Source: UNIVERSITY OF ARKANSAS submitted to
QUALITY EVALUATION OF FOOD PRODUCTS BY SENSORY AND INSTRUMENTAL ANALYSIS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
TERMINATED
Funding Source
Reporting Frequency
Annual
Accession No.
0176554
Grant No.
(N/A)
Project No.
ARK01721
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Oct 1, 2003
Project End Date
Sep 30, 2009
Grant Year
(N/A)
Project Director
Meullenet, J. F.
Recipient Organization
UNIVERSITY OF ARKANSAS
(N/A)
FAYETTEVILLE,AR 72703
Performing Department
FOOD SCIENCE
Non Technical Summary
Sensory evaluation is costly, time consuming, and difficult to implement in an industrial setting. As a result, the development of rapid instrumental tests to predict sensory characteristics, often difficult to assess in foods , would greatly benefit the food industry in Arkansas and elsewhere. The purpose of these studies is to develop instrumental techniques predictive of the sensory perception of food products.
Animal Health Component
30%
Research Effort Categories
Basic
70%
Applied
30%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
5011530201025%
5011530309025%
5013260201025%
5013260309025%
Goals / Objectives
1.To evaluate quality of food products by descriptive sensory evaluation 2.To develop instrumental methods designed to predict sensory characteristics of food products. 3.To develop predictive models of sensory response using a descriptive sensory panel.
Project Methods
1. We will work with researchers at the University and private industry to identify and quantify sensory characteristics important to various food products produced in Arkansas, including rice and poultry. 2. Descriptive sensory evaluation will be performed using the professional sensory panel of the Institute of Food Science and Engineering and personnel of the Food Science Department at the University of Arkansas. 3. Instrumental methods will be developed as needed for the prediction of the sensory characteristics most important to the quality of food products. Sensory characteristics for which instrumental tests will be developed include characteristics such as appearance, aroma, flavor, and texture. Instrumentation that will be used includes texture analyzers, rheometers, NIR spectrometers, GC mass spectrometers, colorimeters, and others. 4. Predictive models of sensory attributes will be developed using instrumental parameters as predictors and multivariate regression principles. 5. All products evaluated by sensory panels will be covered by protocols submitted to and approved by the Human Subjects committee.

Progress 10/01/03 to 09/30/09

Outputs
OUTPUTS: This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods and 2) Development of instrumental methods to evaluate the texture of foods. 1. Understanding Consumer Acceptance. New methodologies developed for understanding the drivers of consumer acceptance of foods were developed. In particular, a new statistical approach, Euclidian distance ideal point mapping (EDIPM) was tested against known methods of preference mapping. In the framework of internal preference mapping, consumers are represented as vectors and an ideal point is not identified. With EDIPM, a consumer's ideal point in relation to existing products is identified through calculation of Euclidian distances and simple correlation with hedonic scores. The compilation of individual ideals allows the creation of a density map, the densest area being the group ideal point. The sensory profile of the ideal point can then be derived. A new unfolding algorithm (PREFSCAL) was tested against more standard data decomposition techniques (PCA). Results indicate a significant improvement in fit offered by PREFSCAL over PCA. This algorithm was tested over 11 different data sets and provided significant improvements for 8 datasets. We have investigated segmentation techniques for studies designed as balanced incomplete block designs. For studies involving the identification of consumer segments, the number of samples evaluated is often large (6-15). Since it is recommended that consumers not evaluate more than 4-5 products within a testing session, consumers have to participate in multiple sessions. This is a significant issue for practitioners because of cost and experiment control. An alternative to complete designs are Balanced Incomplete Block Designs (BIB) where each consumer only evaluates a subset of the samples. However, most segmentation techniques require observations for each consumer on each product. One solution to this problem is to predict the hedonic scores that would be given to products not seen by consumers. Several data replacement techniques were tested including Maximum Likelihood Estimations (MLE). In an initial approach, we have taken complete datasets and remove parts of the data according to a BIB design (as if consumers had not seen all products). The data is then replaced (predicted) and segmentation performed. Results of these studies indicate that as much as 60% of the data can be missing without losing segment structure. 2.Food Texture Prediction. Further studies are on the way on the optimization of an instrumental method designed to measure cooked meat tenderness. The Meullenet Owens Razor Shear Method has been successfully employed to measure poultry meat tenderness. In addition, mastication studies relating muscle activity to tenderness prediction in poultry pectoralis major were conducted. PARTICIPANTS: Jean-Francois Meullenet is professor of sensory science in the department of food science. Mohammed Saleh is a postdoctoral associate in the department of food science. Youngseung Lee is a postdoctoral associate in the department of food science. Casey Owens is an associate professor in the department of poultry science. TARGET AUDIENCES: Industrials interested involved in food product optimization, product development, consumer science or sensory evaluation. Methods developed to assess food texture are of interest by industrials dealing with quality control of food texture quality, in particular tenderness. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Research results (7 abstracts) were presented at the 2009 IFT Annual Meeting, Anaheim, CA, USA, June 6-9 2009 and at the 8th Pangborn Sensory Science Symposium, Florence, Italy, July 26-30 2009. In addition, the research resulted in 5 refereed publications

