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

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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

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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

-

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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 ( 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

Ruan and Chen

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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NMR in Food Quality Analysis

10000 +T1

(20M H z )

+T1(200

MHz)

1

0.01

'

' 0.0001 l.E-10

'

"""'

'

1 .E-09

'

.

"""'

1 .E-08

"""'

'

1 .E-07

'

'"'.rl

1 .E-06

Tc (s)

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

Chen 206

and

Ruan

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

1000

L

T,minimum

n

2

w

100 :

H

I ~

,

10 100

200

I 300

400

Temperature (K) Fig. 35 Spin-latticc relaxation time constant (T,)as a function of absolute temperaturc for maltodextrin samples (DE = 5; moisture content = 25%).

NMR in Food Quality Analysis 1000 loo0

-GE

207

I

n W

1 0 0 ;: 100

10 150

200

250

300

350

400

Temperature (K) Fig. 36 Spin-lattice relaxation time constants as a function of absolute temperature for maltodextrin samples (DE = 25; moisture content = 29.2%).

100

n rA

3.

W

G

Transition point

10 100

200

300

400

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

for

and

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Chen

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

1000 c t . 8.9%

+10.4%

+12.8%

10

'

200

250

300

350

400

Temperature (K)

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

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NMR in Food Quality Analysis

1.35 A 14.70%

:

h

1.25

v

-3

1.15

1.os

x18.30&

0.95 I 250 200 150

300

350

400

Temperature (K) Fig. 39 Effect of moisture contenton spin-spin relaxation time constantTzfor maltodextrin (DE = 5 ) .

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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REFERENCES I.

A Abraham. ThePrinciplesofMagneticResonance.London:OxfordUniversity

Press,1960. 2. TL James, GG McDonald. Measurement of the self-diffusion coefficient of each component in a complex system using pulsed-gradient Fourier transform NMR. J MagnReson11:58-61,1973. 3. LD Field. Fundamental aspects of NMR spectroscopy. In: LD Field, s Sternhell, eds. Analytical NMR. Chlchester: John Wiley & Sons, 1989:5-39. 4. TC Famar. Pulsed NMR Spectroscopy. Madison. WI: The Farragut Press. 1989:211 . 5. EL Hahn. Spin echoes. Phys Rev 80:580-594, 1950. 6. HY Carr, EM Purcell. Phys Rev 94:630, 1954. 7. PC Lauterbur. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 243: 190, 1973. 8. A Kumer, D Welti, RR Ernst. N M R Fourier zeugmatography. J MagnReson28: 69-83,1975. 9. CNChen, DI Hoult.Biomedicalmangeticresonancetechnology.Bristol:Adam Hilger, 1989340. 10. WG Proctor, FC Yu.The dependenceof nuclear magnetic resonance frequency upon chemical compound. Phys Rev 72:717, 1950. 1 I . N Ishida, T Kobayashi, M Koizumi, H Kano. ‘H-NMR imaging of tomato fruits. Agric Biol Chem 53(9):2363-2367, 1989. 12. P Chen, MJ McCathy, R Hauten. NMR for internal quality evaluation of fruits and vegetables. Trans ASAE 32:1747, 1989. 13. CY Wang, WPC. Non-destructive detection of core breakdown in ‘Bartlett’ pears with nuclear magnetic resonance imaging. HortScience 24( I): 106, 1989. Nondestructive detectionofwatercoreinapplewith 14. CYWang.WPC,MFaust. nuclear magnetic resonance imaging. Sci Hort 35(3-4):227-234, 1988. 1s. B Zion, P Chen, MJ McCarthy. Nondestructive quality evaluation of fresh prunes by NMR spectroscopy. J Sci Food Agric 67(4):423-429, 1995. 16. JL Maas. MM Millard, MJ Line, GJ Galletta, JL Maas, GJ Galletta. Nuclear magnetic resonance microimaging of strawberry fruit. Acta Hort 348:375-377, 1993. 17. BR Rosen. VJ Wedeen, TJ Brady. Selective saturation NMR imaging. J Computer Assisted Tomography 8:813. 1984. 18. A Haase. J Frahm. WH Nicke,DMatthaei.IH NMR chemicalshiftselective (CHESS) imaging. Phys MedBiol30:341.1985. 19. PJ Keller, WWJ Hunter, P Schmalbrock. Multisection fat-water imaging with chemical shift selective presaturation. Radiology 164539. 1987. 20. RC Semelka, W Chew, H Hricak, E Tomei. CB Higgins. Fat-saturation MR imaging of the upper abdomen. AJR 155:I I 1 I . 1990. 21. J Mao, J Gao, H Yan, JR Ballinger. Susceptibility artifact reduction in fat suppression. MRM 33582-587. 1995. 22. K Chang, RR Ruan, PL Chen,RG Fulcher, E Bastian. Moisture, fat and temperature mapping of cheese block during cooling using MRI. Paper N0.956535 presented at ASAE meeting, Chicago, 1995.

