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Statistics in food science and nutrition

Auteur : Are Hugo Pripp
Éditeur : New York, NY : Springer, ©2013.
Collection : SpringerBriefs in food, health, and nutrition.
Édition/format :   Livre électronique : Document : AnglaisVoir toutes les éditions et tous les formats
Base de données :WorldCat
Résumé :
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  Lire la suite...
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Détails

Genre/forme : Electronic books
Type d’ouvrage : Document, Ressource Internet
Format : Ressource Internet, Fichier informatique
Tous les auteurs / collaborateurs : Are Hugo Pripp
ISBN : 9781461450108 1461450101
Numéro OCLC : 811249946
Description : 1 online resource : ill.
Contenu : 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.
Titre de collection : SpringerBriefs in food, health, and nutrition.
Responsabilité : Are Hugo Pripp.
Plus d’informations :

Résumé :

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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