Pripp, Are Hugo.
Statistics in Food Science and Nutrition [electronic resource] / by Are Hugo Pripp. - VIII, 66 p. 15 illus., 3 illus. in color. online resource. - SpringerBriefs in Food, Health, and Nutrition . - SpringerBriefs in Food, Health, and Nutrition .
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. .
9781461450108
10.1007/978-1-4614-5010-8 doi
Chemistry.
Food--Biotechnology.
Nutrition.
Biostatistics.
Statistics.
Chemistry.
Food Science.
Biostatistics.
Nutrition.
Statistics for Life Sciences, Medicine, Health Sciences.
TP248.65.F66
641.3 664
Statistics in Food Science and Nutrition [electronic resource] / by Are Hugo Pripp. - VIII, 66 p. 15 illus., 3 illus. in color. online resource. - SpringerBriefs in Food, Health, and Nutrition . - SpringerBriefs in Food, Health, and Nutrition .
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. .
9781461450108
10.1007/978-1-4614-5010-8 doi
Chemistry.
Food--Biotechnology.
Nutrition.
Biostatistics.
Statistics.
Chemistry.
Food Science.
Biostatistics.
Nutrition.
Statistics for Life Sciences, Medicine, Health Sciences.
TP248.65.F66
641.3 664