1406, 2024

Investigating control-centred results after uncentred principal component analysis

John Castura/ June 14, 2024/ Oral Presentation/ 0 comments

Test-control paired comparisons can be investigated after principal component analysis (PCA). We show that a centred PCA of test-control paired comparisons is equivalent to an uncentred PCA of control-centred results. We demonstrate how to conduct these analyses. We illustrate key properties of centred and uncentred PCA. We justify why coordinates of control-centred results can be visualized in principal components obtained

1005, 2024

Investigating control-centred results after uncentred principal component analysis

John Castura/ May 10, 2024/ Preprint/ 0 comments

This study examines how to carry out test-control paired comparisons after performing principal component analysis (PCA). Different approaches are proposed here, involving either centred or uncentred PCA, and their respective key properties are highlighted. In particular, we show centred PCA of test-control paired comparisons is equivalent to uncentred PCA of control-centred paired differences. It is customary to use a column-centred

1809, 2023

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

1611, 2022

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