Publications

  • Saha, A. A. V. S. Perumalla, Y.S. Lee, J. F. Meullenet, and C. M. Owens. 2009. Tenderness, Moistness, and Flavor of Pre- and Post-Rigor Marinated Broiler Breast Fillets Evaluated by Consumer Sensory Panel. Poultry Science. 88:1250-1256.
  • Saha, A., Y. Lee, J. F. Meullenet, and C. M. Owens. 2009. Consumer acceptance of broiler breast fillets marinated with varying levels of salt. Poult. Sci. 88: 415-423.
  • Tubbs, J., G. Oupadissakoon, Y. S. Lee and J. F Meullenet. 2010. Performance and Representation of Euclidian Distance Ideal Point Mapping (EDIPM) using a third dimension. Food Quality and Preference, 21: 278-285.
  • Dooley, L., Y. S. Lee, and J. F. Meullenet. 2010. The application of check-all-that-apply (CATA) consumer profiling to preference mapping of vanilla ice cream and its comparison to classical external preference mapping.Food Quality and Preference 21:394-401.
  • Lee, Y. S., C. M. Owens, and J. F. Meullenet. 2009. Tenderness perception of poultry major pectoralis muscle during mastication. J. Food Sci. Vol. 74, Nr. 9, S413-422.
  • Lee, Y. S., C. M. Owens, and J. F. Meullenet. 2009. Changes in Tenderness, Color, and Water-Holding Capacity of Broiler Breast Meat during Post-Deboning Aging. J. Food Sci. Vol. 74, Nr. 8, E449-454.


Progress 01/01/08 to 12/31/08

Outputs
OUTPUTS: This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods and 2) Development of instrumental methods to evaluate the texture of foods. Progress in these areas is as follows: 1) Understanding Consumer Acceptance. New methodologies developed for understanding the drivers of consumer acceptance of foods were further optimized. In particular, a new statistical approach, Euclidian distance ideal point mapping (EDIPM) was tested against known methods of preference mapping. EDIPM allows the identification of what the sensory characteristics of an ideal product would be. In the framework of internal preference mapping, consumers are represented as vectors and an ideal point is not identified. With EDIPM, a consumer's ideal point in relation to existing products is identified through calculation of Euclidian distances and simple correlation with hedonic scores. The compilation of individual ideals allows the creation of a density map, the densest area being the group ideal point. The sensory profile of the ideal point can then be derived. A new unfolding algorithm (PREFSCAL) was tested and compared for consumer fit distributions to the standard practice of data decomposition known as principle components analysis (PCA). Preliminary results indicate a significant improvement in fit offered by PREFSCAL over PCA. This algorithm was tested over 11 different data sets and provided significant improvements for 8 datasets. 2) Food Texture Prediction. Further studies are on the way on the optimization of an instrumental method designed to measure cooked meat tenderness. The Meullenet Owens Razor Shear Method has been successfully employed to measure poultry meat tenderness. More recently the method has been applied to the assessment of pork and beef tenderness. PARTICIPANTS: Jean-Francois Meullenet is professor of sensory science in the department of food science; Mohammed Saleh is a postdoctoral associate in the department of food science; Youngseung Lee is a postdoctoral associate in the department of food science; Casey Owens is an associate professor in the department of poultry science. TARGET AUDIENCES: Industrials interested involved in food product optimization, product development, consumer science or sensory evaluation. Methods developed to assess food texture are of interest by industrials dealing with quality control of food texture quality, in particular tenderness. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Regarding objective 1, the research results were shared at the 8th Sensometrics Conference held at Brock University Catharines, Ontario, Canada. July 20-23 2008. Subsequently to these reports, the PREFSCAL algorithm was tested for a major Food Corporation. Regarding objective 2, the quality control methods developed have been shared with two instrument manufacturers and the investigators have collaborated with several end-users of the methods to facilitate implementation.

