Les financiers. / de Bleton P.

• Record Nr.: UNINA990002809520403321
• Autore: Bleton, Pierre <1924-1985>
• Locazione: ECA
• Collocazione: 9-380-TB
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Multivariate and probabilistic analyses of sensory science problems [[electronic resource] /] / Jean-François Meullenet, Rui Xiong, and Christopher J. Findlay

• Record Nr.: UNINA9910144391303321
• Autore: Meullenet J.-F (Jean-Francois), <1968->
• Pubbl/distr/stampa: [Chicago, Ill.], : IFT Press; Ames, Iowa, : Blackwell Pub., 2007
• ISBN: 1-282-36555-X; 9786612365553; 0-470-27753-X; 1-61583-205-X; 0-470-27631-2
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (258 p.)
• Collana: IFT Press
• Altri autori (Persone): XiongRui; FindlayChristopher J
• Disciplina: 664/.07
• Soggetti: Food - Sensory evaluation - Statistical methods; Multivariate analysis
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 Mapping; 3.1. 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; Chapter 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; 8.2. 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; 10.6. Use of Dummy Variables to Model JAR DataReferences; Index
• Sommario/riassunto: Sensory 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 for