This tinding was

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(large deformations) results where the contribution of exo-polysaccharide to shear stress is reported to be significant (80). Ronnegird and Dejmek (81) studied the development of gel structure in G’ data for the set yogurt by oscillatory shear measurements and compared the to find that modulus of the set yoghurt with that for commercial stirred yogurt latter is about 10 times smaller. Paulsson et al. (72) studied gelation of heat-induced P-lactoglobulin by dynamic rheometry at different pH levels (4.5, 5 , 7) and protein concentrations (3, 4, 5% mass/vol). They reported that the temperature at the start of gelation of G* is is mostly independent of pH and protein concentration but the value influenced mainly by the protein concentration and to a lesser degree by the pH. It is interesting to note that the exponent n in the relation JG*Jr. (cy, where c is the protein concentration, varied between 2.2 and 2.6 for rennet and acid milk gels (7 1 ) as well as heat-induced P-lactoglobulin gels (72) when pH was below 7. A higher value for n is reported when the pH was 7 (72). Viscoelastic properties of acid casein gels made by slow acidification with glucono-&lactone (GDL) have also been determined using oscillatory shear tests (82,83).Somesimilaritiesanddifferencesarenotedbetweenrennet-induced (skim) milk gels (71,73) and acid casein gels made with GDL (83). Forboth types of gels, gelation time increased with decreasing gelation temperature. The gelation time of GDL-induced gels is about 5-15 times that of rennet gels depending on the gelation temperature. The G’ of gels with GDL reached plateau values of 500-600,100-200and <20 Pa at 20, 30, and 4 0 ° C respectively, whereas the G‘ of rennet gels was still increasing after 72 hours of aging at 20°C and reached plateau values of 125 and 75 Pa at 30 and 4 0 ° C respectively. Salvador and Fiszman (84) used dynamic rheometry to study properties of acidic and neutral milk gels produced by gelation of commercial gelatin. I t is by gelatin than found that the values of G’ were higher for milk gels formed those for aqueous gelatin gels. Thisresult is taken to indicate stabilization of the gel network by milk components (84). Navarro et al. (85) recently reported on the linear viscoelastic properties of commercial samples of D u k e de Leche (a typical Argentine sweet spread) with different compositions. The linear viscoelastic limit for these sweet spreads varied between 3.5 and 9%, the larger value belonging to the confectionery type, which contained agar as thickener. Dynamic rheological data indicated that the behavior of the common and low-calorie Duke de Leche was more like that of the concentrated solutions and dispersions, whereas the behavior of the confectionery type was similar to that of a weak gel (85). Another use of the SAOS test is demonstrated by Ozer et al. (86) where rheologicalproperties of six differentlabneh(concentratedyogurt)samples were compared using data from SAOS, penetrometer, and viscosity tests. The destructive penetrometer and viscosity measurements failed to reveal expected

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differencesamonglabnehsamplespreparedafterdifferenttreatments.However, oscillatory shear tests clearly differentiated between the samples and, therefore, are considered to be more reliable to determine rheological properties of labneh. the qualityof prepared foods Melting characteristicsof cheese are critical to containing cheese (e.g., pizza). Several methods have been developedto measure meltability of cheese (87). There is, however, a lack of correlation among traditional methods (88), and thus new methods are needed. Riiegg et al. (87) stated 6, could be potentially used as a predictor for cheese that the loss tangent, tan meltability. Indeed, the use of dynamic rheometry to study cheese properties at high temperatures has been increasing (89-91). (G', G", Ustunol et al. (89) correlated the dynamic rheological properties and G*) of Cheddar cheese up to 90°C with the meltability data from the traditional Arnott test. The minimum complex modulus G* is suggested as a possible meltability indicator as it showed a significant correlation ( I = -0.80) with the empirical meltability results. A recent study on the melt characteristics of imitation cheese reports a higher correlation coefficient ( r = 0.96) between the maximum tan 6 values and meltability results obtained by the empirical Olson-Price tube method (90). Meltability of process Cheddar cheese made with different emulsifying salts has been measured by dynamic stress rheometry (91). In this study, the parameter to compare behavior of cheeses was the so-called transition temperature, which is defined as the lowest temperature at which tan 6 = 1. The change from more elastic to more viscous behavior occurs at a lower temperature for process cheese containing trisodium citrate (56.5"C) than that containing disodium phosphate (64.6"C) (91). A significant concern in dynamic rheometry at high temperatures is the slippage (92), which results in inconsistency in the rheological data (93). Howto the plates and/or using serrated ever, remedies such as bonding the sample plates or compressing the specimen slightly have been applied to reduce the slippage problem (89-91,94-96). Another technique to study properties of melted cheese, which does not suffer from slippage problem, is the lubricated squeezing flow. This technique is a simple but fundamental alternative to the traditional empirical meltability tests and has been frequently usedto study cheese meltability (67,97-99). The sample and test geometry of the squeeze flow method makes it also suitable for performing creep and stress relaxation tests within linear viscoelastic region (65). 2. Determining Gel Point in Polymeric Systems

Another area of research where SAOS method has found wide use is in determining gel point, i.e., the moment at which a polymer/biopolymer system changes

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from a viscous liquid(sol) to an elastic solid (gel) during the course of the gelation process. Determining the gel point from rheological properties such as steady shear viscosity fortheliquidstateandequilibriumshearmodulusforthesolid state requires extrapolation (Fig. 1 1 ) and suffers from singularity at the transition (100). SAOS method,however,providescontinuousrheologicaldata for the entire gelation or curing process. Hence, SAOS has become widely used of maturegels (100to investigatethesol/geltransitionandtheproperties 104). Ross-Murphy (105) listed a number of rheological means to detect gel point or gel time: (a) when the signal from gelling system becomes just greater than the background noise, (b) whenG’ becomes higher than a chosen threshold value, (c) when G’ becomes just greater than G” (the cross-over method), and (d) when tan 6 becomes independent of frequency (the Winter-Chambon method). Method I suffers from being dependent on the instrument’s minimum torque value, which may vary among commercial rheometers. Method 2 requires prior knowledge of 3 , as Tung and Dynes(106) pointed out, the proper value for gel strength. Method depends on the frequency of the oscillation test. Method4, based on fundamental arguments that have been experimentally supported, appears to be the preferred way in recent studies to detect gel point.

Fig. 11 Illustration of discontinuityinthesteadyshearviscosity/equilibriummodulus data at gel point also known as the critical point (t,) during sol-gel transition (Adapted fromRef.100.)

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In the study by Tung and Dynes (106), it was suggested that the time at which G’ and G” curves cross each other can be used for determining the gel point. This studyalso reported that the gel time determined as such was a function of frequency of the oscillatory test. Recognizing that the gelation of a material must not depend on the frequency of the rheological test, Winter and Chambon (100) havedeveloped a newmethodforlocatinggelpoint.According to the Winter-Chambon criterion, tan 6 (= G”/G’) at the gel point becomes independent of frequency (104) (Fig. 12). Moreover, the cross-over method is a special case of the Winter-Chambon method (102,104). Lopes da Silva and Gonqalves (107) studied rheological properties of curing high-methoxyl pectin/sucrose gels at different temperatures using SAOS experiments. The G’-G” cross-over method could not be used asa criterion to identify the gel point. They instead applied the Winter-Chambon criterion. Michon et al.( 1 08) applied the Winter-Chambon criterion to determine critical parameters of gelation (e.g., time, temperature) at different polymer concentrations for systems involving iota-carrageenan and gelatin. When gelatin was cooled from 60°C the gelling time was found to be 44 minutes. In another set of experiments, the gelation temperature of iota-carrageenan was determined to be 53.5OC based on the same criterion. These researchers provided phase diagrams where critical temperature of sol/gel transition was charted against concentration of the polymers. These graphs showed that at a given temperature, a gelatin with mean molecular mass of 70.000 daltons would require a much higher concentration for the sol/ gel transition than that with a mean molecular mass of 182,000 daltons.

. 4

8

c (TI

. I -

0.1

Gelation time (min) Fig. 12 At thegel point, loss tangent (tan 8 ) is independent of frequency (w). (Data fromRef. 103.)

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Labropoulos and Hsu (109) have used the SAOS method to investigate gelforming ability of whey protein isolate (WPI) dispersions subjected to different heattreatmentsandotherprocessingvariables(e.g.,pH,concentration).Employing the Winter-Chambon criterion for gel point detection, a wide range of gelation times from 12 to 164 minutes is obtained depending on the experimental conditions. Information from such studies is expected to allow processors to obtain desired gel properties by controlling the variables during gelation of WPI dispersions.

3. Characterizing Food EmulsionGels Different types of emulsions are widely encountered in several food products and maysuch as ice cream, margarine, butter, beverages, sauces, salad dressings, onnaise (1 10).The stability of food emulsions is arather important and complex phenomenon that greatly influences the quality and performance of a product. Good emulsion stability means that the size distribution and the spatial arrangea ment of droplets do not change significantly during the observation time. In recent review article, Dalgleish (1 11) discussed different types of instabilities that may occur in food emulsions. The instabilities observedin oil-in-water emulsions are illustrated in Fig. 13.

Emulsion droDlets may Which mav:

0

Cream

lead to: A

Creaming 01

Flocculate (seml-reversibly)

0 01

Aggregate (irreversibly) andlor

or

Coalescence flocculation or

Q0

Flocculate by bridging ~~~~~

. " +

3

@-0

~

Fig. 13 Instabilities inanoil-and-water

emulsion. (Adaptedfrom Ref. 111.)

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Rheological properties of food emulsions play important roles in the stability as well as texture and mouthfeel of these products. In this section we present examples on the use of SAOS method to characterize food emulsion gels, to understand stability mechanisms, and to determine the roles of various factors contributing to the emulsion stability. Dickinson and Golding (1 12) used the SAOS method (0.04 Pa, 0.1 Hz) to examine viscoelastic changes due to development of a particle network in emulsions prepared at pH 6.8 with sodium caseinate as the only emulsifier( I -6% by mass) and n-tetradecane as the dispersed phase (10, 35, or 45% by volume). The 2% (by mass) caseinate emulsion (35% by volume n-tetradecane, pH 6.8, 30°C) behaved like a viscous liquid with G” > G’ at all times. This behavior is associated with a nonflocculated system. In contrast, for the 6% by mass caseinate emulsion (35% volume n-tetradecane, pH 6.S,3O0C), there was an initial decrease in the dynamic moduli followed by a steady-state region, which was followed by a steady increase of both quantities (G’ faster thanG”) until a cross-over point (G’ = G”). Thisbehavior is associatedwiththe floc reorganizationtomore closely packed structures and the formation of a gel network by the flocculated particles. Muiioz and Sherman( 1 13) measured the viscoelastic properties of commerSAOS tests cial mayonnaise, reduced-calorie mayonnaise, and salad creams using with a controlled stress rheometer (1 Hz, 8.96-1 8.51 Pa). They have explained the observed differences in viscoelastic properties by variations in the ingredients of these emulsions. For instance, the lower G’ value of one mayonnaise compared to another is related to the presence of sugar in the former, as sugar molecules exerted a shielding effect on protein groups involved in interaction and network formation among the oil droplets. The lowest G’ values for salad creams are attributed to the lower oil content, yielding a lower concentration of oil droplets in the emulsion. This is in accord with the results of Dickinson and Golding ( 1 12) in that even when protein concentrationwas high (>8% by mass), emulsion made with lower volume fraction of dispersed phase (oil) had a weaker structure. Other studies by Gallegos et al. ( 1 14) and Ma and Barbosa-Cinovas ( I 15) confirm that higher oil contents result in higher dynamic moduli for commercial and modelmayonnaisesamples.Moreover,thestrainsweepmeasurements at I O rad/s on model mayonnaise samples indicated a narrower linear viscoelastic region for samples with lower oil content ( 1 14). Past work on whey proteinshas shown that co-homogenizationor preemulsification of the oil with lecithin reduces significantly the strength of heat-set emulsion gels ( 1 16). Consequently, Dickinson and Yamamoto ( I 17) used the SAOS method (1 Hz, maximum strain amplitude of0.5%) to examine thoroughly the influence of lecithin added after emulsion formation on the properties of heatset P-lactoglobulin emulsion gels. The dynamic data(G’ and G”) clearly showed that the addition of lecithin at a concentration of 4.4% by mass increased G’ of

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the heat-set @-lactoglobulin emulsion gel at least 10 times. It is suggested that once P-lactoglobulin becomes adsorbed at the surface of oil droplets, the role of lecithin is then toformcomplexes withadsorbedandfreeP-lactoglobulin to reinforce the heat-set emulsion gel network (1 17). The positive effect of lecithin lesser extent. is also observedin G’ of heat-set @-lactoglobulin gel (no oil) buta to It is concluded that the lecithin-containing emulsion gels behaved more like a “strong” gel since G’ is less frequency-dependent in the range 10” to 2 Hz as compared to the @-lactoglobulin gels made without lecithin. to determine effects of proDynamic measurements are also employed cessing parameters, such as emulsification temperature and machine type, on the stability of salad dressing emulsions (1 18). Franco et al. ( I 18) reported that the linear viscoelastic region of emulsions is significantly affected by the processing parameters. Moreover, their data indicated that higher energy input during emulsification and elevated processing temperature (50°C) result in higher values of dynamic moduli because of enhanced network formation of flocculated oil droplets. This in turn improved the stability of the emulsions.

4. DeterminingSensoryPerception Linear viscoelastic tests have also been applied to study the interaction between texture, flavor, and taste. It has been shown that complex viscosities q* from dynamic tests rather than steady shear viscosities give better correlation with sensory thickness for weak gel systems such as liquid and semisolid foods( 1 19121). Hill et al. (122) investigated the relationship between the perceived (sensory) thickness, taste and flavor and rheological parameters of a lemon pie tilling. They established a general linear relationship between perceived thickness and q* over theviscosityrange of 1,000-70,000mPa.s. (Fig. 14). Richardsonet

2.5 3.5 3.0

4.0 log (q’, rnPa.s)

4.5

5.0

Fig. 14 Linear relationship between perceived sensory thickness and complex viscosity. (Data fromRef. 122.)

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al. (1 19) covered the viscosity range of 10-10,000 mPa.s using model systems thickened by xanthan, guar, and starch. These results indicate that q* measured at a frequency of 50 rad/s can be considered a suitable criterion for predicting perceived thickness in widely differing systems (122). The relationship between taste and flavor and rheological properties seem to depend on the structure of the solution and the concentration. It is well established that the intensity of perceived taste and flavor decreases as the product thickness increases ( I 23). In model solutions containing random coil polysaccharides, suppression of taste and flavor starts when the polysaccharide concentration exceeds the critical concentration where the polymer coil starts to entangle. The exception to this is xanthan gum where good flavor release is achieved at a concentration in excess of the critical concentration ( 120,121,124,125). At comparable viscosities, cornstarch suppresses sweetness far less than random coil polysaccharide, which is thought to be a consequence of a weak gel structure ( 1 22). Windhab et al. (126) established stability domains for emulsions processed under various conditions using different rotor/stator gap geometries. They reported a linear relation between the sensory thickness impression from the spoon test and the ratio of G‘/G” from the dynamic rheometry for optimally processed and stable emulsion samples. R@nnet al. (127) recently reported on the predictionof sensory properties from dynamic rheological measurements for low-fat spreads. Ten commercial low-fat spreads are evaluated bothby a trained sensory panel and by SAOS measurements. Two of the I 3 properties evaluated by the panel, namely, meltability of the temperature sweeps and graininess, are successfully predicted from features (G* vs. T) from the rheological measurements.

5. Determining GlassTransitionTemperature Materials with amorphous or partially amorphous structures undergo a transition from a glassy solid state to a rubbery viscous state at a material-specific temperature called the glass transition temperature, Tg (29,38,44). Glass transition in amorphous food materials generally occurs over a range of temperature rather than at a single temperature (44,128,129). The technological importanceof the glass transition phenomenon for different types of foods has been discussed in detail in the literature (44,130-136). The glass transition or the associated parameter Tg has a great effect on the processing,properties,quality,safety,andstabilityoffoods(137). Tg or the difference between the temperature of a material and its Tg (Le., T-Tg) affects the physical and textural properties of foods (e.g., stickiness, viscosity, brittleness, crispness, or crunchiness), the rates of deteriorative changes (e.g., enzymatic reactions, nonenzymatic browning, oxidation), and the success of many processes (e.g., flavor encapsulation, crystallization). Hence, the knowledge of Tg is essen-

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tial in assuring the quality, stability, and safety of various foods such as confectionery products, breakfast cereals, baked goods, coated flavors, frozen products, and food powders (128,130,133,134,137-146). At the glass transition temperature,* properties such as the thermal expansion coefficient, the dielectric constant (for polar materials), and the heat capacity exhibit a discontinuity. Thus, techniques measuring such property changes have of Tg (e.g., dilatometry, calorimebeen developed for experimental determination try) (29,39). Differential scanning calorimetry (DSC) is probably the most commonly used technique for determining Tg. Besides DSC, the utility and advantages of other techniques such as nuclear magnetic resonance (NMR), electron spin resonance spectroscopy (ESR), and dynamic mechanical (thermal) analysis (DMA or DMTA) have been increasingly appreciated (148-153). Kalichevsky et al. (151) stated that the dynamic mechanical techniques are more sensitive than DSC to the molecular motions around Tg and the relaxations below Tg. When dynamic mechanical spectroscopyis employed within the linear viscoelastic regime to determine Tg, the storage and loss moduli (G' and G" or E' and E"), and loss tangent (tan6 = G"/G' or = E"/E') are measured as a function of temperature at a constant frequency and a selected heating or cooling rate. In of biological and synthetic polymers exhibits glass transition, the storage modulus a sharp drop with temperature. whereas the loss modulus or tan 6 shows a characteristic peak (29,39,152,154-157). The decrease observed in modulus of amorphous synthetic polymers is typically about 3 orders of magnitude (29), whereas that observed in biopolymers is about one order of magnitude (148,151-153). Moreover, as Peleg (158) demonstrated, the plot of stiffness or rigidity (E', G') versus temperature in the transition region of biomaterials has a downward concavity, which cannot be described by conventional models, such as WLF ( 1 59), developed for synthetic polymers. Peleg, therefore, suggested another model and demonstrated its applicability to describe the stiffnessor rigidity versus temperature relationship of biopolymers at the transition region ( I 29,155,158,160- 162). Different definitions have been used to facilitate experimental determination of Tg from the dynamic mechanical data: (a) temperature corresponding to the onset of drop in storage modulus T,,,,,, (b) temperature corresponding to the (c) temperamidpoint of the glass transition region for storage modulus Tlllldpolnl, turewheretheextrapolatedline of theinitialmodulusintersectsthat of the (d) temperature corresponding to the loss modulusG" peak steepest slope TInlerrec,, 6 peak Tlans.These are schematiTG",and (e) temperature corresponding to the tan of Tg for the same material cally illustrated in Fig. 15. It must be noted that values may differ slightly or significantly depending on the definition, the experimental conditions (e.g., heating/coolingrate in DSC and test frequency in DMA or

* No phase change is involved as

the word "transition" may possibly ,imply (147).

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Steepest slope Initial slope

Moduli=0.5x Initial moduli

T .,

'tan

6

Temperature

Fig. 15 Schematic drawing of different ways of obtaining glass transition temperature from dynamic mechanical data.

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DMTA), and the technique (e.g., DSCvs. DMA) (29,153,154). Hagen et al. (163) reported that Tg values, for instance, of an unfilled natural rubber differby about 10°Cwhen determined by using two commercial dynamic mechanical instruments (DMA vs. DMTA). Frequently the temperature corresponding to G” or tan 6 peak is used as a marker ofTg (29,148,149,153,164). However, Peleg (157) showed by computer 6 peak location does simulations and published experimental data that the tan not always correspond to the transition zone even for the same material with different moisture contents. Peleg (157) has instead suggested, as a more meaningful index of Tg, to use the temperature at which 50% of the initial stiffness (i.e., storage modulus, G‘) is lost. Several factors, such as composition,* molecular weight, and features of chemical structure (e.g., cross-links, side groups) can alter the glass transition temperature of materials. Among the constituents of foods, water is an effective andubiquitousplasticizer,whichlowersthe Tg of mostbiologicalmaterials (134). Plasticizers are relatively low molecular weight materials that, when added it to amorphous polymers, lead to a large increase in mobility and thus make easier for changes in molecular conformation to take place. Cocero and Kokini (148) demonstrated the plasticizing (or softening) effect of water, as measuredby the storage modulusG’, on the major protein component (i.e.. glutenin) in wheat Hour. Hallberg and Chinachoti (166) used DMAto study phase transitions in shelf-stable MRE (“meal, ready-to-eat”) bread. Three distinct transitions are reportedin fresh MREbread and among those the main transition temperature decreased from about 160 to - 1 1°C as the moisture content increased from about 2.6 to 28.8%. It is also found that transition temperatures ( 166). Gontard et al. ( 167) remained nearly constant throughout 3 years of storage on mechanical reported on the strong plasticizing effect of water and glycerol and barrier properties of edible wheat gluten films. Kalichevsky and Blanshard of amylopec(164) studied the effectof fructose and water on the glass transition tin to find that the effect of fructose on the Tg of amylopectin is greater at lower water contents. The depression of Tg, due to plasticization of amorphous components by water or other plasticizers, to the vicinity of ambient temperatures may have a significant effect on the shelflife and stability of foods (128,168).The importance of plasticization by water becomes more evident when one considers the hygroof water plasticiscopic nature of most dehydrated foods. A typical manifestation zation is the loss of crunchiness or crispness in snack foods and breakfast cereals (161,169,170). It is important to note that the loss of crispness in the corn Hour

* It

is interesting to note here that Matveev et al. (165) calculated Tg of proteins from the amino acid composition and reported good agreement with the experimental results.

lastic

Linear

extrudates and in the corn cakes occurred within the glassy state, meaning that the knowledge of Tg alone may not be sufficient to predict crispness (142,171). in thelowWater can also exertanantiplasticizingeffectparticularly moisture or low-a, region by causing an increase in puncture strength, modulus, and brittleness (167,172). Moreover, addition of low molecular weight diluents other than water (e.g., fructose, glycerol) to glassy polymers (e.g., amylopectin, starch) on the one hand lowers Tg but at the same time exerts an antiplasticizing effect on the mechanical properties of the polymer ( 172- 175).

