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Date IIIIIIIIIIIIIIIIIIIIIIIIIIII 3 1293 10698 732 Iflx-xwmw-u.‘ inf-fit? 81 L: 0-! an- -- -& i n-:‘;: o f" l - .‘W‘fl ”n 9" _"_.I 'd" g-vvw U;§ icli: :- This is to certify that the thesis entitled USE OF A TIME-TEMPERATURE INDICATOR IN MONITORING QUALITY OF REFRIGERATED SALADS presented by Louise Anne CampbeTI has been accepted towards fulfillment of the requirements for M - S - degree in _EQQ.d_S.Cience / .. . 1' Major professor May 15, 1986 0-7639 MS U i: an Aflimative Action/Equal Opportunity Institution RETURNING MATERIALS: IV‘ESI_J PIace in book drop to remove this checkout from 4:22:2225i_ your record. FINES wil] be charged if book is returned after the date stamped below. 3:53.; t 1% USE OF A TIME-TEMPERATURE INDICATOR IN MONITORING QUALITY OF REFRIGERATED SALADS By Louise Anne Campbeii A THESIS Submitted to Michigan State University in partiai fuifiiiment of the requirements for the degree of MASTER OF SCIENCE Departnent of Food Science and Human Nutrition 1986 ABSTRACT USE OF A TIME-TEMPERATURE INDICATOR IN MONITORING QUALITY OF REFRIGERATED SALADS By Louise Anne Campbell A time-temperature indicator was used for monitoring quality of refrigerated salads. The indicator was a pressure-sensitive label imprinted with a standard bar code (for product identification) and a color-changing time-temperature integrating polymer (TTIP). Bar code information was scanned and TTIP color measured as % Reflectance (% R) by a hand-held computer equipped with an optical wand. In a control study, TTIP performance was evaluated at three constant temperatures, and reaction rates and activation energies were determined. TTIP color change followed first order kinetics. In the same study, salads were observed for quality parameters which were sensitive to thehmal history. In a field study, salads labeled with indicators were traced through commercial distribution and retrieved at intervals during shelf life. Salad quality was determined by microbial, sensory, and color parameters and indicators were scanned and data recorded. No correlation between microbial quality and TTIP reflectance value was found. Sensory flavor scores were positively correlated with % R. Chlorophyll degradation of certain green vegetables in the salads was correlated with % R. Likely areas of thermal abuse in the distribution chain were identified. TO BOB AND CAROL ii ACKNOWLEDGMENTS A very special thank you is extended to Dr. Ronnie G. Morgan, major professor and chairman of the guidance committee, for encourage- ment in supporting this research, and for his infinite patience in editing the thesis. Thanks are alSo extended to the other members of the guidance committee: Dr. John H. Allen, Dr. Dennis R. Heldman, and Dr. JohnlL Partridge for their friendship, support, and encouragement. I am deeply indebted to Dr. Dennis R. Heldman and the Campbell Soup Company for providing the incentive and appropriating funds to conduct research in carrying out and completing this project. I would like to acknowledge the assistance of members of the Department of Food Science and Human Nutrition at Michigan State University: thanks are in order to Dr. T.R. Dutson for his advice and encouragement throughout my program at MSU; to Dr. James Pestka for his generosity in sharing laboratory space and equipment for the microbiological portion of the laboratory study, and to Ms. Marguerite Dynnik and Dr. Susan Cuppett for technical advice and assistance; thanks to Drs. C.M. Stine and Bruce Harte for advice and assistance in planning and carrying out the vibration work; and to Ms. Rhonda Crackel, Ms. Terri Drumm, Ms. Betsy LaDuke, Ms. Elaina Ryder, and Dr. T. Nishnetsky, fbr assisting in other aspects of the lab study. A very special thank you to Mr. Costas Tsalakidis for many, many hours spent on computer work, and to Mr. Gary Garfield for assistance in preparing charts and graphs. Special recognition is in order for the personnel at Campbell Institute for Research and Technology and Campbell Soup Company for their active involvement and participation in the laboratory and field studies: thanks to Mr. Gene Ford for setting up the communications system; to Ms. Ellen Barr for always being in the right place at the right time and for accomplishing a myriad of tasks: packing, unpacking and delivering samples, performing endless chlorophyll extractions and for many hours spent in the walk-in cooler scanning indicators; to Dr. Robert Fisher for assistance in developing the chlorophyll extraction procedure; to Mr. George Evancho and Dr. Don link for advice and assistance in planning the microbiological portion of the study, and to Ms. Althea Wilson for plating and counting numerous salad samples. A special thank you to the Sensory Evaluation team: Mr. Vince Brusco, Ms. Doris Aldridge, and Ms. Antoinette D'Angelo and to the members of the taste panel (you know who you are) for making time and space available for the completion of this project. Thanks to Dr. John Scanlon, Mr. Richard Metivier, Ms. Barbara Horlbeck, Mr. Ted Haunton and Ms. Karen Voellinger for providing helpful background information for the field study, and to Ms. Vivian Carter for expert secretarial assistance. I am indebted to the members of the "LifeLines" Technology Team at Allied Corporation for their generosity in sharing their knowledge and talents and for assistance throughout the study: thanks to Mr. Steve Fields for his advice and encouragement; to Ms. Barbara Nicholas for processing mountains of data; to Dr. Paul Friedman, Mr. Frank Patris, iv and Mr. Robert Rack for technical assistance; to Dr. Fred Grabiner for efforts well beyond the call of duty in setting up communications; and most especially, to Dr. Ted Prusik for solving nearly every problem that arose, and for always remaining calm, collected, and cheerful . Thanks are also in order to Mr. Fred Kirby of Mrs. Crockett's Kitchens for his generosity and cooperation in carrying out the field study; to Mr. Steve Norton and the production staff on the "Fresh Chef" salad line; and most especially to Mr. Hiram Rivera for his generous assistance in technical matters of packing, labeling, and shipping salads. And finally, a very special thank you to Dr. Leora Shelef of the Department of Family and Consumer Resources at Wayne State University, whose friendship, support, and guidance lead me in this direction; thanks also to Dr. Michael Zemel for instilling good habits in writing laboratory reports, and to Dr. James Jay for an appreciation of the wonders of microbiology. To my family and many friends, especially Bob and Carol, and Judy and Beverly, who have supported and encouraged me, mere thanks are not enough. Aren't you glad it's finished? TABLE OF CONTENTS Page LIST OF TABLES ......................... ix LIST OF FIGURES ........................ xi INTRODUCTION .......................... l I. BACKGROUND ....................... l A. THE TREND TOWARD REFRIGERATED FOODS ........ l B. PROBLEMS ASSOCIATED WITH REFRIGERATED FOODS . . . . l C. QUALITY LOSS .................... 2 D. ROTATION OF STOCK ................. 2 E. ABUSES DO OCCUR .................. 3 F. A POTENTIAL SOLUTION ................ 3 II. OBJECTIVES ....................... 4 A. OVERALL ...................... 4 B. SPECIFIC ............... , ....... 4 LITERATURE REVIEW ....................... 6 I. PERISHABLE FOODS AND TODAY'S FOOD INDUSTRY ....... 6 A. PERISHABLE FOODS DEFINED .............. 6 B. THE NEED FOR MONITORING THERMAL HISTORY ...... 6 II. TIME-TEMPERATURE INDICATORS .............. 8 A. DEFINITION ..................... 8 B. DESIRED CHARACTERISTICS .............. 8 C. CATEGORIES OF INDICATORS .............. 9 D. HISTORY ...................... lO First Patent Issued ................ 10 First Practical Device ............... ll Device Contains Message .............. ll Disposable Indicator ................ l2 Enzyme/Substrate Indicator ............. 12 LifeLines Computerized System ........... 13 vi Page E. PERFORMANCE EVALUATIONS .............. l3 III. QUALITY LOSS IN FOODS DURING STORAGE ......... l4 IV. APPLICATION OF INDICATORS TO THE FOOD INDUSTRY . . . . l6 EXPERIMENTAL DESIGN AND PROCEDURES .............. 17 I. MATERIALS ....................... l7 A. INDICATORS .................... 17 B. THE "LIFELINES" SYSTEM .............. 19 C. SALADS ...................... 21 1'1. LABORATORY STUDY ................... 23 A. INDICATORS .................... 23 B. SALADS ...................... 23 III. FIELD STUDY ...................... 25 A. SALAD MANUFACTURE ................. 25 B. INDICATOR APPLICATION AND SCANNING ........ 29 C. QUALITY ASSESSMENT ................ 30 Microbiological .................. 32 Sensory ...................... 32 Color ....................... 34 RESULTS AND DISCUSSION .................... 35 I. LABORATORY STUDY ................... 35 A. INDICATORS .................... 35 Mean Reflectance Values of TTIPs ......... 35 Individual Scans of Indicators .......... 39 Reaction Rates and Activation Energies ...... 4l B. SALADS ...................... 48 Physical Characteristics ............. 48 Microbiological Characteristics ...... . . . . 55 II. FIELD STUDY ...................... 56 A. SALAD QUALITY ................... 56 Microbiological .................. 56 Sensory ...................... 58 Color.‘ ...................... 65 vii B. THERMAL ABUSE ................... CONCLUSIONS .......................... APPENDIX 1. Typical Data Collection Sheet for Lab Study on Physical Conditions of Salads .......... APPENDIX II. Baseline Values for Field Study ......... APPENDIX III. Procedure for Color Analysis of Green Vegetable Salad Components ................. APPENDIX IV. Plots of Mean Reflectance Values of Individual Indicators vs. Time .......... APPENDIX V. Occurrence of Syneresis and of Separation in Salads ............. ,- . ....... APPENDIX VI. Observation of Salad Color During Shelf Life . . APPENDIX VII. Microbiological Data from Lab Study ...... APPENDIX VIII. Microbiological Counts for Control and Variable Salads Evaluated During Field Study. . . APPENDIX IX. Statistical Summary of Salad Mean Flavor Scores and Normalized Reflectance Values of Indicators . BIBLIOGRAPHY ......................... viii Page 74 85 87 89 92 94 100 105 110 116 119 121 Table mVOTU'Ith 1O 11 12 13 14 15 16 LIST OF TABLES Reaction rates of TTIPs stored at constant temperature. . Description of "Fresh Chef" salad varieties ....... Flow chart of field study activities ........... Typical "Fresh Chef" salads manufacturing schedule. .'. . Baseline values for selected salads/packages ....... Quality analysis schedule for field study ........ Types of microbial counts, media, and incubations . . . . Mean reflectance values observed fbr TTIP material code 57 during controlled temperature storage ......... Mean reflectance values observed for TTIP material code 61 during controlled temperature storage ......... Statistical summary of mean 'differences' of individual scans for two material codes stored at three constant temperatures ....................... Calculated reaction rate constants (k) for two TTIP material codes at each of three constant temperatures . . Comparison of kinetic parameters calculated in lab study and reported by manufacturer. . . . ......... . . Summary comparison of reaction rate constants at three constant temperatures .......... . ....... Summary of Appendix V. Scores for syneresis and separation in salads ..... , .............. Summary of color evaluations for selected salads ..... Salads sensory evaluation; analysis of variance ..... ix Page l9 22 26 - 28 3O 31 33 36 37 4O 45 47 48 50 52 59 Table 17 18 19 20 21 22 Linear regression of mean sensory f1avor scOre vs. normalized reflectance values .............. Absorbance values (tripliCate samples) of total chlorophyll extracted from green peppers in five salad varieties during shelf life ............... Absorbance values (triplicate samples) of total chlorophyli extracted from celery in three salad varieties during shelf life ...... . ........ Absorbance values (triplicate samples) of total chlorophyll extracted from broccoli in Vegetable Garden Salads during shelf life ................ Reflectance values for control and variable Holiday Cole Slaw salads during shelf life .............. Reflectance values for control and variable Vegetable Garden Salads during shelf life ............. Page 61 66 72 73 76 81 LIST OF FIGURES Figure Page 1 "LifeLines" Time-Temperature Indicator .......... 18 2 Schematic Diagram of "LifeLines" Inventory Management System .......................... 20 3 Summary of Normalized Ref1ectance vs. Time for Material Codes 57 and 61 at l.7°C ................. 42 4 Summary of Normalized Reflectance vs. Time for Material Codes 57 and 61 at 4. 5° C ................. 43 5 Summary of Normalized Reflectance vs. Time for Material Codes 57 and 61 at 7. 2°C ................. 44 6 Plot of Reaction Rate Constants vs. Inverse Absolute Temperatures ....................... 46 7 Plot of Average First Day of Observed Color Loss vs. Inverse Absolute Temperature ............... 54 8 Italian Pasta Salad Mean Flavor Scores vs. Normalized Reflectance ....................... 62 9 Seafood Pasta Salad Mean Flavor Scores vs. Normalized Reflectance ....................... 63 10 Mean Flavor Scores vs. Normalized Ref1ectance for Six Salad Varieties ..................... 64 ll Chlorophyli Absorbance (Values from Individual Trials) vs. % Reflectance (Normalized) for Five Salad Varieties Containing Green Pepper ................. 69 12 Chlorophyll Absorbance (Average Values) vs. % Reflectance (Normalized) for Five Salad Varieties Containing Green Pepper .......................... 7O 13 Chlorophyll Absorbance vs. % Ref1ectance (Normalized) for Green Pepper in Seashell Macaroni Salad ....... 71 Figure 14 15 16 17 18 19 IVa. IVb. IVc. IVd. IVe. IVf. Normalized Reflectance Values vs. Time for Control and , Variable Holiday Cole Slaw Salads ............ Comparison of Thermal Treatment for Control and Variable Holiday Cole Slaw Salads ................ Normalized Ref1ectance Values vs. Time for Control and Variable Vegetable Garden Salads ............ Comparison of Loss of Ref1ectance for Salad Varieties Produced on Two Days .................. Loss of Reflectance for One Salad Variety After One Day Storage .............. . .......... Comparison of Reflectance Values for Samples of One Production Lot Retrieved from Different Locations. . . . Plot of Mean Reflectance V lues of Individual Indicators of Material Code 57 at l. C Storage vs. Time ...... Plot of Mean Reflectance Values of Individual Indicators of Material Code 57 at 4.5°C Storage vs. Time ...... Plot of Mean Reflectance Values of Individual Indicators of Material Code 57 at 7.2°C Storage vs. Time. . . . . . Plot of Mean Reflectance Values of Individual Indicators of Material Code 61 at l.7°C Storage vs. Time ...... Plot of Mean Reflectance Values of Individual Indicators of Material Code 61 at 4.5°C Storage vs. Time ...... Plot of Mean Reflectance Values of Individual Indicators of Material Code 61 at 7.2 C Storage vs. Time ...... xii Page 75 78 79 83 84 94 95 96 97 98 99 INTRODUCTION 1. BACKGROUND A. THE TREND TOWARD REFRIGERATED FOODS A few years ago, the refrigerated display cases in most grocery stores were stocked mainly with such standard items as dairy products and meat. Recent advances in food processing technology, coupled with consumer demand and ever-increasing competition among manu- facturers to create new products and gain market share, have resulted in a proliferation of product offerings in refrigerated display cases. The deli section has been born and is growing rapidly. Dairy products - milk, cheeses, and yogurts - are increasing in number and variety. The meat section has expanded to include fresh fish and poultry. Refrigerated salads, sauces, and soups, as well as prepared sandwiches and entrees, are now available. B. PROBLEMS ASSOCIATED NITH REFRIGERATED FOODS The proliferation of refrigerated products has been accompanied by its share of problems. Refrigerated foods are complex systems, highly perishable, and subject to rapid deterioration if not stored and handled properly (Campbell et al., 1985). Except for a few products, such as fruits and vegetables, which reach maturity during the distribution process, perishable foods continually lose some amount of quality from the time they leave the processor until they are consumed (Farquhar, 1982). Loss of quality may also be due to increases in microbial populations, changes in color or texture, separations in gels or emulsions, etc. A significant factor affecting rate of change of many of these quality parameters is temperature. C. QUALITY LOSS Food deterioration typically follows a zero order reaction (constant rate-of-loss) or a first order reaction (shelf life declines exponentially) (Labuza, 1979). In the case of perishable foods, the rate of quality loss usually increases with rising temperature, fbllowing the Arrhenius relationship (Labuza, 1980). Therefore, careful monitoring of time-temperature exposure of a product becomes an important element in estimating product quality and remaining shelf life. D. ROTATION OF STOCK Rotation of most refrigerated products is based on a First In, First Out (FIFO) system. Product packages are marked with a code date, which may be a "use by" or a "sell by" date. The basic flaw in this system is that products are rotated strictly on the basis of age, and storage temperature throughout handling is not considered. Jeffrey (1985) reported that a typical food product is handled and moved at least 17 times between harvest or manufacture and point of consumption. Certainly a great deal of variability in storage temperatures is encountered in this handling. These variabilities, which can severely affect product quality and shelf life, are ignored by a rotation system which is based only on product age. E. ABUSES DO OCCUR In spite of efforts by the food industry to optimize conditions during handling and distribution, time and temperature abuses do occur (Farquhar, 1977; Jeffrey, 1985). Since most food products are packaged for retail sale, quality of a product may not be evident until time of consumption. There is a need for a system which accurately monitors time-temperature history of perishable foods and is compatible with existing inventory management information systems (Fields, 1985). F. A POTENTIAL SOLUTION M A system which monitors a product's thermal history during storage and distribution without damaging product or package integrity could provide information to facilitate rotation on the basis of actual product quality. Such a system could offer significant benefits, including: 1. Improvement in consistency of product quality, thus contributing to brand loyalty and repeat sales. 2. Strengthening of the distribution chain by pinpointing areas of abuse. 3. Reduction in product loss due to spoilage. 4. Improved inventory management. A number of systems have been devised to solve this problem. One system has been introduced by Allied Corporation of Morristown, NJ under the trade name "LifeLines Inventory Management System."1 This system is proposed as a means of monitoring time-temperature history of perishable products at any point between time of manufacture and time of retail sale. It consists of three components: indicator labels, a hand-held computer for recording label infbrmation, and accompanying software. The label is imprinted with a standard bar code for product identification, and an indicator code. The indicator code contains a time-temperature integrating polymer (TTIP) which changes color as a function of time and temperature. A variety of polymers are available, which change color at different rates, and thus provide for a wide range of temperature environments. II. OBJECTIVES A. OVERALL The overall goal of this study was to evaluate the performance of the indicator component of the "LifeLines" system in monitoring shelf life quality of a refrigerated food product, namely, delicatessen-type salads. B. SPECIFIC 1. To evaluate perfbrmance of the time-temperature integrating polymer (TTIP) in monitoring thermal histories. IReference to the trade names "LifeLines" or "LifeLines Inventory Management System" does not constitute endorsement by the author or Michigan State University. a. To determine thermal reaction rates of TTIPs at various refrigeration temperatures. b. To determine activation energies of TTIPs at temperatures encountered in the distribution and sale of refrigerated salads. 