A preliminary review of multiple group Principal Component Analysis for descriptive sensory data
Principal component analysis (PCA) is frequently used to analyse sensory descriptive analysis data to better understand the multivariate sensory space. Consider that even well-trained descriptive sensory panelists might retain some distinctive characteristics, including a tendency to use somewhat different scale levels and ranges than other panelists. Panelists might also show other innate differences in sensitivity to particular attributes or differences in response patterns due to attribute understanding. Often these differences are averaged out prior to conducting PCA. We explored multiple group principal component analysis (MGPCA; Thorpe, 1988) as an alternative multivariate approach.
MGPCA is a relatively simple technique related to canonical variate analysis (CVA; Hotelling, 1936; Thorpe, 1988). Where PCA might perform singular value decomposition on the variance-covariance matrix obtained (conventionally) from panel averages, MGPCA can be performed by singular value decomposition of a pooled variance-covariance matrix derived from the weighted average of the panelists’ variance-covariance matrices. MGPCA provides a within-class analysis that derives a consensus sensory space in which the individual panellist responses for products are also represented. Agreement amongst panelists is readily evaluated by inspection.
In this respect, MGPCA provides richer output than PCA. It derives a similar consensus space as generalized Procrustes analysis (GPA) without performing translation, rotation, isotropic scaling transformations. This preliminary investigation reveals some advantages to MGPCA for sensory data, and interpreted results from previous descriptive analysis studies were comparable to those obtained from other multivariate approaches, indicating that the MGPCA approach warrants further investigation.
Li, M., Browne, R. P., Findlay, C. J., McNicholas, P. D., & Castura, J.C. (2013). A preliminary review of multiple group Principal Component Analysis for descriptive sensory data. In 10th Pangborn Sensory Science Symposium. August 11-15, 2013. Rio de Janeiro, Brazil.