Individual Differences in Sensory and Consumer Science: Experimentation, Analysis and Interpretation presents easily readable, state-of-the-art coverage on how to plan and execute experiments that give rise to individual differences, also providing the framework for successful analysis and interpretation of results. The book highlights the different methodologies that can be applied and how to select the correct methodology based on the type of study you are performing, be it product research and development, quality control or consumer acceptance studies.  Written by an experienced team of statisticians and sensory and consumer scientists, the book provides both academics and industry professionals with the first complete overview of a topic of ever-increasing importance.Identifies how to plan and execute experiments in sensory and consumer scienceAnalyzes and interprets individual variances in sensory and consumer researchDifferentiates best practices for examining product development, quality control and consumer acceptance

Individual Differences in Sensory and Consumer Science: Experimentation, Analysis and Interpretation (Woodhead Publishing Series in Food Science, Technology and Nutrition)

Engineering & Transportation

Næs, Tormod

Berget, Ingunn

Varela, Paula

Engineering

Woodhead Publishing

As we move further into the 21st Century, sensory and consumer studies continue to develop, playing an important role in food science and industry. These studies are crucial for understanding the relation between food properties on one side and human liking and buying behaviour on the other. 
This book by a group of established scientists gives a comprehensive, up-to-date overview of the most common statistical methods for handling data from both trained sensory panels and consumer studies of food. It presents the topic in two distinct sections: problem-orientated (Part I) and method orientated (Part II), making it to appropriate for people at different levels with respect to their statistical skills. This book succesfully: <ul>
<li>Makes a clear distinction between studies using a trained sensory panel and studies using consumers.</li> <li>Concentrates on experimental studies with focus on how sensory assessors or consumers perceive and assess various product properties.</li> <li>Focuses on relationships between methods and techniques and on considering all of them as special cases of more general statistical methodologies</li> </ul>
It is assumed that the reader has a basic knowledge of statistics and the most important data collection methods within sensory and consumer science. This text is aimed at food scientists and food engineers working in research and industry, as well as food science students at master and PhD level. In addition, applied statisticians with special interest in food science will also find relevant information within the book.

Statistics for Sensory and Consumer Science

Business & Money

Brockhoff, Per Bruun

Tomic, Oliver

Marketing & Sales

wiley

Multivariate Calibration

Multivariate Calibration Harald Martens, Chemist, Norwegian Food Research Institute, Aas, Norway and Norwegian Computing Center, Oslo, Norway Tormod Næs, Statistician, Norwegian Food Research Institute, Aas, Norway The aim of this inter-disciplinary book is to present an up-to-date view of multivariate calibration of analytical instruments, for use in research, development and routine laboratory and process operation. The book is intended to show practitioners in chemistry and technology how to extract the quantitative and understandable information embedded in non-selective, overwhelming and apparently useless measurements by multivariate data analysis. Multivariate calibration is the process of learning how to combine data from several channels, in order to overcome selectivity problems, gain new insight and allow automatic outlier detection. Multivariate calibration is the basis for the present success of high-speed Near-Infrared (NIR) diffuse spectroscopy of intact samples. But the technique is very general: it has shown similar advantages in, for instance, UV, Vis, and IR spectrophotometry, (transmittance, reflectance and fluorescence), for x-ray diffraction, NMR, MS, thermal analysis, chromatography (GC, HPLC) and for electrophoresis and image analysis (tomography, microscopy), as well as other techniques. The book is written at two levels: the main level is structured as a tutorial on the practical use of multivariate calibration techniques. It is intended for university courses and self-study for chemists and technologists, giving one complete and versatile approach, based mainly on data compression methodology in self-modelling PLS regression, with considerations of experimental design, data pre-processing and model validation. A second, more methodological, level is intended for statisticians and specialists in chemometrics. It compares several alternative calibration methods, validation approaches and ways to optimize the models. The book also outlines some cognitive changes needed in analytical chemistry, and suggests ways to overcome some communication problems between statistics and chemistry and technology.

Science & Math

Martens, Harald

Chemistry

Analysis of Variance (ANOVA) is a statistical technique used in a number of chemical areas including the food industry. This handbook is a highly readable guide to the uses of the very important technique of ANOVA applied to sensory analysis. It is unique in stressing the practical implications of the topic rather than the theoretical background. This is important since ANOVA is a complex statistical technique which can be misunderstood and misused by sensory analysts. Typical problems from sensory analysis are used as examples.

Analysis of Variance for Sensory Data

Lea, Per

Rødbotten, Marit

Biological Sciences

User Friendly Guide to Multivariate Calibration and Classification

Nir Pubns

Multiblock Data Fusion in Statistics and Machine Learning Explore the advantages and shortcomings of various forms of multiblock analysis, and the relationships between them, with this expert guide Arising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist. Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems. Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package. The distinguished authors have created an accessible and useful guide to help readers fuse data, develop new data fusion models, discover how the involved algorithms and models work, and understand the advantages and shortcomings of various approaches. This book includes: <ul> <li>A thorough introduction to the different options available for the fusion of multiple data sets, including methods originating in psychometrics and chemometrics</li> <li>Practical discussions of well-known and lesser-known methods with applications in a wide variety of data problems</li> <li>Included, functional R-code for the application of many of the discussed methods</li> </ul> Perfect for graduate students studying data analysis in the context of the natural and life sciences, including bioinformatics, sensometrics, and chemometrics, Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is also an indispensable resource for developers and users of the results of multiblock methods.

Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences

Smilde, Age K.

Liland, Kristian Hovde

The field of chemometrics in general, and multivariate calibration and classification in particular, is now an essential tool for many analytical techniques. This book provides a readable text, for non-mathematicians, as an introduction to these areas for people with little or moderate knowledge of chemometrics. The book has been designed in an attractive and easily read format, with many diagrams and the use of margin notes to highlight important features.The authors are all very well known in the field and they bring a complementary range of experience and interests to this project. Tormod Naes has a PhD in statistics and is Senior Scientist at Nofima and Visiting Professor at the University of Copenhagen. His main interest is developing and applying multivariate methods. The late Tomas Isaksson was Professor of Food Science at the Agricultural University of Norway. He worked in the area of non-destructive quality analysis and chemometrics. Tom Fearn is Professor of Applied Statistics at University College London. He has worked in the area of multi-variate calibration and discrimination for over 30 years. Tony (A.M.C.) Davies first used multiple regression analysis in 1965. He works as an NIR consultant and has been writing about chemometrics since 1989.This Second Edition includes Digital Object Identifiers (DOIs) for references where these are -available, facilitating the finding of references online.

A User-Friendly Guide to Multivariate Calibration and Classification 2017

IM Publications LLP

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