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.
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
Light-struck taints in wine develop when they are exposed to sunlight and elevated temperature. Affected wines undergo a colour change, a decrease in fruit flavours and a development of off-flavours that have been described as cooked cabbage and/or corn nuts. This poses a problem in retail stores that have a large number of external facing windows that can potentially expose
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.