Publications

  • Nishinari, K., F. Hayakawa, C-F. Xia, L. Huang, J-F. Meullenet, J-M. Sieffermann. 2008. Comparative study of texture terms: English, French, Japanese and Chinese. J. Texture Studies. 39(5):530-568.
  • Lee, Y.S, C.M. Owens, and J-F. Meullenet. 2008. On the quality of commercial boneless skinless broiler breast meat. J. Food Science. 73(6):S253-261.
  • Lee, Y. S., C. M. Owens, J-F. Meullenet. 2008. A Novel Laser Air Puff and Shape Profile Method for Predicting Tenderness of Broiler Breast Meat. Poult. Sci. 87:1451-1457.
  • Lee, Y.S., C.M. Owens and J-F. Meullenet. 2008. The Meullenet-Owens Razor Shear (MORS) for Predicting Poultry Meat Tenderness: Its Applications and Optimization. J. Texture Studies. 39:655-672.
  • Lee, Y.S., Saha, A., Xiong, R., Owens, C.M. and J.F. Meullenet. 2008. Changes in Broiler Breast Fillets Tenderness, Water-Holding Capacity and Color Attributes during Long-Term Frozen Storage. J. Food Sci. 73(4):E162-168.


Progress 01/01/07 to 12/31/07

Outputs
OUTPUTS: This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods and 2) Development of instrumental methods to evaluate the texture of foods. Progress in these areas is as follows: 1) Product Optimization. New methodologies developed for understanding the drivers of consumer acceptance of foods were further optimized. In particular, a new statistical approach, Euclidian distance ideal point mapping (EDIPM) was tested against known methods of preference mapping. EDIPM allows the identification of what the sensory characteristics of an ideal product would be. In the framework of internal preference mapping, consumers are represented as vectors and an ideal point is not identified. With EDIPM, a consumer's ideal point in relation to existing products is identified through calculation of Euclidian distances and simple correlation with hedonic scores. The compilation of individual ideals allows the creation of a density map, the densest area being the group ideal point. The sensory profile of the ideal point can then be derived. 2) Food Texture Prediction. (a) A novel instrument was developed to predict the tenderness of beef carcasses. The testing of the UA Tendertek (UATT) was performed in two beef plants at the grading station. UATT tender beef was found to be significantly more acceptable by consumers that UATT tough beef. (b) In addition, further studies are on the way on the optimization of an instrumental method designed to measure cooked meat tenderness. The Meullenet Owens Razor Shear Method has been successfully employed to measure poultry meat tenderness. More recently the method has been applied to the assessment of pork and beef tenderness. PARTICIPANTS: Jean-Francois Meullenet is associate professor of food sensory science in the department of food science Mohammed Saleh is a postdoctoral associate in the department of food science Casey Owens is an associate professor in the department of poultry science TARGET AUDIENCES: Industrials interested involved in food product optimization, product development, consumer science or sensory evaluation. Methods developed to assess food texture would be of interest by industrials dealing with quality control of food texture quality, in particular tenderness.

Impacts
Further enhancements of consumer data mining methods were made to better understand likes and dislikes of consumers. Quality control tools for poultry, pork and beef meat tenderness provide a significant enhancement of existing methods. The identification of carcasses yielding tender beef could have a major impact on the beef industry by allowing the creation of guaranteed tender meat labels.

Publications

  • Meullenet, J-F., Lovely, C., Threlfall, R. Moris, J.R. and Striegler, R.K. 2008. An ideal point density plot method for determining an optimal sensory profile for Muscadine grape juice. Food Quality and Preference 19(2):210-219.
  • Saleh, M.I. and J-F. Meullenet. 2007. The effect of moisture content at harvest and surface lipids on the texture properties of cooked long-grain rice. Cereal Chemistry 84(2):119-124.
  • Saleh, M.I. and J-F. Meullenet. 2007. Effect of protein disruption by proteolitic treatment on cooked rice texture properties. J. Texture Studies. 38:423-437.


Progress 01/01/06 to 12/31/06

Outputs
This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods, (2) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (3) Development of instrumental methods to evaluate the texture of foods, and (4) Develop an understanding of the perception of texture in foods by humans. All four sub-projects have revolved around the use of multivariate analysis techniques to understand the perception or predict sensory characteristics of foods. Progress in these areas is as follows: (1) New methodologies developed for understanding the drivers of consumer acceptance of foods were further optimized. In particular, a new statistical approach, Euclidian distance ideal point mapping (EDIPM) was tested against known methods of preference mapping. EDIPM allows the identification of what the sensory characteristics of an ideal product would be. In the framework of internal preference mapping, consumers are represented as vectors and an ideal point is not identified. With EDIPM, a consumer's ideal point in relation to existing products is identified through calculation of Euclidian distances and simple correlation with hedonic scores. The compilation of individual ideals allows the creation of a density map, the densest area being the group ideal point. The sensory profile of the ideal point can then be derived. (2) Spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. This method has shown extremely promising results to predict texture characteristics. In addition, various modeling techniques (Artificial Neural Networks) have been combined to SSSA to provide accurate predictions of the perception of food texture attributes. (3) A novel instrument was developed to predict the tenderness of beef carcasses. Its preliminary testing revealed interesting possibilities for its implementation on-line in the beef industry. (4) Projects are underway to better understand the perception of texture attributes by humans. In particular, chewing patterns are being studies as a function of food mechanical properties. The Bite Master II, an instrument imitative of the human bite (BITE master II) was used to study foods such as gelatin gels, cheeses and poultry meat.