IV. SUMMARYANDFUTURETRENDS Evaluation of food quality parameters has been traditionally performed via sensory panel tests and/or destructive mechanical tests suchas TPA and the uniaxial compression and tension. Such methodsstill remain popular as they relate reasonablywell to the consumer perception of macroscopic food quality. However, increasing consumer demand for consistently high-quality products and the comto obtain fundamental information petitive marketplace have led the food industry to improve quality assurance and/or control operations. Linear viscoelastic methods arewidely used for obtaining information regarding structural organization of the food materials. Since food texture is largely a manifestation of microstructural organization, viscoelastic methods provide the basic information needed to understand the factors that influence quality. The transient methods such as stress relaxation and creep have been the most commonly used viscoelastic methods. By appropriate mechanical models, transient test data can be used to obtain structural information and determine the viscoelastic properties of the materials. The theory of dynamic viscoelastic tests has long been known (176) and has been used in the polymer industry (46,177). However, the application of dynamic tests for studying food materials have been to eitherlack of properinstrumentation delayed until relativelyrecentlydue and/or highcost. The advances in computerandinstrumentationtechnology have led to improved rheometer design at an affordable cost. This has promoted widespread use of dynamic rheological testing of foods. While the transient tests are relatively easier to perform, they may take a long time, whereas the dynamic a tests allow us to obtain viscoelastic parameters fairly accurately and within shorttime (at frequenciesabove 1 Hz). In addition,theability of dynamic rheometers to perform tests at a wide range of frequencies provides information not easily obtainable by other methods. In the case of high-speed processing of foods, for example, viscoelastic characterization at a high frequency is very critical. Determining the fundamental structural characteristics of foods is still the main focus of linear viscoelastic tests. Nevertheless, new research efforts have

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been afoot in correlating transientand dynamic rheological parametersto various sensory quality attributes and some relevant functional properties. We expect in such attemptswill come only if the methsuch efforts to continue. But success ods and the properties are well rooted in the fundamental structural aspects of both the food materials and the targeted properties. In this regard, we hope that more researchers will use the linear viscoelastic data to elucidate the structurefunction relationshipsof foods rather than to report trends and/or to obtain simple statistical correlations with some enduse properties. Studies on glass transition phenomena in amorphous foods are expected to increase at a faster rate since techniques otherthan DSC, such as DMA, DMTA, NMR, and ESR, are being introduced and used more and more. Complementary data from allof these techniqueswould enable researchers to understand changes taking place at and around the glass transition temperature in wide range of systems, from simple solutions to complex frozen foods. The plasticizing and antiplasticizing effectsof water and other diluents obviously deservemore investigation. Also, more work is expected to be done on the changes that occur in the glassy state itself and how these changes affect the quality or textural attributes of foods. Although linear viscoelastic methods are useful, they suffer from the fact that they employ very small strains to be relevant to study material properties during real processes that often employ large and rapid deformations (e&, food mixing and extrusion). Novel rheometers have been developed to address these issues, which are nonlinear (28,178). These rheometers have expanded the ability of rheologists to obtain information heretofore unavailable but critical for successful food process design and/or product development (179). We expect that further research in the nonlinear viscoelasticity in conjunction with the results obtained via linear viscoelastic tests will bring food rheologists closer to characterizing food material properties and quality attributes more completely than is now possible.

Dedication: The authors dedicate this Chapter to the memory of thousands of victims of the earthquakes in Turkey on August 17 and November 12, 1999.

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Biosensors in Food Quality Evaluation Sudhir S. Deshpande Signature Bioscience, Inc., Burlingame, California

1.

INTRODUCTION

Quantitative determination of the composition and propertiesof both raw materials and processed food products is important from the viewpoint of quality and safety of the food. Quality depends on color, flavor, aroma, texture, nutritional attributes, and microbial content. Food safety relatesto the content of pathogenic microorganisms and toxic compounds, including microbially produced toxins, pesticides, and naturally occurring toxic and antinutritional compounds, as well as compounds produced by processing. Food analysis thus presents several chalof foods available, lenges, in large part because each analyte in the wide range from fluid products such as milk and fruit juices to processed meats, is complex and nonhomogeneous. Quality control testingin the food industry has been based upon traditional analytical methods that require hours, if not days, to complete. In recent years, the food industry is increasingly adopting food safety and quality management systems that are more proactive and preventive than those to rely on end-product testing and visual used in the past, which have tended inspection. The regulatory agencies in many countries are promoting one such management tool, Hazard Analysis Critical Control Point (HACCP), as a way to achieve a safer food supply and also as a basis for international harmonization of trading standards. For HACCP to be effective and successful, rapid methods for monitoring and verifying performance need to be available. This, in turn, has provided an impetus for developing newer and improved detection methods and technologies that are “cheaper, better, and faster.” Until recently, rapid quality-monitoring methods for on- and near-line use were considered not to be very accurate or very reliable. The consensus was that

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the fastest resultis not always the best. The 199Os, however, have seen the procession of traditional laboratory analyses to the factory production floors. Measurement of constituents such as fat, protein, carbohydrates, moisture, and solids is being incorporated into quick, convenient instruments designed for use in close physicalorfunctionalproximitytotheprocess.Withtimelyfeedback,better process decisions are made, helping boost production volume, reduce inventory and product in hold, and free up laboratory personnel. Sometimes, incorporation of sophisticated instrumentation on-line requires high initial investments.But the tremendous cost savings that rapid methods can help realize, in terms of wasted product and time lost pending analytical results, should not be taken lightly. Although such investment depends on the product and production volume, often it pays off within a matter of months. Along with costs, several points need to be considered in selecting on-line sensors for monitoring food process and quality control. Some of these are summarized in Table I . In modern food-processing plants, computer control has already become an important part of factory operation. As yet unavailable, or scarcely used, are

Table 1 Factors to Consider in Using On-line Sensor Technologies for Food Process

and Quality Control Factor How does the method's accuracy compare to standard methods? What is the percent recovery? Does the method identify and measure parameters of interest? What Specificity steps are taken to ensure a high degree of specificity'? Precision Is the method's precision within batch. batch-to-batch, or day-to-day variation'? What steps in the procedure contribute the greatest variability? Ruggedness Does the instrument operate consistently in tough conditions'? Is it insensitive to fluctuations in pressure and temperature prevalent in food plant settings? Hardware What are the initial and ongoing operational and maintenance costs? How often does the instrument have to be calibrated. and what is involved? What are their costs and availability? Are there any disposal and exReagents and posure concerns? consumables Are the Reproducibility How reliable is the method from the standpoint of precision? results of one set of measurements comparable to those from another set of measurements by the same instrument and those from another similar instrument/method? Does the method give results quickly enough'! What is the on-line Speed compatibility of the instrument with the existing and future computer control systems in the plant'?

Accuracy

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sensors to simultaneously monitor the multiple parameters of the production lines and report the data to the computer. Furthermore, traditional monitoring usually relies on surveillance of physical and/or chemical parameters of a process such as the time and temperature of heating or pH. However, the issue of foodborne pathogens has recently captured the attention and concern of the scientific community, academia, government agencies, and consumers. Much hasbeen written about the number of cases, outbreaks, foods, and microorganisms involved and as the about the costs of foodborne diseases to the nation’s economy as well potential negative impact of lack of food safety in the nation’s food supply ( 1 4). The culprits are the pathogenic microorganisms and their toxins and metabolites in our food supply. Thus, there is an increasing need for the introduction of in-line microbiological analysis and the production of data relevant to product safety and shelf life. There is some controversy as to whether microbiological tests can be used to monitor critical control points during food processing because of the length of time needed to generate results and the sampling strategy required for meaningful results. Verification that the process is safe must involve microbiological testing, but the results need not be generated in real time. However, considerable advantages may accrue if verification can be achieved quickly. The HACCP model’s primary goal-on- or at-line monitoring control-has indeed driven a focus on the role of microbiological methods for use in such programs. a new, rapid typeof moniBiosensor technology can offer the food industry toring and measuring device whose speed, sensitivity, stability, and ease of use exceed the current methodology. The ability of the food microbiologist, for example, to monitor food productionon line with an “organism” probe, whose utilization is as simple and straightforward as that of a pH meter, is not yet a reality. of biosensors may rapidly perform a variety However, in the future an array ofanalyticalprocedureswithhighreproducibility,specificity,andsensitivity. Potential uses for biosensor technologies in the food industry include, among others, proximate analysis, nutritional labeling, pesticide residues, naturally occurring toxins and antinutrients, processing changes, microbial contamination, enzymatic inactivation, and BOD (biological oxygen demand) of wastes. In this chapter, the basic principles of the biosensor technology as relevant to the food industry, a variety of biosensors for analytical purposes, emerging detection technologies in this field and their future in the food QA/QC laboratories are briefly described.

II. WHATAREBIOSENSORS?

A sensor canbe defined as a devicethat respondsto a physical stimulus producing a response or signal that can be used for measurement, interpretation, or control. It is usually constructed from three components: the detector, which recognizes

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the physical stimulus; the transducer, which converts the stimulus into a suitable signal; and the output system itself, which involves amplification, analysis, processing, and display that is usually electrical. The term “biosensor” is now generally applied to those devices that employ a biologicaUbiochemica1 detection system. The reactive surface (e.g., the biochemical or signal transducer) generates a measured response upon binding with an analyte. The device and associated instrumentation (the physical transducer component) convert analyte-binding events into qualitative or quantitative units of analyte concentration. Thus, the conversion of the analyte by the biochemical transducer into another chemical species and/or physical property is sensed and converted into a measurable signalby the physical transducer. Immunosensors are essentially biosensors that use antibodies as signal transducers. Molecular recognition of a specific sample analyte by the respective receptor is realized by the “lock-and-key’’ principle, which is the biocomponent or the biocatalyst that gives the biosensor its specificity and selectivity ( 5 - 8 ) . It is also involved in rapid recognition and detectionof specific molecules or chemical reactions. This molecular recognition and detection mechanism, in general, creates a chemical change in or by the biological element proportional to the reaction, which can then be monitored by the physical transducer. The biocomponent must, therefore, have intimate contact with a suitable transducing system. Enzymes, receptors, organelles, tissues, nucleic acids, antigens, or antibodies, as well as whole cells of bacterial, fungal, plant, or animal origin, can be used as a biological detection system. The physical transducers or measuring principles generally vary with the type of biological detection system used in the biosensor. The transduction systems commonly used in biosensors are summarized in Table 2 . For example,

Table 2 CommonlyUsedBiosensorTransductionSystems

Transduction system

Options

ElectrochemicalPotentiometry,amperometry,voltametry,ion-sensitiveelectrode (ISE), ion-sensitive field effect transistor (ISFET), immuno-field effect transistor (IMFET) Electrical Surface conductivity, conductivity, capacitance Thermal Calorimetry, enzyme thermistor (heat of reaction or absorption; TELISA or thermistor ELISA) Paramagnetism Magnetic Optical Fluorescence, luminescence, reflection, absorption, surface plasmon resonance, scattering, evanescent waves PiezoelectricQCM.SAQ, SH/APM, Lambwave,Lovewave

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activity of an enzyme biocomponent can be monitored by measuring pH change, production of oxygen or hydrogen peroxide in the chemical reaction, nicotinamide adenine dinucleotide (NADH) fluorescence or absorption, conductivity, or changes in temperature. Similarly, reactions involving the antibody as the biocomponent can be monitored via a surface plasmon resonator grating coupler, interferometer piezoelectric device, fluorescence or enzyme-linked assays, elec(7). In contrast, trodes, or absorption, fluorescence, or luminescence measurement oxygen (0,)and carbon dioxide (CO,) electrodes are preferred transducers when microbial cells or plant and animal tissues are usedas the main biological components (9). The transducer can be viewed as the interpreter of the biosensor device. It takes the energy impulse from the biocatalyst and translates it into a usable signal relative to the conditions of the measured analyte. Proper electronics then amplify the signal and transferit into an output that can be interpreted by the user. Interference and distortion of the signal are minimized,as the transducer is usually adjacent to the biocatalyst. The interface between the biocatalyst and the transducer often constitutes the major hurdle in the development of a practical device. The attachment or the immobilization of the biocatalyst to the transducer surface is critical to the efficiency of the biosensor. The immobilization process must produce durable and repetitive binding of the biocatalyst to the transducer without impairing the activityor specificity of the biomolecule for the target analyte. This biocomponent element of the biosensor may be protected by a membrane that is permeable to certain molecules in the environment. In many cases, the biological receptor is directly immobilized or adsorbed onto the surface of the transducer. a generalized biosensor is shown in Fig. 1. A A schematic drawing for target analyte (illustrated by solid circles) in the external medium (the sample) must be able to enter the biosensor. The external membrane of the biosensor must be permeable to the analyte and,if possible, exclude other chemical species that the biosensor might also be sensitive to. The biological element inside the biosensor then interacts with the analyte and responds in some manner that can be detected by a transducer. The biological element may (a) convert the analyte to another chemical species (represented by open circles) through a biochemical reaction, (b) produce or release another chemical product in response to the analyte stimulus, (c) change its optical, electrical, or mechanical properties, or (d) make some other response that can be reliably quantified. There may be another internal membrane near the transducer, which might have different permeability a biosensor deproperties than the external membrane. The output signal from pends on the type of transducer it uses. The transducer may be a conventional electrochemical sensor or may be based on another technology. It is quite evident that advances in both biotechnology and electronics have accelerated the development of biosensors. The biotechnological advances include an increased understanding of biomolecules and biomolecular interactions.

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I

Semipermeable membrane

medium

Electrolyte

Signal

/ Transducer Internal membrane

Fig. 1 General principlesof a biosensor. A specific chemicaltarget (analyte, represented by solid circles)is recognized by the biological element,creating a stimulus (opencircles) to the detecting transducer, from which a reproducible signal is measured.

The availability of morerefined and improved transducer elements is largely due to the progress made in the microelectronics, microfabrication, and communication industries. The biosensor technology thus encompasses the molecular recognition and amplification inherent in many biochemical reactions and combines them with the signal processing and transmission capabilities of electronics and fiberoptics technology. By providing valuable real-time information, biosensor technologies have the potential to revolutionize a wide spectrum of biochemical analyses for health care, food-processing, agricultural, and pollution-monitoring applications. The developmentof a biosensor, therefore, requires an integrated, multidisciplinary team of biologists, chemists, physicists, engineers, and computer experts. This blend of expertise is generally not found in every establishment, so biosensor developments have primarily resulted from interinstitutional collaborative projects.

111.

CHARACTERISTICS OF AN IDEAL BIOSENSOR

A principal impetusin progressing towards the development of biosensor systems has been the need to produce a simple, very rapid, sensitive, and easy-to-use

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analytical system that doesnot need trained specialists to produce reliable results. The aim isto develop a small, portable unit into which the sample canbe inserted without preprocessing. The result should be obtained within seconds, and the answer should not be subject to interference or modification by the matrix, the user, or environmental factors. The optimum design of a biosensor is dictated by several basic physical properties of the measuring system, as well as those of the media in which the measurement is made (10,l I). Some of the most pertinent properties and characteristic behaviors of an ideal biosensor are described below.

A.

Sensitivity

The sensitivity is usually defined as the final steady-state changein the magnitude in concentration of a of the biosensor output signal with respect to the change specific chemical species (10). In other instances where measurements are based on the dynamic response of the biosensor, it is the change in the signal with time for a given change in concentration or some other relationship that depends on time. Time integration, frequency analysis, or other data processing of the time varying signals may also be of value in relating them to the concentration of the analyte. Factors that affect the sensitivityof a biosensor forits target analyte include the physical size of the sensor, the thickness of the membranes and resulting and mass transport of chemical species from the sample to the sensing region, various processes that deactivate the biosensor or otherwise impairits operation over time. Ideally, the sensitivity of a given biosensor should remain constant during its lifetime and should be sufficiently high to allow convenient measurement of the transducer output signal with electronic instrumentation.

B. Calibration An ideal biosensor should be easily calibrated by exposing it to prepared standard solutions or gases containing different known concentrations of the target analyte. It is preferable to perform a calibration procedure only once to determine the sensitivity of the biosensor for subsequent measurements. However, in practice it is usually necessary to make periodic calibrations at regular intervals to characterize changes in the sensitivity with time.

Linearity C. A biosensor need not be linear to be practically useful, as long as the calibration curve can be obtained with sufficient accuracy to interpret the biosensor signal. Ideally, the measurements should be made in the linear range of the calibration curve.

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D.DetectionLimit Ideally, the range of expected analyte concentrations for an intended application be useful. Theoretically, the mustbe detectable in order for the biosensor to lowest limit of detection is dependent upon the resolution of the electronic instrumentation used for the measurement.In practice, other considerationsmay often cause the lower limit of detection to be higher. For example, electrochemical transducers using potentiometric measurements may have interference from other ions and surface reactions that limit the measurement.

E. BackgroundSignal The background signal may make the determination of the lower limit of detection for a biosensor difficult to determine accurately. Current leakage, small potential differences in the electronic instrumentation or due to dissimilar metalto-metal contacts in the wire leads of the biosensor, or electrochemical factors are often the major causes for the background signal.

F. Hysteresis If the outputof a sensor follows different paths during increasing and subsequent decreasing inputs,it is said to suffer from hysteresis.In essence, it can be considered as an effect of measurement history. An ideal biosensor should not be affected by its past history of measurements and would have zero hysteresis. Hysteresis could also affect the transient responses of the biosensor. In some cases it can be minimized by making slow changes.

G. DriftandLong-TermStability Drift is a slow change in output without any change in input quantity, and it directly relates to the long-term stability of the measurements takenwith a biosensor. An ideal biosensor should have zero driftand constant sensitivity for its It entire lifetime, or at least during the time the measurements are being made. is usually necessary to recalibrate the biosensor at frequent intervals during its for thedrift in sensitivity with time. use, so thatthesignalcanbecorrected Generally, drift is approximately linear with time between calibrations (10).

H. Specificity(Interference) The response of a biosensor should be specific to only the changes in concentration of the target analyte andnot be influenced by the presence of other chemical

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species. But normally, variations in other analytes and/or environmental factors will affect the output. Such effects (considered as noise) should be minimized.

1.

DynamicResponse

The physical properties and relative size of a biosensor determine how quickly it will respond to a change in concentration of the target analyte it measures (10,ll). The principal mechanism that affects the dynamic response is usually simple diffusion of the chemical species from the sample to the active surface of the transducer. Itis desirable that the response be as fast as possible (but comparable to the speed of data acquisition).

J.

Flow Sensitivity

Generally, the response time of a biosensor decreases as flow and convective mass transport increase. A biosensor that is not operating in a diffusion-limited mode may be subject to measurement errors (stirring artifact) when flow is increased.

K. TemperatureDependence Since all the physical properties are dependentupon temperature, biosensor measurements should ideally be made under isothermal conditions. Special efforts should be made to minimize effect of varying temperatures on the output.

L.Signal-to-NoiseRatio A large signal-to-noise ratio is an indication that the biosensor is responding strongly to the analyteof interest, and the other extraneous effects are kept rather small. The signal-to-noise ratioof the biosensor measurements can be improved by using digitalfiltering techniques to clean up the signal, by ensemble averaging that is synchronizedwith the repetitive stimulus,by reducing the cutoff frequency of the amplifier, or by passing the output through analog filters.

M. Lifetime The biological elementsused in a biosensor are generally the least stable components of the system. The lifetime of a biosensor may be dependent on the total number of measurements made or may depend on the magnitude of the analyte concentrations measured. Higher concentrations may lead to more rapid losses in sensitivity. It may also be necessary to store the biosensor under refrigeration,

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or the biological element may need to be supplied with a specific chemical environment to maintain its bioactive properties. also need to be In addition to the above properties, several other factors considered during the design and development of biosensor products, including the following: Instruments should be small, self-contained, cheap and robust, capable of interfacing with existing central laboratory systems. The user interface should be simple for use by even unskilled operators. No volumetric measurement of the specimen, e.g., by pipette, should be necessary. The test specimen should be the only addition; no further reagent addition should be required. Results should be unaffected by the test specimen matrix. The time between presentation of the specimen and final result should be less than 5 minutes. Built-in standardization and controls are required. Results obtained need to be quantitative, with a printed copy of the results available. The detection limit should be subnanomolar. A wide analytical range is required, with a capability for immunochemistry, clinical chemistry, enzymology, DNA probe measurements, and a variety of other applications. The potential for simultaneous measurement of multiple analytes should be considered. There should be a good correlation of results with existing test methods. Biosensor consumables must be cheap to manufacture in bulk.

IV.

