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ESTIMATION OF HEDONIC RESPONSES FROM DESCRIPTIVE SKIN SENSORY DATA BY CHI‐SQUARE MINIMIZATION
Author(s) -
ALMEIDA I.F.,
GAIO A.R.,
BAHIA M.F.
Publication year - 2006
Publication title -
journal of sensory studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 53
eISSN - 1745-459X
pISSN - 0887-8250
DOI - 10.1111/j.1745-459x.2006.00049.x
Subject(s) - categorical variable , bootstrapping (finance) , mathematics , statistics , sensory system , partial least squares regression , econometrics , pattern recognition (psychology) , computer science , artificial intelligence , psychology , cognitive psychology
Six topical formulations were evaluated by a trained panel according to a descriptive analysis methodology and by a group of consumers who rated the products on a hedonic scale. We present a new approach that describes the categorical appreciation of appearance, texture and skinfeel of the formulations by the consumers as a function of related sensory attributes assessed by the trained panel. For each hedonic attribute, a latent random variable depending on the sensory attributes is constructed and made discrete (in a nonlinear fashion) according to the distribution of consumer‐hedonic scores in such a way as to minimize a corresponding chi‐square criterion. Standard partial least squares (PLS) regression, bootstrapping and cross‐validation techniques describing the overall liking of the hedonic attributes as a function of associated sensory attributes were also applied.Results from both methods were compared, and it was concluded that chi‐square minimization can work as a complementary method to the PLS regression.