Impacts
Significant progress was made toward better understanding biomechanical processes involved in texture perception. Further enhancements of consumer data mining methods were made to better understand likes and dislikes of consumers. Quality control tools for poultry meat tenderness provide a significant enhancement of existing methods. The identification of carcasses yielding tender beef could have a major impact on the beef industry.

Publications

  • iong R; L.C. Cavitt, J-F. Meullenet and C.M. Owens. 2006. Comparison of Allo-Kramer, Warner-Bratzler and razor blade shear for predicting sensory tenderness of broiler breast meat. J. Texture Studies:37(2) 179-199.
  • Meullenet, J-F. and R.K. Gandhapuneni. 2006. Development of the BITE Master II and its application to the study of cheese hardness. Physiology & Behavior 89(2006) 39-43.
  • Baublitz, R.T., J-F. Meullenet, J.T. Sawyer, J.M. Mehafey and A. Saha. 2006. Pump rate and cooked temperature effects on pork loin instrumental, sensory descriptive and consumer-rated characteristics. Meat Science 72, 741-750.
  • Xiong, R. and J-F. Meullenet. 2006. A PLS dummy variable approach to assess the impact of jar attributes on liking. Food Quality and Preference. 117:188-198.


Progress 01/01/05 to 12/31/05

Outputs
This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods, (2) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis, (3) Development of instrumental methods to evaluate the texture of foods, and (4) Develop an understanding of the perception of texture in foods by humans. All four sub-projects have revolved around the use of multivariate analysis techniques to understand the perception or predict sensory characteristics of foods. Progress in these areas is as follows: (1) New methodologies were developed for understanding the drivers of consumer acceptance of foods. In particular, a new statistical approach, ideal point preference mapping (IPPM), was developed. IPPM allows the identification of what the sensory characteristics of an ideal product would be. In the framework of internal preference mapping, consumers are represented as vectors and an ideal point is not identified. With IPPM, a consumer's ideal point in relation to existing products is identified through calculation of Euclidian distances and simple correlation with hedonic scores. The compilation of individual ideals allows the creation of a density map, the densest area being the group ideal point. (2) Spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. This method has shown extremely promising results to predict texture characteristics. In addition, various modeling techniques (Artificial Neural Networks) have been combined to SSSA to provide accurate predictions of the perception of food texture attributes. (3) Projects were initiated to understand the perception of texture attributes by humans. In particular, chewing patterns are being studies as a function of food mechanical properties. The Bite Master II, an instrument imitative of the human bite (BITE master II) was used to study foods such as gelatin gels and cheeses.

Impacts
Significant progress was made toward better understanding biomechanical processes involved in texture perception. Further enhancements of consumer data mining methods were made to better understand likes and dislikes of consumers. Quality control tools for poultry meat tenderness provide a significant enhancement of existing methods.

Publications

  • 1. Cavitt, L.C., J-F.C. Meullenet, R. Xiong, C.M. Owens. 2005. The relationship of razor blade shear, Allo-Kramer shear, Warner-Bratzler shear and sensory tests to changes in tenderness of broiler breast fillets. Journal-of-Muscle-Foods. 16(3): 223-242.
  • 2. Cavitt, L.C., J-F. Meullenet, R.K. Gandhapuneni, G.W. Youm, C.M. Owens. 2005. Rigor development and meat quality of large and small broilers and the use of Allo-Kramer shear, needle puncture, and razor blade shear to measure texture. Poultry-Science. 84(1):113-118.
  • 3. Xiong, R. and J-F. Meullenet. 2005. A PLS dummy variable approach to assess the impact of jar attributes on liking. Food Quality and Preference. 16(2005).
  • 4. Owens, C.M., L.C. Cavitt, G.W. Youm, and J-F. Meullenet. 2005. Using a novel razor blade shearing method to measure poultry meat tenderness. ZooTechnica International, Number 1 January 2005, 56-59.
  • 5. Finney, M., and J-F. Meullenet. 2005. Measurement of biting velocities, and pre-determined and individual crosshead speed instrumental imitative tests for predicting sensory hardness of gelatin gels. J. Sensory Studies, 20(2):114-129.


