CGAIGRSep 16, 2018

Testing SensoGraph, a geometric approach for fast sensory evaluation

arXiv:1809.06911v17 citations
Originality Incremental advance
AI Analysis

This work addresses sensory evaluation for domains like wine tasting or consumer picture comparison, offering a faster and more scalable method, though it is incremental as it builds on existing geometric and clustering techniques.

The paper tackles the problem of fast sensory evaluation by introducing SensoGraph, a geometric approach that clusters and visualizes sample similarities from multiple assessors. The method produces consensus graphics comparable to Multiple Factor Analysis (MFA) while providing extra connection information and handling significantly more assessors computationally.

This paper introduces SensoGraph, a novel approach for fast sensory evaluation using two-dimensional geometric techniques. In the tasting sessions, the assessors follow their own criteria to place samples on a tablecloth, according to the similarity between samples. In order to analyse the data collected, first a geometric clustering is performed to each tablecloth, extracting connections between the samples. Then, these connections are used to construct a global similarity matrix. Finally, a graph drawing algorithm is used to obtain a 2D consensus graphic, which reflects the global opinion of the panel by (1) positioning closer those samples that have been globally perceived as similar and (2) showing the strength of the connections between samples. The proposal is validated by performing four tasting sessions, with three types of panels tasting different wines, and by developing a new software to implement the proposed techniques. The results obtained show that the graphics provide similar positionings of the samples as the consensus maps obtained by multiple factor analysis (MFA), further providing extra information about connections between samples, not present in any previous method. The main conclusion is that the use of geometric techniques provides information complementary to MFA, and of a different type. Finally, the method proposed is computationally able to manage a significantly larger number of assessors than MFA, which can be useful for the comparison of pictures by a huge number of consumers, via the Internet.

Foundations

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