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

John Castura/ August 26, 2021/ 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.

Using contrails and animated sequences to visualize uncertainty in dynamic sensory profiles obtained from temporal check-all-that-apply (TCATA) data

John Castura/ December 23, 2016/ Peer-reviewed Paper/ 0 comments

Approaches for analyzing temporal check-all-that-apply (TCATA) data are further developed and illustrated using data arising from a Syrah wine finish evaluation. Raw and smoothed trajectories are obtained using principal component analysis. Virtual panels are obtained from a partial bootstrap, and the attribute citation proportions are then projected into the solution space to form contrails.

Multivariate and probabilistic analyses of sensory science problems

Sara King/ August 20, 2007/ Book/ 0 comments

Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration

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