2. To identify, in a controlled temperature storage study, quality parameters of refrigerated salads which are sensitive to thermal history and are significant in determining remaining product shelf life. 3. To measure changes in these salad quality parameters during commercial distribution and to correlate those with changes in color reflectance of the TTIP. 4. To demonstrate the use of the TTIP in identifying areas of thermal abuse in the commercial distribution channel. LITERATURE REVIEW I. PERISHABLE FOODS AND TODAY'S FOOD INDUSTRY A. PERISHABLE FOODS DEFINED . Perishable foods are defined by Farquhar (1982) as "...those products which must be stored under special, controlled temperature conditions from the time they are harvested, slaughtered, produced or processed until the time they are consumed, if a satisfactory degree of original quality is to be maintained. In modern technology, perishable products include fresh, chilled or refrigerated products which have been stored at temperatures slightly above freezing and those food products that must be kept below the freezing point." B. THE NEED FOR MONITORING THERMAL HISTORY By definition, quality of perishable foods is dependent upon proper temperature handling. The effect of time and temperature on frozen food quality is well known and has been the subject of investigation by a number of authors: Van Arsdel et a1. (1969), Singh and Wang (1977), and Jul (1984). Recent trends in consumer demands for foods that are "fresh" and "natural" coupled with rising sales in this area have focused attention on quality and shelf life of refrigerated foods (Fields, 1985). An A.C. Nielson study of consumer complaints (1983) in regard to grocery items cited lack of freshness as a major problem, especially in regard to frozen and refrigerated items. Perishables constitute approximately 50% of grocery purchases (Farquhar, 1982) and therefore represent a key component of the food industry. In a recent presenta- tion, Fields (1985) pointed out several new technologies impacting the food industry: aseptic processing, automated warehousing, use of bar codes and scanning devices, controlled atmosphere storage, and flexible packaging. He concluded that "...these changes and trends are bringing product shelf life and the need for better temperature management into sharp focus." The occurrence of thermal abuse during handling of perishables is common and has been the subject of many studies (Sanderson-Walker, 1975, 1979; Farquhar, 1977, 1982; Jeffrey, 1985). Sanderson-Walker (1979) stated that "the monitoring of temperatures in the distribution chain has indicated a need to evolve improved protection to the product for better quality assurance." An unpublished report by the American Frozen Food Institute (Farquhar, 1977) confirmed the mishandling of frozen foods throughout the distribution chain, and that time-temperature exposures commonly exceeded recommended guide- lines. Commonly used stock rotation systems are based on product age and do not account for variations in temperature to which a product has been subjected (Fields, 1985). Consequently, these rotation methods may be inadequate in optimizing stock rotation based on product quality or remaining shelf life. There exists a need to more accurately monitor temperatures encountered in the distribution channel. The ideal monitoring program would act as "...an early warning system, where product that has experienced some temperature abuse can be moved swiftly to retail with sufficient high quality life remaining" (Farquhar, 1982). II. TIME-TEMPERATURE INDICATORS A. DEFINITION A time-temperature indicator is a device which may be affixed to the outside of a product package to provide evidence of the thermal history to which the product has been exposed (Campbell et al., 1985). Ideally, the indicator could serve as a monitoring or decision-making tool, aid in stock rotation, improve inventory management, reduce spoilage losses, and assist in insuring delivery of the optimum quality product to the consumer. To date, at least 60 such devices have been patented (Farquhar, 1982), but none are in widespread use. 8. DESIRED CHARACTERISTICS Desirable attributes of the 'ideal' time-temperature indicator were listed by Renier (1962): 1. Easy to read by unskilled personnel. Reasonably accurate. Small in size in comparison to product. Size and shape easy to use on or with package. Infinite shelf life. . Easily activated. \lOtU'l-th Maintenance free. 8. Irreversible. 9. Self-powered. 10. Inexpensive. More recently, in a discussion of the new technologies presently impacting the food industry, Fields (1985) added these requirements: 1. Employ objective measurements (so decisions made are consistent). 2. Compatibility with existing management information systems. 3. Imnediately correlatable with product quality. C. CATEGORIES OF INDICATORS Farquhar (1982) described four 'systems' for monitoring food handling: spot temperature checks, time/temperature recorders, code or open dating, and time temperature indicators. Schoen and Byrne (1972) examined 44 'defrost' indicators and classified them into six categories based on the type of information they provided. The authors emphasized the importance of recognizing the specific product characteristic an indicator is to measure and stated that "no one indicator...cou1d possibly indicate a given level of quality for all products, since all products differ in their time/temperature tolerance." Byrne (1976) classified time-temperature indicators into three categories: 1. 'Defrost indicators' - those which react in some way (e.g., a color change) when a preselected temperature is reached; 10 2. 'Time-Temperature Integrators' - those which react to a combination of time and temperature, coming to an endpoint (e.g., a 'final' color) at a preselected time-temperature combination, but do not give any indication of how far past the endpoint they may be; 3. 'Time-Temperature Integrator/Indicators' - those which may be activated at a preselected temperature and indicate change (usually by color) along a graduated scale; and are thought to show the most promise. Byrne (1976) identified three important criteria when considering implementation of a time-temperature indicator: 1. Accuracy of reaction time to within 10% and temperature to within 1.l°C acceptable; 2. Shelf life may be limited - performance may diminish with storage time; 3. The user should be aware that the indicator responds to temperature in its immediate area, and so is a monitor of storage condition rather than of product temperature. 0. HISTORY Several comprehensive reviews of time-temperature indicators have been published (Schoen and Byrne, 1972; Byrne, 1976; Kramer and Farquhar, 1976; Farquhar, 1977, 1982). First Patent Issued According to Kramer and Farquhar (1976), "the earliest indicator for which a patent was approved was that of Midgley (1933), which was 11 based on the distortion of an ice figure upon thawing." Since then, other indicators have been developed which have depended on the use of bacteria or enzymes which undergo a change in appearance with tempera- ture change; liquid-filled capsules which rupture with freezing and thawing (Kramer and Farquhar, 1976). First Practical Device Credit for the first 'practical' time-temperature indicator offered (for use with frozen foods) is given to Minneapolis-Honeywell (Renier et al., 1962). This indicator was designed to meet the needs of the United States Army Quartermaster Corps in the shipment of frozen foods overseas. The device had a yellow time-temperature scale which retained its appearance if stored or shipped within the temperature range of 0°F to +125°F. Fracture of a vial activated the device with release of an electrolytic solution onto an absorbent paper scale, resulting in a color change from yellow to red. The irreversible color change proceeded at a rate dependent on the temperature of the device. Composition of the electrochemical solution could be altered for different absorption rates. This device is no longer available. Device Contains Message Hu (1971) reported on a time-temperature indicator designed to reveal a preprinted message. This indicator makes use of the rate of oxygen permeation through plastic film, which is a reaction dependent on time and temperature. A colored solution contained within the pouch 'hides' the message at the outset. During storage, oxygen 12 permeates the pouch and reacts with the solution, rendering the solution colorless and revealing the message. More than one pouch, containing solutions of different concentrations, could be used to reveal sequential messages such as 'USE NON' or 'DISCARD'. Disposable Indicator A disposable device offered by Bio-Medical Sciences was described for use in gauging product shelf life (Anon., 1975). The device is a label which shows a color bar accompanied by a scale. The indicator is activated at the time of packaging and cumulative exposure to time and temperature results in irreversible color advance along the scale. The indicator is programmed for specific storage conditions, e.g., for a product with a shelf life of one year at 0°F, color advances one unit of the ten unit scale every 40 days. At a higher temperature, the color advances more quickly and reaches the 'expired' and of the scale in less days. This indicator is small, so it assumes the temperature of the food rapidly, and is flexible, so it can be attached to a package for easy reading and without disturbing product integrity. Enzyme/Substrate Indicator Function of the 'i-point' monitor, proposed for use with products which deteriorate as a result of enzymatic degradation, is described by Rose (1981): breakage of a seal between two pouches allows reaction between an enzyme and a substrate containing a pH indicator. As the substrate is consumed by the enzyme, the system changes from alkaline 13 to acidic, and change in pH is indicated by color change. Rate of reaction, as affected by temperature, is controlled by choice of substrates. ~LifeLines Computerized System Fields and Prusik (1983) described a computerized system for monitoring shelf life of temperature sensitive products. The system is composed of three key components: "LifeLines" indicators, a hand- held computer, and accompanying software. The indicator consists of bar code fbr product identification and a color-changing polymer. The polymer is composed of diacetylenes which change color as a function of time and temperature. Bar code and polymer color are 'read' via an optical wand and information is stored in the hand-held computer. The hand-held computer is designed to correlate measurements of the polymer color with predetermined time-temperature characteristics of the product and provide a readout of product quality. On a larger scale, this system could be used as a tool in inventory management. E. PERFORMANCE EVALUATIONS A number of devices have been evaluated for performance by Schoen and Byrne (1972), Arnold and Cook (1977), Mistry and Kosikowski (1983), and Hells and Singh (1985) and others. Kramer and Farquhar (1976) evaluated performance of several commercially available indicators, which fit Categories 2 or 3 previously described. They concluded that the indicators behave with a sufficient level of precision, but that modifications in terms of 14 methods of activation, scaling, readout, and reliability are desirable. A device evaluated by Arnold and Cook (1977) failed to meet the manufacturer's performance claims. The device functions by color change of a dye-impregnated paper strip. The color change takes place as ammonia gas diffuses over the paper strip. The gas is released upon breakage of a glass vial and is contained within an impermeable transparent plastic tube where it diffuses over the paper strip. The manufacturer's claim that color change would move at uniform speed over constant temperature was not met. Wells and Singh (1985) examined performance of four different indicators for use in frozen food transport. The amount of manipulation required for activation of the indicators and hand work necessary for attachment were pointed out as the most serious limitations of their USE. III. QUALITY LOSS IN FOODS DURING STORAGE The relationship between temperature and rate of quality loss in foods has been explored by many authors, including Desrosier and Desrosier (1977), tabuza (1979, 1982), and Saguy and Karel (1980). Desrosier and Desrosier state: “As a rule of thumb, reducing the temperature of a stored food 10°C doubles the storage life. Increasing the temperature 10°C cuts the storage life in half. The temperature of a storage chamber is important in retaining the quality of foods as well as for extending the period of time during which that quality is maintained." 