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APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS
Author(s) -
TAN J.,
GAO X.,
GERRARD D.E.
Publication year - 1999
Publication title -
journal of sensory studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 53
eISSN - 1745-459X
pISSN - 0887-8250
DOI - 10.1111/j.1745-459x.1999.tb00108.x
Subject(s) - sensory system , computer science , artificial intelligence , artificial neural network , fuzzy logic , sensory analysis , pattern recognition (psychology) , mathematics , psychology , cognitive psychology , statistics
This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy‐set‐based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi‐judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.