LEADER 05084nam 22006974a 450 001 996199251203316 005 20230721004114.0 010 $a1-282-36555-X 010 $a9786612365553 010 $a0-470-27753-X 010 $a1-61583-205-X 010 $a0-470-27631-2 035 $a(CKB)1000000000687167 035 $a(EBL)468622 035 $a(OCoLC)609847375 035 $a(SSID)ssj0000303787 035 $a(PQKBManifestationID)11228564 035 $a(PQKBTitleCode)TC0000303787 035 $a(PQKBWorkID)10276382 035 $a(PQKB)10146270 035 $a(MiAaPQ)EBC468622 035 $a(Au-PeEL)EBL468622 035 $a(CaPaEBR)ebr10296506 035 $a(CaONFJC)MIL236555 035 $a(EXLCZ)991000000000687167 100 $a20061030d2007 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultivariate and probabilistic analyses of sensory science problems$b[electronic resource] /$fJean-Franc?ois Meullenet, Rui Xiong, and Christopher J. Findlay 205 $a1st ed. 210 $a[Chicago, Ill.] $cIFT Press ;$aAmes, Iowa $cBlackwell Pub.$d2007 215 $a1 online resource (258 p.) 225 1 $aIFT Press 300 $aDescription based upon print version of record. 311 $a0-8138-0178-8 320 $aIncludes bibliographical references and index. 327 $aMultivariate 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 Mapping 327 $a3.1. 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; References 327 $aChapter 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. Introduction 327 $a8.2. 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 Analysis 327 $a10.6. Use of Dummy Variables to Model JAR DataReferences; Index 330 $aSensory 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. 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