15 Labuza (1982) discussed modes of food quality deterioration such as rancidity, microbial growth, enzymatic reactions, and loss of color, flavor, and nutrients. Labuza (1979) indicated that deterio- ration of many foods can be treated as a zero or first order reaction. Rate of quality loss can thus be represented by the equation 95.: dt -k(A)n (l) where %%-= rate of degradation of A with time A = quality factor t = time k = rate constant n = reaction order (zero or one) The Arrhenius equation is used to describe the influence of temperature on reaction rate (Labuza, 1979): k = k'e'Ea/RT (2) where k' = rate constant at infinitely large T Ea = activation energy R = gas constant T = absolute temperature The Arrhenius relationship indicates that a plot of ln k vs the reciprocal of absolute temperature yields a straight line. The slope of the line is equal to the activation energy divided by the gas 16 constant (Labuza and Riboh, 1982). If reaction rate at several high temperatures is known, the reaction rate at lower temperatures can be predicted by extrapolation of the line. This relationship can be used to predict product shelf life. IV. APPLICATION OF INDICATORS TO THE FOOD INDUSTRY The use of time-temperature indicators to reflect quality of foods has been discussed (Schoen and Byrne, 1972; Kramer and Farquhar, 1976; Schubert, 1977; Mistry and Kosikowski, 1983). Schoen and Byrne (1972) caution that the first criteria for using indicators as monitors of quality is to define what is meant by quality and that indicator selection should be governed by choice of quality attribute. Labuza (1980) applied simple kinetics (zero or first order) to mathematically model loss of food quality. The "LifeLines" indicator utilizes a color-changing polymer which approximates first order kinetics with an Arrhenius dependence on temperature over a wide temperature range. Because both the indicator and food deterioration obey similar time—temperature kinetics, it has been proposed that simple relationships can be established to adequately correlate indicator reading to product quality (Fields and Prusik, 1983). EXPERIMENTAL DESIGN AND PROCEDURES This study was carried out in two phases: a laboratory study, in which indicators and salads were evaluated under controlled conditions; and a field study, in which indicators and product were traced through a commercial distribution chain. I. MATERIALS A. INDICATORS Time-temperature indicators were manufactured and supplied by Allied Corporation of Morristown, NJ. A diagram of the indicator is shown in Figure 1. The indicator is a pressure-sensitive label imprinted with a standard bar code symbology and a color-changing time- temperature integrating polymer (TTIP). The polymer is a diacetylene that gradually and irreversibly changes color in response to cumulative temperature exposure (Fields, 1985). The TTIP becomes 'activated' at above-freezing temperatures. A variety of polymers, which change color at different rates, were available from the manufacturer. Polymers are identified by a one or two digit 'Material Code,‘ which is part of the bar code symbology. The material code is also a means of identifying the rate at which the polymer changes color. In the laboratory study, polymers with Material Codes 57 and 61 were evaluated. In the field study, polymers with Material Codes 63 and 65 were used. 17 18 65 65019908 Product . Material Identification Code Polymer Figure l. "LifeLines" Time-Temperature Indicator. 19 Table 1 lists four material codes used in this study and their reaction rates and activation energies as reported by the manufacturer (Friedman, 1985). Table 1. Reaction rates of TTIPs stored at constant temperature. Material Code Reaction Rate (k') 53 (day ) (kcal/g mol) 57 1.668x1016 22.6 61 1.234x1o‘5 20.7 63 3.533x1o15 21.4 65 2.535x1015 21.4 B. THE "LIFELINES" SYSTEM The time-temperature indicator evaluated in this study is part of a commercially available system for monitoring shelf life quality of perishable products. A diagram of the system components and pathways of information transfer is shown in Figure 2. The system is computer- based, and the TTIP is the system's sensor. The entire system consists of three elements: "LifeLines" indicator labels, a hand-held computer, and accompanying software. The hand-held computer (the I'HC-lOO") is equipped with an optical wand for scanning the bar code information and polymer color. Polymer color is measured by the wand as "% Reflectance." The computer uses 20 LifeLines Inventory Management System h/n/ Figure 2. Schematic Diagram of "LifeLines" Inventory Management System. 21 software specifically developed for a given product to correlate indicator reading with predicted change in product quality. The HC-lOO, supplied by Allied, was used to scan and store information from the indicators. Each unit stores up to 3,000 indicator label readings in its memory before being downloaded to a host computer via telephone modem. C. SALADS Nine varieties of delicatessen-type salads manufactured and supplied by the Campbell Soup Company of Camden, NJ1 were used in the study. Salads were of the following varieties: chicken, ham, fruit, Italian, slaw, vegetable, macaroni, potato, and seafood. Salads were marketed under the brand name "Fresh Chef" and are further described in Table 2. Salads had an expected shelf life of 23 days. They were packaged in single serving size plastic containers and then encased in a paperboard sleeve. Individual units were packed in corrugated shipping containers, each shipper holding six units. Manufacture and packing of salads were handled by an independent manufacturer (under contract to Campbell Soup) at a location in Ft. Worth, TX. Retail distribution market of the salads was in the Denver, CO area. 1References to the brand "Fresh Chef" or to the manufacturer, Campbell Soup Company, are not intended as endorsement of the product by the author or Michigan State University. 22 Table 2. Description of "Fresh Chef" salad varieties. Salad Name Code Pkg. Size Major Ingredients Chicken with CSA 6 1/2 oz. Cooked chicken meat, celery, Almonds Salad mayonnaise, sweet peppers, almonds Ham and Cheddar HCCS 6 1/2 oz. Cooked ham, processed Cheese Salad cheddar cheese, celery, sweet peppers, mayonnaise Holiday Cole Slaw HCS 12 oz. Cabbage, carrots, celery, mayonnaise, red wine vinegar Italian Pasta IPS 6 1/2 oz. Macaroni, tomatoes, Salad artichoke hearts, mushrooms, black olives, red wine vinegar, olive oil Old Fashioned OFPS 12 oz. Potatoes, hard cooked eggs, Potato Salad mayonnaise Seashell Macaroni SMS 14 oz. Macaroni, green peppers, Salad red peppers, salad dressing Seafood Pasta SP5 6 1/2 oz Macaroni, shrimp, crabmeat, Salad hard cooked eggs, green peppers, celery, mayonnaise, sour dressing Tropical Delight TDFS 6 1/2 oz. Pineapples, grapes, oranges, Fruit Salad coconut, cream, yogurt Vegetable Garden V65 6 1/2 oz. Broccoli, cheddar cheese, Salad cauliflower, mayonnaise, cider vinegar 23 II. LABORATORY STUDY The primary objectives of the laboratory study were to determine effects of time, handling, and storage temperature on TTIP color change and to identify significant salad quality parameters. A. INDICATORS TTIPs of Material Codes S7 and 61 were evaluated to determine activation energies and reaction rate constants at each of three temperatures. Prior to application, indicators were stored at -30°C. Two indicators (one of each material code) were applied to each of 60 or more product packages. At least 20 of each were stored in each of three environmentally controlled cubicals maintained at constant dry bulb temperatures of 1.7, 4.5, and 7.2°C i 1°C. At five time intervals over a 20-day period, bar codes and % Ref1ectance (color) of the TTIPs were measured by scanning with the optical wand. Scanning was repeated immediately five consecutive times for each indicator label reading. 8. SALADS Fresh samples of salads were obtained from the manufacturing plant in Ft. Worth, TX and shipped by air to Michigan State University in East Lansing, MI. Upon receipt, salads were packed in corrugated shipping containers in the same manner as for retail distribution. Several samples of each salad variety were immediately stored in the same cubicals as the indicators. The remainder were stacked in three columns of 15 shippers each and wrapped (as a single unit) with stretch 24 wrap around the sides but not over the top. The wrapped stacks were subjected to vibrations to simulate normal truck transportation (Harte, 1985). Using an MTS electrohydraulic vibration table (Richmond, 1982), a frequency search of 3 to 100 Hz was conducted to determine resonance of the stack. The wrapped stack was vibrated at a frequency of 6.3 Hz, with a constant acceleration of 0.5 G, for a period of 15 minutes. Following vibration, at least 10 samples of each salad variety were placed in each of the 3 storage'cubicals. Quality of the vibrated salads was evaluated by subjectively noting and recording changes in physical condition (such as surface appearance, color change, separation and syneresis) and by plating for microbial count at five intervals over a 33 day period (10 days beyond expected shelf life). A typical data collection sheet for physical condition is shown in Appendix I. All physical evaluations were conducted by the same person, in the same room (227 Food Science), and under similar light. For microbiological evaluation, 25 gram samples were diluted 1:10 with 0.1% peptone broth and homogenized for one minute in a Stomacher Lab Blender. For total plate count, appropriate dilutions were cultured on Plate Count Agar (PCA, Difco, Detroit, MI) and incubated 35°C for 48 hours; psychrotrophic count was made on PCA, incubated at 7.2°C for one week; and yeast and mold count on Potato Dextrose Agar (PDA, Difco, Detroit, MI) acidified to pH 4.5 and incubated at 25°C fbr 48 hours (Pestka, 1985). All samples were prepared by the pour plate method. 