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A COMPARISON OF TWO MULTIVARIATE METHODS FOR THE ANALYSIS OF SENSORY PROFILE DATA
Author(s) -
HUNTER E.A.,
MUIR D.D.
Publication year - 1995
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.1995.tb00006.x
Subject(s) - principal component analysis , jackknife resampling , sensory system , statistics , mathematics , multivariate statistics , curse of dimensionality , pattern recognition (psychology) , dimensionality reduction , principal (computer security) , computer science , artificial intelligence , biology , neuroscience , estimator , operating system
Tables of means, over assessors, are often used to summarize the results of sensory profile experiments. These tables are sometimes further summarized by Principal Components Analysis (PCA) to give plots of the samples in the principal sensory dimensions. An alternative procedure is to use Generalized Procrutes Analysis (GPA) on the assessor data to allow for differences in usage of the vocabulary and in the proportion of the scale used. It is shown that these methods give different configurations in the principal sensory dimensions when applied to the data from a study of cheeses (Muir et al. 1995). Using a Jackknife method to calculate the variability of the samples in the principal sensory dimensions, the results from the GPA method are shown to have a higher dimensionality than from the PCA method. Jackknife estimates of variability are used to calculate confidence ellipses to attach to the sensory space maps.