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
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
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
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
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
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
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
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
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.
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