  The so-called rapid sensory methods have proved to be useful for the sensory
study of foods by different types of panels, from trained assessors to
unexperienced consumers. Data from these methods have been traditionally
analyzed using statistical techniques, with some recent works proposing the use
of geometric techniques and graph theory. The present work aims to deepen this
line of research introducing a new method, mixing tools from statistics and
graph theory, for the analysis of data from Projective Mapping. In addition, a
large number of n=349 unexperienced consumers is considered for the first time
in Projective Mapping, evaluating nine commercial chocolate chips cookies which
include a blind duplicate of a multinational best-selling brand and seven
private labels. The data obtained are processed using the standard statistical
technique Multiple Factor Analysis (MFA), the recently appeared geometric
method SensoGraph using Gabriel clustering, and the novel variant introduced
here which is based on the pairwise distances between samples. All methods
provide the same groups of samples, 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.
