05084nam 22006974a 450 99619925120331620230721004114.01-282-36555-X97866123655530-470-27753-X1-61583-205-X0-470-27631-2(CKB)1000000000687167(EBL)468622(OCoLC)609847375(SSID)ssj0000303787(PQKBManifestationID)11228564(PQKBTitleCode)TC0000303787(PQKBWorkID)10276382(PQKB)10146270(MiAaPQ)EBC468622(Au-PeEL)EBL468622(CaPaEBR)ebr10296506(CaONFJC)MIL236555(EXLCZ)99100000000068716720061030d2007 uy 0engurcn|||||||||txtccrMultivariate and probabilistic analyses of sensory science problems[electronic resource] /Jean-François Meullenet, Rui Xiong, and Christopher J. Findlay1st ed.[Chicago, Ill.] IFT Press ;Ames, Iowa Blackwell Pub.20071 online resource (258 p.)IFT PressDescription based upon print version of record.0-8138-0178-8 Includes bibliographical references and index.Multivariate and Probabilistic Analyses of Sensory Science Problems; Table of Contents; Introduction; Chapter 1. A Description of Sample Data Sets Used in Further Chapters; 1.1. A Description of Example Data Sets; References; Chapter 2. Panelist and Panel Performance: A Multivariate Experience; 2.1. The Multivariate Nature of Sensory Evaluation; 2.2. Univariate Approaches to Panelist Assessment; 2.3. Multivariate Techniques for Panelist Performance; 2.4. Panel Evaluation through Multivariate Techniques; 2.5. Conclusions; References; Chapter 3. A Nontechnical Description of Preference Mapping3.1. Introduction 3.2. Internal Preference Mapping; 3.3. External Preference Mapping; 3.4. Conclusions; References; Chapter 4. Deterministic Extensions to Preference Mapping Techniques; 4.1. Introduction; 4.2. Application and Models Available; 4.3. Conclusions; References; Chapter 5. Multidimensional Scaling and Unfolding and the Application of Probabilistic Unfolding to Model Preference Data; 5.1. Introduction; 5.2. Multidimensional Scaling (MDS) and Unfolding; 5.3. Probabilistic Approach to Unfolding and Identifying the Drivers of Liking; 5.4. Examples; ReferencesChapter 6. Consumer Segmentation Techniques 6.1. Introduction; 6.2. Methods Available; 6.3. Segmentation Methods Using Hierarchical Cluster Analysis; References; Chapter 7. Ordinal Logistic Regression Models in Consumer Research; 7.1. Introduction; 7.2. Limitations of Ordinary Least Squares Regression; 7.3. Odds, Odds Ratio, and Logit; 7.4. Binary Logistic Regression; 7.5. Ordinal Logistic Regression Models; 7.6. Porportional Odds Model (POM); 7.7. Conclusions; References; Chapter 8. Risk Assessment in Sensory and Consumer Science; 8.1. Introduction8.2. Concepts of Quantitative Risk Assessment 8.3. A Case Study: Cheese Sticks Appetizers; 8.4. Conclusions; References; Chapter 9. Application of MARS to Preference Mapping; 9.1. Introduction; 9.2. MARS Basics; 9.3. Setting Control Parameters and Refining Models; 9.4. Example of Application of MARS; 9.5. A Comparison with PLS Regression; References; Chapter 10. Analysis of Just About Right Data; 10.1. Introduction; 10.2. Basics of Penalty Analysis; 10.3. Boot strapping Penalty Analysis; 10.4. Use of MARS to Model JAR Data; 10.5. A Proportional Odds/Hazards Approach to Diagnostic Data Analysis10.6. Use of Dummy Variables to Model JAR DataReferences; IndexSensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration of consumer data, including segmentation and identifying drivers of liking. Applied in approach and written forIFT Press series.FoodSensory evaluationStatistical methodsMultivariate analysisFoodSensory evaluationStatistical methods.Multivariate analysis.664/.07Meullenet J.-F(Jean-Francois),1968-855731Xiong Rui855732Findlay Christopher J855733MiAaPQMiAaPQMiAaPQBOOK996199251203316Multivariate and probabilistic analyses of sensory science problems1910562UNISA