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23. WJ Scott. Water relations of food spoilage microorganisms. Advances Food Res 7: 83-127,1957. 24. JA Troller. Influence of water activity on microorganisms in foods. Food Technol (5):76-83,1980. 25. S Schwimmer. Influence of water activity on enzyme reactivity and stability. Food Technol (5):64-74, 1980. 26. JA Troller. Water activity and food quality. In: TM Hardman, ed. Water and Food Quality. London: Elsevier Applied Science, 1989: 1-3 I . 27. F Frank. Hydration phenomena: an update and implications for food processing industry. In: H Levine, L Slade, eds. Water Relationships in Foods-Advances in the 1980s and Trends in the 1990s. New York: Plenum Press, I99 1 : 1- 19. of food safety and quality? Trends Food 28. F Frank. Water activity: a credible measure SciTechnol:68-72,1991. 29. L Slade, H Levine. Beyond water activity: recent advances based on an alternative approach to assessment of food quality and safety. Crit Rev Food Sci Nutr 30: 115360,1991. ORFennema, ed.FoodChemistry. New York: 30. RCLindsay.FoodAdditives.In: MarcelDekker,1985:629-687. 31. H-M Lai, SJ Schmidt. Mobility of water in various sugar-water systems as studied by oxygen-I7 NMR. Food Chem 4655-60, 1993. 32. MJ Tai. A Suggett, F Frank, S Ablett, PA Quickenden. Hydrogen of monosaccharides: a study by dielectric and nuclear magnetic relaxation. J Solut Chem 1 :131151,1972. 33. PS Belton. KM Wright. An 170 nuclear magnetic resonance relaxation-time study of sucrose-water interaction. J Chem SOC Faraday Trans 1(82):451-456, 1972. 34. GW Padua. Water States Associated with Skim Milk Components as Determined by NMR. Urbana, IL: University of Illinois, 1989. 35. HS Lai, SJ Schmidt. Water mobility and crstallization action of lactose-water systems by oxygen-17 and carbon-13 NMR. J Food Sci 55:1435-1440, 1990. 36. SJ Richardson, IC Baianu, MP Steinberg. Mobility of water in wheat flour suspensions as studiedby proton and oxygen- 17 nuclear magnetic resonance.J Agric Food Chem 34:17-23, 1986. 37. SJ Richardson, IC Baianu,MPSteinberg.Mobilityofwaterinsucrosesolutions determined by deuterium and oxygen-17 nuclear magnetic resonance. J FoodSci 52:806-809,1987. 38. A Mora-Gutierrez, IC Gaianu. IH NMR relaxation and viscosity measurements on solutions and suspensions of carbohydrates and starch from corn: the investigation of carbohydrates hydration and stereochemical and aggregation effects in relation to "0 and '>CNMR data for carbohydrate solutions. J Agric Food Chem 37: 14591465,1989. 39. L Kakalis, IC Baianu, TF Kumosinski. Oxygen-17 and proton nuclear magnetic relaxation measurements of soy protein hydration and protein-protein interactions in solution. J Agric Food Chem 38:639-647, 1990. 40. BP Hills. Multinuclear NMR studies of water in solutions of simple carbohydrates. I. Proton and deuterium relaxation. Molec Phys 72: 1099- I 121, 1991. 41. SJ Schmidt, H Lai. Use of NMR and MRI to study water relations i n foods. In: H

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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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Coupland and McClements

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 ( 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

312

Gunasekaran

Ak and

(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 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

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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

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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-

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..

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-

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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

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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)

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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

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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.

416

Tan

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