25 III. FIELD STUDY Major objectives of the field study were: to measure changes in salad quality during commercial distribution; to correlate those with changes in TTIP color reflectance; and to demonstrate use of the TTIP in identifying areas of thermal abuse in the distribution channel. A flow chart of activity sequence for the field study is shown in Table 3. A. SALAD MANUFACTURE Of the nine varieties of salads, four or five varieties were manufactured and packed on Thursday of each week, and the remaining varieties on Friday. A typical manufacturing schedule is outlined in Table 4. Each variety was prepared and packaged separately, with processing machinery being cleaned between varieties. Packaged salads remained in the processing room (average temperature approximately 16.6°C) for various lengths of time (usually no more than six hours) before storage in a refrigerated warehouse. Temperature in the warehouse as observed by the author fluctuated as much as i 4.l°C about an average of 4.5°C. On Saturday morning, salads were shipped by refrigerated truck from the manufacturing plant and arrived at grocery store ware- houses in the Denver, CO area on Monday morning. This study dealt only with salads distributed through the Safeway grocery chain. Average storage temperature on the trucks and at the Safeway warehouse was l.7°C 26 * Table 3. Flow chart of field study activities. Activity Salad Shelf Life Day Apply indicator labels to sleeves; store in 1-2 days before refrigerated warehouse salad production Apply indicator labels to shippers Day 0 Pack salads in plastic containers; put Day 0 sleeves on containers; scan 30-40 indicators Pack containers in shippers; scan 15-20 Day 0 indicators Stack shippers on pallet Day 0 Remove 'control' samples; ship directly to CIRT Truck picks up salads Day 1, 2 Truck arrives at Safeway warehouse Day 3, 4 Pick up salads at warehouse; scan Day 3, 4 indicators; ship to CIRT Receive salads at CIRT; scan Day 4, 5 indicators; remove samples for quality analyses Scan indicators; perform quality Day 5, 6 analyses (M, S, C) Pick up salads at retail stores; scan Day 14, 15 indicators; ship to CIRT 27 - * Receive salads at CIRT; scan Day 16, 17 indicators; remove samples for quality analyses Perform quality analyses (M, C) Day 18 Perform quality analysis (S) Day 22 Pick up salads at retail stores; scan indicators; Day 22, 23 ship to CIRT Receive salads at CIRT; scan Day 24 indicators; remove samples for quality analyses Perform quality analyses (M, C) Day 25 Quality Analysis Codes: M=Microbiologica1; S=Sensory; C=Color 28 Table 4. Typical "Fresh Chef" salads manufacturing schedule. Day Time Salad Thursday morning Old Fashioned Potato Salad Seashell Macaroni Salad afternoon Holiday Cole Slaw Italian Pasta Salad Vegetable Garden Salad Friday morning Chicken with Almonds Salad Ham and Cheddar Cheese Salad afternoon Seafood Pasta Salad Tropical Delight Fruit Salad 29 (Kirwin, 1985). From each lot of salads produced, a 'control' group (labeled with indicators) was shipped by air directly from the manufacturing plant to research labs at Campbell Institute for Research and Technology (CIRT) in Camden, NJ. 'Control' salads and indicators were stored at ~0ptimum conditions (l.7°C) for at least their expected shelf life of 23 days. Control salads were used for comparisons in sensory and microbial analyses. Salads were retrieved from the Safeway warehouse on the Monday morning following production, and from retail stores at various intervals throughout the expected shelf life. Indicators were scanned as soon as possible after salads were retrieved (usually within two hours). Salads were shipped overnight by air to research laboratories at CIRT for quality evaluations. Upon receipt, indicators were scanned and salads were removed for immediate quality analyses (micro- biological, color). Some salads were held at 7.2°C (to simulate retail storage conditions) fbr quality analyses (sensory, color) which were conducted beyond expected shelf life. B. INDICATOR APPLICATION AND SCANNING Prior to application, indicators were stored at -30°C. For the field study, indicators were manually applied to product sleeves (secondary packages) and stored in a refrigerated warehouse for 24 to 48 hours. Sleeves were applied to primary containers immediately after manufacture of the salads. Indicators were applied to shippers at time of salad manufacture. Indicators on sleeves and shippers were 3O scanned at the time of salad manufacture (Day = 0) to obtain an initial reflectance value. For each salad variety, 30 to 40 sleeve indicators and 15 to 20 shipper indicators were each scanned three consecutive times. The numerical average of these three scans was computed as a MEAN SCAN. Mean scans for sleeves and shippers were recorded separately by salad variety. The average of mean scans on Day=0, for each variety/ package is defined as the BASELINE. A partial list of BASELINES is shown in Table 5 with a complete list given in Appendix II. Table 5. Baseline values for selected salads/packages. Salad Lot Material Code Package Baseline SPS* l 63 shipper 78 SPS 1 63 sleeve 61 SPS 2 65 shipper 76 SPS 2 65 sleeve 72 SPS 3 65 shipper 78 SPS 3 65 sleeve 72 *Seafood Pasta Salad C. QUALITY ASSESSMENTS Determination of salad quality was based on microbial, sensory, and color evaluations. Analyses were performed on selected salads in each lot according to the scheme outlined in Table 6. 0f salads 31 Table 6. Quality analysis schedule for field study.* Lot 1 Lot 2 Lot 3 Production date (6/20-21) (7/11-12) (8/1-2) Salad** Chicken C S M C C Ham C S M C C ant C S M X C Italian - C C S M C Slaw C C S M C Vegetable X C S M C Potato C C C S M Macaroni C C C S M Seafood C C C S M *C= Color analysis; S = Sensory evaluation; M = Microbial counts; X= Not produced. 1"'*Complete salad names given in Table 2. 32 produced on June 20-21, CSA, HCCS, and TDFS A were analyzed for sensory evaluation and microbial counts. All varieties produced were analyzed for color. 0f salads produced on July 11-12, HCS, IPS, and VGS were subjected to sensory and microbial evaluation, and all for color. OFPS, SMS, and SPS produced on August 1-2 were analyzed for microbial count and sensory, and all salads for color. Microbiological Microbiological evaluations were performed on salads retrieved from the warehouse or retail stores on approximately Days 4, l4, and 23 from date of manufacture and on 'control' salads of the same age which had been shipped directly to CIRT and held at l.7°C. Thirty gram samples of each salad were diluted 1:10 and homo- genized for approximately 1 minute with a Stomacher Lab Blender (Model 400). Table 7 lists counts, media, and incubation times and temperatures. Sensory Six of the salads (CSA, HCCS, TDFS, HCS, IPS, and VGS) were judged twice; the first time at about one week after production, and the second time just prior to the expiration date. The remaining three salads (OFPS, SMS, and SPS) were judged three times: one week after production, just prior to 'expiration', and 12 days after 'expiration'. Retail samples and 'controls' (those samples held under optimum storage conditions) were evaluated by a panel of trained and experienced 1A11 salad codes are listed in Table 2, p. 22. 33 Table 7. Types of microbial counts, media, and incubations. Count Medium Incubation Incubation Temperature time Total aerobic glnte count agar 35°C 48 hrs 0 Coliform Violet red bile 35°C 48 hrs + MUG* (D) Lactobacillus Lactobacillus 35°C** 48 hrs MRS agar (D) Psychto- Phytone Yeast 7°C 10 days trophic Extract (0) Yeast and Trypticase Soy 25°C 72 hrs mold Agar (B) (D) - Difco, Detroit, MI (B) - Baltimore Biological, Cockeysville, MD. *4-methylumbelliferyl-B-d-glucuronide, 100 ug/ml VRB **Incubated (anaerobic) in gas-pak jar or in C02 chamber, depending on availability. 34 taste testers for overall flavor quality. All sensory measurements were made using a nine point category scale in which 5.0 is set as the minimum score for flavor acceptability (Brusco, 1985). C9195. Degradation of chlorophyll content of broccoli, celery, and green pepper was determined by extraction of pigment and spectrophotometric analysis on a Lambda 5 spectrophotometer. The procedure, a modifica- tion of the AOAC (AOAC, 1985) procedure for chlorophyll extraction, is described in Appendix III (Fisher and Barr, 1985). RESULTS AND DISCUSSION 1. LABORATORY STUDY A. INDICATORS The effect of time and storage temperature on TTIP color change were evaluated in a controlled temperature storage study. Mean Ref1ectance Values of TTIPs At least 20 indicators each of Material Codes (MC) 57 and 61 were placed on product packages and stored at each of three temperatures: 1.7, 4.5, and 7.2 : 1°C. Tables 8 and 9 list population sizes (n), mean reflectance values (X), standard deviations (5.0.), and normalized reflectance values (adj—) for each set of indicators at initial storage time (Day=0) and at five succeeding intervals. MEAN reflectance values for each set of indicators were obtained by averaging the mean values of five consecutive scans for each indicator in the set. The initial mean reflectance value for any set of indicators is called the BASELINE. Baselines for MC 57 stored at 1.7, 4.5, and 7.2°C were, respectively, 81, 81, and 79; and fbr MC 61 were 83, 84, and 82. Baselines were less than 100 due to loss of reflectance during shipping of indicators and holding time prior to initial scanning. NORMALIZED reflectance values were used to standardize some comparisons between groups of indicators. Normalized values were 35 36 Table 8. Mean reflectance values observed for TTIP material code 57 during controlled temperature storage. Day Temp (°C) 11 A Y 8.0. ade 0 1.7 21 81 3.22 100 7 1.7 24 74 3.71 91 10 1.7 7 72 3.17 89 14 1.7 21 66 3.22 81 18 1.7 21 62 2.80 76 20 1.7 8 60 2.41 74 0 4.5 27- 81 3.30 100 6 4.5 27 69 3.12 85 10 4.5 27 62 2.84 76 13 4.5 28 57 3.02 70 18 4.5 27 50 2.44 62 20 4.5 26 46 _3.00 57 0 7.2 99 79 3.53 100 6 7.2 99 64 3.38 81 9 7.2 100 56 3.05 71 13 7.2 98 48 3.22 61 17 7.2 66 39 2.24 49 20 7.2 73 37 2.09 47 *Define n = number of samples scanned 7' = mean reflectance values 5.0. = standard deviation normalized reflectance values cu ft XI 11 37 Table 9. Mean reflectance values observed for TTIP material code 61 during controlled temperature storage.