Before revision:  The results show that all the consensus graphics provide the same groups of samples, depending mostly on the proportion of chocolate chips, with the blind duplicates being correctly identified as the closest pair of samples, more clearly when using geometric techniques. The groups given by SensoGraph with distances are a bit clearer than those provided by SensoGraph with Gabriel clustering, although this latter positioning is shown to be closer to the one given by MFA, using Procrustes distance and RV coefficient. Last, but not least, for such a large number of consumers the computation time turns out to be considerably smaller for the geometric techniques than for the statistical one, as theoretically predicted before .
After revision:  The results show that all the consensus graphics provide the same groups of samples, depending mostly on the proportion of chocolate chips, with the blind duplicates appearing close together. Finally, the stability of the results is studied using bootstrapping and the RV and Mantel coefficients. The results suggest that, even for unexperienced consumers, highly stable results can be achieved for MFA and SensoGraph when considering a large enough number of assessors, around 200 for the consensus map of MFA or the global similarity matrix of SensoGraph .