Progress 01/01/04 to 12/30/04

Outputs
This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods, (2) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (3) Development of instrumental methods to evaluate the texture of foods, and (4) Develop an understanding of the perception of texture in foods by humans. All four sub-projects have revolved around the use of multivariate analysis techniques to understand the perception or predict sensory characteristics of foods. Progress in these areas is as follows: (1) New methodologies were developed for understanding the drivers of consumer acceptance of foods. Methods such as preference mapping are being investigated. Novel statistical techniques such as proportional odds modeling and risk assessment have been successfully employed to the prediction of food acceptance. (2) Spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. This method has shown extremely promising results to predict texture characteristics. In addition, various modeling techniques (Artificial Neural Networks) have been combined to SSSA to provide accurate predictions of the perception of food texture attributes. (3) Instrumental methods are being developed for foods such as poultry meat, rice, apples, cheese, yogurt, frankfurters and gels. (4) Projects were initiated to understand the perception of texture attributes by humans. In particular, chewing patterns are being studies as a function of food mechanical properties. In addition, an instrument imitative of the human bite (BITE master II) was conceptualized and built for studies on biomechanics.

Impacts
Significant progress was made toward better understanding biomechanical processes involved in texture perception. Further enhancements of consumer data mining methods were made to better understand likes and dislikes of consumers. Quality control tools for poultry meat tenderness provide a significant enhancement of existing methods

Publications

  • No publications reported this period


Progress 01/01/03 to 12/31/03

Outputs
This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods, (2) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (3) Development of instrumental methods to evaluate the texture of foods, and (4) Develop an understanding of the perception of texture in foods by humans. All four sub-projects have revolved around the use of multivariate analysis techniques to understand the perception or predict sensory characteristics of foods. Progress in these areas is as follows: (1) New methodologies were developed for understanding the drivers of consumer acceptance of foods. Methods such as preference mapping are being investigated. Novel statistical techniques such as proportional odds modeling and risk assessment have been successfully employed to the prediction of food acceptance. (2) Spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. This method has shown extremely promising results to predict texture characteristics. In addition, various modeling techniques (Artificial Neural Networks) have been combined to SSSA to provide accurate predictions of the perception of food texture attributes. (3) Instrumental methods are being developed for foods such as poultry meat, rice, apples, cheese, yogurt, frankfurters and gels. (4) Projects were initiated to understand the perception of texture attributes by humans. In particular, chewing patterns are being studies as a function of food mechanical properties. In addition, an instrument imitative of the human bite (BITE master II) was conceptualized and built for studies on biomechanics.

Impacts
These methods need further development but shows great potential for monitoring many food products' texture quality. These studies should assist the U.S. food industry in ensuring that texture properties of food products are optimized and that, consequently, consumer satisfaction is maximized.

Publications

  • Meullenet, J-F., R. Xiong, J.A. Hankins, P. Dias, S. Zivanovic, M.A. Monsoor, T. Bellman-Horner, Z. Liu, H. Fromm. 2003. Modeling Preference of Commercial Toasted White Corn Tortilla Chips Using Proportional Odds Models. Food Quality and Preference, 14:603-614.


Progress 01/01/02 to 12/31/02

Outputs
This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods, (2) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (3) Development of instrumental methods to evaluate the texture of foods, and (4) Develop an understanding of the perception of texture in foods by humans. All four sub-projects have revolved around the use of multivariate analysis techniques to understand the perception or predict sensory characteristics of foods. Progress in these areas is as follows: (1) New methodologies were developed for understanding the drivers of consumer acceptance of foods. Methods such as preference mapping are being investigated. Novel statistical techniques such as proportional odds modeling and risk assessment have been successfully employed to the prediction of food acceptance. (2) Spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. This method has shown extremely promising results to predict texture characteristics. In addition, various modeling techniques (Artificial Neural Networks) have been combined to SSSA to provide accurate predictions of the perception of food texture attributes. (3) Instrumental methods are being developed for foods such as poultry meat, rice, apples, cheese, yogurt, frankfurters and gels. (4) Projects were initiated to understand the perception of texture attributes by humans. In particular, chewing patterns are being studies as a function of food mechanical properties. In addition, an instrument imitative of the human bite (BITE master II) was conceptualized and built for studies on biomechanics.

Impacts
These methods need further development but shows great potential for monitoring many food products' texture quality. These studies should assist the U.S. food industry in ensuring that texture properties of food products are optimized and that, consequently, consumer satisfaction is maximized.

Publications

  • Meullenet, J-F., M.L. Finney, and M. Gaud. 2002. Measurement of Biting Velocities, and Predetermined and Individual Crosshead Speed Instrumental Imitative Tests for Predicting Cheese Hardness. J. Texture Studies 33:45-58.
  • Meullenet, J-F., R. Xiong, M. Monsoor, T. Bellman-Horner, S. Zivanovic, P. Dias, H. Fromm, Z. Liu. 2002 Preference Mapping of Commercial Toasted White Corn Tortilla Chips. J. Food Sci. 67:1950-1957.
  • Carson, K.H. and J-F. Meullenet. 2002. Spectral Stress Strain Analysis and Partial Least Squares Regression to Predict Sensory Texture of Yogurt using a Compression/Penetration Instrumental Method. J. Food Sci. 67:1224-1228.
  • Xiong, R., J-F. Meullenet., W.K. Chung, and J.A. Hankins 2002. Relationship between sensory and instrumental hardness of commercial cheeses, J. Food Sci. 67:877-883.
  • Suwansri, S., J-F. Meullenet, J.A. Hankins, and K. Griffin. 2002. Preference Mapping of Domestic/Imported Jasmine Rice for U.S. Asian Consumers. J. Food Sci. 67:2420-2431.