BIOLOGICALTRANSDUCERS

As described earlier, the biorecognition element

is the definitive component of the biosensor. It is also the most crucial, being responsible for the selective recognition of the analyte, generating the physicochemical signal monitored by the physical transducer and, ultimately, the sensitivity of the device (12). The bioreas typified cognition element can be categorized into two distinct types: catalytic, by enzymes, and irreversible or affinitive, of which antibodies and the receptors a third group, amplified or hybrid are the best known examples. Occasionally, configurations of the catalytic and affinitive types, can also be distinguished. The catalytic types of biorecognition systems include single-enzyme systems, multiple enzymes, organelles, whole cells or organisms, and slices of animal or plant tissue, the latter typically containing numerous enzymes and various

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cofactors with which they function more efficiently. Enzymes still remain the most popular choice, providing a high level of amplification of the biorecognition process with a high degree of selectivity. The most important enzymes from an analytical viewpoint are the oxidoreductases, which use oxygenor NAD to catalyze the oxidation of compounds, and hydrolases, which catalyze the hydrolysis of compounds (13,14). For example, glucose oxidase catalyzes the oxidation of glucose to gluconic acid, which is the basis of glucose monitoring for diabetics. Singleenzymesystems,however, maynot alwaysproveideal,through either instability via thermal denaturationor interaction with products or intermediates. They can also lose their activityin the immobilization step. To overcome these limitations, multienzyme-detection systems often can be used to regenerate an expensive coenzyme or to produce a detectable product not available from a single enzyme system. The use of plant or animal tissue slices or whole cells can often permit a complex sequence of reactions because coenzymes and other cofactors are present in a more stable and natural environment. Although a loss of specificity is often seen in such systems, the detectionof a species rather than a specific analyte may be advantageous in some applications. Unlike the catalytic receptors, the affinity class of biorecognition element is even more specific in nature of the binding. These biocomponents are often more suited to a single detection use rather than monitoring applications, since the binding is essentially irreversible. lmmunoreceptors (antibody-antigen) are the dominant type of affinity receptors as far as biosensor applications are concerned. Others include G-protein coupled receptors and lectins, which also exhibit a high degree of specificity and affinity. Biological transducers can be immobilized on a solid support in a variety of ways. There are a number of requirements that the immobilization technique must satisfy if biosensors are to be of practical use. These include the following: The biological component must retain substantial biological activity when attached to the sensor surface. The biological film must remain tightly associated with the sensor surface while retaining its structure and function. The immobilized biological film must have long-term stability and durability. The biological material needs to have a high degree of specificity to a particular biological component. Physical adsorption, entrapment in a gel or polymer, covalent binding to a carrier, and cross-linkingof proteins are techniques routinely usedin biosensor development (7). The immobilization matrix may function purely as a support or may also be concerned with mediation of the signal transduction mechanism. Physical adsorption of the biocomponent based on van der Waals attractive forces is the oldest and simplest immobilization method. A major disadvantage of this

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method, however, is that the binding forces between the biocomponent and the in matrices support cannot be controlled easily. Entrapping the biocomponent such as gels, polymers, pastes, or inks considerably improves its stability. However, to improve the performance of the biosensor, it may be essential to covalently link the biological transducer to the solid support. A prerequisite for these immobilization procedures is the availability of functional groups on the protein as well as on the solid support. Covalent binding of the biocomponent to the solid phase often results in greater precision and accuracy of the assay by eliminating within- and between-assay variation.

V.

PHYSICALTRANSDUCERS

Almost all biosensor technologies fall into four methods of signal transduction: optical, mass, electrical, and thermal. The nature of the sensor surface, the sensor’s ability to measure in “real time,” and whether the sensor can be reused tend to be distinctive in each method. Similarly, the merits of each method are dictated by the application and breadth that it brings to the diagnostics industry compared to technologies and products currently in the marketplace. The basic characteristics of these four classesof signal transduction technologies are briefly described below.

A. Optical Transducers Signal transduction by monitoring changes in optical properties can be measured by fluorescence, absorbance, refractive index, interferometry, diffraction,or polarization. Optical transducers can be broadly divided into two further categories: extrinsic and intrinsic. In the extrinsic mode, the incident light passes through the sample phase and interacts directly with the sample. In the intrinsic mode, the incident wave is not directed through the bulk sample but propagates along a wave guide. It thus interacts with the sample only at the surface within the evanescent field.

6. MassTransducers The surface acoustic and piezoelectric methods of mass signal transduction are primarily based on the measurement of interdigitating arrays of the planar devices. This type of biosensor is sensitive to changes in density, elasticity, or electrical conductivity of the surface on whichthe acoustic wave propagates. Piezoelectricsystemsareessentiallymicrobalancesbased on quartzcrystals. These crystals are mechanically distortedwhen subjected to an electric potential.

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C.ElectricalTransducers There are six typesof electrical transduction: potentiometric, amperometric, conductimetric, capacitance, impedance, and oxidation state. Of these methods, potentiometric (monitoring pH changesof the medium) and amperometric (monitoring the redox potential) have found the widest applications in the biosensor field.

D. ThermalTransducers Since most enzyme- or microorganism-catalyzed reactions are accompanied by considerableheatevolution,thermaltransducers,whichmonitortemperature changes by thermistor devices, may find broader applications than other physical transducers in the food industry. A summary of various transducers used in biosensor construction, with their potential advantages and disadvantages, is presented in Table 3.

Table 3 AComparisonofTransducersUsedinBiosensorConstruction

Transducer Sluggish response, requires a stable reference electrode. susceptible to electronic noise Simple, high selectivity Low sensitivity Simple, high selectivity Low sensitivity Remote sensing. low cost. Interference from ambient miniaturized, can be free light, requires high-energy from electrical interference sources, only applicableto a narrow concentration range Fast response, simple stable Low sensitivity in liquid apoutput signal, low cost for plications, interference rereadout dcvicc. 110 spectal sulting from nonspecific sample handling binding Vcrsatility, free from optical Expensive. cumbersome. reinterferences such a s color quires a large amount of and turbidity enzyme Low cost. mass production, sta-Temperature-sensitive, fabrible output. rcquires very cation of different layers small amount of biological on the gate is not pcrfccted material. can monitor several analytes simultaneously

Ion-selective electrodes Simple, reliable

Oxygen electrode H 2 0 zelectrode Optical systems

Piezoelectric

Calorlmetric Ion-selective tieldeffect transistor (ISFET)

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VI.

BIOSENSOR TYPES

Biosensors can be classified based on either the biocomponent used (e.g., enzymatic, immunological, or cellular) or the signal transducing system (e.g., potentiometric, amperometric, optical, thermal, etc.). The latter classification is used in this discussion.

A.

Optical

Optical biosensors make use of the physical properties of light in various ways to detect small changes in analyte concentration. Based on the principle used, as surface plasmon resooptical biosensor devices could be broadly classified nance (SPR), total internal reflection (TIR), or photon correlation spectroscopy (PCS) instruments. SPR instruments containing an antibody layer detect minute changesin the refractive index on binding of the antigen (15). These changes are related to the size of the antigen as well as the conformational change of the antibody-antigen complex on binding. The latter involves solvent reorganization and protein unas a shift in the angle folding. The change in the refractive index is then detected of total absorption of incident light on a metal layer (usually gold or silver) carrying the antibodies. This system can be usedas a biosensor because the surface plasmon resonance, which extends horizontally along the metal surface for about 100 nm, has an associated, exponentially decreasing, evanescent field that extends a few hundred nanometers. When vertically into the surrounding medium for molecules bind at the surface within the evanescent field (antibodies are about 10 nm in diameter), they perturb the field and hence the surface plasmon resonance, which therefore alters the angle of the light at which SPR occurs. A schematic diagram of an SPR device consisting of a prism on a glass slide carrying a thin metal layer is shown in Fig. 2. SPR-based instruments have been successfully used for the characterization of dye monolayers (1 6), for the measurement of antibody concentration in liquids (15), and for the binding of the anti-human serum albumin without prior incubation or separation steps (17). Inexpensive immunosensing devices based on SPR technology could be manufaca metalized diffraction grating on a tured using holographic techniques using plastic support (18). These devices could be used for the detection of microbial pathogens, toxins, and pesticide residues in plant and animal food products. TIR-based biosensors detect internal reflections in a light guide. The guide surface is coated with antibodies that come in contact with the analyte (7,s).TIR immunosensors make use of the evanescent wave penetrating only a fraction of a wavelength into the optically rarer medium when light coming from an adjacent denser medium is incident on the interface with an angle above the critical angle (Fig. 3). Changes in the surface refractive index or absorptivity then reduce the

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Incident light

- Glass substrate

- Metal Sensitizing layer ( antibody ) """"""""

"""""""" """""""" """""""" """"""""

Sample solution

"""""""" """""""" """"""""

( antigen )

""""""""

"""""""" """"""""

""""""""

Fig. 2 Biosensorsbasedonsurfaceplasmonresonance (SPR) principle to measure analyte/antigen concentrations in solution kinetically without prior incubation or separation steps. The device measures changes in the refractive index upon analyte-antibody binding.

transmission of light through the guide. Kress-Rogersand Turner (19) suggested the use of fluorescent techniques with TIR immunosensors, since the fluorescent at the surfaceis coupled evanescent wave originating from fluorescent complexes back into the guide. This gives a high fluorescence intensity at the angle of total internal reflection.

"_""""""_ _""""""""

"""""""" """""""" """""""" """""""" """"""""

Light ~-guide

Incident light

Sample solution Sensitizing layer

-Filter Detected light

Fig. 3 A schematic diagram ofa biosensor based on total internal reflection (TIR) geometry. Based on the evanescent wave principle, it measures the effects of analyte binding to its antibody or receptor immobilized on the surface.

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A TIR immunosensor based on fluorescence evanescent wave technique (fluorescence capillary-fill device, or FCFD) is commercially being marketed by Serono Diagnostics Limited (Allentown, PA) (20). This FCFD system is capable of achieving nanomolar detection limits and can be used for immunological detection of both small and large analyte molecules. Disposable optical devices based on this principle, with automatic definition of sample volume by capillary tubes, have been patented by Hirschfield (21). PCS optical techniques for particle sizing in the submicron range measure the time constantof the Brownian motion of particles by dynamiclight scattering (7). In the diagnostics industry, PCS devices could be used to monitor the changes in particle size as a result of agglomeration following the formation of antibodyantigen complex.

B. Acoustic Acoustic biosensors sensitive to changes in the density, elasticity, or electrical conductivity of a surface on which the surface acoustic wave (SAW) propagates have potential in agriculture and veterinary diagnostics for monitoring food quality. The variety and ingenuity of these devices is extensive. Two extensive reviews cover the use of piezoelectric transducers in detection of mass (22). The methods rely, in general, on measuring the changes in vibrational resonant frein mass on quency of piezoelectric quartz oscillators that result from changes oscillator’s surface. A SAW immunosensor consists of a piezoelectric crystal such as quartz or lithium niobate carrying thin-film interdigital electrode arrays. Radiofrequency excitation of the electrode pair creates a synchronous mechanical surface wave that is propagated on the surface of the piezoelectric substrate and recorded by either another electrodepair on a SAW delay line or by the same pair after refleca chemical tion on a SAW resonator device (7,19). A schematic diagram for sensor based on a SAW delay line is shown i n Fig. 4. Other acoustic devices such as a quartz microbalance or a piezoelectric oscillator, based on the dependence of the resonance frequency of a vibrating quartz crystal on its mass, can also be used as biosensors. The quartz crystals in a liquid, and if the oscillate when immersed either partially or completely solution properties such as density, viscosity, and conductivity are kept constant, the change in mass of the quartz crystal can be detected (8,23). Imrnunosensors based on the piezocrystal microbalance principles have already been commercially introduced (24). Using conventional antibody-antigen interactions on thin substrates, detection limits of nanomolar down to picomolar concentrations have been claimed with “ideal” test samples, although this is perhaps unlikely to be achieved with normal food samples. Manufacture of the devices to produce cheap, disposable

351

Biosensors Sample channel with sensitizing layer for specific bindingof the analyte

I

lnterdigital electrodes

-

- I' 1

*

Piezoelectric substrate

I

I

SAW delay

1

Fig. 4 Chemical sensor based on a surface acoustic wave (SAW) delay line.

units with uniform characteristics is also likely to pose problems, and provision of multianalyte analysis on a single unit will be a considerable challenge.

C. Semiconductor Field-effect transistor (FET) devices similar to the ion-sensitive FET devices(ISFET) have been used for the detection of minute potential changes associated with the formation of an antibody-antigen complex (25). Such direct immunosensors are called IMFETs. ISFET and other microelectronic gas or ion sensors can also be used in indirect electrochemical immunoassays using enzyme labels instead of the traditional ion-selective electrodes of gas probes (8,19,26). The fast response,good signal-to-noise ratio,and small sizeof FET devices are particularly useful in flow-injection analysis techniques. Because of manual handling in conventional ELISAs (chloramphenicol acetyl transferase, or CAT assays), the variations in the binding time between the reactants need to be eliminated by extending the timeso that slight variationswill not influence thefinal outcome. In contrast, in flow-injection analysis, the only factor that varies is the time during

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which the pulse of the substrate is introduced. This makesit possible to keep the variation between assays to less than 2%. Potentially, the FET devices can be constructed as inexpensive disposable units, although their full potential has not yet been explored by the food diagnostics industry. A schematic diagram of a prototype IMFET devicein which the metal gate is replaced by an antibody or antigen immobilized on a membrane is shown in is controlled Fig. 5. The conductivity of the n-channel region in the p-type silicon by the strength of the electric field at the gate and is measured by the application its proper of a voltage between the source and drain electrodes. A prerequisite for functioning, however, is to have the solution-membrane interface remain ideally polarized and thus impermeable to the passageof the charge. Failure to meet this criterion results in the poor sensitivity of the instrument. Yet another semiconductor device, TELISA (or thermistor ELISA), has great promise in the diagnostics industry. The heat-sensitive enzyme thermistor in a small,wellmeasurestheheatevolved in anenzyme-catalyzedreaction of an enzymethermistor is shown insulatedchamber.Aschematicdiagram in Fig. 6. A small sample volume is injected in a comparatively rapid buffer stream. A short sample pulse results in a temperature peak that is proportional a linear operating to the concentration of the analyte (enzyme substrate) over range of concentration. Normally, the thermograms are evaluated by peak height

Encapsulant Membrane insulator """""

n - Si source p-Type silicon substrate

-

n Si drain

<

Antigen Immobilized antibody

Fig. 5 Immuno-Field-Effect Transistor (IMFET) sensor for direct potentiometric monitoring of the antibody-antigen complex.

353

Biosensors

5

Polyurethane

1

/’/

Heat Exchanger

\ Thermostatted

\

Insulation

Aluminum Block

Auxiliary or

Column Reference Column Enzyme Fig. 6 A schematic representation of an enzyme thermistor that usesflow injection analysis for analyte detection.

determination. Continuous sample introduction leadsto a shift of the temperature this shiftisalsoproportionaltothe signal to a newsteady-statelevel.Since analyte concentration, the instrument can easily be used for continuous monitoring. The system shown in Fig. 6 contains two identical flow lines, which can be independently used for two different enzyme columns for different analyses. Alternatively, one of the columns can be used as a reference column to compensate for any nonspecific reactions. In normal operation, however, the temperature of the enzyme columnis registered versus the temperature measured witha reference thermistor mounted in the heat sink of the calorimeter. as albumin, gentaTELISA hasbeen used for the detection of such antigens micin, insulin, insecticides, and heavy metal ions, and the procedure has been automated for monitoring hormones and proteins produced by biotechnological methods (27,28).

D. Electrochemical Electrochemicalbiosensorssimilartoenzymeelectrodescan also beusedin developing rapid assays for food quality control. They have a unique blend of sensitivity with simplicity that resultsin a family of low-cost, rapid, and portable chemical sensors capable of operating in turbid solutions such as food analytes (29).

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Electrochemical transducers can be divided into two general categories: potentiometric devices that require the derived voltage to be determined with reference to a second electrode under conditions of essentially zero current flow, and amperometric devices that measure the current flowing between two electrodes in response to the application of a defined voltage. Potentiometric devices measure a logarithmic response and are critically dependent on a precise reference electrode. The responses, however, are largely independent of mass transport effects. In contrast, amperometric electrodes produce a linear response and are relatively insensitive to fluctuations in the reference voltage (7,8,19). However, since the species being measured is consumed, they are affected by changes in the rate of diffusion of the analyte to the electrode. Janata (30) first reported that the changes in electrical charge on antibodyantigen interaction could be detected directly using a simple potentiometric system. Subsequently, Yamamoto et al. (3 1 ) covalently immobilized anti-human chorionic gonadotrophin (anti-hCG) on a cyanogen bromide-activated titanium a titanium reference electrode wire and monitored the potential difference against on addition of hCG (Fig.7). These researchers suggested thatit would be feasible

e5mV

n

I " Reference Electrode

Titanium Wire Electrode

Human chorionic gonadotrophin (hCG)

Immobilized anti-hCG Fig. 7 A direct potentiometric immunosensor based on a titanium wire electrode.

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to monitor voltage differences as low as 0.05 mV using their apparatus, although they did encounter problems resulting from nonspecific binding. IMFET devices, described earlier, also fall in the category of direct potentiometry-based immunobiosensors.

7.

Potentiometric

Potentiometric membrane electrodes, such as pH, solid-state, polymer, and gassensing electrodes, have been used as internal sensing elements for biosensors. A typical classification of these ion-selective electrodes (ISE) is shown in Table 4. Gas-sensing electrodes are by far the most popular choice because of the inherent selectivity of gas-permeable membranes (32,33). Potentiometric biosensors based on gas-sensing membrane electrode consist of a hydrophobic gas-permeable membrane anda pH electrode separatedby a thin layer of internal electrolyte. The differences between the several gas-sensing electrodes are mainly apparent with respect to their internal filling solutions and their operating pH ranges. Gas-sensing electrodes are quite selective since their membranes are permeable only to gases.Samplesolutionshavetobetotally aqueous since the hydrophobicity of these membranes would be compromised by organic solvents, thereby leading to membrane failure. The usefulness of gas-sensing electrodes is often hampered by pH limitations imposed by samples or by the biocatalysts used in the construction of biosensors. Compromises often have to be made in order to satisfy both the pH requirements of the electrodes and those of the biological components, with the latter usually having stringent pH limitations due to their biological nature. The pH requirements of the gas-sensing electrodes themselves arise from the fact that the electrodes are responsive onlyto the gaseous formof the measured substrate. Ion-selective electrodes, however, can tolerate a certain degree of turbidity in samples and are thus especially suited for food analysis. Someexamples of contemporarypotentiometricmembraneelectrodes are summarized in Table 5. The lifetimes of these biosensors vary from hours

Table 4 Classification of Ion-Selective Electrodes Based on Sensor Membrane Composition Type of electrode Composition Glass

Solid state Liquid ion-exchange membrane With coating over membranes of ion-selective electrodes

ofmembrane sensor

H', Na', monovalent cations F-, Cl-, Br-, I-, CN-, SCN-, Cd?+, Cd', Pb2+ Ca", CI-, divalent cations, BFJ-, N03-, cIoJ-,K+ C02, NH3, H2S, NO2, SOz

Table 5 SelectedExamplesofPotentiometricBiocatalyticMembraneElectrodes

Internal sensing Lifetime Substrate

Ref.

Enzyme electrodes Adenosine L-Alanine Glutamine Guanine Gluconate Histidine Salicylate Tyrosine Bacterial electrodes L-Arginine L-Aspartate L-Cysteine L-Glutamate Glutamine L-Histidine NAD+ Nitrate Nitrilotriacetic acid Pyruvate Serine Sulfate Sugars Tyrosine Uric acid Plant tissue electrodes Ascorbate Cysteine Dopamine Glutamate Phosphatelfluoride Pyruvate Urea L-GlutaminehAsparagine Tyrosine Spermidine Animal tissue electrodes AMP Guanine Glucosamine 6-phosphate kidneyPorcine Glutamine

(d)

element Biocatalyst

Adenosine deaminase L-Alanine dehydrogenase Glutaminase Guanase Gluconate kinase-6-phospho-~gluconate dehydrogenase Histidine decarboxylase Salicylate dehydroxylase L-Tyrosine decarboxylase

9

Streptococcus fueciutn Bacterirtm caduveris Proteus morgnnii Escherichia coli Sarcina jlava Pseudomonas spp. Esclzerichia colilNADase Azotobacter vinelundii Pseudomonas spp. Streptococcus faecium Clostridium ncidiurici Desulfovibrio desulfuricans

20

Bacteria from dental plaque Aeromonas phenologenes Pichi tnernhranae fnciens

IO 1

42 4 30 12 0.42 IO

6 21 14 21 s 7 14 30

34 35 36 37 38 39 40

41 42 43 44 45 46 47 48 49 50

14

51

3

52

IO

53 54

3 8

55

50

56

Cucumber Cucumber leaf Banana pulp Yellow squash Potato tuberlGlucose oxidase Corn kernel Jack bean meal Magnolia flower

5-6

57

28

58 59

7 28 7 94 14

60

Sugar beet

Pea seedling

8 20

65 66

Rabbit muscle Rabbit liver Porcine kidney

28 14 21

67 68 69

30

70

10

61 62 63 64

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to weeks, depending upon the biocomponent used in the preparation of the electrode.

