1610, 2023

Principal component analysis of sensory panel results for a reference and multiple prototypes

John Castura/ October 16, 2023/ Conference Proceedings/ 0 comments

A panel of trained sensory assessors often evaluates samples by quantifying the intensities of sensory attributes. In some cases, samples are instances of in-market or prototype products. To explore results from the panel, it is conventional to obtain a products-by-attributes table of means, center and variance-standardize its columns, then conduct principal component analysis (PCA). The principal components that extract variance

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0106, 2023

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

John Castura/ June 1, 2023/ Peer-reviewed Paper/ 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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2401, 2023

Investigating paired comparisons after principal component analysis

John Castura/ January 24, 2023/ Peer-reviewed Paper/ 0 comments

Principal component analysis (PCA) is often used to explore sensory and consumer test data about products on multicollinear sensory attributes. In this paper, we propose an approach for investigating paired comparisons between products and their uncertainties in the principal components. We use the truncated total bootstrap (TTB) procedure to simulate virtual panels from the original data set. The virtual-panel results

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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

Read More