This work uses projective mapping with n=349 consumers to evaluate nine commercial chocolate chips cookies , including a blind duplicate of a multinational best-selling brand and seven private labels. The data obtained are processed using both a statistical technique , Multiple Factor Analysis , and two geometric techniques, the recently appeared  method SensoGraph using Gabriel clustering, and a novel variant introduced here which is based on the pairwise distances between samples. 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 .
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 .
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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
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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
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