2. Amperometric Amperometric biosensors are usually based on oxidoreductase enzymes, which commonly use oxygen, NAD', or NADP' as the electron acceptor that recycles as the electron the enzyme after the substrate reaction. Enzymes using oxygen acceptor are commonly called oxidases, while those usingNAD' or NADP' are called dehydrogenases or reductases. Of these two groups, oxidase enzymes are the most commonly used as biocomponents. The first amperometric biosensors used the Clark oxygen electrode as the physical transducer. The oxygen electrode is generally constructed using a twoelectrode system, e.g.,a platinum cathode and a silver anode. A gas-only permeable membrane (e.g., Teflon) covers the tip of the cathode. A biochemical layer (most often an enzyme) is then placed on the oxygen electrode and secured with a coarse membrane that allows the passageof analyte, thus forming a biosensor. A negative potential is then applied to reduce oxygen at the cathode. The reduction current is directly proportional to the oxygen concentration. In the presence of substrate, the enzymatic reaction decreases the oxygen concentration, and the decrease in the oxygen reduction current is proportional to substrate concentration. Theprincipaladvantage of anoxygenelectrode-basedbiosensor is its excellentselectivity. The selectivityoftheenzyme is not compromised by the oxygen electrodes, because only gases can pass through the membrane and only substances that are reducible at the applied potential can interfere. The limitations of this type of biosensor are that it is sensitive to ambient oxygen concentrationsandthegas-onlypermeablemembranecanbecomeblockedor clogged, which often necessitates the use of a second coarse membrane (e.g., a dialysis membrane). As the membrane layers become thicker and more complex, the response and recovery times increase, and the biosensor may require more frequent calibration (71). The maintenance of calibration for many biosensors is dependent not only on enzymatic activity but also on the constancy of the diffusional pathways for reactants and products. The thicker and more complicated the biochemical layer and its associated membranes, the more difficult it is to maintain constant diffusion of reactants and products, thus increasing the frequency of calibration. Oxygen electrode-based biosensors have been used to determine a variety of enzymatic substrates including glucose, monosaccharide, hypoxanthine, lac(8). tate, amino acids, sulfite, salicylate, oxalate, and pyruvate Hydrogen peroxide-and dehydrogenase-based biosensors also operate on similar principles.The former functionby oxidizing hydrogen peroxide,a product

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of the enzymatic reaction, while the latter utilize the enzymatic oxidation of NADH or NADPH. The mainproblemwiththedehydrogenase-basedbiosensors is thatthe NADt/NADH and NADP+/NADPH redox couples are not always completely reversible, and when the reduced formis reoxidized, some product can form. this oftenirreversiblyadsorbsontheelectrodesurface,causingelectrodefouling. After repeated use, the oxidized cofactor decreases in concentration if the electrochemical recycling is not 100% efficient or if it is not confined to the electrode surface. Thus, it may become a limiting factor in the enzymatic reaction. In conjunction with this problem, if the undesired product fouls the electrode surface, the current response of the electrode decreases with time because the active electrode area is decreasing. Careful attention to the choice of electrode material and electrolysis conditions can minimize these problems. Alternatively, a mediator with better electrochemical properties can be used to recycle the NAD'/NADH redox couple. This approach was used in the construction of ethanolandlacticacidsensors, in whichflavinmononucleotide (FMN) was the mediator, and its reduced form, FMNH?, was oxidized at a platinum electrode, producing a current directly proportional to substrate concentration (72). Amperometric biosensors that use oxidase enzymes normally rely on oxygen as an electron acceptorto recycle the enzyme after conversion of the substrate to the product. The upper linear rangeof these biosensors is limited at high substrate concentrations because oxygen becomes a limiting factor due to its limited solubility. As the substrate concentration increases, more oxygen is used in the enzymatic reaction; when the concentration of the oxygen becomes too low, it becomes a limiting factor. Consequently, each increasein substrate concentration results in less of an increase in the overall reaction rate, and the biosensor response begins to level off, eventually becoming independentof substrate concentration and limited by the oxygen concentration. Also, hydrogen peroxide proin sufficient concentration, is known to duced from the oxygen, when present deactivate many enzymes (7 1). Electron mediators can be used to alleviate both problems by replacing oxygen as the electron acceptor. This approach usually extends the linear range and often lowers the working potential, thereby reducing noise, background curto interferences.Byeliminatinghydrogenperoxide,the rent,andsignalsdue operating lifetime of the biosensors is often extended. The commonly used electronmediatorsincludetetrahiafulvalene,hexacyanoferrate,N-methylphenazinium, and ferrocene and its derivatives (7). Ferrocenes are popular because they are hydrophobic, exhibit good electrochemistry, and can be structurally altered to change their redox potential. Hydrophobicity is important because it prevents the mediator from leaching from the electrode surface when used in aqueous systems.

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Table 6 ApplicationsofBiosensorsintheFoodIndustry

Compound measured Applications Amino acids

Alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glutathione, histidine, leucine, lysine, methionine, N-acetylmethionine, phenylalanine, sarcosine, serine, tyrosine, tryptophan, valine Amines,amides,hetero-Aminopyrine,aniline,aromaticamines,acetylcholine,chocyclic compounds line, phosphatidylcholine, creatine, guanidine, guanosine, penicillin, spermine, creatinine, uric acid, urea, xanthine, hypoxanthine Carbohydrates Amygdalin, galactose, glucose, glucose-6-phosphate, lactose, maltose, sucrose, starch Carboxylic acids Acetic acid, formic acid, gluconic acid, isocitric acid, ascorbic acid, lactic acid, malic acid, oxalic acid, pyruvic acid, succinic acid, nitriloacetic acid Gases NHI, Hz, CH,, SO?, NO NAD(P)H, ATP, Cofactors AMP, Hz02 Inorganic ions Fluoride. nitrite, nitrate, phosphate, sulfate, sulfite, Hg", Zn" Complex variables Antibiotics, assimilable carbohydrates, assimilable substances, biological oxygen demand, freshness of meat, mutagens, vitamins Alcohols, phenols Acetaldehyde, bilirubin, catechol, cholesterol, cholesterol ester, ethanol, glycerol, glycerol esters, methanol, phenol

Selected examples of the applications of biosensors in food analysis are summarized in Table 6.

VII.

DNAPROBETECHNOLOGY

Recombinant DNA techniques and DNA probe-based diagnostics will play an increasingly important rolein the determination of pathogenic microorganisms in foods. These techniques depend on the selective cleavage of DNA by restriction endonucleases and on the localization of the specific sequences of nucleotides after hybridization with known DNA or RNA fragments (probes) labeled with a radionuclide (e.g., zzP, '"I, j5S, zH)or an alternative label. The latter includes fluorescent labels (e.g., fluorescein. rhodamines, ethidium, rare-earth chelates), luminescent labels (luminol derivatives, acridinium esters, luciferase). enzyme of two markers (alkaline phosphatase, horseradish peroxidase), or a combination

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or more of these label systems. Therefore, the detection systemsused in the field of immunoassay are also applicable to DNA probing. Nucleotide-probing techniques rely primarily on the ability of the labeled DNA and RNA probes to detect antigen by binding to the complementary arrangements of target nucleotides. Since such binding, under appropriate assay conditions, can be extremely strong and specific, DNA/RNA probe assays can be extremely specific and sensitive. These characteristics are mandatory for devising sensitive and specific diagnostic assays. Furthermore, these probes can be amplified by polymerasechainreaction(PCR)usingappropriateenzymesystems, thereby providing the starting material in great quantity for testing.The PCR is a three-step process that duplicates a DNA fragment bound by two oligonucleotide primers.Eachprimer is complementary to one strand of thedouble-stranded DNA template used. The first step of the process is initiated by denaturing or separating the double-stranded DNA target into its single-stranded components by heating the sample to 90-95°C in the presence of a large excess of the two primers. In the second step, the temperature of the reaction is lowered to about 10°C below the melting temperature of the primers, and the primers are allowed to anneal or hybridize to their complementary sequence on the DNA template molecule. The third step of the reaction is carried out by raising the temperature of the reaction to 70-73"C, the optimal temperature for extension of the primers by a DNA polymerase. One cycle of denaturation, annealing, and primer extension results in a doubling of the DNA target sequence. By repeating the cycles as many as 25-30 times, the DNA sequence flanking the primers can be amplified as much as a millionfold. The PCR can also be used to assay RNA in a sample if the RNA template is first transcribed into DNA by reverse transcriptase. Blackburn et al. (73) described an electrochemiluminescence (ECL) for the detection of PCR reaction products using ruthenium(I1) tris(bipyridy1) (Ru(bpy)32+)to label the probe. ECL assays detect the hybridization of labeled of PCR products is demonstrated probes to nucleic acid sequences. Quantification in Fig. 8. I n this format, the PCR is first used to amplify the specific genes by use of two primers, one of which is biotinylated. The double-stranded DNA is then captured on streptavidin-coated microparticles and washed with an alkaline solution to denature and separate the strandsof DNA. Incubation of the particlebound DNA with Ru(bpy)32'-labeled probes is followed by washing the sample and quantifying the particle-bound label. Other recent amplification systems include the ligase chain reaction (LCR), Q-p replicase,Syva'sPA-singleprimeramplification,andself-sustainedsequence replication (3SRJ (8). Although the current nucleotide probe-based techniques are slow and laborious in comparison to technologies such as immunoassays, several commercial products are already being marketed. DNA probe technology will find its general use in medicine, clinical sciences, and food technology. In the food technology

361

Biosensors

PCR Reaction Biotin

B

mmmOiigo Polymerase extension

Bindina to Beads

BS

a

Denature to Separate Strands

/"".

Hvbridize, Wash. and Read

WBb

Fig. 8 Diagram of the procedure for the detection of polymerase chain reaction (PCR) products in the DNAprobe assay. B = Biotin, S = streptavidin-coated microparticles;

*

=

Ru(bpy),".

area, conventional microbiological testing of a food product often requires as long as 4-6 days, a time frame food processors could ill afford to wait, given of mostfreshandmanyprocessedfoods. DNA thehighlyperishablenature probes have been used for the identification of certain bacterial cells. Other examples are the detection of Escherichia coli, Yersinia erlterocolitica, Listeria spp., and Salmonella in foods and drinking water.

VIII. FLOW CYTOMETRY The flowcytometrymicrofluorimeter (FCM), or fluorescence-activatedcell sorter, is one of the most sophisticated machines dedicated to cell analysis and

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sorting available today. The majority of applications of FCM at present relate to biomedical research, although increasing uses are foreseenin veterinary and food sciences, especially in microbiological quality testing. of cell properties on Flow cytometry simultaneously measures a number an individual basis at speeds of up to 5000 cells per second. There is no need first to isolate the cells of interest from the rest of the population. The cells are usually tagged with fluorescent markers indicative of biological properties prior to their passing individually through a focused laser beam. Strategically placed photosensors then measure optical changes as each cell passes through the beam. These changes are characteristic of each cell type, and the resulting signals are analyzed by a data processing system. Cell sorting is accomplished by breaking up the fluid stream under pressure into tiny droplets that are electrically charged according to the type of cell that each contains. An actual physical separation is achieved electrostatically (8). Flow cytometry is quiteuseful in studying cell surface markers fluorescently labeled with antibodies or lectins. This allows rapid identification of subpopulations in a heterogeneous population of cells, thereby alleviating the need for isolation of the desired cells. Any cell surface molecule can be used as a cell population marker provided it can be made immunogenic. Twoor more antibodies can beusedtogether if they are conjugated to fluorescent dyes thathave nonoverlapping emission spectra. A fundamental difference between FCM and ELISA, however, is that FCM must be performed in the liquid phase, whereas ELISA is a solid-phase assay. The former is thus ideally suitedto microbiological testing. FCM also allows for rapid assay turnover times of 15 minutes or less, since the unbound fluorophore is not detected by the machine, thereby making all the washing steps unnecessary. In addition, the binding of fluorophores used in FCM occurs immediately, whereas the chromogenic changes that form the basis of ELISA take time to develop. Immunoassays using fuorophores are also 10-20 times more sensitive than colorimetric assays; thus. an approximately 50fold greater dynamic range is obtainable from FCM techniques compared to microtitration plate-based ELISA. FCM, therefore, is increasingly being reviewed as the future technology in microbiological and clinical testing.

IX.

BIOLUMINESCENCE ASSAYS

Luminescence techniques based on the detection of light generated by enzymemediated reactions represent someof the fastest assays currently availablefor the of the few rapid technologiesto detection of microbial contaminants and are some be currently utilized extensively in the food industry. Techniques based on this phenomenon can be divided into two broad categories: ATP bioluminescence and bacterial bioluminescence.

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The ATP bioluminescence technique was first described in the 1960s by National Aeronautics and Space Administration (NASA) scientists(74). It makes use of the fact that all living cells contain ATP, which is the universal energy donor for metabolic reactions. An enzyme-substrate complex, luciferase-luciferin, present in firefly tails converts the chemical energy associated with ATP into light by a stoichiometric reaction (Fig. 9). The amount of light emitted is proportional to the concentration of ATP present and can be easily quantified using a luminometer. The ATP pool within a cell is normally constant (75). A bacterial cell typically contains one femtogram (1 X IO-" g) of ATP. Thus, the measurement of cellular ATP is a good indicator of biomass. The light produced during the reaction is directly related to the number of metabolically active cells present in the assay. The sensitivity of the ATP bioluminescence assay is such that approximately 1000 cells can be detected. Modifications of this basic technology, using a coupled cycling reaction involving myokinase and pyruvate kinase, can detect fewer than 100 cells (76).In another variation of this technique, cellular adenylate kinase is reacted with ADP to form ATP, which is then measured using the firefly luciferase reaction. Sharpe et al. (77) were the first to apply the method to the detection of microorganisms in food, but the high levels of ATP from nonmicrobial sources

Luciferase

LUCIFERIN.AMP PPI

Fig. 9 Basicprinciple of anATPbioluminescenceassay.ATP = Adenosinetriphosphate; Luciferin. AMP = luciferyladenylate;Ppi = pyrophosphate;AMP = adenosine monophosphate.

facturer

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Table 7 CommerciallyAvailableATPBioluminescenceHygiene Monitoring Kits

name Instrument/Test Bio-Orbit" GEM@ Hy-Lite' InspectoPlSystem SureTM Lightning@ Lumac@ Luminator"/PocketSwabTM Uni-Lite@'/Uni-Lite@Xcel Profile"

Bio-Orbit Oy, Turku, Finland GEM Biomedical Inc., Hamden, CT E. Merck, Darmstadt, Germany Celsis International plc, Cambridge, U.K. Idexx Laboratories, Westbrook, ME Lumac bv, Landgraaf, The Netherlands Charm Sciences, Inc., Malden, MA Biotrace Ltd., Bridgend, U.K. New Horizons Diagnostics, Columbia, MD

reduced the sensitivity substantially. Although there continued to be an interest in the application of ATP bioluminescence for assessing microbial contamination of food, it was not until the early 1990s that the technique came of age in the food industry (78-80). Bioluminescent methods have found several niches in the food processing industry. Perhaps the most prominent is astool a to monitor the efficacyof sanitation. ATP hygiene monitoring has been used in a variety of processing situations, including breweries, dairy plants, meat processing plants, and fruit juice opera7). tions. Several companies now offer ATP hygiene monitoring kits (Table Bioluminescence is also used in reporter gene technology, which is rapidly gaining acceptance in the biological sciences as a sensitive and convenient alter(76). The native to conventional chloramphenicol acetyl transferase (CAT) assays bioluminescent reporter gene is linked to a geneof interest and expression of the bioluminescent protein, e.g., firefly luciferase, marine bacterial luciferase,Rerzillu luciferase, green fluorescent protein, etc.. used to monitor the activation of the gene.

X.

EMERGINGTECHNOLOGIES

Overthe past two decades,enormousactivityhas takenplace in the field of sensor technology. Biosensors,in particular, have attracted considerable attention because of their extraordinary sensitivities and specificities. However, such devices often lack storage and operational stability because they are based on fragile biological recognition elements: enzymes or antibodies. For this reason, biosenin the early euphoric sors have not become quite the commercial success expected

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development phase. Two emerging technologies, electronic nose and molecular imprinting, however, could provide an alternative. These are briefly described below.

A.

ElectronicNose

The world of electronic nose technology mimicking the human nose and its utility in the flavor industry as both a research and quality assurance tool literally exploded in the United States in late 1994. Traditionally, much of the flavor testing in the food and beverage industries as well as flavor and fragrance manufacturers has relied on sensory evaluation by trained expert panels, such as U.S. Food and Drug Administration (FDA) inspectors, who assess seafood freshness, coffee and tea tasters, or brewmasters and wine stewards. Flavor and fragrance companies rely heavily on sensory evaluation throughout the development process to final production. The results areusually highly subjective, prone to error, and difficult to relate to other analyses. Current research seems to indicate that no two humans really perceive the same aroma quite the same, nor can they communicate a common descriptor easily due to an array of cultural, presuppositive, and associative memory effects (81). 20-300 daltons) Flavor molecules are generally small (molecular weight and polar and can be detected by humans at levels below one part per billion. To date, more than 6000 flavor-active compounds have beenidentified, and more are being discovered each passing day. Recognizable flavors or odors arise from the specific contribution of complex mixtures of many odorous molecules, each of different concentration. Attempting to detect complex odors at these levels by conventional analytical techniques is very expensive and not always possible. It is therefore not surprising that traditional (organoleptic) methods of odor assessment have survived for so long. Analytical techniques can objectively discriminate odors, but the sample must be separated into its individual components using gas chromatography/mass spectroscopy ( G U M S ) techniques. While these techniques can provide a chemical tally of volatiles in a food, flavor, or fragrance, aroma, judge its character, or none can tell if the identified component has an predict synergistic effects with other compounds. In contrast, human olfaction can discriminate aromas without separating mixtures into individual compounds. In the human brain, a kind of pattern recognition may also take place to decode signals sent from receptor cells located in the human olfaction system (82). In recent years, significant interest in the use of sensor arrays to discriminate between odors has arisen. If an array of nonspecific sensors could be compiled to rival the human olfactory system, then the sample need not be separated (83,84). Analysis would prove and could be monitored analytically as a whole rapid, nondestructive, and continuous. The term “electronic nose” (or “artificial

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nose”) has been coined to describe such an array of chemical sensors, where each sensor has only partial specificity to a wide range of odorant molecules, coupled with a suitable pattern recognition system software. In order to discriminate aromas by mimicking the human olfaction system, attempts to construct the so-called “artificial nose” by utilizing various gas sensors have been made since the early 1980s (85.86). Metal oxide semiconductor sensors (85,87-89), surface acoustic waves (SAW) sensors (90), and quartz resoits own nator sensors (91) seem potential methods. Although every sensor has advantages and limitations, there are not such sensors that can show perfect selectivity to specific compounds or a group of compounds. Pattern recognition of into every analytical the responses from a multisensor array was incorporated procedure due to the nonselectivity of gas sensors. Early efforts were largely focused on detecting specific gases or hazardous compounds on the basisof their response patterns (88-90). From the viewpoint of aroma analysis, discriminating is essential. As the first step for a particular gas mixture from other mixtures developing a simple aroma-monitoring system, pattern recognition analysis for responses to food aromas from a gas sensor array was attempted using semiconductor gas sensors (92.93). Such an array may consist of as many as six or more semiconductor gas sensors. These are preferred over other types of sensors because of their durability, high sensitivity for most reducing compounds. and insensitivity to water vapor. The most popular sensors used to develop electronic noses suitable for use within the food industry are described below.

7. Semiconductor Metal Oxide Chemoresistive Sensors Much interest has been devoted to the development of chemoresistive arrays of inorganic semiconducting materials such as oxides and catalytic metals(94).The oxide materials contain chemisorbed oxygen species with which interaction of odor molecules alters the conductivity of the oxide. These devices operate at elevated temperatures (400-600°C) to avoid interference from water and to aid rapid response and recovery times. This resultsin high power consumption. They are quite sensitive to combustible materials such as alcohols but are less good at detecting sulfur- or nitrogen-based odors (95). Alpha M.O.S. America, Inc. (Belle Mead,NJ) has introduced an electronic nose based on this technology. It consists o f 6, 12, or 18 metal oxide sensors. It is an open-ended instrument platform that has the potential to use sensors based o n other technologies within the instrument.

2. Quartz Resonator Sensors Electronic nose systems based on this principle consist of a piezoelectric quartz or crystal oscillator coated with a sensing membrane such as acetyl cellulose

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lecithin. Adsorption of odor molecules on the membrane leads to changes in the resonant frequency of the device due to a change in mass, provided that the viscoelasticeffectsarenegligible.Differentmembranescanbeused,andthe changes in their resonant frequencies in the presence of different odors can be analyzed. Electronic noses based on this technology are commercially not yet available.

3. Conducting Polymers The unique electrical properties of conducting polymeric materials have been exploited in the preparation of electronic noses. They have the advantage that a wide variety of suitable materials exists. Two main classes are poly(pyrro1e)s and poly(ani1ine)s. The materials are easyto process, so preparation of reproducible gas sensors is possible. One of the first applications of conducting polymers as odor sensors was described by Persaud and Pelosi (86). Several properties of the polymers can be used for measurement, including changes in the work function of the polymers, as well as Inass changes that can be monitored when polymers are depositedonto quartz resonator type substrates. The most popular parameter to monitor, however,is the conductivity of the polymers, which is known to change rapidly and reversibly in the presence of odors. The adsorbed odor molecules are believed to cause a swelling of the polymers and to interfere with charge transfer within the polymer and interchain hopping mechanisms (95). These sensors can be used at ambient temperatures and have quite good sensitivities, typically 0.1- 100 ppm. Electronic noses based on conducting polymer arrays are available commercially. Examples include the e-Noseo// 4000 Aroma Analysis System (NeotronicsScientific Inc., FloweryBranch, GA) andthe AromaScannero//from Aromascan, Inc. (Hollis, NH). The electronic nose technology does provide objective and reproducible aroma discrimination on a wide variety of sample types with a sensitivitycomparable to human nose. Analysis times are on the order of minutes, yet the results compare very favorably with GUMS, and sensory panel analyses that can take much longer. There continues to be ongoing research into new sensor technologies, materials, and fabrication methods. At least one company offers multiple hybrid systems. Toko has reported a taste sensor that has successfully discriminatedthebasic taste qualities-sourness,saltiness,bitterness,sweetness,and umami-in a number of food products and materials(8 I ) . The integrationof taste sensors with electronic nose technology could truly revolutionize flavor analysis. all To date, all attempts to develop universal electronic nose suitable for applications have failed. Problems associated with sampling. calibration, sensor drift, and control of humidity and temperature are the 1na.jor causes for concern. Improvements in associatedtechnologiessuchasnewodor-sensingmaterials,

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development of hybrid noses, smarter pattern recognition techniques, and miniaturization are essential if the market potential of such an instruments is to be realized (84,95). Ultimately, the development of application-specific noses that are tailor-made for a given problem may prove more successful than attempts to develop a single system suitable for universal applications.

B.

