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

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

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. ​

This book is a compendium of Alok Bhargava's most important contributions in longitudinal econometric methods and its application to problems of food, nutrition and health. It demonstrates the usefulness of rigorous econometric and statistical methods in addressing issues of under-nutrition and poor child health in developing countries, as well as obesity in developed countries.The close connection between the issues and themes analyzed in disciplines such as economics, nutrition, psychology, demography, epidemiology and public health, provides a sound basis for the formulation of public policies.

During the past decade, tremendous growth has occurred in the use of nutrition symbols and rating systems designed to summarize key nutritional aspects and characteristics of food products. These symbols and the systems that underlie them have become known as front-of-package (FOP) nutrition rating systems and symbols, even though the symbols themselves can be found anywhere on the front of a food package or on a retail shelf tag. Though not regulated and inconsistent in format, content, and criteria, FOP systems and symbols have the potential to provide useful guidance to consumers as well as maximize effectiveness. As a result, Congress directed the Centers for Disease Control and Prevention (CDC) to undertake a study with the Institute of Medicine (IOM) to examine and provide recommendations regarding FOP nutrition rating systems and symbols. The study was completed in two phases. Phase I focused primarily on the nutrition criteria underlying FOP systems. Phase II builds on the results of Phase I while focusing on aspects related to consumer understanding and behavior related to the development of a standardized FOP system. Front-of-Package Nutrition Rating Systems and Symbols focuses on Phase II of the study. The report addresses the potential benefits of a single, standardized front-label food guidance system regulated by the Food and Drug Administration, assesses which icons are most effective with consumer audiences, and considers the systems/icons that best promote health and how to maximize their use.