Investigating paired differences for data sets with special structures after principal component analysis

John Castura/ September 18, 2023/ Oral Presentation/ 0 comments

Principal component analysis (PCA) is a popular technique for summarizing and exploring multivariate data sets. We propose how to conduct PCA of results from sensory studies that have a special structure, where only a subset of the product paired comparisons are of interest. We illustrate the proposed approach with two data sets, both from trained sensory panels. In the first

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Evaluation of complementary numerical and visual approaches for investigating pairwise comparisons after principal component analysis

John Castura/ November 16, 2022/ Oral Presentation/ 0 comments

We propose and evaluate numerical and visual methods for investigating paired comparisons after principal component analysis (PCA). PCA results can be visualized to facilitate an understanding of the relationships between the products and the sensory attributes. But identifying and visualizing significant product differences in multiple PCs simultaneously is not straightforward. A benefit of the proposed methods is that they provide

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