2509, 2024

Investigating test-control paired differences in flavour-molecular correlations

John Castura/ September 25, 2024/ Oral Presentation/ 0 comments

We investigate supervised principal component regression (SPCR) its sensory and instrumental results from a data set of eight pinot noir wines. Data include measured concentrations of volatile organic compounds from headspace—solid phase micro-extraction—gas chromatography—mass spectrometry (HS-SPME-GC-MS) and sensory descriptive analysis results on two selected sensory attributes evaluated orthonasally. We show the solution from SPCR as conducted conventionally is equivalent to

0509, 2024

Investigating sensory-instrumental relationships in a subset of test-control paired comparisons by partial least squares regression

John Castura/ September 5, 2024/ Oral Presentation/ 0 comments

Relationships between sensory and instrumental variables are often investigated using partial least squares regression (PLSR). We describe the conventional PLSR solution. Next, we describe two new approaches for conducting PLSR based on paired comparisons of objects. First, we describe the PLSR of all paired comparisons. Conducting PLSR of all paired comparisons is equivalent to the conventional solution in the sense

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

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

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