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

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

1008, 2023

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

John Castura/ August 10, 2023/ Poster/ 0 comments

A common task for trained sensory assessors is to evaluate samples by characterizing and quantifying the intensities of sensory attributes. In some cases, the 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

2007, 2023

Investigating only a subset of paired comparisons after principal component analysis

John Castura/ July 20, 2023/ Peer-reviewed Paper/ 0 comments

Principal component analysis (PCA) is often used to summarize and explore multivariate data sets, including sensory evaluation data sets. We propose how to conduct PCA of a results matrix in which only a subset of the paired comparisons is of interest. We illustrate the proposed approach with two data sets, both from trained sensory panels. In the first example, assessors

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

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

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