The recording and analysis of food data are becoming increasingly sophisticated. Consequently, the food scientist in industry or at study faces the task of using and understanding statistical methods. Statistics is often viewed as a difficult subject and is often avoided because of its complexity and a lack of specific application to the requirements of food science. This situation is changing – there is now much material on multivariate applications for the more advanced reader, but a case exists for a univariate approach aimed at the non-statistician. This book provides a source text on accessible statistical procedures for the food scientist, and is aimed at professionals and students in food laboratories where analytical, instrumental and sensory data are gathered and require some form of summary and analysis before interpretation. It is suitable for the food analyst, the sensory scientist and the product developer, and others who work in food-related disciplines involving consumer survey investigations will also find many sections of use. There is an emphasis on a ‘hands on’ approach, and worked examples using computer software packages and the minimum of mathematical formulae are included. The book is based on the experience and practice of a scientist engaged for many years in research and teaching of analytical and sensory food science at undergraduate and post-graduate level.

Mathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical foundations. Using clear examples and case-studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians, and may therefore be used by scientists, students and food industry professionals at different levels and with varying degrees of statistical skill.

Many statistical innovations are linked to applications in food science. For example, the student t-test (a statistical method) was developed to monitor the quality of stout at the Guinness Brewery and multivariate statistical methods are applied widely in the spectroscopic analysis of foods. Nevertheless, statistical methods are most often associated with engineering, mathematics, and the medical sciences, and are rarely thought to be driven by food science. Consequently, there is a dearth of statistical methods aimed specifically at food science, forcing researchers to utilize methods intended for other disciplines. The objective of this Brief will be to highlight the most needed and relevant statistical methods in food science and thus eliminate the need to learn about these methods from other fields. All methods and their applications will be illustrated with examples from research literature. ​

The recording and analysis of food data are becoming increasingly sophisticated. Consequently, the food scientist in industry or at study faces the task of using and understanding statistical methods. Statistics is often viewed as a difficult subject and is often avoided because of its complexity and a lack of specific application to the requirements of food science. This situation is changing – there is now much material on multivariate applications for the more advanced reader, but a case exists for a univariate approach aimed at the non-statistician. This second edition of Statistical Methods for Food Science provides a source text on accessible statistical procedures for the food scientist, and is aimed at professionals and students in food laboratories where analytical, instrumental and sensory data are gathered and require some form of summary and analysis before interpretation. It is suitable for the food analyst, the sensory scientist and the product developer, and others who work in food-related disciplines involving consumer survey investigations will also find many sections of use. There is an emphasis on a ‘hands-on’ approach, and worked examples using computer software packages and the minimum of mathematical formulae are included. The book is based on the experience and practice of a scientist engaged for many years in research and teaching of analytical and sensory food science at undergraduate and post-graduate level. This revised and updated second edition is accompanied by a new companion website giving the reader access to the datasets and Excel spreadsheets featured in the book. Check it out now by visiting www.wiley.com/go/bower/statistical or by scanning the QR code below.

Statistical Methods in Food and Consumer Research, Second Edition, continues to be the only book to focus solely on the statistical techniques used in sensory testing of foods, pharmaceuticals, cosmetics, and other consumer products. This new edition includes the most recent applications of statistical methods, and features significant updates as well as two new chapters. Covering the application of techniques including R-index, the Bayesian approach for sensory differences tests, and preference mapping in addition to several other methodologies, this is the comprehensive reference needed by those studying sensory evaluation and applied statistics in agriculture and biological sciences. Research professionals working with food, beverages, healthcare, cosmetics, and other related areas will find the book a valuable guide to the variety of statistical methods available. - Provides comprehensive coverage of statistical techniques in sensory testing - Includes data compiled from real-world experiments - Covers the latest in data interpretation and analysis - Addresses key methods such as R-index, Thursonian Discriminal Distances, group sequential tests, beta-binomial tests, sensory difference and similarity tests, just-about-right data, signal-to-noise ratio, analysis of cosmetic data, Descriptive Analysis, claims substantiation and preference mapping

Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make valid decisions. The development of modern statistical packages makes the analysis of data easier than before. The book focuses on the application of statistics and correct methods for the analysis and interpretation of data. R statistical software is used throughout the book to analyze the data. - Contains numerous step-by-step tutorials help the reader to learn quickly - Covers the theory and application of the statistical techniques - Shows how to analyze data using R software - Provides R scripts for all examples and figures

The practical approached championed in this book have led to increasing the quality on many successful products through providing a better understanding of consumer needs, current product and process performance and a desired future state. In 2009, Frank Rossi and Viktor Mirtchev brought their practical statistical thinking forward and created the course "Statistics for Food Scientists. The intent of the course was to help product and process developers increase the probability of their project's success through the incorporation of practical statistical thinking in their challenges. The course has since grown and has become the basis of this book. - Presents detailed descriptions of statistical concepts and commonly used statistical tools to better analyze data and interpret results - Demonstrates thorough examples and specific practical problems of what food scientists face in their work and how the tools of statistics can help them to make more informed decisions - Provides information to show how statistical tools are applied to improve research results, enhance product quality, and promote overall product development