Progress 01/01/01 to 12/31/01

Outputs
This project covers the following sub research projects: (1) Development of modeling techniques for understanding consumer acceptance of foods, (2) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (3) Development of instrumental methods to evaluate the texture of foods, and (4) Develop an understanding of the perception of texture in foods by humans. All four sub-projects have revolved around the use of multivariate analysis techniques to understand the perception or predict sensory characteristics of foods. Progress in these areas is as follows: (1) New methodologies were developed for understanding the drivers of consumer acceptance of foods. Methods such as preference mapping are being investigated. Novel statistical techniques such as proportional odds modeling are being tested and compared to more conventional methods (i.e. Partial Least Squares Regression). (2) Spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. This method has shown extremely promising results to predict texture characteristics. In addition, various modeling techniques (Artificial Neural Networks and Fuzzy Logic) are being investigated. (3) Instrumental methods are being developed for foods such as rice, apples, cheese, yogurt, frankfurters and gels. (4) Projects were initiated to understand the perception of texture attributes by humans. In particular, chewing patterns are being studies as a function of food mechanical properties.

Impacts
These methods need further development but shows great potential for monitoring many food products' texture quality. This technology should assist the U.S. food industry in ensuring that texture properties of food products are optimized and that, consequently, consumer satisfaction is maximized.

Publications

  • Meullenet, J-F., Xiong, R., Monsoor, M., Bellman-Horner, T., Zivanovic, S., Dias, P., Fromm, H., Liu, Z. 2001. Preference Mapping of Commercial Toasted White Corn Tortilla Chips. J. Food Science. (In Print).
  • Carson, K.H. and Meullenet, J.-F. 2001. Spectral Stress Strain Analysis and Partial Least Squares Regression to Predict Sensory Texture of Yogurt using a Compression/ Penetration Instrumental Method. J. Food Science. (In Print).
  • Xiong, R., Meullenet, J.-F., Chung, W.K., and Hankins, J.A. 2001. Relationship between sensory and instrumental hardness of commercial cheeses. J. Food Science. (In Print).
  • Sitakalin, C. and Meullenet, J.-F. 2001. Prediction of Cooked Rice Texture using an Extrusion Test in Combination with Partial Least Squares Regression and Artificial Neural Networks (ANN). Cereal Chem. 78:391-394.
  • Meullenet, J-F., Mauromoustakos, A., Bellman Horner, T., and Marks, B.P. 2001. Prediction of the Texture of Cooked Rice by Near-Infrared Reflectance Analysis of Whole-Grain Milled Samples. Cereal Chem. (In Print).
  • Breuil, P. and Meullenet, J.-F. 2001. A Comparison of Three Instrumental Tests for Predicting Sensory Texture Profiles of Cheese. J. Texture Studies. 32:41-55.
  • Sesmat, A. and Meullenet, J.-F. 2001. Prediction of Cooked Rice Texture from a Compression Test and a Novel Stepwise Model Optimization Method. J. Food Science. 66:124-131.


Progress 01/01/00 to 12/31/00

Outputs
This project covers the following sub research projects: (1) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (2) Correlation of Instrumental and Sensory Flavor Measurements of Cooked Rice, (3) Development of Instrumental Methods to Evaluate Texture of Cooked Rice, and (4) Prediction of Sensory Quality of Cooked Rice using NIR Spectroscopy. All four sub-projects have revolved around the use of multivariate analysis techniques to better predict sensory characteristics of foods using instrumental methods. In particular, the spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. The method considers the force deformation curve generated instrumentally as a spectrum which is later used to build predictive models of sensory attributes using Partial Least Squares Regression. This method has shown extremely promising results to predict rice texture characteristics such as hardness, stickiness, cohesiveness of mass, roughness of mass, toothpull and toothpack. In addition, various modeling techniques (Artificial Neural Networks and Fuzzy Logic) are being investigated for predicting food texture from instrumental testing. NIR spectroscopy was used to attempt the prediction of texture characteristics of cooked rice measured via sensory evaluation techniques. Data collected so far shows that the prediction of cooked rice texture from NIR spectroscopic measurements are as accurate as destructive methods.

