Statistics in food science and sutrition
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. Neve...
Gespeichert in:
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| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
New York, NY :
Springer New York
2013.
Cham : Springer Nature |
| Schriftenreihe: | SpringerBriefs in Food, Health, and Nutrition
|
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Statistics in Food Science and Nutrition, Texte imprimé, 9781461450092 |
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| 100 | 1 | |a Pripp, Are Hugo, |d 19..- | |
| 245 | 1 | 0 | |a Statistics in food science and sutrition |c by Are Hugo Pripp. |
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| 520 | |a 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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