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Titolo:
Alternatives to data averaging of consumer preference data
Autore:
Tang, C; Heymann, H; Hsieh, FH;
Indirizzi:
Univ Missouri, Dept Food Sci & Human Nutr, Columbia, MO 65211 USA Univ Missouri Columbia MO USA 65211 & Human Nutr, Columbia, MO 65211 USA Unilever Res US, Consumer Sci, Edgewater, NJ 07020 USA Unilever Res US Edgewater NJ USA 07020 sumer Sci, Edgewater, NJ 07020 USA
Titolo Testata:
FOOD QUALITY AND PREFERENCE
fascicolo: 1-2, volume: 11, anno: 2000,
pagine: 99 - 104
SICI:
0950-3293(200001/03)11:1-2<99:ATDAOC>2.0.ZU;2-V
Fonte:
ISI
Lingua:
ENG
Soggetto:
FOOD;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
13
Recensione:
Indirizzi per estratti:
Indirizzo: Heymann, H Univ Missouri, Dept Food Sci & Human Nutr, Columbia, MO 65211 USA Univ Missouri Columbia MO USA 65211 tr, Columbia, MO 65211 USA
Citazione:
C. Tang et al., "Alternatives to data averaging of consumer preference data", FOOD QUAL P, 11(1-2), 2000, pp. 99-104

Abstract

The relationship between a consumer preference data set and a corresponding sensory profile on eight cooked wheat noodles with different formulas wasexamined using several multivariate techniques. Individual consumer hedonic responses (100 noodle/pasta consumers) and eight appearance and texture sensory attributes were collected. The consumer preference data were treatedin two different ways: mean values averaged across all consumers or principal components extracted from individual responses. The mean preference scores were submitted to both principal component stepwise regression and partial least squares regression (PLSI), whereas the summarized major preference components were subjected to canonical correlation analysis, as well as partial least squares regression (PLS2). The results suggested that in case of complex consumer data, using mean value can only capture the most manifest trends in consumer preference patterns, while studying individual responses and by further categorizing major preference patterns provide an opportunity to discover the hidden information that are masked by data averaging. (C) 1999 Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 06/04/20 alle ore 01:34:58