Impacts
These methods need further development but shows great potential for monitoring many food products' texture quality. This technology should assist the U.S. food industry in ensuring that texture properties of food products are optimized and that, consequently, consumer satisfaction is maximized.

Publications

  • Sesmat, A. and J-F. Meullenet. 2001. Prediction of Cooked Rice Texture from a Compression Test and a Novel Stepwise Model Optimization Method. Accepted for publication in J. Food Science.
  • Breuil, P. and J-F. Meullenet. 2001. A Comparison of Three Instrumental Tests for Predicting Sensory Texture Profiles of Cheese. Accepted for publication J. Texture Studies.
  • Lenjo, M. and J-F. Meullenet. 2000. Prediction of Rice Sensory Texture Attributes using Spectral Stress Strain Analysis and the Jack-Knife Model Optimization Method. Discovery, 1: 31-37.
  • Meullenet, J-F., E.T. Champagne, K.L. Bett, A.M. McClung, and D. Kauffmann. 2000. Rapid Assessment of Cooked Rice Texture Characteristics: A Method for Breeders. Cereal Chem. 77:512-517.
  • Sitakalin, C. and J-F. Meullenet. 2000. Prediction of Cooked Rice Texture using Extrusion and Compression Tests in Conjunction with Spectral Stress Strain Analysis. Cereal Chem. 77:501-506.
  • Meullenet, J-F., C. Sitakalin, and B.P. Marks. 1999. Prediction of Rice Texture by Spectral Stress Strain Analysis: A Novel Technique for Treating Instrumental Extrusion Data used for Predicting Sensory Texture Profiles. J. Texture Studies. 30:435-450.
  • Meullenet, J-F., C. Sitakalin, and B.P. Marks. 1999. Prediction of rice texture by spectral stress strain analysis: a novel technique for treating instrumental extrusion data used for predicting sensory texture profiles. J. Texture Studies 30:265-274.
  • Horner, T.B., B.P. Marks, and J-F. Meullenet. 1999. Near-Infrared Prediction of Functional Characteristics of Milled Rice. Rice Research Studies, Arkansas Agricultural Experiment Station. p:344-351.
  • Meullenet, J-F., Jason Gross. 1999. Modeling of sensory perception of texture using instrumental parameters from single and double compression tests. J. Texture Studies 30:167-180.
  • Meullenet, J-F., B.G. Lyon, J.A. Carpenter, C.E. Lyon. 1998. Relationship between sensory and instrumental texture attributes. J. Sensory Studies 13 (1998). 77-93.
  • Meullenet, J-F., Gross, J., Marks, B.P., and Daniels, M. 1998. Sensory Profiling of Cooked Rice and its correlation to instrumental parameters using an extrusion cell. Cereal Chemistry 75(5):714-720.
  • Meullenet, J-F, Gross J.A., Marks, B.P., and Siebenmorgen, T.J. 1998. Sensory profiling of cooked rice and its correlation to instrumental parameters using an extrusion cell. Rice Research Studies, Arkansas Agricultural Experiment Station. p:213-222.


Progress 01/01/99 to 12/31/99

Outputs
This project covers the following sub research projects: (1) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis (2) Correlation of Instrumental and Sensory Flavor Measurements of Cooked Rice, (3) Development of Instrumental Methods to Evaluate Texture of Cooked Rice, and (4) Prediction of Sensory Quality of Cooked Rice using NIR Spectroscopy. All four sub-projects have revolved around the use of multivariate analysis techniques to better predict sensory characteristics of foods using instrumental methods. In particular, the spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. The method considers the force deformation curve generated instrumentally as a spectrum which is later used to build predictive models of sensory attributes using Partial Least Squares Regression. This method has shown extremely promising results to predict rice texture characteristics such as hardness, stickiness, cohesiveness of mass, roughness of mass, toothpull and toothpack. In addition, various modeling techniques (Artificial Neural Networks and Fuzzy Logic) are being investigated for predicting food texture from instrumental testing. NIR spectroscopy was used to attempt the prediction of both flavor and texture characteristics of cooked rice measured via sensory evaluation techniques. The amount of data collected so far is too small to report meaningful results. However, results look promising.

