Statistics In Food Science And Nutrition

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.

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

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.

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

Introduction to Nutrition and Health Research aims to fill a critical gap in dietetics, nutrition and health education literature by providing a comprehensive guide to conducting research and understanding the research of others. Using actual articles, this book teaches how researchers identified problems; how they framed those problems; and how they reported, interpreted and implemented their findings. Step by step, the chapters cover an overview of the process, statistical and measurement concepts, types of research (including experimental, quasi-experimental, descriptive, and qualitative research), how to present results and computer techniques for data analysis. While this book is primarily aimed at masters and doctoral level students and beginning researchers, it will also have strong appeal for teachers, technicians and counselors.

Several changes in the United States over the past two decades have implications for diet, nutrition, and food safety, including patterns of food consumption that have produced an increase in overweight and obese Americans and threats to food safety from pathogens and bioterrorism. The changes raise a number of critical policy and research questions: How do differences in food prices and availability or in households' time resources for shopping and food preparation affect what people consume and where they eat? How do factors outside of the household, such as the availability of stores and restaurants, food preparation technology, and food marketing and labeling policies, affect what people are consuming? What effects have food assistance programs had on the nutritional quality of diets and the health of those served by the programs? Where do people buy and consume food and how does food preparation affect food safety? To address these and related questions, the Economic Research Service (ERS) of the U.S. Department of Agriculture (USDA) asked the Committee on National Statistics to convene a panel of experts to provide advice for improving the data infrastructure on food consumption and nutrition. The panel was charged to review data needs to support research and decision making for food and nutrition policies and programs in USDA and to assess the adequacy of the current data infrastructure and recommend enhancements to improve it. The primary basis for the panel's deliberations, given limited resources, was a workshop on Enhancing the Data Infrastructure in Support of Food and Nutrition Programs, Research, and Decision Making, which the panel convened on May 27-28, 2004. This report is based on the discussions at the workshop and the deliberations of the panel. The report outlines key data that are needed to better address questions related to food consumption, diet, and health; discusses the available data and some limitations of those data; and offers recommendations for improvements in those data. The panel was charged to consider USDA data needs for policy making and the focus of the report is on those needs.

Food Security, Poverty and Nutrition Analysis provides essential insights into the evaluative techniques necessary for creating appropriate and effective policies and programs to address these worldwide issues. Food scientists and nutritionists will use this important information, presented in a conceptual framework and through case studies for exploring representative problems, identifying and implementing appropriate methods of measurement and analysis, understanding examples of policy applications, and gaining valuable insight into the multidisciplinary requirements of successful implementation. This book provides core information in a format that provides not only the concept behind the method, but real-world applications giving the reader valuable, practical knowledge. * Identify proper analysis method, apply to available data, develop appropriate policy * Demonstrates analytical techniques using real-world scenario application to illustrate approaches for accurate evaluation improving understanding of practical application development * Tests reader comprehension of the statistical and analytical understanding vital to the creation of solutions for food insecurity, malnutrition and poverty-related nutrition issues using hands-on exercises