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Statistics in food science and nutrition

Autore: Are Hugo Pripp
Editore: New York, NY : Springer, ©2013.
Serie: SpringerBriefs in food, health, and nutrition.
Edizione/Formato:   eBook : Document : EnglishVedi tutte le edizioni e i formati
Banca dati:WorldCat
Sommario:
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  Per saperne di più…
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Dettagli

Genere/forma: Electronic books
Tipo materiale: Document, Risorsa internet
Tipo documento: Internet Resource, Computer File
Tutti gli autori / Collaboratori: Are Hugo Pripp
ISBN: 9781461450108 1461450101 1461450098 9781461450092
Numero OCLC: 811249946
Descrizione: 1 online resource : ill.
Contenuti: 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.
Titolo della serie: SpringerBriefs in food, health, and nutrition.
Responsabilità: Are Hugo Pripp.
Maggiori informazioni:

Abstract:

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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