Molecular Imprinting

The technology of molecular imprinting could alleviate several problems associated with the biorecognition element of the biosensors. It provides highly stable synthetic polymers that possess selective molecular recognition properties because of recognition sites within the polymer matrix that are complementary to the analyte in the shape and positioning of the functional groups. Some of these polymers have high selectivities andaffinity constants, comparable with naturally occurring recognition systems such as monoclonal antibodies or receptors. This makes them especially suitable as constituents in chemical (biomimetic) sensors. Molecular recognition between a molecular receptor (host) and a substrate (guest) in a matrix containing structurally related molecules requires discrimination and binding. This can happen only if the binding sites of the host and guest molecules complement each otherin size, shape, and chemical functionality. Biological systems, such as enzyme-substrate, antibody-antigen, and hormone-receptor systems, demonstrate molecular recognition properties that have developed by natural selection. One of the most intriguing areas for host-guest chemistry is the development of biomimetic recognition systems. As described earlier, a wide range of analytical procedures depend on reliable and sensitive biological recognition elements such as antibodies, receptors, and enzymes. Because such biomolecules can suffer from stability problems, synthetic counterparts are desirable. One such approach to biomimetic recognition is the fabrication of molecularly imprinted polymers (MIPS) (96). Molecular imprinting is a powerful method for preparing synthetic recognitionsiteswithpredeterminedselectivityforvarioussubstances. It canbeap(97)andthepreorganized proached in twoways:theself-assemblyapproach approach (98). These two approaches, which differ with respect to interaction mechanism in prepolymerization, follow common molecular recognition terminology. The self-assembly nlolecular imprinting approach involves host-guest complexes produced from weak intermolecular interactions (e.g., ionic or hydrophobic interactions, hydrogen bonding, and metal coordinations) between the analyte and the monomer precursors (97,99). These complexes are spontaneously established i n the liquid phase and are then sterically fixed by polymerization with a high degree of crosslinking. After removal of the print molecules from the re-

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sulting macroporous matrix, vacant recognition sites that are specific to the print molecule are established.The shapeof the sites, maintained by the polymer backbone and the arrangement of the functional groupsin the recognition sites, results in affinity for the analyte. In the preorganized molecular imprinting approach, strong, reversible, covalent arrangements (e.g., boronate esters, imines, and ketals) are formed between the monomers and the print molecules before polymerization. print The molecule, therefore, needs to be derivatized with the monomers prior to actual imprinting. After cleaving the covalent bonds that hold the print molecules to the macropoto the analyte remainin the polymer rous matrix, recognition sites complementary (98). MIPs have been successfully prepared with affinities for proteins, amino acid derivatives, sugars and their derivatives, vitamins, nucleotide bases, pesticides, and a wide varietyof pharmaceuticals (98). The binding of some of these of some natural monoclonal polymers has been comparable with the binding antibodies ( 100,lO1). The primary advantageof MIPs is that imprints can be made of compounds against which it is difficult to raise high-affinity antibodies. Other advantages include their long-term stability and resistance to chemically harsh environments ( 102).

MIPs have unique properties that make them especially suitable for sensor of medical, technology. They exhibit good specificity for various compounds environmental, and industrial interest, andthey have excellent operational stability. Their recognition properties are unaffected by acid, base, heat, or organic in phase treatment (102), making them highly suitable as recognition elements chemical sensors. Some examples are shown in Table 8. To date, the most convincing demonstration of the usefulness of a “real” biomimetic sensor based on molecular imprints is an optical fiber-like device in which a fluorescent amino acid derivative (dansyl-L-phenylalanine)binds to the polymer particles, resulting

Table 8 ExamplesofBiolnilneticSensorsBasedonMolecularlyImprintedPolymers (MIPs)

Range

Analyte Morphine Vitamin K, Phenylalanine anilide Dansyl-L-phenylalanine Atrazine Conductometry Sialic acid

(pg/mL) Ref. 0- 10 0-4

Qualitative 33-3300I05 0-30

Transducer

Amperometry Ellipsometry Capacitance Potentiometry fluorescence Fiberoptic

0-0.5 0-3

fluorescence Optical

102 103 I04 106 I07 I08

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in fluorescent signals that vary as a function of the concentrationof the derivative (106). Chiral selectivity was shown by using the corresponding D-enantiomer as a control. A major limitation of using MIP-based biomimetic sensors is the long response time (15-60 min) for the measurement of the desired analyte (99). This delay could be minimized by optimizing the kinetics and selectivityof polymers. Generally, the use of highly rigid polymers favors selectivity but increases responsetimes.Similarly,polymerporosityincreasespolymer-bindingcapacity and response time. Using small polymer particles or thin polymer layers should improve diffusion rates and thus the apparent binding kinetics, giving far lower response times. Alternatively, the initial binding (preequilibrium) rate could be used to determine analyte concentration. The current generationof MIP biomimetic sensors is 100- to 1000-fold less sensitive than other types of biosensors. Although further improvements in MIPS are likely to decrease the sensitivity gap and lead to useful applications, biomimetic and other types of biosensors will most probably find their own niches in the future.

XI.

FUTURE TRENDS

The ability of biosensors to provide real-time information and their relatively simple design should result in a wide variety of applications for monitoring the quality and safety of our food supply. Indeed, sensors basedon ATP bioluminescence and electronic noses, to some extent, are already being used extensively in the food industry. Amperometric and potentiometric biosensors have also found widespread acceptance for measuring a range of food analytes. Optical biosensors will find potential applications for measuring and monitoring concentrations of such compounds as acetaldehyde, alanine, malate, lactate, nitrate, glucose, glycerol, ethanol, xylitol, isocitrate, glutamate, sorbitol, and galactose in a variety of food processing operations; for detecting food contaminants such as Salrnotzella, E. coli, Listeria, and Staphylococcus; and for monitoring contamination of meat and dairy products by residues of antibiotics, growth hormones, and veterinary drugs. Thermal transducer-based biosensors have already been used successfully formonitoringseveralcompoundsrelevant to foodqualitycontrol,including ascorbic acid, glucose, lactose, triglycerides. cholesterol, galactose, ethanol, sucrose, antibiotic and therapeutic agents, and oxalic acid. These biosensors could also be applied to detect and monitor microbial contamination of food products. Althoughthebiosensorsdescribedabovehavebeenimprovedtremendously, they have not yet established themselves as providing a technology that is cheap and versatile. Immunosensing is also unlikely to be achieved as easily or

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as cheaply as with the other typesof biosensors described. Among the biosensor technologies described here, the mass-detection piezoelectric sensorsin the form of acoustic wave guide devices, or one of the four or five optical devices described, are the most likely candidates as general purpose bioanalytical sensors to produce and possess a potential for use over a of the future. They are easy wide rangeof analyte concentrations. They also approach the low detection limits required for immunosensing, and the reproducibility of results is similar to that achieved by conventional analytical methods. These devices rely on relatively well-understood physical principles, and the associated instrumentation is quite in the optical sensor is, however, easy to construct. The analyte detection element much cheaper to construct and is more flexible in the uses to which it can be put than the piezoelectric devices. The fluorescent methods used with the optical sensors, although intrinsically more sensitive than the other optical methods, suffer from potentially greater interference effects arising from the test specimen matrix. It is for this reason that the surface plasmon resonance (SPR) or integrated optical sensors are likely to be commercialized as the next generation of general purpose analytical tools. These devices have not yet met all the criteria listed for the “ideal” biosensor, but they have met most of these criteria, and future developments are likely to enable all of them to be met within the next few years. Although biosensors hold considerable promise and potential for on-line still in its quality and safety monitoring in the food industry, the technology is of biosensors have been developed commercially. infancy. Only a limited number The problems associated with reliable mass production and commercialization of this technology are enormous. They include long-term stability of enzymes, antibodies, and bioreceptors; sterilization of sensors; nonspecific adsorption of other species; immobilization and mediation of enzyme-based sensors; reduction of interferences from other substancesin the food; miniaturization of sensors and sensor arrays; and variability in large-scale manufacturing. Progress on all these fronts is continually being made in this rapidly growing area of research. of fields involved in the developmentof biosenBecause of the wide variety sors, successful research and developmentof biosensors will require a multidisciplinary approach. Biologists, chemists, physicists, and electrical and food and bioprocess engineers will have to join together to solve the technical problems facing biosensors. Nevertheless, it will not be too long before this technology will become the mainstay of quality and safety monitoring in the food industry.

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10 New Techniques for Food Quality Data Analysis and Control Jinglu Tan University of Missouri, Columbia, Missouri

1.

INTRODUCTION

Food quality data are gathered for various purposes and subjected to different analyses. Instrumental food quality data are often analyzed and used for two important objectives. First, to establish the usefulness of an instrumental measurement as a food quality characteristic,it usually needs to be compared with human sensory responses, which are the primary standard for food quality measurement. Instrumental measurements are thus used as direct or indirect indicators of sensory quality attributes. Second, instrumental measurements are frequently used for quality control purposes because instrumental techniques can be noninvasive, rapid, convenient, and implemented on-line. of variance and deterStatistical techniques are commonly used for analysis mination of functional relationships. The routinely used techniques include correlationanalysis,analysis of variance(ANOVA),regressionanalysis,principal component analysis (PCA), andpartial least squares (PLS) analysis. The conventional statistical methods are well known and can be found in many textbooks. In this chapter, we describe some recent developments in food quality data analysis and food quality control. First, we present a fuzzy set-based paradigm for sensory data interpretation, which is followed by a neural network approach to establishingrelationshipsbetweeninstrumentalmeasurementsandsensory responses. Then we discuss the use of neural networks as recognizers of patterns in quality data for the purpose of statistical process control (SPC). Finally, we in real-time statisdemonstrate the use of instrumental food quality measurements tical process control.

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II. FUZZY SETS ANDNEURALNETWORKS Since instrumental food quality measurements are frequently comparedwith human sensory evaluations, proper methods for interpreting sensory data and effective techniques for establishing functional relationships are extremely important for food quality data analysis.In this section,we point out some problems associated with conventional techniques and introduce a new approach based on fuzzy sets and neural networks ( I ) . Sensory analysis involves evoking, measuring, analyzing, and interpreting human judge responses to product sample characteristicsby the senses of sight, smell, taste, touch, and hearing. A variety of scales are used to measure sensory responses, and the choice of scales depends on the objectives of evaluation. Stevens ( 2 ) classified sensory scales into the following four categories: Nominal scales for classification or naming Ordinal scales for ordering or ranking Interval scales for measuring magnitudes Ratio scales for measuring magnitudes In a nominal scale, sensory attributes are classified into categories that are or magnitude relationship. Classifydifferent but do not have any particular order ing ice creams according to their flavors is an example application of nominal scale. An ordinal scale uses categoriesor levels ordered from “low” to “high,” ‘‘least’’ to “most,” etc.; however, no assumptions are made regarding the interval size between two levels. An example ordinal scale would be the word sequence “none, slight, moderate, strong, and extreme,” used for assessing the intensity of an attribute. Interval and ratio scales are like ordinal scales except that the intervals between two adjacent levels are assumed equal-equal increment for interval scales and equal ratio of adjacent levels for ratio scales. The widely used nine-point hedonic scale from “extremely dislike” to “extremely like” is considered an interval scale. The popular graphical line scale with two a type interval anchors of extreme marked near the line ends is also considered of scale. Expressing the perceived intensity of a stimulus in numerical values is an application of ratio scale because sensory responses are roughly proportional to the stimulus intensity ratio (3). One distinctive characteristic of sensory responses is that they are vague andimprecise; Le., they arefuzzy. For example, we cannotpreciselydefine “moderate” and “strong” and exactly tellthe difference between the two in quantitative terms. A sensory score of, say, 5 is not intended to mean exactly 5. As a result, the levels in a sensory scale, denoted with either words or numbers, are always symbolic characterizations of sensory responses ratherthan numerical measures of the response magnitude. These levels are often assigned numerical values and treated as numbers for the convenience of statistical analysis, but

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they do not possess the arithmetical meanings of numerical values. Obviously, assigning numerical values to nominal and ordinal scales is difficult to justify because a logic relationship does not exist for the categories in a nominal scale and the spacings between the levels in an ordinal scale are unknown. Although as true equal spacing is always assumed for interval and ratio scales and taken a soundbasis. in practice,thisassumption is notreadilyverifiableandlacks Because of the fuzzy nature of sensory responses, the scales cannot be defined in a space of instrumentally measurable variables. Any attachment of numerical values to a scale will automatically imply some assumed logic relationship and spacing of the levels, which are not necessarily reasonable and are difficult to validate. Fuzzy set and neural network techniques are very applicable tools for overcoming some of the difficulties in sensory analysis. A fuzzy set is defined over some domain of interest called the universe. The degree to which elements of the universe belong to a fuzzy set is specified by a membership function. The membership function values are drawn from the interval 0 1 (in mathematical notation, from interval [0, I]). A membership grade of 0 indicates a nonmember, I indicates a full member, and other values indicate partial members of a fuzzy set. Thisis in contrast to the conventional crisp sets where the membership grades are either 0 1 (in mathematical notation, drawn from set { 0, 1)), meaning that an element in the universe is either a nonmember or full member of a crisp set. The existence of partial memberships in fuzzy sets results in fuzzy boundaries between two sets; in other words, an element in the boundary area may be a partial member of more than one fuzzy set. When a fuzzy set is defined over a universe of real numbers, it is called a fuzzy number. A variable whose states are represented with fuzzy sets or fuzzy numbers is a fuzzy variable. A group of fuzzy sets may be used to represent linguistic concepts such as “low,” “medium,” and “high,” which are referred to as linguistic values. A fuzzy variable with linguistic values is a linguistic variable. Detailed coverages of such basic concepts can be found in many textbooks (e.g., Ref. 4). Although primarily motivated by the imprecise nature of human linguistic descriptions and reasoning (5), fuzzy set and fuzzy logic techniques have found extremely rare applications in sensory analysis. Lincklaen Westenberg et al. (6) used twoquestionnaires to evaluate 15 qualityattributes of fourtypes of fat spread. In addition to the conventional graphical line scale, they asked the panelists to respond with “true,” “borderline,” or “false” to statements suchas “The product is hard.” The panel responses to the statements were tallied in the formof fractions (or percentages) and usedas membership grades to classify the samples through a fuzzy logic inference procedure. Different inference rules and attributes were tried. They concluded that the approach was promising and deserved further development. An artificial neural network relates one set of variables to another through

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interconnecting neurons, eachof which represents a linearor nonlinear functional relationship. A neural network thus forms a complex, nonlinear model structure for two setsof variables. The complexity of such a structure and the many adjustable parameters involved make a neural network extremely powerful for establishingfunctionalrelationships.Comparedwithconventionalregressiontechniques, neural networks have distinct advantages in modeling complex relationships between multi-input and multi-output data. This makes neural networks a tool of great potential for establishing relationships between instrumental quality measurements and sensory responses.

A.

Fuzzy Set Interpretations of Sensory Scales, Attributes, and Responses

By and large, sensory analysis is about modeling human senses and using human senses as sensors for measurements. Human senses are sophisticated and powerful, and yet they are inaccurate and lack repeatability. Exact quantification of sensory responses is difficult if not impossible because they are naturally fuzzy. As a result, sensory scales and attributes are also fuzzy. The following are fuzzy set interpretations of some commonly used sensory scales, sensory attributes, and sensory responses.

1. Sensory Scales as Fuzzy Sets A sensory scale is usually a set of terms that describe the intensity levels or the categories of some stimulus attributesof interest. The attributes and their intensity ranges or categories specify a space or universe in which the set of terms are defined. For example, sweetness in a range may be the attribute of interest and evaluated with a scale consisting of the term set (slight, moderate, very much). The range of sweetness is the universe in which the terms are defined. In reality, however, the terms in a sensory scale are only vaguely described. Each term may represent a stimulus intensity in a general neighborhood, and there is not a clear fit the boundary between two adjacent terms. Such linguistic terms obviously to description of fuzzy sets. In fact, they are exactly what fuzzy sets are used represent. A sensory scale is simply a battery of fuzzy sets.

a. Notninal and Ordinal Scales. In nominal and ordinal scales, a sample is classified into a category to which the sample is closest. Once classified, the sample is considered 100% belonging to that category; i.e., it has a membership grade of 1 in that set. This may appear to be a caseof the conventional crisp set, in which a sample either fully belongs to a set or it does not. A crisp set has a crisp boundary, B, in the universe, x, between two adjacent sets, C, and C,+l(Fig. I). The membership function, p, is a constant 1 over the domain of each set. TO the left of B, a sample is classified as C, and otherwise C,,,. In sensory scales,

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x

Fig. 1 Two crisp sets, C, and C,,,, a crisp boundary, B, in universe, x.

however, a crisp boundary B never exists. As a result, an alternative and more reasonable representation of nominal and ordinal scales is a battery of singleton fuzzy sets (Fig. 2 ) . A singleton fuzzy set is one which has only one member. This member has a membership grade of 1 in the set. This agrees with the fact that the categories in nominal and ordinal scales are considered mutually exclusive and a member in a category (set) is always a full member. We may further consider that each singleton set is defined over a general vicinity of a point in the universe rather than precisely at the point. In other words, if a sample is near a singleton set, it is classified as a full member of the set. The fuzziness of the word “near” implies that there is not a clear-cut boundary between two sets. Furthermore, the singleton sets are ranked in an ordinal scale and are not in a nominal scale.

0. Interval and Ratio Scales. Among the interval and ratio scales in use today, variations exist. Some do but others do not permit simultaneous partial classification of a sample into more than one level or, simply termed, betweenlevel classification. For interval and ratio scales thatdo not permit between-level classification, a suitable representation would also be a batteryof singleton fuzzy

X

Fig. 2 A battery of singleton fuzzy sets, C , to C,, describing nominal and ordinal scales, and interval and ratio scales that do not permit between-level classification.

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Fig. 3 A battery of fuzzy sets, C, to C,, with triangular membership functions depicting interval and ratio scales that permit between-level Classification.

sets (Fig. 2 ) . For example, the nine-point hedonic scale is considered an interval scale, and a sample must be classified into one of the nine levels. Indeed, such a scale is not fundamentally different from an ordinal scale except that the intervals are assumed equal. Some widely used interval or ratio scales allow between-level classification. The US. Department of Agriculture (USDA) scale for grading marbling abundance in beef steaks is a good example. It has nine levels, from “devoid” to “moderately abundant.” A sample may be given a grade between two adjacent levels with the closeness to the higher level indicated by a degree. For example, a “30-degree slight” grade means that the sample stands at the 70-30% split of the interval between “traces” and “slight.” Such a scale can be exactly represented by a battery of fuzzy sets defined by the membership diagram in Fig. 3. which is routinely seen in the fuzzy set and fuzzy logic literature. The fuzzy sets or linguistic values, C,, C2, etc., are defined by a set of triangular membership functions as shown by the solid lines in Fig. 3. When the intensity of an attribute in the universe, x, its membership grade under evaluation moves from left to right decreases in one set and increases in another in a linear fashion. For instance, a sample with an attribute intensity x, partially belongs to both C,and C2 (Fig. 3 ) . Its membership grades in the two sets are pI(x,) and p?(xl), respectively, with the two summing to 1. Evidently the membership grades are simply a different interpretation of the degrees in the USDA marbling scale, and the fuzzy sets rather accurately depict the marbling abundance levels. The intervals between the vertices of two adjacent membership functions may not be equal in general, but in the case of an interval scale they are often assumed equal as discussed before. The widely used graphical line scale for intensity evaluation is simply a An special case of the interval scale that permits between-level classification. example line scale is shown in Fig. 4a. Two intensity extremes, such as “exof the line. A tremely low” and “extremely high,” are marked near the ends

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Extremely High I

A

P

Extremely High

X

Fig. 4 (a) A graphical line scale. (b) Two fuzzy sets representing the line scale.

judge indicates his or her evaluation of an attribute by putting a mark between the two extreme tics. This mark is thenconvertedinto a numericalscore by measuring its distances from the extremetics. The conversion is most commonly done according to a linear distance-to-score relationship with the low extreme as the zero-score point and the high extreme as the full-score point. Such a scale is apparently well described by the fuzzy sets depictedin Fig. 4b. There are only two sets, “extremely low” and “extremely high,” each of which is defined by a triangular membership function. When the intensity of a measured attribute increases, its membership in “extremely low” decreases and that in “extremely high” increases in a proportional manner. Of course,a nontriangular membership function can be used to describe a nonlinear distance-to-score relationship. Table 1 summarizes the fuzzy set representations of the commonly used sensory scales as discussed.

2. Sensory Attributes as Fuzzy Variables Because sensory scales are fuzzy sets used to represent the intensity values or states of sensory attributes, sensory attributes are fuzzy variables by definition (variables whose states are represented with fuzzy sets). Furthermore, since the fuzzy sets in sensory scales usually denote some linguistic concepts or values, sensoryattributesare also linguisticvariables(fuzzyvariableswithlinguistic values).

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Table 1 SummaryofFuzzySetRepresentationsofCommonlyUsedSensoryScales

Fuzzy

Scale Nominal scales Ordinal scales Interval and ratio scales that do not permit between-level classifications Hedonic scales Interval and ratio scales that permit betweenlevel classifications Line scales

Unranked singleton fuzzy sets Ranked singleton fuzzy sets Ranked singleton fuzzy sets

Singletons defined near a point (see Fig. 2) As above

Ranked singleton fuzzy sets Regular ranked fuzzy sets

As above

Two regular fuzzy sets

As above

Other shapes of membership functions possible (see Fig. 3 ) Other shapes of membership functions possible (see Fig. 4)

3. Sensory Responses as Membership Grades In sensory evaluation, the state of a sensory attribute is measured by one or more human judges. The judges arefirst trained according to some predefined sensory scale, which is usually presented as a set of standard samples. This is simply the process of sensor (human senses) calibration against a standard. After training, the judges are usedto evaluate the sensory attributeof product samples.A judge’s or her measure of the state of the sensory sensory response to a sample is his attribute. Since a sensory attribute is a fuzzy variable, whose states are expressed in fuzzy sets, the sensory response is simply the judge’s evaluation of the membership grades of the sample in the fuzzy sets of the scale used. In all the scales that can be described by singleton fuzzy sets (including nominal scales, ordinal scales, and interval and ratio scales that do not permit between-level classification), judges are asked to assign a sample to one of the linguistic categories represented by fuzzy sets C,, C:, . . . C,,. If judge j decides that a sample with attribute intensity x belongs to category C,, then the sample membership grade in fuzzy set C, given by judge j, pi,,(x),is 1; otherwise, it is 0; i.e.,

Pi,jtX) =

1

if C, is chosenfor x by judge j 0 otherwise

(1)

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In an interval or ratio scale that permits between-level classification, a judge may select a point between two adjacent levels, C, and C,,,(i = 1, 2, . . . , n I), for a sample with attribute intensity x. The membership grades of the sample in C, and C,,,determined by judge j are, respectively, pl,,(x) and pltl,J(x), which sum to 1 ; i.e., Pi.J(X) +

CLi+l.J(X)

= 1

(2)

The specific values of P,,~(x) and P,+~.,(X) depend on the membership functions. in Figs. 3 and 4, the In the case of triangular membership functions as shown pI+I,J(x)vary in an inversely proportional manner between two values of V,,~(X) and adjacent levels. For example, a “30-degree slight” grade for marbling abundance given by a judge to a steak sample means that the sample has a membership in fuzzy set “traces” and 0.3 (P,+,,~)in “slight,” and a “40grade of 0.7 (p,,,) degree slight” grade means a membership grade of 0.6 in “traces” and 0.4 in “slight.”

