Abstract
This paper presents an intelligent technique based method for analyzing and interpreting sensory data provided by multiple panels for the evaluation of industrial products. In order to process the uncertainty existing in these sensory data, we first transform all sensory data into fuzzy sets on a unified scale using the 2-tuple fuzzy linguistic model. Based on these normalized data sets, we compute the dissimilarities or distances between different panels and between different evaluation terms used by them, defined according to the degree of consistency of data variation. The obtained distances, expressed with crisp numbers, are turned into fuzzy numbers for a better physical interpretation. Thus, these fuzzy distances permit to characterize in an easier way the evaluation behaviour of each panel and the quality of the evaluation terms used. Also, based on soft computing techniques and the dissimilarity between terms, we develop procedures for interpreting terms of one panel using those of another panel and a model for setting the relationships between the physical product features and the evaluation terms. Then, we introduce a new method to forecast the consumer preference from the sensory evaluation provided by an expert panel. This general approach has been applied to two kinds of industrial products concerning both cosmetic and textile industries.
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Koehl, L., Zeng, X., Zhou, B., Ding, Y. Sensory Quality Management and Assessment: from Manufacturers to Consumers. In: Ruan, D., Chen, G., E. Kerre, E., Wets, G. (eds) Intelligent Data Mining. Studies in Computational Intelligence, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11004011_19
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DOI: https://doi.org/10.1007/11004011_19
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26256-5
Online ISBN: 978-3-540-32407-2
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