* Day Temp (°C) 11 Y 5.1). ade 0 1.7 21 83 2.24 100 7 1.7 24 66 2.38 80 10 1.7 7 61 1.28 73 14 1.7 21 54 1.76 65 18 1.7 21 47 1.46 57 20 1.7 8 44 1.64 53 0 4.5 27 ' 84 3.91 100 6 4.5 27 60 2.17 71 10 4.5 27 50 1.14 59 13 4.5 28 41 1.90 49 18 4.5 27 31 1.73 37 20 4.5 26 28 1.61 33 0 7.2 99 82 2.83 100 6 7.2 99 52 2.88 63 9 7.2 100 41 2.01 50 13 7.2 98 30 1.70 36 17 7.2 66 20 1.94 24 20 7.2 73 18 1.76 22 * Define n = number of samples scanned X' = mean reflectance values 5.0. = standard deviation ade' normalized reflectance values 38 obtained by dividing actual mean reflectance values by their baselines. Population sizes vary due to losses of some data during transfer between computer systems. Ref1ectance values repeated for any individual TTIP are expected to be within 10 units, the difference due largely to light reflectance as measured by the optical wand (Prusik, 1985). Variability between TTIPs manufactured as one Material Code lot and stored until use may be reflected in the standard deviation (SD), as shown in Tables 8 and 9. For MC 57, over time and temperature, mean reflectance values (X) ranged from 81 to 37 and SD ranged from approximately 2.1 to 3.7. For MC 61, over time and temperature, mean reflectance values ranged from 84 to 18, and the SD ranged from approximately 1.1 to 2.8 (with one exception; Day 0, 4.5°C).1 In spite of the large range in mean reflectance values over time, standard deviations within each material code were not significantly affected by temperature. The mean of all standard deviations for MC 57 (n=18), across time and temperature was equal to 2.99, while the mean of all standard deviations for MC 61 (n=17) was 1.91. The difference between the mean 505 for the two material codes was significant at the 90% confidence level (Student T-Test). This implies that variance of MC 61 mean scans was significantly less than for MC 57. All indicators were scanned by the same person, and with the same wand. 1The exception is in Day 0 reflectance values for MC 61 stored at 4.5°C. Investigation of raw data shows two of the indicators in this group (n=27) with unusually high mean reflectance values. The SD for this group was 3.91, and is considerably higher than the SDs for all other mean reflectance values for MC 61. 39 Increased variability in MC 57 is probably due to one or more of the following: difference in holding conditions prior to use, differences inherent to the polymer, or in the laminated coating over the polymer. Applying the principle that 95% of observed values of a normal population will lie within i two $05 of the mean, the range of observed mean scans for MC 57 was approximately 12%, and 7.6% for MC 61. The variance of mean scans for Material Codes 57 and 61 tested in the lab study was not affected by reflectance over the range of reflectance values (80 to 18) observed. Variance for MC 57 was observed to be significantly different from MC 61. Individual Scans of Indicators In the laboratory study, scanning was repeated five consecutive times for each indicator. Mean reflectance values were obtained by averaging the values of these five scans. Data were also analyzed to assess variability between individual reflectance values for the same indicator. This section addresses the results of variability of individual‘scans. 'Difference' is defined as the highest reflectance value of any indicator minus the lowest value for the same indicator, when scanned consecutively at any given time. For a given temperature, statistical analysis revealed that there was no significant correlation (90% confidence level) between the 'difference' of scans and the mean reflectances. Simply stated, the range of the five individual reflectance values fbr one indicator was not affected by the level of 4O reflectance, at the ranges encountered in this study. Therefore, data for all time intervals were pooled according to temperature and material code to determine if 'difference' was dependent on temperature and material code. Table 10 summarizes mean 'differences' and standard deviations for MC 57 and 61 at each storage temperature. Table 10. Statistical summary of mean 'differences' of individual scans for two material codes stored at three constant temperatures. Storage Temperature Material l.7°C 4.5°C 7.2°C Code 7 so 7 so if SD 57 5.43 2.67 4.79 2.62 4.31 2.40 61 4.11 2.32 3.83 1.99 3.92 2.38 The average 'difference' for all temperatures for MC 57 is 4.84 vs 3.95 for MC 61. However, statistical analysis indicated that average 'differences' in Table 10 were not significantly correlated to temperature or to material code. Data in Table 10 indicates that 95% of individual scans for MC 57 ranged 8.6 to 10.8 reflectance units while those for MC 61 ranged 7.7 to 8.2. This compares favorably with Prusik (1985) who indicated that reflectance values for a single indicator, scanned consecutively at a given time, would be within ten units. 41 At the range of reflectance values encountered in this study (80 to 18 for MC 57 and 81 to 36 for MC 61), the expected range of reflectance values for consecutive scans of an individual indicator is fairly constant, at 7.7 to 10.8. Reaction Rates and Activation Energies Statistical summaries of mean reflectance values (actual and normalized) and standard deviations for MC 57 and 61 are provided in Tables 8 and 9. Normalized mean reflectance values for ALL individual indicators stored at each of three temperatures are plotted in Appendix IV, Figures a through f. Figures IVa, IVc, and IVe are plots of all data for MC 57 at 1.7, 4.5, and 7.2°C; and Figures IVb, IVd, and IVf for MC 61. Averages of mean reflectance values were plotted on log coordinates (Y axis) vs. time (X axis). Figures 3, 4,_and 5 are summaries of data in Figures IVa and IVb; IVc and IVd; and IVe and IVf; respec- tively. The two material codes clearly have two distinct reaction rates at a given temperature, as depicted by their different lepes in each graph. Comparison of the slope for MC 57 at the three different temperatures indicates a different reaction rate at each temperature; likewise for MC 61. The straight line relationships of log reflectance vs. time in Figures 3, 4, and 5 indicate a first-order reaction, with the slope of the line being proportional to the reaction rate at a given temperature. Linear regression analysis (SAS, 1976) was used to fit raw data (for a given temperature) of normalized reflectance values to the model fbrm: 42 - o 1. \D \.\. \D\ O\D Ii 50*- E? .. z a? U in. c .3 o d) E: :52 3'2 .— 0 - MC 57 o'- MC 61 Temp. = l.7°C 10 l J l L O 5 10 - 15 20 Time, Days Figure 3. Summary of Normalized Reflectance vs. Time for Material Codes 57 and 61 at l.7°C. 43 1Golf 1 T 1 1 1e ~“\“-. _. U\\\\\\\\ ““~. 1— . O\ . .\.\ .. O\D .. \ 01 O l % Reflectance, Normalized 0 Il- MC 57 c:—- MC 61 Temp. = 4.5°C 1 21 1 1- 10 0 5 10 15 20 Time, Days Figure 4. Summary of Normalized Ref1ectance vs. Time for Material Codes 57 and 61 at 4.5°C. 44 U1 0 l % Reflectance, Normalized D 0- MC 57 13— MC 61 Temp. = 7.2°C 10 .1 1 L 1 O 5 10 15 20 Time, Days Figure 5. Summary of Normalized Reflectance vs. Time for Material Codes 57 and 61 at 7. 2° C. 45 1n (ade) = a + k (time) (3) where k = reaction rate constant for a given temperature and exp(a)= k', the rate constant at infinitely large T. Results for each material code and temperature are shown in Table 11. Table 11. Calculated reaction rate constants (k) for two TTIP material codes at each of three constant temperatures. ‘ Temp. MC 57 MC 61 1/T(K") k(day']) R2 k(day’1) R2 1.7°c 0.003638 0.0154 0.844 0.0309 0.977 4.5°C 0.003602 0.0279 0.936 0.0554 0.985 7.2°C 0.003567 0.0404 0.952 0.0781 0.980 As revealed in Figures IVa through IVf, regression fits were quite good for both material codes at all temperatures, with R-squared exceeding 0.930, except for MC 57 at l.7°C where R-squared = 0.844. All regression analyses were significant at the 0.001 level. The activation energy for each material code was calculated using the Arrhenius relationship. A plot of log k vs. reciprocal of absolute temperature yielded a straight line, with slope being proportional to activation energy (Ea) divided by the gas constant (R). In Figure 6, reaction rates listed in Table 11 are plotted against reciprocals of absolute temperatures. Activation energies of MC 57 and 61 are quite 46 0.7 1 1 " ‘1 - D _ D .x I— c-l .3 C 3 '- O -1 2 O U 8 .. 0 - t6 ‘- O C O I: U to Q) a: '- — e— MC 57 D—- MC 61 ‘0 0.0 1 1 3.50 3.60 3.70 Inverse Absolute Temperature, k‘1x1000 Figure 6. Plot of Reaction Rate Constants vs. Inverse Absolute Temperatures. 47 close, at 26.5 kcal/g mol and 25.5 kcal/g mol, respectively. Table 12 is a summary of kinetic parameters calculated in this study as compared to values reported by the manufacturer (Friedman, 1985). Table 12. Comparison of kinetic parameters calculated in lab study and reported by manufacturer. Material k'(day']) Ea (kcal/g mol) Code Calculated Reported Calculated Reported 57 2.104x1019 1.668x1016 26.5 22.6 61 6.916x1018 1.234x1015 26.5 20.7 The Arrhenius relationship for kinetic reaction rates is expressed as -Ea/RT k = k' e (4) Note that small changes in Ea can result in significant changes in the rate constant k. Therefore, a more appropriate analysis is to compare k observed at each of the storage temperatures with k computed from the manufacturer's kinetic parameters, using equation (4). Table 13 is a summary of that comparison, where all values of k compare favorably with a maximum difference of 4.0% accuracy for MC 61 at 7.2°C. Therefore, the two MC‘s (57 and 61) evaluated performed reliably. Table 13. Summary comparison of reaction rate constants at three constant temperatures. MC 57 MC 61 Temperature Observed Reported Observed Reported 1.7°c 0.0154 0.0154 0 0309 0.0316 4.5°C 0 0279 0.0279 0.0554 0.0554 7.2°C 0.0404 0.0418 0.0781 0.0816 The two TTIPs of MC 57 and 61 evaluated in the lab study performed reliably with respect to first order reaction rates over the range of reflectance values encountered. Observed values for reaction rates and Ea agreed within 15 to 20% of those reported by the manu- facturer. B. SALADS Quality parameters of salads which were sensitive to thermal history and significant to shelf life were determined