4.

Fuzzy Membership Vector and Fuzzy Histogram of Response

Since human sensory responses are intrinsically inaccurate and imprecise, variations occur from judge to judge and from one measurement to the next by the same judge. These variations constitute a measurement noise, which can be reduced by calibration (judge training) but cannot be eliminated.As a result, multiin sensory ple judges are used to minimize the effect of this noise, especially studies where data accuracy is essential. When multiple judges are involved, there exists the question of how the data from multiple judges should be represented; in other words, how can we establish an overall response for a sample? The conventional way of finding an overall response is to treat the individual judge responses as numerical scores and take the average of them. This suffers from the difficulty in providing the underlying equal-interval and linearity assumptions as discussed before. Sincethe responses are values of fuzzy variables, a logical way would be to take the fuzzy average of them. However, the fuzzy operations based on the extension principle (7) and consequently the fuzzy averages (8) require explicit knowledgeof the membership functions.In sensory analysis, the fuzzy setsin the scales are not explicitly defined in terms of some instrumentallymeasurablevariables;thus,theaveragingoperationbasedonthe extension principle is not applicable. While there may be many possible waysto represent and utilize multijudge responses, one method would be the fuzzy histogram or membership vector described as follows. Though a fuzzy average over the fuzzy values is not obtainable without explicit knowledge of the membership functions, the membership grades given by the judges can be averaged within each fuzzy set. Suppose that there

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c, c, c, c, c, c, Fuzzy set or category Ci

c,

Fig. 5 An example plot of fuzzy membership vector

c,

c,

or fuzzy histogram of response.

a are n fuzzy sets (categories) in the scale used and there are m judges. For sample with attribute x, judge( jj = 1,2, . . . m) decides that its membership grade in fuzzy set i (i = I , 2, . . . n) is p,,,(x) as describedin the previous subsection. Note that p),,(x)= 0 if fuzzy set C, is not chosen by judge j. The average membership grade of the sample in fuzzy set C, voted by all the judges is

TP1.,(X) pix) =

,=I

(i = 1 , 2 . . . n)

m

(3)

pi(x) represents an averageor consensus voteby all judges on the degree to which the sample belongs to category C,; in other words, it is an expected membership grade of x in fuzzy set C,. Note that the averaging operation is performed within a fuzzy set and does not require knowledge of the membership functions and the n fuzzy sets in the scale. Eq. intervals between the fuzzy sets. Since there are (3) implies n membership grades for x, which can be written into the following fuzzy membership vector P(X) = [PI(X) P?(X) . . CI,I(X)IT '

(4)

where superscript T stands for transpose. Fuzzy membership vector ~ ( xshows ) the membership grades of x in all n fuzzy sets. Graphically, it can be plotted as a bar graph exemplified by Fig. 5 . Since Fig. 5 is actually a normalized histogram of the judge responses, which

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are membership grades in the fuzzy sets of a scale, we may refer to Fig. 5 as a fuzzy histogram of response. Because no assumptions are involved, the fuzzy of response provides an unadulterated membership vector or the fuzzy histogram depiction of the overall response by a panel of judges. It should be noted that a single judge is also used in practice because of cost considerations as in the case of government-administered grading. A single judge is just a special case of a judge panel with m = 1. The fuzzy membership vector and fuzzy histogram of response defined are still applicable.

5. Defuzzification The fuzzy membership vector or fuzzy histogram of response gives an unaltered representation of judge responses for sensory analysis, and the fact that a sample may be partial membersof multiple categories depicts the fuzzy nature of sensory responses. In commercial applications of sensory evaluation, however,it is sometimes necessary to assign a sample to a single, specific category. Then we need to convert the multijudge responses into a unified consensus, or in other words, to convert the fuzzy membership vector into a scalar of unity corresponding to a single crisp set. The conversion is similartodefuzzification in fuzzylogic control. Several defuzzification methods have been proposed in the fuzzy logic literature, including the maximum method, the mean-of-maximum method, and the center-of-gravity method (9, IO). The concepts of the defuzzification methods may be borrowed to convert membership vectors into crisp sets. By the maximum method, the fuzzy set with the maximum membership grade is chosen as the defuzzified crisp value (or category). Lincklaen Westenberg et al. (6) employed this method to determine the in Fig. 5 final classification. For example, the fuzzy membership vector plotted will be defuzzified by the maximum method to givea crisp classification of category C,. When there is a tie in the maximum membership grade, any one set involved in the tie may be chosen as the crisp value. The mean-of-maximum and the center-of-gravity methods use the center of peaks and the center of gravity, respectively, of a combined membership function. This requires explicit knowledge of the membership functions. As the membership functions are generally unknown in sensory analysis, these two methods are rarely applicable. The maximum method is a logical choice for defuzzification of multijudge sensory responses in many cases. The fuzzy set or category with the maximum membership grade represents the majority opinion of the judges and thus seems to be a natural and logical choice for the crisp value or consensus classification. In contrast, the conventional defuzzificationby arithmetic averaging of assigned numerical values is not as natural because an unverifiable equal-interval assumption must be made, as discussed before, and also because the resulting crisp value does not generally possess the majority-opinion property.

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6. Neural Network Prediction of Sensory Responses From

Instrumental Measurements Sensory responses are reactions of senses to stimuli. Stimuli can be measured and characterized by instrumental measurements. It is of great interest to researchers and product developers to establish relationships between sensory responses and instrumental measurements of stimuli. Such relationships can lead to a fundamental understanding of human senses and help formulate products that people like. Moreover, they establish the usefulness of instrumental measurements as indicators of food quality. As discussed in the previous section, it is unreasonable to represent sensory responses to a product sample as a precise number. The fuzzy membership vector, p, or the fuzzy histogram of response describes the judges' responses without artificial modification. One obvious characteristic of p is that it is multivalued in general. At the same time, characterization of a stimulus or a set of stimuli the stimuli for human responses usually requires a multitude of data. For example, of marbling abundance in a beef steak may include a numberof marbling characof the teristicssuchassize,uniformity,percentarea,andspatialdistribution marbling flecks. As a result, relationships between instrumental measurements and sensory responses are routinely multi-input and multi-output (MIMO) type. Moreover, the relationships areusually nonlinear because of the nonlinear nature of human senses. To establish nonlinear relationships for MIMO data, the conventional statistical methods are extremely awkward.Artificial neural networks, fortunately, provideaneffectivetoolforthispurpose. An artificial neuralnetworknormally consists of a layer of input nodes, a layerof output nodes, and oneor more layers o f hidden nodes in between. Between every two adjacent layers, the nodes are interconnected with neurons, each of which represents some linear or nonlinear relationship with two adjustable parameters: a weight and a bias. Overall, each output node is related to the input nodes through a complex nonlinear relationship. Many algorithms and techniques are available to determine the parameters for an optimal fit of a functional relationship between the inputs and outputs. to many available text(Readers unfamiliar with neural networks are referred books, including Ref. 1 I.) In mathematical notation, an artificial neural network represents the following nonlinear relationship, f

Y

=

f(X)

(5)

with

x = [x, X? . . . XJT [ Y I Y? . . . Y"1' where X and Y are the input and output vectors, respectively, and k and the numbers of inputs and outputs, respectively.

y

=

n are

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For sensory response prediction,Y will be the sensory response represented by fuzzy membership vector p (i.e., Y = p), and X will be a stimulus vector consisting of various measurable characteristics of the stimuli. For example, if Y is sensoy responses of color, X should include various instrumentally measurable color properties that can potentially influence human perceptions of color.

C.ExampleApplications We demonstrate the use of the proposed fuzzy set and neural network paradigm withtwo example applications in beef quality evaluation. The current USDA beef quality grading system is heavily based on visual appraisals of muscle color and marbling abundance over a steak. It is therefore of interest to study human of various sensory response of muscle color and marbling abundance as functions instrumentally quantifiable characteristicsof color and marbling.In these applications, color and marbling were characterized by using color image processing (12). 1.

Samples

Forty-three wholesale beef carcasses were used. Slices of the small end 5 cm thick were cut. Each slice was cutinto two 2.5 cm thick samples. The two freshly cut surfaces, which were congruent mirror images of each other, were used for quality evaluation: one for sensory analysis and the other for image processing.

2. SensoryEvaluation A IO-member panel of 7 males and 3 females was assembled to evaluate beef steak muscle color and marbling abundance. All participants were screened for formal training in fresh beef quality evaluation. The panelists were reacquainted official with the attributesof fresh beef qualityin training sessions with the aid of photographical standards of color and marbling. Representative samples were evaluated during the training sessions and group discussions helduntil the panelists gave relatively uniform scores to the training samples. The laboratory conditions for sensory evaluation were maintained in accordance with the guidelines established by the American Meat Science Association ( 1 3). Muscle color of each steak was scored according to the Beef Steak Color Guide from the National Cattlemen’s Beef Association (formerly National Live Stock and Meat Board, Denver, CO) in an eight-point scale: [Very dark red. Dark red, Moderately dark red, Slightly dark red, Cherry red, Moderately light cherry red, Very light cherry red, Bleached red] Marbling abundance was rated with the aid of USDA marbling score standards in a nine-point scale:

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3. Fuzzy Membership VectorFormulation According to the fuzzy set interpretations, the color scale consisted of eight fuzzy to “bleachedred”(denoted as C, to CR),andthe setsfrom“verydarkred” marbling scale consisted of nine fuzzy sets from “devoid” to “moderately abundant” (denoted as C, to C,). While interval equality cannot be proven, as discussed before, both the color and marbling scales are interval scales since they are used to measure magnitudes of sensory attributes (fuzzy variables): redness and marbling abundance. The color scale does not allow between-level classification; thus, it is represented by a battery of singleton fuzzy sets as shown in Fig. 2. The membership grades were determined from the sensory responses by using Eq. (1). The marbling scale isan interval scale that allows between-level classification;then it is represented by abattery of fuzzysetsdefinedbytriangular membership functions shown in Fig. 3. As mentioned before, a sample falling between two levels is given a grade stated as a degree of closeness to the higher level. The sensory responses were represented as fuzzy membership grades according to Eq. ( 2 ) and the explanations following Eq. (2). The multijudge responses were then formulated as fuzzy membership vectors or fuzzy histograms of response according to Eq. (3). Figure 6 shows the fuzzy membership vectors of marbling for three steaks.

0.7

I

- 4

USample 1

h

X

Sample 2 Sample 3

i- 0.6

0.0

c “ ,

c,

I

c2 c,c,c, c, c,

c,

c9

Fuzzy set or category Ci

Fig. 6 Fuzzymembershipvectors o f marblingabundance for threesteak samples.

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Instrumental Measurement of Stimulus Characteristics

Color image processing was employed to characterize the stimuli instrumentally. Image processing algorithms were developed to segment each steak image into or ribeye muscle, the two subimages: one contained only the longissimus dorsi other had only the marbling flecks. From these two subimages, the color and marbling characteristics were computed. a. ColorStimulusCharacteristics. Thelongissimusdorsimuscleimages were represented in the RGB (red, green, and blue) coordinates. Muscle color was characterized by computing the mean and standard deviation of the of six R, G, and B functions, which led to the following color stimulus vector elements:

where p and (J denote mean and standard deviation, respectively, and subscripts R, G, and B denote the color functions. The means indicated the average color properties of a longissimus dorsi muscle, and the standard deviations showed the color variations over it. b. Marbling Stitnulus Charucterisfics. The size and density of marbling flecks were considered most relevant to sensory responses of marbling abundance. Each marbling fleck was individually identified and its size (area) calcuin 0.5mm2increments into 11 size lated. The marblingfleckswereclassified categories: A 5 0.5, 0.5 < A 5 1, 1 < A 5 1.5 . . . 5 < A 5 6.5, and A > 6.5; where A is fleck area in mm’. The number of marbling flecks in each size in a marbling category was counted and divided by the ribeye area, which resulted for all the sizecategoriesformed a marbling density. The marbling densities density vector as

where d, (i = I , 2 , . . . 1 1 ) is marbling density in size category i. X,,, was the marbling stimulus vector.

5. NeuralNetworkTraining Feedforward neural networks were trained by using the back propagation algoMath rithm to predict the sensory color and marbling responses. Matlab (The Works, Natick, MA) was used for neural network training and testing. The samples were randomly separated into training and testing sets. Two networks, one for color and the other for marbling, were trained by feeding the training data (6) and (7)] in a sequential and recursive manner. The stimulus vectors [Eqs. were the inputs, and the fuzzy membership vectors [Eq. (4)1 were the outputs. Different numbers of layers and neurons and different transfer functions were

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tested to minimize the sum of squared errors. The resulting neural networks had three layers with, respectively, 33, 20, and 8 neurons for color and 20, 25, and 9 neurons for marbling. A linear transfer function was used for the first layer and a sigmoid function used for the second and third layers. The networks were quite complex, consistingof many layers and neurons. This complexity was considered to be a result of the complex nature of sensory responses to stimuli.

6. SensoryResponsePrediction After the neural networks were trained, the test data sets were used to verify them. The inputs in the test data were fed to the networks to generate predicted outputs. The predicted fuzzy membership vectors were compared with the real sensory fuzzy membership vectors. Figures 7 and 8 show comparisonsof predicted and sensory fuzzy membership vectors for a test sample. Results for other test samples were similar. The predicted and sensory fuzzy membership grades had very similar patterns. For a test data set of five samples, the mean squared errors in the predicted membership grades averaged 0.0059 for color prediction and0.0012 for marbling prediction. This shows that the neural networks could predict the sensory responses to a good degree of accuracy for both muscle color and marbling. It should be noted that the network outputs were fuzzy membership vectors

i

Sensory

E2223 Predicted 1

1

Color fuzzy set or category Ci Fig. 7 Comparison of predicted and sensory fuzzy membership vectors of muscle color for a steak sample.

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X

v

i0

0.8

.f3.

0.6

xb 5 B

c,

0.4

E 0 M

2

0.2

e,

2

0.0

c,

c2

c,

c,

c5

C6

c,

c,

c,

Marbling fuzzyset or category Ci Fig. 8 Comparison of predictedand sensory fuzzymembershipvectors abundance for a steak sample.

of marbling

instead of simple crisp scores or grades. The predictions were for sensory reartificial simplification.This sponses in virtuallytheiroriginalformswithout allows analysis of sensory data with their natural fuzziness and complexity and determination of instrumentalmeasurements as indicators of truesensoryresponses. When the maximum method was used to classify the test samples into specific categories, the predictions were 100%correct forboth color and marbling of the test samples.

7. Comment When a new approach is developed, one would naturally expect a quantitative comparison with existing approaches in terms of prediction accuracy and so on. Unfortunately, such a comparison cannot be easily made meaningful because the or do not exist in true values (true grading, true category, etc.) are unknown sensory evaluations. This is usually the case when subjective judgments are involved. Nevertheless, differences existin the ways subjectiveor sensory information is analyzed and interpreted. Some are more logical and reasonable than others.Theparadigmandproceduresdescribedhaveapparentadvantagesover conventional techniques in avoiding unverifiable assumptions and retaining the a more realisintrinsic fuzzy characteristicsof sensory responses and thus provide tic representation of reality.

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111.

PATTERNRECOGNITION

Statistical process control (SPC) has been an important tool for quality control in the processing industry. It has been regaining acceptance and popularity in recent years becauseof the increasing consumer demand for quality products and the intensifying global economic competition.SPC is being employed as a major quality control mechanism for more and more manufacturing processes. It has become an indispensable tool to the manufacturing industry for improving productivity and product quality (14). An SPC system consists of means for product quality data gathering, quality data analysis to detect process abnormalities, and implementation of corrective all product actions when necessary(1 5). Quality variables are evaluated for either units or product samples taken periodically. The quality variables are plotted as functions of time on control charts such as Shewhart and cumulative sum (CUSUM) for production process monitoring (1 6). If a quality variable deviates excessively from normal values or exhibits abnormal patterns, one or more special causes exist and appropriate corrective actions are determined and implemented. Depending on the product and process, one or more steps of the SPC procedure may require human involvement. These may include manual evaluation of data characteristics and manual implementation of corrective actions. The need for manual operations often leadsto a considerable time lag between data gathering and process correction, which can be costly. For a high-throughput process, it is very desirable to have on-line problem detection and process control because of lowany time delay in process adjustment can result in significant amount grade products or wastes. This is especially true for continuous processes that cannot be stopped and restarted frequently. A good example of such a process is food extrusion, which currently relies on constant attention of experienced operators for control. To implement an on-line SPC system, a critical ability is automatic detection of abnormalities. The 30-criterion (three-standard-deviationcriterion) detects the existence of special disturbances by performing limit checking. The abilityto identify special disturbances by recognizing unnatural patterns in quality data is also very important in SPC applications. Swift (1 7) proposed a dichotomous decision tree approach and used a series of statistical hypothesis tests to identify six unnatural patterns in control charts a highly inflated (quality data plots). The approach was found useful but had false rate because of the use of hypothesis tests. The capability of the pattern recognition algorithm depends on the accuracy of the hypothesis tests. Cheng ( 1 8) developed a process deviation reasoning system using a traditional syntactic pattern approach to recognize unnatural patterns. Specific protoin thesystem.Thesetemplatesareusertypepatterns or templatesareused defined and could significantly affect the performance of the resulting algorithm. In addition. the pattern-making processis tedious and time-consuming, especially

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when the number of templates or patterns is large. This has impeded real-time applications of the approach. With the advent of computers, intelligent pattern recognition techniques have been developing rapidly, making automated pattern recognition possible. Hwarng ( 19) reported a neural network model to recognize six common patterns in quality data. The model could successfully recognize computer-generated patterns. However, it requires transformation of real data into binary numbers as input, leading to a large input layer size and making the neural network complicated and slow.In addition, the transformation inevitably changes some statistical real characteristics of quality data, which can make the algorithm less reliable for applications. In this section, a sliding-window schemeis described for developing neural network pattern recognizers for quality data analysis. Synthesized and experimenof the approach. tal data from food extrusion are used to show the usefulness The recognizers are suitable for on-line applications.

A.

PatternsinFoodQualityData

A production process usually involves the operation of a complicated system with many possible causes for variations. Generally, fluctuations in quality data can be classified into two categories: natural and unnatural patterns. The major distinction between a natural pattern and an unnatural one is that the former is random and the data follow a normal distribution, whereas the latter has identifiable patterns and the data do not follow a normal distribution. The Statistical Quality Control Hardbook (20) summarizes 15 unnatural patterns in quality data. Among them, the most commonly occurring patterns include trend, cycle, systematic, stratification, mixture, and sudden shift. Figure 9 illustrates these patterns. A trend is defined as a continuous and gradual movement up or down. A series of periodic upward and downward variation is a cycle pattern. If a pattern is systematically alternating between up and down, thenit is a systematic pattern. A stratification pattern is a series of points that hug a centerline with a few deviations from the centerline. A mixture pattern is characterized by points tending to fall near the upper and lower control limits with an absenceof normal fluctuations near the middle. A sudden shift pattern shows an abrupt change in level. Interested readers are referred to the Statisticd Qunlity Control Hmdbook (20) for detailed descriptions of the patterns.

B. Data Formatting for Pattern Recognizer Training For a neural network to recognize a pattern, a segment of quality data containing the pattern must be given to the neural network. Quality data can be presented

398

Tan

Trend

Cycle

Stratification

I

I

1

Mixture

I Sudden shift

Fig. 9 Six common patterns in qualitydata.

to a neural network in a sliding-window fashion. The window length w must be long enough to contain at least one complete pattern. In other words, quality data sequence y(t) is formatted as neural network input vectors as [y(t - w), . . . Y(t - 11, y(t)l’ ?

t = w , w + 1, . . .

(8)

where t is the current time. Equation (8) simply indicates that each input vector to the neural network is a window or segmentof a data sequence and the window slides forward by one data point for each time increment. To train a neural net-

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work to recognize N patterns, N columnvectors(onefromeachpattern)are presented after each time incrementto the neural network as input in an arbitrarily selected order. An N-bit binary number can be used to represent N different patterns. For example, if there are six patterns of interest, [ I , 0, 0, 0, 0, 0IT indicates pattern I , 10, I , 0, 0, 0, 01 indicates pattern 2, and so on. The binary numbers are used as outputs to train a neural network pattern recognizer.

C.PatternDataGeneration It is generally difficult to acquire real quality data with distinct and repeated patterns of importance for recognizer training. As an alternative, a pattern generator may be used. A pattern generator may be expressed in a general form of a process mean plus two disturbances: Y(t) = I.I + d(t) +

at)

(9)

where y(t) is a quality measure at time t, p is the process mean for y(t) when the process is in control, d(t) is a special disturbance, andE(t) is a random variable following a normal distribution, i.e., E(t) = N(O, ro)

(10)

where o is the process standard deviation when the process is in control and r is the magnitude of random noise in terms of CJ (0 < r < I ) . Special disturbance d(t) determines the typeof pattern. The special disturbances for generating the six common patterns are shown in Table 2.

