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
Discriminability and uncertainty in principal component analysis (PCA) of temporal check-all-that-apply (TCATA) data
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.
Two updated R packages are available: Castura, J.C. (2022). cata: Analysis of Check-All-that-Apply (CATA) Data. R package version 0.0.10.7. http://www.cran.r-project.org/package=cata/ Castura, J.C. (2022). tempR: Temporal Sensory Data Analysis. R package version 0.9.9.20. http://www.cran.r-project.org/package=tempR/
Castura, J.C. (2021). Consumer hedonic studies with incomplete block designs. In: Workshop: A journey of consumer segmentation. History and a discussion of current “preference” segmentation approaches based on incomplete and complete test designs (Zach, J., Rothman, L., Carr, B.T., Thomas, H., Castura, J.C.). 14th Pangborn Sensory Science Symposium, 9-12 August. Online. (Workshop Oral). DOWNLOAD
This study introduces temporal rate-all-that-apply (TRATA) as a new temporal sensory method. It was inspired by rate-all-that-apply (RATA) and temporal check-all-that-apply (TCATA), but is most similar to multiple-attribute time intensity (MATI) in that the TRATA method allows for simultaneous rating of attribute intensities over time. Only attributes that are perceived are scaled. In this case study, the TRATA method was
Temporal ranking for the characterization and better discrimination of protein beverages with different sweeteners
This study proposes the application of ranking to temporal product evaluation to differentiate attribute dominances over time. The ability of the proposed method to discriminate sensory differences was compared to temporal check-all-that-apply (TCATA), a discriminating temporal method for capturing the temporal profile of food and beverage products.
Investigating perception dynamics and uncertainty in temporal sensory data via independent components analysis (ICA)
Wine finish from several Syrah wines are evaluated using temporal check-all-that-apply (TCATA), and results analysed with Independent Components Analysis (ICA) using the Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm.
Analyzing data using the chance-corrected beta-binomial model: Parameter estimates and their confidence regions
Data arising from replicated sensory discrimination test methods with a correct response are often modelled using the chance-corrected beta-binomial distribution. The model can provide maximum likelihood estimates of the mean proportion of correct responses in the population (and discriminal distances under Thurstonian assumptions) and of the assessor heterogeneity (overdispersion). Both of these parameters are estimated with uncertainty. Previously uncertainty associated
Analysis of sensory check-all-that-apply (CATA) data which includes the evaluation of a single ideal product
When evaluating samples in sensory tests, consumers are sometimes asked not only about real samples but also about imagined ideal products. Check-all-that-apply (CATA) questions are one way to understand consumers’ perceptions of products and their ideal product. We propose the following statistical analyses of consumer CATA data: (i) confidence intervals for head-to-head comparisons based on CATA data; (ii) panel (dis)agreement