in a controlled temperature storage study. Salad quality was evaluated on the basis of physical and microbial characteristics. A typical data collection sheet for physical characteristics is shown in Appendix I. Physical Characteristics Effect of Vibration. Three stacks of shippers containing salads were stacked 15 rows high and vibrated as described in Experimental 49 Design, page 23. Salads were wrapped with stretch wrap around the Sides, but not over the top. Except for salads in the top two rows, there were no significant differences noted in the physical character- istics (syneresis, separation, and color) of vibrated vs. unvibrated salads, at any of the three storage temperatures, during the length of the study. The tap two rows experienced considerably more vibration than did the lower rows, due to lack of stretch-wrap constraint. These salads exhibited a greater degree of separation and syneresis than did salads in the lower rows. However, it was determined at a later date that for retail distribution, pallets of salads are stretch-wrapped securely over the top layers. It is unlikely that vibration effects encountered in normal truck transportation have a temperature-dependent or temperature-related contribution to salad quality. Syneresis and Separation. Syneresis was defined as the expulsion of water from the salad or salad dressing. Separation was defined as the breakdown of emulsion in the salad dressing. Syneresis and separation were judged qualitatively as: (-) absent, (/) slight; (+) definite. Evaluations of the vibrated salads in regard to separation and syneresis are tabulated in Appendix V. A score of 0 was assigned to salads which exhibited no evidence of syneresis or separation; 0.5 for slight evidence, and 1.0 for definite evidence. Table 14 shows total scores for each salad variety as observed over the period of the study; and total scores for all varieties (vibrated and non-vibrated) at each temperature. 50 Table 14. Summary of Appendix V. Scores for syneresis and separation in salads. Syneresis Separation 1.7°c 4.5°C 7.2°C 1.7°c 4.5°C 7.2°C CSA 0.5 1.5 1.0 0.5 2.0 1.5 HCCS 1.0 1.5 5.0‘* 0 0 0.5 HCS 0 0 0 0 0 0 IPS 0 0 2.0 1.5 1.5 5.0“ OFPS 2.0 0.5 2.5 0 0.5 2.0 SMS 3.0 4.6“ 4.0” 3.0 4.0* 6.0“* SPS 3.0 3.0 4.0‘* 2.0 2.5 3.5* TDFS 1.0 0 3.0 0 0 3.0 VGS 0 0 2.5 0.5 o 1.0 Total 10.5 11.0 24.0 7.5 10.5 22.5 Vibrated Total' 9.5 11.0 23.5 8.0 ‘10.0 21.0 Non-vibrated *Indicates total score 2 2.0 on or before 'expiration'date. “Indicates consistent syneresis or separation at or near 'expiration' date. Scores: (-)=0; (/)=O.5; (+)=1.0 51 A review of the data listed in Appendix V shows that four of the salad varieties (HCCS, IPS, SMS, and TDFS) exhibited tendency toward increasing syneresis or separation with increasing time. All other salad varieties did not show consistent tendencies toward syneresis or separation until they had exceeded the 23 day shelf life. Only three (salad varieties (HCCS, SMS, and SPS) had total scores > or = 2.0 on or before the 23 day 'expiration'. For four types of salads (HCCS, IPS, SP5, and VGS), scores for syneresis increased with increasing storage temperature over time. For six types of salads (HCCS, IPS, OFPS, SMS, SPS, and TDFS) separation increased with increasing temperature. Although there may be a slight trend toward increasing syneresis and/or separation with time and/or temperature for some salads, results are subjective and data are not conclusive enough to facilitate develop- ment of a correlation between syneresis or separation and thermal history and vibration. 9919:, Salads were observed for overall color daring shelf life and beyond. Results of evaluations on color are tabulated in Appendix VI. Seven (HCCS, HSC, IPS, SMS, SPS, TDFS, and VGS) of the nine salads exhibited a trend toward color loss with increasing time and/or temperature, as shown in Table 15. Five types of salads (HCCS, IPS, SMS, SPS, and VGS) exhibited a definite loss of color was a function of thermal history. For these salads, color was judged 'good' or 'bright' initially; then 'slightly dull', followed by 'dull'. The first loss of color (judgement of 'slightly dull') was determined earlier at higher temperatures (7.2 and 52 Table 15. Summary of color evaluations for selected salads.* Shelf Life Day Color Loss First Observed Salad Storage Temp. 7.2°C 4.5°C 1.7°c HCCS 19 19 23 HCS 27 24 24 IPS 20 24 24 SMS 23 24 27 SPS 22 23 23 TDFS 22 23 20 ves 23 24 34 Average day of first color loss* 21.4 22.8 26.2 *Excluding HCS, TDFS, due to inconsistency of data. 53 4.5°C) than at the lower, 'optimum' storage temperature of l.7°C. For six types of salads (HCCS, IPS, SMS, SPS, TDFS and VGS) stored at 7.2°C, color loss was evident BEFORE the 23 day 'expiration' date. At storage of 4.5°C, three salad varieties (HCCS, SP5, and TDFS) showed color loss before expiration, and at l.7°C, only HCCS and TDFS exhibited color loss before expiration. For two salad varieties (CSA and OFPS) color change as a function of time and/or temperature was not evident during the lab study. CSA is the only one of the nine salads that does not have many different colored components. It is composed of white chicken meat, almonds, mayonnaise, and finely chopped celery. Color of CSA was evaluated as 'bland' or 'dull' throughout the study. OFPS components consisted of potatoes (white), hard cooked eggs (white and yellow), green pepper (green), and pimento (red). Loss of color was not noted except in one sample, and may be due to the effect of preservatives in the formulation. There is a general tendency toward first order rate loss of color of certain salad components over time and with increasing storage temperature. This tendency is illustrated in Figure 7, where average first day of color loss at each temperature is plotted against inverse of absolute temperature. The plot is a straight line which obeys the Arrhenius relationship. The evaluations in this study were subject- ive, but consistent. A more objective means of measuring color loss was used in the field study. First Day of Observed Color Loss 54 100 ‘ 1 I /D c] E] 10 l l 3.55 3.60 3.65 Inverse Absolute Temperature, k’1x1000 Figure 7. Plot of Average First Day of Observed Color Loss vs. Inverse Absolute Temperature. 55 Microbiological Characteristics Salads were evaluated for microbiological populations which might affect quality or shelf life. Samples of salads stored at each of three temperatures (1.7, 4.5, and 7.2°C) were plated for total, yeast and mold, and psychrotrophic counts during shelf life and beyond. A complete listing of microbial counts is shown in Appendix VII. A review of all counts tabulated in Appendix VII indicates no general tendency of microbial counts to change as a function of time or storage temperature. Yeast and mold counts and psychrotrophic counts are very low, generally less than 100 per gram for all varieties at all storage conditions.‘ Total plate counts, as expected, are 2 to 104 per gram. Total counts do somewhat higher, generally from 10 not show appreciable increase over time for any of the samples plated. There is a general tendency for salads stored at the lower temperature (l.7°C) to exhibit Slightly higher counts per gram than salads stored at 7.2°C. This is due to the presence of sorbates and benzoates in the salads, which have a greater antimicrobial effect at the warmer temperature (link, 1985). PH of the salads ranged from 3.8 to 4.9, and this may also have been responsible for low counts. The results of the microbiological study do not indicate an obvious relationship between microbial count and salad quality. Microbial counts did not increase appreciably over time, and were slightly higher with optimum storage temperature. 56 II. FIELD STUDY Changes in salad quality during commercial distribution were measured and correlated with TTIP reflectance value. Use of the TTIP in identifying areas of thermal abuse in the distribution channel was demonstrated. A. SALAD QUALITY Salads had an expected shelf life of 23 days. Packages were code dated with an appropriate 'use by' date and labeled with pertinent storage information ('...must be kept refrigerated'). Salads were retrieved from the grocery warehouse on approximately Day 4; and from retail stores on approximately Days 16 and 23. Micro- bial, sensory, and color analyses were performed on selected salads in each of three production lots according to the scheme outlined in Table 6. Microbial and sensory evaluations were performed on 'variables' (salads retrieved from retail distribution) and on 'controls' (salads stored at Campbell Institute for Research and Technology under optimum conditions - l.7°C - immediately after manufacture). Color evaluations were performed on variable salads only. Microbiological Salads were evaluated for microbiological counts on approximately Days 6, 18, 24, and 34 after manufacture. Day 6 was approximately the 57 first day the salads were likely to be purchased at retail. Day 18 represented the time a product may have been in retail distribution for approximately two days; Day 24 was immediately after expiration; and Day 34 was 10 days past expiration. Salads were enumerated for total, yeast and mold, lactobacillus, psychrotrophic, and coliform counts. Complete data for total, lactobacillus, and psychrotrophic counts are tabulated in Appendix VIII. Coliform and yeast and mold counts for all varieties of variables and controls tested were extremely low ( 0.9 while three other varieties (HCS, OFPS, and SMS) exhibited r > 0.8. Plots of mean scores vs. normalized reflectance values for IPS, SP5, and for six salads pooled are shown in Figures 8, 9 and 10. 61 Table 17. Linear regression of mean sensory flavor score vs. normalized reflectance values. Salad Correlation Coefficient (r) HCS 0.860 IPS . 0.923 VGS 0.568 OFPS 0.874 SMS 0.846 SPS 0.910 ALL 0.662 62 9-0 I 1 1 1 1 1 1 I 8.01- 7.0:- .\ 6.01- (D a ‘ O 8 L. 5.01- O > M E 4.0 - 2 I0 2 3.0- 2.0 _. ." control 0- variable r = 0.923 1.0- 0 l L, 1 1 1 1 1 1 100 80 60 40 20 % Reflectance, Normalized Figure 8. Italian Pasta Salad Mean Flavor Scores vs. Normalized _ Reflectance. 63 Mean Flavor Score 9-0 1 1 I 1 1 1 1 1 1 8.0- 7.0b e\O .. N 6.0- Ii \0 5.0.. 4.0- 3.0- 2 0_ 0— control ' 0- variable r = 0.910 1.0- 0L,, 1 11 1 1 2L. 1 41 l l 100 80 60 40 20 % Ref1ectance, Normalized Figure 9. Seafood Pasta Salad Mean Flavor Scores vs. Normalized Ref1ectance. ‘ Mean Flavor Score 64 9 0 1 1 1 1 T 1 1 1 T 8.0 .— “ 7 0 .. 1| . .— ..\O\O .0 C) “- ll 5.0 _. .. 4.0 _ ._ 3.0 _. .. 2.0 - Q-control -‘u o-variable r = 0.910 1.0 _. .— 0 l L 1 1 1 1 1 i L 100 80 6O 4O 20 % Reflectance, Normalized Figure 10. Mean Flavor Scores vs. Normalized Reflectance for Six Salad Varieties. 