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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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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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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Investigating the temporality of binary taste interactions in blends of sweeteners and citric acid in solution

John Castura/ October 3, 2022/ Peer-reviewed Paper/ 0 comments

This study investigated sweet–sour taste interactions in novel sweeteners using a 3 × 2 factorial design consisting of Sweetening System (three levels: sucrose; d-allulose; and a blend of d-allulose and Monk fruit extract) and Acidity (two levels: with or without citric acid). 110 untrained Chinese subjects participated using the temporal check-all-that-apply (TCATA) method. Mixed-model ANOVA was conducted to investigate the effect of

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Temporal ranking for characterization and improved discrimination of protein beverages

John Castura/ May 24, 2022/ Peer-reviewed Paper/ 0 comments

We propose a new temporal sensory method called temporal ranking (TR) in which assessors indicate and rank the three most noticeable sensations at every time point. The TR method was compared to temporal-check-all-that-apply (TCATA) in two trained-panel studies, one study involving six ready-to-mix (RTM) protein beverages and one study involving seven ready-to-drink (RTD) protein beverages. In each study, the same

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Clustering consumers based on product discrimination in check-all-that-apply (CATA) data

John Castura/ March 4, 2022/ Peer-reviewed Paper/ 0 comments

Consumers can be clustered based on their product-related check-all-that-apply (CATA) responses. We identify two paradoxes that can occur if these clusters are derived from conventional similarity coefficients. The first paradox is that clustering similar consumers can nullify within-cluster sensory differentiation of products. The second paradox is that consumers who check many attributes yet disagree can be clustered together, whereas consumers

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Discriminability and uncertainty in principal component analysis (PCA) of temporal check-all-that-apply (TCATA) data

John Castura/ February 1, 2022/ Peer-reviewed Paper/ 0 comments

Temporal check-all-that-apply (TCATA) data can be summarized and explored using principal component analysis (PCA). Here we analyze TCATA data on Syrah wines obtained from a trained sensory panel. We evaluate new and existing methods to explore the uncertainty in the PCA scores. To do so, we use the bootstrap procedure to obtain many virtual panels from the real panel’s data.

Does the τ estimate from same-different test data represent a relevant sensory effect size for determining sensory equivalency?

John Castura/ July 1, 2019/ Peer-reviewed Paper/ 0 comments

Analysis of data arising from the same-different test method can be submitted to Thurstonian-derived modelling with the goal of estimating the sensory distance between two products (the discriminal distance δ) and the response bias for responding “same” (τ). Previously it has been proposed that it is possible to use τ estimates from same-different test data to represent the consumer-relevant effect

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