Some new tools for visualising multi-way sensory data

In this paper we propose some new plots for three-way analysis of sensory data. One of the plots is called “Manhattan plot” and offers an alternative way of presenting explained variances, while the “Hiding plot” provides information about how the different axes in the consensus space are related to the original measurements. Correlation loadings plots exhibit correlations between the common scores acquired from the consensus matrix and the variables in the original multi-way data. For the computation of the consensus matrices we propose a new tool that bridges Generalised Canonical Analysis and Tucker-1 by defining intermediate combinations of the two. This is accomplished by altering a parameter in a more general model that covers these two methods. Three-way sensory profiling data from cheese are used as input for this general model to compute consensus matrices at different values for the parameter. External validation is pointed out as a useful tool for indicating which of the two statistical methods is most appropriate for computing a consensus matrix representing all assessors.