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Statistics in food science and nutrition

Autor: Are Hugo Pripp
Editorial: New York, NY : Springer, ©2013.
Serie: SpringerBriefs in food, health, and nutrition.
Edición/Formato:   Libro-e : Documento : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:
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  Leer más
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Detalles

Género/Forma: Electronic books
Tipo de material: Documento, Recurso en Internet
Tipo de documento: Recurso en Internet, Archivo de computadora
Todos autores / colaboradores: Are Hugo Pripp
ISBN: 9781461450108 1461450101 1461450098 9781461450092
Número OCLC: 811249946
Descripción: 1 online resource : illustrations.
Contenido: Statistics in Food Science and Nutrition --
Methods and Principles of Statistical Analysis --
Applying Statistics to Food Quality --
Nutritional Epidemiology and Health Effects of Foods --
Application of Multivariate Analysis: Benefits and Pitfalls.
Título de la serie: SpringerBriefs in food, health, and nutrition.
Responsabilidad: Are Hugo Pripp.
Más información:

Resumen:

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.

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