D. ExampleApplications We use two examples reported in Tan and Sun (21) to demonstrate the development and application of neural network pattern recognizers. One example was based on synthesized pattern data, and the other on experimental data.

1. Synthesized DataExample N. Pattern Data. Equation (9) was used to generate data of six patterns by usingthespecialdisturbancesdescribed in Table 2. For robustness of the trained neural network pattern recognizer, the pattern data were generated with different combinations of magnitudes of the special disturbances and noise levels shown in Table 3. Symbol yi,,(t) denotes a data sequence generated with special is nonpedisturbance magnitude i and noise levelj. Since the sudden shift pattern riodic, it was generated in such a way that each vector of size w would contain one shift at a sequentially different point in the window.

Tan

400

Table 2 SpecialDisturbancesforPatternDataGeneration

rameters on

disturbance Notes Special Pattern Trend

d(t) = k(t - ~ J o

k = slope of trend terms in of

o 4, = initial time Cycled(t) = kosin[2x(t - &,)/TI k = amplitude of cycle intermsof o T = cycle period Systematic d(t) = k(- 1)'o k = amplitude of pattern terms in of o, k>0 Stratification d(t) = d d = offset from process the mean (center of stratification) Mixture d(t, r) = k(- 1)"s r = random number, 0 0 If r < p. w = 0, otherwise w = 1 P = prespecified probability value which determines the shifting between distributions Sudden shift d(t, t,) = k(-l)'o t, = time when sudden shift occurs s = 0 if t 2 t,, and shift upward s = 1 if t < t,, and shift downward k = magnitude of shift in terms of o, k = 0 i f t < t,, k > 0 if t 2 t,

Table 3 Magnitude of Special Disturbance and Noise Levels Used for Pattern Data

Generation j

1

Data sequence y,,,(t) 3

I

Magnitude Pattern

2 level. Noise of disturbance, k

3

I

2

r

~~

1.5

1.5

1.75 shift

0.1 2.5 Trend Cycle Systematic 2.5 Stratification Mixture Sudden

2.0 2.0

1 .5

I .5 0. I

0.3

0.5

0. I 0.1 0.0.3 I 0.6 0.4

0.3 0.3 0.3 0.2 0.5

0.5 0.5 0.5

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b. RecognizerTraining. The neuralnetworkrecognizerwasathreeAs a result layer network with an input layer, a hidden layer, and an output layer. of the data format, the input layer consisted of w nodes, and the output layer had six nodes. Twenty-eight nodes wereused for the hidden layer. The transfer function of the hidden layer was a sigmoid function. The transfer function of output layer was a linear function. When 50 data vectors (windows of data) from a pattern were used to train the recognizer and data from the same pattern at the same magnitude and noise levels were used for testing, the results were excellent. When data from the same pattern but at different magnitude and noise levels were used to test the neural network recognizer, the results were poor. For satisfactory robustness of the recognizer, the training data were not only from different patterns, but also from different magnitude and noise levels. The first 300 training vectors consisted of 50 vectors from each of the six patterns at magnitude and noise levels of yz,z(t). The next 100 training vectors were from the cycle and the mixture patterns at magnitude and noise levels of y,,,(t). Thelast 50 training vectors were from the sudden shift pattern at magnitude and noise levels of y3,3(t). The backward propagation algorithm was used to train the neural network. Momentum and adaptive learning rate features were used to facilitate the training process. The Matlab Neural Network Toolbox was used for network training and testing. c. RecognizerPerformance. The performance of thetrainedneuralnetwork pattern recognizer was evaluated with test data (data not used for training). Because the recognizer might correctly or incorrectly identify a pattern or might even fail to recognize a pattern as one of the six, three performance measures were used.The target rate (TR) was the percentage of correctly identified patterns. The error target rate (ETR) was the percentage of incorrectly identified patterns. The false target rate (FTR) was the percentage of patterns unrecognized by the recognizer(neithercorrectlynorincorrectlyidentified).Thefalsetargetrate showed how frequently the recognizer failed to recognize a pattern. Table 4 shows the target rate and error target rate for testing sequences of different magnitude and noise levels (indicated by the subscripts of y; see Table 3). All target rates were more than 79% with a majority being perfect or nearly or perfect. All error target rates were less than 5.7%, with a majority being zero nearly zero. Given the differences in the patterns and in the magnitude and noise a properly levels, the results are considered very good. This demonstrates that trained neural network has robust ability to recognize patterns. The false target rate depended on a threshold value used. Since the neural network output could not always be a perfect binary number with one bit being 1 and the rest being 0, a threshold must be defined. When an output had a bit greater than the threshold, the output was considered to indicate an identified

402

Tan

Table 4 PerformanceoftheNeuralNetworkRecognizer for Patterns of Different Magnitude and Noise Levels

Data sequence Target rate

(%)

Error target rate

97-100 99- 100 95- 100 99- 100

0-0.17 0

100 100

IO0 88- 100

0 0 0 0-2. I7

79- 100

0-5.67

(%)

0-0.17 0-0.17

pattern corresponding to that bit. It is not surprising that the threshold value had 10 shows the false target rate significant effects on the false target rate. Figure as a function of threshold value for three magnituddnoise combinations. A high to a high false target rate. A low threshold value threshold value would lead would reduce the false target rate but increase the error target rate; in other words, with a lower threshold value, the recognizer would more easily claim data as recognizable patterns but also more easily misclassify them. Selecting an appropriate threshold value is, therefore, important. From this work, a threshold value of 0.6 appeared most reasonable.

Threshold value

Fig. 10 False target rate as a function of threshold value (a value above which a target was considered identified),

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The performance of the recognizer differed for different data sequences as shown in Table 4 andFig. 10. Forexample,theperformancemeasuresfor magnitudehoise combinations y,,l(t),y?.?(t),and yi3(t) were different. The differences were evidently dueto the amount of data used for neural network training. Three hundred input vectors from R,?(t),100 from y,,,(t), and50 from y3,3(t) were used to train the neural network. Consequently, the test results for yz,?(t) were the best with a target rate of 100% and a zero error target rate (Table 4). The a target rate of 97% andan error target results for y,,l(t) were slightly worse with rate of 0.17%. The results for y3.3(t)were the poorest with a target rate of 79% and an error target rate of 5.67%. The false target rates in Fig. 10 also show the same trend. This shows, as can be expected, that the performance of the neural network recognizer for a pattern depends on the amount of training data drawn from that pattern.

2. ExperimentalDataExample The methods and procedures described in the last section were usedin an experimental application in food extrusion. Although 15 patterns have been identified and considered common in the literature, experimentally acquiring data of those patterns from a specific process is not always practical. First, the responses of quality variables depend on the process dynamics. It is usually difficult to determine by experimental trial and error what disturbances would result in a certain pattern in quality data. On the other hand, patterns induced by disturbances frequently occurring in a process may somehow differ from the representative patterns described in the literature. Second, to obtain sufficient data of various pata huge and exhaustive array of experiments terns for neural network training, may be necessary, which can be practically unfeasible. The food extrusion process is expensive to run and cannot be subjected to extensive experimentation. To facilitate the experiment process and to minimize the number of experiments necessary, process modeling was used to advantage. A major source of process variation was identified and perturbed with a pseudorandom binary sequence (PRBS). A quality variable of interest was measured and modeled as a function of the disturbance variable. The model was used to determine the typeof disturbance that would rendera certain quality data pattern, and further experiments were then conducted.The model was also used to generate pattern data for neural network training. Finally, the neural network recognizer was tested on the experimental data. a. The Process. Afoodextruder is a high-temperature,short-timeprocess that can transform a variety of raw materials into intermediate and finished In the productssuch as ready-to-eatfoods, flat breads,andbreakfastcereals.

404

Tan

process, food materials are pressed through a barrel by a set of screws. They are heated, pressurized, and subjected to shear. The materials often become highpressure, high-temperature, and gelatinized extrudate when they reach the die. After exiting the die, the extrudate expands and is cut into a desirable size. The product size is one of the most important quality attributes of many extruded food products. Since itreflects the degree of expansion and bulk density of expanded products andis relatively easy to measure, it is a routinely monitored quality variable for quality control in food extrusion. For constant material feed rate, cutter speed, and other processing conditions, the product size depends on the degree of expansion, whichis affected by material properties. Since variations rein material properties are inevitable, the product size varies. Based on past search, a major source of process disturbance is feed moisture content. It significantly affects quality attributes such as product size.

h. EquipmentandExperiments. An APV-BakerMPF-50125twin-screw food extruder wasused. The extruder consistedof two setsof intermeshing screw elements of different geometry fitted in an enclosed barrel. A die was mounted at the end of the barrel. The screws were driven by a 28 kW DC motor, and the screw speed could vary from 0 to 500 rpm. The feeder was a KTRON model T35 twin-screw volumetric feeder. An IVEK Digifeeder system was used to inject water into the barrel. The extruder had nine zones with independent barrel temperature controllers. The screw speed, feed rate, moisture addition rate, and cutter speed were controlled by a host microcomputer. To measure the extruded product size on-line, a computer vision system 1 color frame was developed. The system consisted of a Data Translation DT-287 grabber, a DT-2878 advanced processor, two programming libraries (AURORA and AIPL), a Sony DXC- 15 I CCD color video camera and a Sony PVM-I 342Q color video monitor hosted by a microcomputer. Each digitized image frame had 512 X 480 pixels. The pixel value range was 0-255 (3 X 8 bit resolution). The same exposure and focal distance were used for all images. The selected exposure was such that the image intensity histograms were roughly centered at the middle of the full-scale range (0-255), which gave the best resolution and clearest images. The focal distance was set so that a desired number of product samples could fit in the image frame. An image processing algorithm was developed in the C programminglanguagetosegmenttheproductobjectsfromthebackground, identify each sample individually, compute the size (side view area in of several samples as square millimeters) of each sample, and take the average the measured product size. A sampling mechanism was fabricated to bring a numberof samples from the product streamto the camera view area at each sampling instant.The sampling a mechanism and the vision computer acted as slaves to the host computer. On signal from the host, the sampling mechanism would take samples, and the vision

New Techniques

1

201

405

401

601

801

1001

1201

Time (s)

Fig. 11 The pseudo-random binary sequence (PRBS).

computer would capture a sample image, compute the product size, and transmit the measurements to the host computer. The sampling period used was 4 s. The extrusion experiments were performed around an operating condition of screw speed of 300 rpm, feed rate of 45 kg/h, and total moisture content of 19% (wet basis). Yellow corn meal was used to make a puffed product. The barrel temperature profile was held constant as described in Chang and Tan (22). To produce the effect of natural moisture variations in feed materials, the moisture addition rate was perturbed with various disturbances while the product size was measured. First, a pseudo-random binary sequence (PRBS) was designed and applied to the moisture addition rate to excite the process across its frequency bandwidth. The PRBS signal is shown in Fig. 1 1 . The amplitude of the disturbance was such that the total extrudate moisture content varied between 17 and 21% (wet basis). The PRBS data were used to develop a model of product size versus feed moisture content. From the model, other types of disturbances were selected and applied to the moisture addition rate for more experiments around the same average operating conditions of the process. c. Process Modeling. The response of product size to moisture disturbance was modeled with the following ARMAX (auto-regressive movingaverage with auxiliary input) model:

(1

+ a,q-’ + a2q-*)A(t)= (mlq-’ + m2q-’)M(t - d) + (1 + c,q-‘)E(t)

( 1 1)

where A(t) is product size (mm’), M(t) is moisture content (%, wet basis), E(t) is a white noise sequence, d is time delay (s), and a,, a?, m,, m!, and c I are constant coefficients. The model was developed using the PRBS experiment data. The model structure and time delay were determined using a systematic search algorithm

406

Tan

Table 5 Model Coefficients and Time Delay

27-0.904 -2.980 13.774 -0.081 -0.863

(22). The recursive least-squares algorithm in Matlab was used to determine the coefficients. The time delay and coefficients are shown in Table 5. d. Puttertz Recognizer Training and Testing. From the model (Eq. [ 1 I ] ) it was found that a sinusoidal wave, a square wave, and a step disturbance would, respectively, induce a cycle, a mixture, and a sudden shift pattern in the product size data. These three disturbances were then applied to moisture addition rate in new experiments. The amplitude (or magnitude) of the disturbances was the same as that used for the PRBS disturbance (17-21 %). The period of the sinusoidal and square wave disturbances was 360 s. One set of experimental data is plotted in Fig. 12. The data approximately exhibit the three patterns. There are significant noisesin the data, which resulted from natural disturbances and were not part of the patterns resulting from the special disturbances. After a zero-phase-shift digital filter was used to remove the noises, the filtered data and those predicted by the model are in good agreeof the special disturbances ment. This shows that the model described the effects very well. Pattern data were generated with Eq. ( I I ) for the same three disturbance inputs as those used in the experiments. The data were used to train the neural network pattern recognizer by following the same procedures described in Sec. 1II.D. Data sets measured on-line with the computer vision system were used to test the trained neural network. The target rate was more than 98% for the cycle pattern, 95% for the mixture pattern, and 91% for the sudden shift pattern. The false target rate was zero. These results further verify the usefulnessof the procedure used to develop the neural network pattern recognizer. In addition, a process model derived from multifrequency perturbation is helpful to save experimental efforts.

IV.

REAL-TIMESTATISTICALPROCESSCONTROL

As mentioned in Sec. 111, it is very desirable to have real-time process control because any time delay in process adjustment can result in a significant amount of low-grade products or wastes. This is especially true for high-throughput con-

New Techniques

407

..

350

m i

200 181

1

240

361

541

72 1

901

1081

4 1

271 91

181

541 361

451

Time (s)

Fig. 12 Comparison of model-predicted patterns with measurements: (top) cycle, (middle) mixture, and (bottom) sudden shift. Dotted lines are measured, thicker solid lines are filtered measurements, and thinner solid lines are predicted.

Tan

408

tinuous processes that cannot be stopped and restarted frequently. Implementation of real-time SPC would require means for on-line measurement of quality variables, automated detection of abnormalities, and automated determination and implementation of corrective actions. Many techniques describedin this book can be used for on-line food quality measurement. Automatic detection of excessive deviations is simple, and automatic recognition of abnormal patterns is achievable (Sec. 111). With the modern computer-based process modeling and control techniques, appropriate process corrective actions can be automatically determined and implemented. Instrumenin improving tal measurements of food quality therefore have great potential quality control in food processes. in Tan et al. (23) In this section, we use an example application reported to demonstrate the use of instrumental food quality measurements in the implementation of an automated, real-time statistical process controller.

A.

ProcessandEquipment

The application process was twin-screw food extrusion. The process and equipment used were the same as those described in Sec. 1II.D. Yellow corn meal was used to make a puffed product, and the quality variable of interest was product to measure the product side view size. The computer vision system was used area in square millimeters, and length and width in millimeters.

B. ProcessVariations During an extrusion run, feed material properties can vary considerably. For example, the moisture content of the corn meal used in this work could vary from 9 to 13% (wet basis)as a result of differences in batch, supply source, and storage conditions. This variationcan significantly affect the product size and other quality attributes. To shorten the experimentalrun time, the effects of material moisture variation on product size was demonstrated by introducing a disturbance into the moisture addition rate. The disturbance was a sequence of step changes as shown in Fig. 13, which caused the overall moisture content to vary from 17 to 21%. The variations in product area are shown in Fig. 13. The dotted line in the figure shows the area variations when a single sample was measured at every sampling instant. The plot indicates that the variations consisted of two major components: a fast or high-frequency variation on top of a slow trend. The fast component of variationwasduetorandomornaturaldisturbances,andthe slow component was due to anassignableorspecialdisturbance,whichwas moisture change in this case. Natural disturbances are undeterminable, and thus random variations can-

409

New Techniques 400 r

1

..""

__

"_

50

Single-sampleoreo Sinqle-somole oreo Subgroup-averagedare0 Sudgroup-averaged Mosturecontent Mosture content

350

300 _.

-

250 200 I

,

"""

I"""

30

20

.""_I

1 50

0

50

1 00

150

200

250

Subgroup or Sample Number

Fig. 13 Product area variations resulting from natural and special disturbances (changes in moisture content).

not be eliminated. For process control, it is important to detect the existence of special disturbances. Subgrouping (low-pass filtering) is often used to separate the effects of the two types of disturbance. Subgroups are chosen so that within a subgroup variations are considered to be only due to natural disturbances and A rational betweentwosubgroupsvariationsareduetospecialdisturbances. subgroup is chosen in various ways depending on the manufacturing process. For a continuous process, the key factor for subgrouping is the subgroup size (number of samples), which determines if the subgroup possesses the properties described above. Proper selection of the subgroup size usually requires many trials. For this work, a subgroup size of 10 was found appropriate through experiat ments. When 10 samples were taken to compute a subgroup-averaged area every sampling instant, the area variations areas shown by the solid line in Fig. 13. The fast or high-frequency variations associatedwith natural disturbances are largely filtered out by the averaging operation. It is clear from the solid line that the subgroup-averaged area was inversely related to moisture content. In addition to identificationof special patterns in quality data, control charts are used to determine if a process exhibits excessive deviations. TheX Shewhart control chart is widely employed to monitor the subgroup mean of a quality variable by examining if it is between an upper control limit (UCL) and a lower control limit (LCL) (16). The UCL and LCL are usually expressed as: UCL = X

+ aR

LCL = X - a R

Tan

. ~ . ..

Length Width Moisture c o n t e n t

UCL=23.8

h h.

Subgroup Number

Fig. 14 Productdimensionvariationsresultingfrommoisturecontentchanges and LCL are upper and lower control limits, respectively).

(UCL

where X is the grand average of a measured quality variable over a long run (average of subgroup means), a is a constant depending on the subgroup size, and R is the average of within-subgroup ranges. Since R indicates the magnitude by Eqs. (12) and of random variations in a process, the control limits defined (13) reflect the process capability to maintain uniformity in a quality variable. The control limits were determined through experiments. For the product area, X = 300 mm? and R = 80 mm'. For a subgroup size of 10, a = 0.308 ( 16). Then, UCL = 325 mm2 and LCL = 275 mm' for the subgroup-averaged product area (solid line in Fig. 13). Figure 13 is the of Shewhart control chart for the product size (area) without implementation corrective actions. The size was out of the control limits because of the moisture variation and the process was not in a state of statistical control. The product length and width variations are shown in Fig. 14. The length reduced with an increasein moisture content or vice versa. For the product length, X = 22 mm, R = 6 mm, and a = 0.308 (subgroup size = lo), which give UCL = 23.8 mm and LCL

= 20.2 mm

As shown in Fig. 14, the product length was also out of its control limits as a result of the moisture variation. The product width exhibited little variation relating to moisture change as

New Techniques

41 1

shown by Fig. 14. The size variations were almost exclusively reflected on the product length. As a result, improved control of the product width was unnecessary for minimizing the effects of moisture variation on size uniformity of the test product.

C. CorrectiveAction To bring the process to a state of statistical control, appropriate corrective actions must be implemented. Sincethe process was continuous, quick actions are important. Upon detection of a state out of statistical control, corrective actions were determined by using a simple feedback control scheme. The size measurement by the vision system was used as the feedback signal to determine a proper cutter speed to compensate for the effects of moisture variations. The block diagram of the control system implementedis shown in Fig. 15. In the figure, product size refers to either product area or length depending on which one of the two is of interest for control. The process block stands for a functional relationship (transfer function) from cutter speed to product size. The disturbance effect block represents the unknown relationship from moisture conof the controller was to minimize product size tent to product size. The role variation under the disturbance of moisture variations. Since the process dynamics between cutter speed and product size was simple, the following PI (proportional and integral) controller was considered appropriate for determining corrective actions: u(t) = u(t - 1 )

+ K,[e(t)

-

e(t

-111 + K,e(t) (14)

Cmtent

Effect

Cutter

Cmtroller

speed

Process

P r a h t %e

Fig. 15 Block diagram of the vision-based real-time process control system.

412

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E E E E v 0

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300 LCL=275

250

- 25

. . . . . ,*.,. . . . . . . . . . . . . . . . . UCL=_7_338, ,.,.% , I. \,......_, *.\ ,l-s.l... ,,, ......_-___ :4, ,.' . .- ,.,~ '< . ...:* \,

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212

0

LCL=20.2:

,

""_

~

-,

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:..lzz-,l 100

1 50

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50

19

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- __ Area .....- .- L e n g t h ""

1 00

23

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.._.I.,

200

-

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- 13 11

250

Subgroup Number

Fig. 16 Shewhart control chart showing performance of the real-time LCL are upper and lower control limits, respectively).

SPC (UCL and

where u is the cutter speed control signal, e is the difference (error) between the K, and K, are gain desired product size (process mean) and the measured size, constants, and t stands for the current sampling instant or subgroup number. The proportional and integral gains, K, and K,, could be designed if a process model were known. As an alternative, they were determined through experimental tuning as widely practicedin industry by using the Ziegler-Nichols tuning procedure (see, e.g., Ref. 24). The two controller gains were determined as K,= 0.01, K, = 0.03 for product area control andK, = 0.14 and K, = 0.42 for product length control. The vision-basedcontrolsystemwasimplementedon-lineandtested against moisture disturbances. Figure 16 shows a control chart for both product area and length when the process was subjected to the same moisture disturbance shown in Figs. 13 and 14. The control chart illustrates the performance of the system. In comparison with the uncontrolled curves in Figs. 13 and 14, the system improved the product size uniformity significantly. The controlled product area and length were always within UCL and LCL, meaning that the process was in a state of statistical control in spite of the moisture disturbance. The controlled curves do not have anidentifiablepattern,indicatingthatthecontrolsystem largely eliminated the variations caused by the special disturbance. Figures 17 and 18 are, respectively, the product area and length histograms with and without the vision-based process control. The histograms show that the

413

New Techniques 0.14

1

0.12

-

0.10

-

0.08

-

x W 3 0-

1

1

1

1

1

1

1

1

EZd

1

1

,

-

Uncontrolled . Controlled .

