Statistics in Food Science and Nutrition

Are Hugo Pripp

ISBN 10: 1461450098 / ISBN 13: 9781461450092

Published by Springer New York Sep 2012, 2012

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 76 pp. Englisch.

Seller Inventory # 9781461450092

Bibliographic Details

• Title: Statistics in Food Science and Nutrition
• Publisher: Springer New York Sep 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1461450098
• ISBN 13: 9781461450092
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 5D millimeters 235 millimeters width by 155 millimeters height by 5 millimeters depth
• Weight: 131 grams

About this title

Synopsis

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.