Variable selection in PCA in sensory descriptive and consumer data

Sammendrag

This paper presents a general method for identifying significant variables in multivariate models. The methodology is applied on principal component analysis (PCA) of sensory descriptive and consumer data. The method is based on uncertainty estimates from cross-validation/jack-knifing, where the importance of model validation is emphasised. Student's t-tests based on the loadings and their estimated standard uncertainties are used to calculate significance on each variable for each component. Two data sets are used to demonstrate how this aids the data-analyst in interpreting loading plots by indicating degree of significance for each variable in the plot. The usefulness of correlation loadings to visualise correlation structures between variables is also demonstrated. (C) 2003 Elsevier Science Ltd. All rights reserved.

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DOI : doi.org/10.1016/S0950-3293(03)...