0.06 -

LL

0.04 ,0.02 0.00 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380

Area (mm*mm)

Fig. 17 Histograms of product area with and without the real-time SPC.

-

0.1 4 0.1 2

0.1 0

Uncontrolled Controlled

x

0

6 0.08 3 0-

0.06 0.04 0.02

0.00

1 61 71 8

1 9 20 21

21

22 2 3 24 25 26 27 28 29

Length (mm)

Fig. 18 Histograms of product length with and without the real-time

SW.

Tan

414

uncontrolled product area and length gathered in three clusters corresponding to the three distinct levels of the moisture disturbance. Within a cluster, the product size also varied; but the roughly normal shape of the histogram for each cluster shows that the within-cluster variation was a result of random disturbances. The standard deviations of uncontrolled product were 27.7 mm' for area and 1.96 mm for length, indicating considerable ranges of variation. The controlled histograms, on the other hand, are essentially limited to the middle clusters despite the presence of the disturbance. Their roughly normal shapes indicate that the effects of the special disturbance were mostly eliminated or the process performance was near optimal in terms of product size uniformity.The standard deviations of the controlled product were 7.6 mm' for area and 0.64 mm for length, which represented, respectively, 73% and 67% reductions over the uncontrolled. The results show a considerable improvement of product size uniformity by the application of the vision-based process control system.

V.

FUTURETRENDS

This chapter describes some recent developments in the application of instrumental measurements for food quality analysis and control. Many of the concepts and techniques are new to food applications. Active future research and developments are expected. Fuzzy set and neural network techniques have great potentialin food quality data analysis. The discussion in this chapter represents only some groundwork of a fuzzy-set-based paradigm for food quality data analysis and demonstrates how fuzzy set and neural network techniques may lead to a natural way for food quality data interpretation. We will see many more future research efforts geared a fundamentally sound towardsrefining the methodology and procedures into and user-friendly system. The method will be tested with various quality datato establish its reliability and consistency. Computer software applications will appear to facilitate the practical use of the methodology. Quality data pattern recognition by neural networks will see more and more applications. Along with the steadily improving availability of nondestructive instrumental means for quality measurements, such automated techniques will play an increasingly important rolein quality control in the food industry. Future research in this area will emphasize multivariate or multiattribute cases. Automated real-time statistical process control is the future of SPC. With the development of computer technology and pattern recognition techniques such as those discussed in this chapter, automated real-timeSPC has become a reality. We will see increased integration of process control with SPC andSQC (statistical quality control). In other words, process control will be implemented in the

New Techniques

415

context of quality control. Furthermore, as food quality is ultimately judged by the consumers, SPC systems, which are usually based on instrumental quality measurements, will be increasingly linked with sensory and consumer responses. Fuzzy set and neural network techniques are important for this link.

REFERENCES J Tan, X Gao, DE Gerrard. Application of fuzzy sets and neural networksin sensory analysis. J Sensory Stud 14: I 19-138, 1999. 2. SS Stevens.Mathematics,measurementandpsychophysics.In: SS Stevens,ed. Handbook of Experimental Psychology. New York: Wiley, 195 I . 3 . H Stone, JL Sidel. Sensory Evaluation Practices. 2nd ed. San Diego, CA: Academic

I.

Press,1993. 4. GJ Klir, NB Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Englewood Cliffs, NJ: Prentice Hall, 1995. 5. LA Zadeh. Fuzzy sets. Information Control 8:338-353, 1965. 6. HW Lincklaen Westenberg, S De Jong, DA Van Meel, JFA Quadt, E Backer,RPW Duin. Fuzzy set theory applied to product classification by a sensory panel. J. Sensory Stud 4155-72, 1989. 7. WM Dong, HC Shah, FS Wong. Fuzzy computations in risk and decision analysis. Civ Eng Syst 2:201-208, 1985. 8. WM Dong, FS Wong. Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst 21:183-199, 1987. 9. CC Lee. Fuzzy logic in control systems: fuzzy logic controller, part 11. IEEE Tran Syst Man Cyber 20:419-435, 1990. IO. J Tan, Z Chang. Linearityand a tuning procedure for fuzzy logic controllers. Trans ASAE37:973-979,1994. 11. B Kosko. Neural Networks and Fuzzy Systems. Englewood Cliffs, NJ: Prentice Hall, 1992. 12. DE Gerrard, X Gao. J Tan. Determining beef marbling and color scores by image processing, J Food Sci 61:145-148, 1996. 13. AMSA. Guidelines for Meat Color Evaluation. Chicago: American Meat Science Association, I99 1. 14. CRauwendaal.SPC-StatisticalProcessControl in Extrusion. NewYork:Hanser Publishers,1993. 15. GW Sturm,SAMelnyk, MA Yousry,CLFeltz,JEWolter.Sufficientstatistical process control: measuring quality in real time. In: JB Keats, DC Montgomery, eds. Statistical Process Control in Manufacturing. New York: Marcel Dekker, 1991. 16. WS Messina. Statistical Quality Control for Manufacturing Managers. New York: Wiley,1987. 17. JA Swift. Development ofa knowledge-based expert system for control chart pattern recognition and analysis. PhD dissertation, Oklahoma State University, Stillwater, OK. 1987.

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18. CS Cheng. Group technology and expert systems concepts applied to statistical process control in small-batch manufacturing. PhD dissertation, Arizona State University, Tempe, AZ, 1989. 19. HB Hwarng. Back-propagation pattern recognizers for; control charts: methodology and performance. Computers Indust Eng 24:219-235, 1993. Co. StatisticalQualityControlHandbook.NewYork:Western 20.WesternElectric Electric Co. 1958. 21. J Tan, Y Sun. Quality data pattern recognition for on-line statistical process control. Proc. 4th Int’l. Sym. on Automatic Control of Food and Biological Processes, Goteborg, Sweden,1998. 22. Z Chang, J Tan. Determination of model structure for twin-screw food extrusion 1: Multi-loop. Trans IChemE 71(C2): 1 1-19, 1993. 23. J Tan, Z Chang, F Hsieh. Implementation of an automated real-time statistical process controller. J Food Proc Eng 19:49-61, 1996. 24. D Seborg, TF Edgar, DA Mellichamp. Process Dynamics and Control. New York: Wiley,1989.

Index

Absorbance, 6, 17, 119, 220 Absorption, 18, 24, 30, 12 1 coefficient,139 Absorptivity, 6, 8. 348 Acoustic: image, 223 microscopy, 223 Adenosine triphosphate (ATP), 100 Adenosine monophosphate (AMP), 363 Aflatoxin,124 bruise, 20,21, 110. 143,150, 151 DLEintensity,109 firmness, 244, 249, 252, 270-275 gloss, 20 maturity, 19 spectral characteristics, 18 stiffness, 255 sugar, 19 watercore.21,137,143,147,156 Apricot,DLEintensity,103,109 Artificial intelligence, 40, 74, 92 Attenuated total reflectance (ATR), 14, 15

Avocado firmness, 256, 274. 275 Bacteria: Catnpylobacter, 129 Escherichiu coli. 361

[Bacteria] Listeria spp.. 361 Salrnonellu, 129, 36 1 Yersinia enterocolitica, 361

Banana: DLEintensity,103,106-109 gloss, 20 maturity, I 1 1 Bayesianclassifier,154,155, 158 Beans, color evaluation, 74 Beef color and marbling, 391-393 fat content, 26 grading, 150 Beer-Lambert Law, 6, 8, 139 Bell pepper: DLE intensity, 109, I 1 1 gloss, 20 picking, 150 orientation and shape, 1 10 Bioluminescence, 363-364, 370 Biosensors, 335, 337 acoustic, 350 amperometric, 357, 370 applications in food industry, 359 biocatalyst, 338-339 biocatalytic membrane, 356 biocomponent, 338-339, 345-346 biomimetic, 369-370 417

418 [ Biosensors]

biomolecules, 339 biorecognition, 344 electrochemical, 353 optical, 348, 370 potentiometric, 370 Blueberry, firmness, 270 BOD (biological oxygen demand), 337 Boltzmann constant, 203, 205 Bound water, 201 Butter: composition, 28-29 creep, 306 Camera: area-array, 55 BCCD, 57-58 calibration, 72 CCD, 55-57, 87, 90, 122, 124 CID, 56 CMOS, 55-57 digital, 58 frame transfer, 56 line-scan, 55, 58, 90 progressive scan, 58 TDI (time delay integrate), 55, 90 Cantaloupe: firmness, 272 maturity,112 Carotene (carotenoid), 20, 109, 1 I O Cam-Purcell pulse sequence, 179, 183 Carr-Purcell-Meiboom-Gill(CPMG), 179, 195- 199 Cavitation, 2 19 Cheese: Cheddar, 307-3 I3 cut time, 227 fat globules, 84-86 meltability, 309 mozzarella, 304 process American, 307 shreds, 66 Cherry: firmness, 272-273 ripeness, sugar, 19

index CHESS (chemical shift selective) sequence,190 Chlorophyll: content, 99, 107-1 IO, 126, 257 lossldegradation,17, 19, 99,109 Chocolate: blooming, 43, 56 melting profile, 226 Chromaticity: coordinates, 4-5, 19 diagram, 5 CIE (Commission de Internationale de I'Eclairage), 4-5, 19, 69 CLSM (confocal laser scanning microscopy), 83 Cod fillet: firmness, 248 sealworms and bones, 223 Color: calibration, 7 1 diagram, 5 index, 22 memory, 1 model. 5 Munsell, 5 rendering index (CRI), 70-71 temperature, 43 Colorimeter, Agtron, 19 Computer tomography (CT), 139- 147, 159 dual energy gamma, 159 Computer vision, 39-92, 1 IO, 130, 404-41 1 Corn: color evaluation, 74 defects, 23. 24 extrudate characteristics,68 quality factors, 1I O shape inspection, 66-67 Cracker, shape inspection, 67 Creep compliance, 291 -295, 301 -3 I O retardation time, 294-308 Cucumber, chilling injury, 1 1 1 Cytometry, 129, 36 I , 362 Deborah number, 290 Debye-Stockes theory, 205

Index

Delayed light enussion (DLE),99- I 16, 130 decay,103, 105, 257 discovery, 99 luminescence, 101 Discrete cosine transform(Dff), 148. 149 Discrete Fourier transform (DFT), 148 DNA probe, 344, 359, 360, 361 DRIFTS (diffuse reflectance infrared Fourier transform spectroscopy), 15 DSC (differential scanning calorimeter), 210, 227, 320, 324 Dynamic: mechanical (thermal) analysis (DMA, MDTA), 320-324 rheometer, 297-299, 3 12, 313 tests, 301, 304 viscosity, 301 Egg: blood spot, 75 defects, 25 MR image,186 shell color, 25 yolk, 5 Eggplant, gloss, 20 Elastography, 232, 237 Electrochemical sensor, 339 Electrochemiluminescence, 360 Electromagnetic: radiation, I 17- I 18, 2 I7 spectrum, 1-3, 6, 31, 118 Electronic nose, 365-370 ELISA, 351-352, 362 thermistor ELISA (TELISA). 352-353 Emulsion: creaming, 23I flocculation, 23I , 3 16 gel, 316-317 oil-in-water, 3 16 Energy: acoustic, 221 attenuation, 53 chemical, I O 0 sound, 220 vibrational, 253 Expert systems, 63

419

Fast Fourier transform (FFT), 256, 257 Feature extraction, 41, 61, 62, 65-66 variant and invariant methods, 66 Fiber optics, 31, 44, 52-55, 340 Field effect transistor (FET), 35 1-352 immuno FET (IMFET), 351-352,355 ion-sensitive FET (ISFET), 347, 351 Fixed path length resonator, 222 Fluorescence, 99, 100, 1 16- 130 auto and induced, 1 18- I27 cytometry,129 image, 86, 122 immunosensors, 349-350, 371 labeling,129 microscopy,123,129 Food safety, 3 1, 39, 191, 209, 237, 287, 319, 320, 335, 337, 370 Frame grabber: calibration, 72 gain and off set control, 57-59 Free induction decay (FID), 175, 194. 196, 212 multiexponential decay, 196 French fry, color, 73, 75 Fresnel equations, 7 Fruit juice, solids concentration, 126, 224 FTIR (Fourier transform infrared), I , 11-15, 22-31 Fuzzy logic (sets), 63, 74, 75, 92, 154, 156, 379-392 defuzzification, 389 Gel point: determination, 3 13 gel time, 133, 315 sol-gel transition, 3 14 Winter-Chambon method, 314-315 Genlocking, 61 Glass transition temperature, 201-212, 304, 3 19-323 MRI mapping, 209 Gloss, 20 Grain: admixture, 23 moisture content, 26

420

Grapefruit, surface defects, 22 Gray level quantization, 144 HAACP (hazard analysis critical control point), 335, 337-338 Handling: data, 39. 86 postharvest, 20, 257 Herz contact theory, 266 Hooke’s law, 289, 295 Hookean, 292-300 HSI (hue, saturation, intensity) system, 42, 68-74, 542 Hysteresis, 29 I , 342 Illumination system, 42-54 Image: acquisition, 41 blur, 43, 90 features, 65-66, 86, 143 Fourier transform, 182 histogram, 73 morphology, 64-65, 147-148 noise, 143 reconstruction, 84, 144 segmentation, 6 1-62, 73 texture, 67-68 thinning, 65 thresholding, 73 understanding, 41, 63 Injury: chilling. I I I , 258 impact, 2 I mechanical. 20, 15I Interferometer,13.14.222.339 Ion-sclectivc electrode, 355 IR (infrared), 1-2. 12- 19, 27-30. 86 imaging. 86, 88 spectroscopy, 1 1 - 12, 26, 28 Isochronal. 295-296 Karhunen-Loeve transform, 148- 149 Kclvin-Voight model. 294-295.307-308 Kiwifruit: tirmness, 48, 249-259, 270-275 maturity, 188

Index

[Kiwifruit] modulus, 266 MR image, 188 texture, 256 Knowledge base, 41, 62-63 Kubelka-Munk, 8, I O Larmor equation, 166 frequency,168,171, 180 Laser air-puff firmness detector, 244, 259, 265 Laser Doppler vibrometer, 256-257, 275-276 Lemon: color grade, I I O DLE intensity, 107-1 10 Ligase chain reaction (LCR), 360 Lighting: arrangements, 43 sources, 43 strobe, 43, 90 Linescan,139-141, 153 Loss tangent (tan 6). 300, 3 14-315, 321 -322 Lubein, 20 Lycopene,109 Machine vision, 39-92 Magness-Taylor (MT) puncture test, 244-246, 253. 263, 269, 271 Magnetic dipoles, 166 Mango, firmness, 256, 274 Maxwell model, 292, 294. 301, 306 Melon. firmness. 244. 248, 254, 270, 274-275 Microorganismlmicrobial:

activity. 201, 209 contamination. 337, 362, 364, 370 infestation,I23 in-line analysis. 337 rapid analysis, I29 testing, 361 -362 toxins,123-124,335.348 Microscopy: acoustic. 223 confocal laser scanning (CLSM). 83

index

421

[Microscopy] electron, 83 fluorescence,123,129 3-D, 83 Microwave, 275 Milk: coagulation, 84, 310. 31 1 gel, 310-312 composition, 26, 30, 224 Modulus: bulk, 219 complex, 300 elastic, 219, 264 loss (viscous), 298 shear, 289 storage, 298, 320, 322 Young's, 232, 259, 289 Moisture content, measurement, 26 Molecular imprinting, 368-370 MRI (magnetic resonance imaging), 165,179-185, 2.57 Munsell color atlas. 5 Muscle food, fat thickness, 232 Muskmelon, DLE intensity, 113 Mycotoxins, I23

Oil: adulteration, 28 composition,28-29,127,129 Olives, DLE intensity, 109 Onion: diseases,137, 151 DLE intensity, 109 firmness, 273 gloss, 20 line scan image, 153 separation from clods, 252 On-line: control, 63 firmness sorting, 278 inspection,142 moving scene analysis, 89 quality and safety monitoring, 371 statistical process control, 396 viscometer, 236 Optical density (OD), 7. 18, 20-26, 1 19 Orange: DLE intensity, 102-1 10 firmness, 247, 252, 270, 273 gloss, 20 juice, 22 surface defects. 22

Nectarine: DLE intensity, 109 firmness, 252 Neural network, 63, 74-75 92, 154, 157, 379-398 Neuro-fuzzy systems, 75 Newton's law, 289 Newtonian. 292-293, 297 NIR (near-infrared), I , 9, 1 1 , 18, 21 31, 54, 57, 86, 88. 115, 227, 244. 253, 280 image, 86-88 instruments, I2 sensors. 160 spectroscopy. 26, 257, 275 NMR (nuclear magnetic resonance), 165-21 1 imaging.165 spectroscopy,165,166,288

Papaya: DLEintensity,103,108 maturity,112.113 Partial least squares (PLS), 379 Pattern recognition, 63, 64, 154, 232, 396-406 Pea, firmness, 275. 276 Peach: DLE intensity,102, 109 firmness, 246, 249, 259. 270-275 ripeness, 19, 1 13 Peanut: maturity, 20 moisture content, 26 Pear, firmness, 249, 270-273 Penetrometer, 245 Persimmon,DLEintensity.103.105, 109,113 Phase locked loop (PLL), 60

422

Phosphorescence, 7, 100 Photoluminescence, 1 13, 1 16 photoluminography,I30 Photonintensity,139 Photosynthesis, 100, 130 Pineapple, surface color, 22 Pistachio, color classification,75 Pixel, 41 square, 55, 59 jittering, 60 Planck’s constant, 1 I , 167 Plum, DLE intensity, 109, 113 Poisson’s ratio, 251, 264, 266 Polymerase chain reaction, 360 Pomegranate, DLE intensity,109 Pork, muscle quality, 24-25 Potato: color evaluation, 74 diseases, 23 firmness, 273 hollow heart, 23, 234 Principal component analysis (PCA), 22, 379 discriminant analysis, 22 Proportional and integral controller, 41 1 Prune: ripeness, I9 sorting, I 14 Quanta, 1 I8 quantumnumber,166,168 Quenching,119,120 Quartz resonator sensor, 366 Radiofrequency (RF) pulse, 167- 174, 183,185,190 Reduced mass, 1 1-12 Reflection: body, 9 coefficient, 223 diffuse, 7, 10, 17 regular, 7 specular, 7, IO, 43 total internal, 52, 348-349 Refractive index, 4, IO- 12,52-53.348

Index Relaxation time, 17 I - 172, 184- 185, 189, 220, 289-290, 305 Retardation time, 294, 305 RGB (red-green-blue) system, 5, 42, 68-74, 393 Rice: degree of milling, 24 gelatinization, 24 quality factors, I 10 RNA probe, 359-360 SAOS (small amplitude oscillatory

shear), 288, 296, 300-319 temperature and frequency sweep, 309 Scattering: classical theory, 220 coefficient, 8, 12 losses, 227 of muscle, 25 Raleigh, I I9 sound, 229 thermal, 229 viscous, 229 Semiconductor metal oxide chemoresistive sensor, 366 Signal-to-noise ratio, 22, 343 Sing-around method, 22 1 Snell’s law, 6, 52 Soybeans, quality factors, 1 10 Spectrophotometer,12,26,113,120 Spectroscopy: ATR.14,15 diffuse reflectance, 9, 15 dynamic mechanical, 320 ITIR, I , 13, 14, 15, 25 NIR, I , 12, 22, 25, 88, 257 optical, 220 photo correlation, 348, 350 ultrasonic. 217, 227, 236 visible, 1 Spin-lattice and spin-spin relaxation, 17 1 - 172, 192-209, 257 spin-echo pulse, 177- 178 Spirits, alcohol concentration, 224 Starch gelatinization, 24

Index

Statistical process control (SPC), 379, 396,406, 414 statistical quality control, 414 Strawberry: firmness, 270 maturity, 188 MR image, 188 Stress relaxation, 293, 305, 306 Surface plasmon resonance (SPR), 348, 370 Tea leaves, DLE intensity, 108, I 1 I Texture, 233-244 profile analysis (TPA), 287, 288 3-D measurement techniques: food quality analysis, 39 stereo, 8 1-82 structured light, 79-81 time of flight, 76-78 triangulation, 78-79 Time constant, 171, 172, 204-209, 257 Time-temperature superposition, 304 Tomato: DLE intensity, 103- I 10 firmness, 247, 270, 275 gloss, 20 internal color, 19 maturity,I87 stern and blossom end, 1 I O Transducer types, 338-347 piezoelectric. 250, 253, 272-274, 338, 348, 350 Transmittance, 6, 7, 10 Ultrasonic: attenuation, 219, 222, 227, 230, 233 firmness sensing, 254 image, 23 1

423

[Ultrasonic] pulse-echo method, 22 I , 232 pitch-and-catch method, 22, 222 through-transmission method, 22 I velocity, 219,221,224,226,227,230 UV (ultraviolet), I , 54, 86, 100, I 15117,121,124 Viscoelasticity, 288-289, 301, 309. 324 Viscosity, 205. 243, 289, 307, 350 measurement, 3 12 complex, 3 18 Voxel,140,142 Water activity, 191,192 Waves: acoustic, 291 evanescent, 348, 350 longitudinal, 2 18 mechanical, 2 17-2 18 shear, 2 18, 236 sound, 217 surface acoustic (SAW), 236, 350 Wheat: fat content, I28 protein content, 28 smut content, 24 Xanthophyll, 20 X-ray: absorption coefficient, 141,146 dualenergy,160 image, 86, 89, 139,141,150, 158 photons,144 source,139,146 Yogurt, gel stiffness, 3 I I