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

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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

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Screening respondents to increase data quality in consumer tests

John Castura/ October 31, 2023/ Peer-reviewed Paper/ 0 comments

Product tests are often conducted to understand consumer opinions and perceptions. A screener is often used to determine which respondents are consumers. Our goal was to determine how filtering consumers who gave low-quality screener responses affects test results. Respondents in Finland (n = 343) and Turkey (n = 342) completed an online questionnaire. The questionnaire began with a screener. Respondents who consumed category products

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Why use component-based methods in sensory science?

John Castura/ October 31, 2023/ Peer-reviewed Paper/ 0 comments

This paper discusses the advantages of using so-called component-based methods in sensory science. For instance, principal component analysis (PCA) and partial least squares (PLS) regression are used widely in the field; we will here discuss these and other methods for handling one block of data, as well as several blocks of data. Component-based methods all share a common feature: they

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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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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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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

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An approach for clustering consumers by their top-box and top-choice responses

John Castura/ July 21, 2023/ Peer-reviewed Paper/ 0 comments

Cluster analysis is often used to group consumers based on their hedonic responses to products. We give a motivating example in which conventional cluster analyses converge on a solution where consumers do not agree on which products they like. We show why this occurs. We state a goal: to group together consumers who have a shared opinion of which products

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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

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Identifying temporal sensory drivers of liking of biscuit supplemented with brewer’s spent grain for young consumers

John Castura/ June 1, 2023/ Peer-reviewed Paper/ 0 comments

Brewer’s spent grain (BSG), a by-product of the brewing industry, has great potential as food additive. BSG is particularly rich in protein and fibre content which makes it an ideal nutritional fortifier for biscuits. However, adding BSG to biscuits can lead to changes in sensory perception and consumer acceptance. This study explored the temporal sensory profiles and drivers/inhibitors of liking

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