Food Product Design: A Computer-Aided Statistical Approach / Edition 1

by Ruguo Hu

Overview

Statistical experimental design is currently used as a quality control technique to achieve product excellence at the lowest overall cost. It can also function as a powerful tool to optimize food products and/or processes, to accelerate food development cycles, reduce research costs, facilitate the transition of products from research and development to manufacturing and troubleshoot manufacturing problems. Food Product Design: A Computer-Aided Statistical Approach familiarizes readers with the methodology of statistical experimental design, and its application in food product design, with the aid of commonly available modern commercial software.

Food Product Design presents basic concepts of food product design, then focuses on the most effective statistical techniques and corresponding computer applications for trial design, modeling, and experimental data analysis. The book presents very few theories about mathematics and statistics. Instead, it contains detailed descriptions of how to use popular computer software to solve the real mathematical and statistical problems that occur in product design. Even those with very limited knowledge of statistics and mathematics will find this a useful and highly practical book. Food Product Design: A Computer-Aided Statistical Approach will be a valuable tool for professional food engineers, technologists, scientists, and industrial personnel who want to update and expand their knowledge about computer-aided statistical methods in the field of food product design. Those involved in applied research at universities in food and agriculture, biological and chemical engineering, and statistics will also find it useful and informative.

Product Details

ISBN-13:	9781566767439
Publisher:	Taylor & Francis
Publication date:	04/27/1999
Edition description:	New Edition
Pages:	240
Product dimensions:	6.00(w) x 9.00(h) x (d)

Table of Contents

Preface Introduction Problems of Food Product Design Classical One-Factor Experimental Method General Remarks about Statistical Methods Software: A Step toward Simplicity A Brief Historical PerspectiveProblems of Food Product Design System Identification and Definition Black-Box Modeling and Input-Output Consideration Production Process Problem Recipe Problem Difference of Modeling for Process and RecipeFood Process Modeling and OptimizationBasic Concepts of Food Process Design Identification of Modeling System Selection of Suitable Models Design of Statistical Experimental Plans Realization of experimental Designs Model Building Effect Analysis of Process Variables Prediction and Optimization Exploration and Confirmation Transfer of Optimum Results and Evolutionary Operation A Complete Practical Example Food Recipe Modeling And Optimization Introduction Target Definition and Food Ingredient Screening Design of Mixture Experimental Plan Realization of Mixture Experimental Designs Model Building Validity of Established Models Analysis of Components Effect Recipe Optimization and Prediction Modeling and Optimization for Both Food Recipe and ProcessIntroduction Principle of Combined Designs Model Building Analysis of Variable Effects Food Quality Optimization and Prediction Practical Example Expert System for Food Product Development Introduction Structure of Expert Systems Theory of Fuzzy Logic Principle of Neural Network Building of an Expert System Bibliography Appendix Index

Editorial Reviews

A reference for food engineers, technologists, scientists, and other professionals in the industry, and suitable as a core text for a one- semester graduate or upper-level course on practical computer-aided statistical experimental methods or as a supplement for a two-semester course on basic statistical theory and statistical experimental methods. After introducing statistical methods in general, Hu, himself in the food industry, discusses problems of product design, modeling and optimizing food processes and recipes, and expert systems for developing food products. He points out that statistical experimental design is currently used as a quality control technique, but argues that it can also optimize food products and processes, accelerate food development cycles, reduce research costs, facilitate the transition from research and development to manufacturing, and troubleshoot manufacturing problems. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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