Impacts
(N/A)

Publications

  • Meullenet, J-F, E.T. Champagne, K.L. Bett, A.M. McClung, D. Kauffmann. 2000. Rapid Assessment of Cooked Rice Texture Characteristics: A Method for Breeders. Accepted. Cereal Chemistry.
  • Meullenet, J-F., C. Sitakalin, and B.P. Marks. 1999. Prediction of rice texture by spectral stress strain analysis: a novel technique for treating instrumental extrusion data used for predicting sensory texture profiles. J. Texture Studies 30: 265-274.
  • Meullenet, J-F., Jason Gross. 1999. Modeling of sensory perception of texture using instrumental parameters from single and double compression tests. J. Texture studies 30:167-180.
  • Meullenet, J-F., B.G. Lyon, J.A. Carpenter, C.E. Lyon. 1998. Relationship between sensory and instrumental texture attributes. J. Sensory Studies 13 (1998). 77-93.
  • Meullenet, J-F., Gross, J., Marks, B.P., and Daniels, M. 1998. Sensory Profiling of Cooked Rice and its correlation to instrumental parameters using an extrusion cell. Cereal Chemistry 75(5):714-720.
  • Meullenet, J-F, Marks, B.P., Sharp, C., and Daniels, M.J. 1998. Effects of rough rice wet holding, drying temperature, storage temperature and storage duration on sensory profile of cooked rice. Rice Research Studies, Arkansas Agricultural experiment Station. p:288-298.
  • Meullenet, J-F, Gross J.A., Marks, B.P., and Siebenmorgen, T.J. 1998. Sensory profiling of cooked rice and its correlation to instrumental parameters using an extrusion cell. Rice Research Studies, Arkansas Agricultural experiment Station. p:213-222.


Progress 01/01/98 to 12/31/98

Outputs
This project covers the following sub research projects: (1) Modeling of sensory texture perception of foods from instrumental Spectral Stress Strain Analysis; (2) Correlation of Instrumental and Sensory Flavor Measurements of Cooked Rice; (3) Development of Instrumental Methods to Evaluate Texture of Cooked Rice; and (4) Prediction of Sensory Quality of Cooked Rice using NIR Spectroscopy. All four sub-projects have revolved around the use of multivariate analysis techniques to better predict sensory characteristics of foods using instrumental methods. In particular, the spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed in our laboratory to predict textural characteristics of food samples measured via descriptive sensory evaluation. The method considers the force deformation curve generated instrumentally as a spectrum which is later used to build predictive models of sensory attributes using Partial Least Squares Regression. This method has shown extremely promising results to predict rice texture characteristics such as hardness, stickiness, cohesiveness of mass, roughness of mass, toothpull and toothpack. In addition, various modeling techniques (Artificial Neural Networks and Fuzzy Logic) are being investigated for predicting food texture from instrumental testing. NIR spectroscopy was used to attempt the prediction of both flavor and texture characteristics of cooked rice measured via sensory evaluation techniques. The amount of data collected so far is too small to report meaningful results. However, results look promising.

Impacts
(N/A)

Publications

  • Meullenet, J-F., Lyon, B.G., Carpenter, J.A., Lyon, C.E. 1998. Relationship between sensory and instrumental texture attributes. J. Sensory Studies 13 (1998). 77-93.
  • Meullenet, J-F., Gross, J., Marks, B.P., and Daniels, M. 1998. Sensory Profiling of Cooked Rice and its correlation to instrumental parameters using an extrusion cell. Cereal Chemistry. 75(5):714-720.
  • Meullenet, J-F., Gross, Jason. 1999. Modeling of sensory perception of texture using instrumental parameters from single and double compression tests. "In Print". J. Texture studies.
  • Meullenet, J-F., Marks, B.P., Sharp, C., and Daniels, M.J. 1998. Effects of rough rice wet holding, drying temperature, storage temperature and storage duration on sensory profile of cooked rice. Rice Research Studies, Arkansas Agricultural Experiment Station. P. 288-298.
  • Meulenet, J-F., Gross, J.A., Marks, B.P., and Siebenmorgen, T.J. 1998. Sensory profiling of cooked rice and its correlation to instrumental parameters using an extrusion cell. Rice Research Studies, Arkansas Agricultural Experiment Station. P. 213-222.


Progress 01/01/97 to 12/31/97

Outputs
Research was initiated that focused on: (1) correlation of instrumental and sensory texture measurements for various commercial foods, (2) correlation of instrumental and sensory flavor measurements of cooked rice, (3) development of instrumental methods to evaluate texture of cooked rice, (4) prediction of sensory quality of cooked rice using NIR spectroscopy, and (5) sensory characterization of cooked rice as a function of post-harvest history. Research directions have revolved around the use of multivariate analysis techniques to better predict sensory characteristics of foods using instrumental methods. In particular, the spectral stress-strain data analysis method, a novel instrumental data analysis method, was developed to predict textural characteristics of food samples measured by descriptive sensory evaluation. The method considers the force deformation curve generated instrumentally as a spectrum which is later used to build predictive models of sensory attributes using Partial Least Square Regression. This method has shown extremely promising results to predict texture characteristics of cooked rice such as hardness, stickiness, cohesiveness of mass, roughness of mass, toothpull and toothpack. NIR spectroscopy was used to attempt the prediction of both flavor and texture characteristics of cooked rice measured via sensory evaluation techniques.

Impacts
(N/A)

Publications

  • No publications reported this period