65 Analysis of sensory evaluation data indicated that age (time) of product had greater significance in scoring than did storage conditions (temperature). Scores for salads tested near or after 'expiration' of Shelf life (Days 22 and 35) were significantly lower than scores for 'fresh' salads (Day 6). However, none of the salads tested were judged to be unacceptable. This suggests that none of the salads was subjected to extreme thermal abuse. Due to large variability in individual scores, linear correlation of reflectance values with individual f1avor scores was poor. However, MEAN f1avor scores were found to be positively correlated with reflectance, with r > 0.84 for five of six salads tested. £212: Fennema (1976) has shown that almost any type of food processing (thermal, dehydration, freezing, irradiation) alone or combined with storage contributes to the deterioration of chlorophyll. Deterioration in chlorophyll can be quantified by changes in the maxima (660 nm) of the absorption spectra (AOAC, 1975). Chlorophyll was extracted from green peppers, celery, and broccoli in different salad varieties according to the procedure outlined in Appendix III (Fisher and Barr, 1985). Table 18 is a list of absorbance and normalized reflectance values for each of five salads containing green pepper. A review of the data shows that as absorbance (Abs) decreases (i.e., as chlor0phyll deteriorates), reflectance values also decrease. Least squares regression analysis was used to develop a correlation between Abs and 66 Table 18. Absorbance values (triplicate samples) of total chlorophyll extracted from green peppers in five salad varieties during shelf life. Day 4 Day 18 Day 25 Holiday Cole Slaw % R 65,‘ 35 29 ABS 0.18 0.057 0.083 0.22 0.075 0.083 0.26 0.066 0.083 Avg. 0.22 0.066 0.083 Old Fashioned Potato Salad % R 56 33 ABS 0.201 0.11 0.098 0.081 No product 0.105 0.051 available Avg. 0.13 0.081 Italian Pasta Salad % R 59 40 25 ABS 0.33 0.048 0.039 0.50 0.065 0.075 0.35 0-086 0.058 Avg. 0.39 0.066 0.057 % R ABS Avg. % R ABS Avg. 62 0.51 0.47 0.49 0.49 75 0.28 0.53 0.42 0.41 67 Seashell Macaroni Salad 44 0.105 0.12 0.052 0.092 Ham and Cheddar Cheese Salad 49 0.057 0.089 0.080 0.075 25 0.025 0.045 0.057 0.042 30 0.059 0.075 0.056 0.063 68 % R, and resulted in the best fit relationship of the form: 1n Abs = a (r R) + b (5) Data from individual chlorophyll analyses for all five salads are plotted against Ref1ectance values in Figure 11. Figure 12 is a somewhat simpler version of Figure 11, showing the average values of Abs vs. % normalized Reflectance for each of the five salads. R-squared in both cases was reasonably good, 0.792 for individual absorbance values vs. % normalized R, and 0.723 for average absorbance values vs. % normalized R. 0f the five salads considered individually, average values of Abs correlated well with % R, for example, Rasquared = 0.952 for SMS variety which are plotted in Figure 12. Results of analyses of chlorophyll extraction from celery and broccoli are inconclusive. Table 19 lists results of chlorophyll analysis for celery in 3 salad varieties. For HCCS, absorbance values at Days 18 and 25 are nearly the same (0.057 and 0.058), and for CSA, values at Days 18 and 25 are exactly the same. Chlorophyll concen- tration in celery is less than in green pepper, and measurements at lower levels are subject to greater error. The amount of celery available for extraction was also small (usually no more than 15 grams), and may also have been a source of error. Table 20 lists results of chlorophyll extraction from broccoli in one salad variety. The absorbance values for Day 18 are not statistic- ally different from those for Day 4, indicating possible error in procedure. Day 25 and Day 34 values are somewhat less, suggesting the possibility of a relationship similar to that of green pepper. 69 1.0 -l .. __1 )— -1 —' '1 m .. 8 8 S. O 8 ,_ 0.11- ._ E " .. a. " q 0 $- 1— —1 0 SE -' ‘- Q _ 2 = 0.792 T 0.01 l l l 1 100 80 60 40 20 0 % Reflectance, Normalized Figure 11. Chlorophyll Absorbance (Values from Individual Trials) vs. % Reflectance (Normalized) for Five Salad Varieties Containing Green Pepper. Chlorophyll Absorbance 7O 1.0 _ l I 1 1 1 b— an L. .. 00] I— .1 P— .- _ R2 = 0.723 _ 0.01 I l 1 I g 100 80 60 »40 20 0 % Reflectance, Normalized Figure 12. Chlorophyll Absorbance (Average Values) vs. % Ref1ectance (Normalized) for Five Salad Varieties Containing Green Pepper. Chlorophyll Absorbance 71 1.0 1 1 1 1 l l l I III 1 1 1 C — fl — —i 0.1 1— -1 1— . d - q — .— _ ‘ b _ .. C .. R2 = 0.906 0.01 I l 1 1 100 80 60 40 20 0 % Reflectance, Normalized Figure 13. Chlorophyll Absorbance vs. % Reflectance (Normalized) for Green Pepper in Seashell Macaroni Salad. 72 Table 19. Absorbance values (triplicate samples) of total chlorophyll extracted from celery in three salad varieties during shelf life.‘ Day 4 Day 18 Day 25 Ham and Cheddar Cheese Salad % R 75 49 30 ABS 0.11 0.072 0.053 0.13 0.057 0.059 0.11 0.042 0.063 Avg. 0.12 0.057 0.058 Chicken with Almonds Salad % R 75 38 33 ABS 0.30 0.040 0.050 0.32 0.030 0.040 0.16 0.040 0.050 Ave. 0.26 0.040 0.040 Old Fashioned Potato Salad % R 58 33 ABS 0.12 0.12 0.045 0.102 No product 0.095 0.036 available Avg. 0.087 0.086 73 Table 20. Absorbance values (triplicate samples) of total chlorophyll extracted from broccoli in vegetable garden salad during shelf life. % R = Normalized Ref1ectance Va1ues Vegetable Garden Salad Day 4 Day 18 Day 25 Day 34 % R 60 44 27 24 ABS 0.15 0.14 0.12 0.092 0.14 0.14 0.091 0.101 0.13 0.17 0.14 0.12 Avg. 0.14 0.15 0.12 0.104 74 Each of the salad varieties is a mixture of different components - vegetables, dressing, preservatives, etc. It is not known what effect the particular 'mix' of one formula may have on chlorophyll deterio- ration in one vegetable as opposed to another. Data for chlorophyll deterioration in green peppers in salads was positively correlated with TTIP reflectance, while no significant correlations were found between chlorophyll in celery or broccoli and reflectance. Due to the difference in chlorophyll concentration in different vegetables, a refinement of the procedure used for chlorophyll extraction may be in order. 8. THERMAL ABUSE In the field study, salads labeled with indicators were traced through commercial distribution and retrieved at intervals. Indicators on salad packages were scanned and reflectance values of the TTIPs were recorded. Indicators were scanned on Day 0 and a baseline established for each salad variety. A11 reflectance data were normalized by dividing the mean reflectance by the baseline. This facilitated standardized comparisons between salads of different varieties and with different manufacture dates. Table 21 lists TTIP mean reflectance values recorded on several days for two groups of salads from one variety Holiday Cole Slaw (HCS), manufactured on a Thursday. Figure 14 is a plot of those data. One group (solid circles) was stored under controlled conditions . immediately after manufacture, while the other (hollow circles) went through normal distribution. Day 0 (Thursday) values for both 75 10° 1 r 1 r 1 e— control 0— variable 4O "' \0 ‘- % Reflectance, Normalized 20r- ._ 1 I I 241 l 0 5 10 15 20 25 Time, Days Figure 14. Normalized Ref1ectance Values vs. Time for Control and Variable Holiday Cole Slaw Salads. 76 Table 21. Reflectance values for control and variable Holidayllole Slaw Salads during shelf life. Day 0 1 4 15 19 22 Control 100 93 90 - 65 59 Variable 100 85 61 44 - 37 groups were recorded at the manufacturing site. All control values were recorded at Campbell Institute for Research and Technology (CIRT), where salads were stored at a constant temperature of l.7°C. Variable salads were retrieved and indicators scanned at the manufacturing plant on Day 1 (Friday), the grocery (Safeway) warehouse on Day 4 (Monday), and from retail stores on later days. The shaded area in Figure 14 indicates an extreme difference in thermal treatment of control and variable salads between Days 0 and 4. Between Days 0 (Thursday) and 4 (Monday), salads were held at the manufacturer's warehouse (in Texas) for two days and then shipped by refrigerated truck to the grocery warehouse in Colorado. Indicators show a loss of 15% Ref1ectance between Days 0 and 1, while still at the manufacturing plant, and an additional loss of 24% Reflectance between Days 1 and 4. The time between Days 1 and 4 included some time at the plant, and transport time between Texas and Colorado. This topic will be addressed in more detail later. Beyond Day 4, the Slopes of the two lines are nearly parallel, indicating similar thermal history f0r both controls and variables for the remainder of the observed period. 77 Data from Table 21 are plotted in Figure 15, illustrating thermal history differences between variable and control salads. The dashed arrow indicates similar thermal treatments for controls and variables but at different times. Indicators on variable salads had a mean reflectance of 61% on Day 4, while indicators on control salads did not reach a comparable level (59%) until Day 22, a difference of 18 days. Table 22 lists TTIP mean reflectance values recorded for controls and variables from another variety, Vegetable Garden Salad (VGS). VGS was manufactured on Friday of the same week as HCS previously discussed. VGS salads were stored and retrieved in the same manner as HCS salads. Figure 16 is a plot of reflectance values vs. time for VGS salad indicators, and illustrates the differences in thermal treatment of control and variables. Indicators on variable VGS salads show a loss of 30% Ref1ectance between Days 0 (Friday) and 3 (Monday). The shaded area in Figure 16 (indicating difference in thermal treatment between controls and variables) is considerably smaller than the shaded area in Figure 15, suggesting that salads manufactured on Friday and held until Saturday are subjected to less thermal abuse than salads inanufactured on Thursday and held until Saturday. The dashed arrow illustrates that VGS variable salads on Day 3 had received equivalent thermal treatment to control salads at Day 15. Figure 17 further illustrates the difference in thermal treatment of salads manufactured on Thursday and Friday. The left Side of the graptiillustrates the CHANGE in reflectance for indicators on salads manufactured on a Thursday, the right for salads manufactured on Friday 78 1 l I T I 100 - e— control 0— variable 80 .— ‘U 21’ "E o 60 "‘ z a? 8 .3 g 40 -1 E: 82’ as 20 J L 0 25 Time, Days Figure 15. 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