Application of fuzzy sets and neural networks in sensory analysis

This paper describes an initial but fundamental attempt to lay some groundw ork 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 s ensory data interpretation. Sensory scales are described as fuzzy sets, sen sory attributes as fuzzy variables, and sensory responses as sample members hip grades. Multi-judge responses are formulated as a fuzzy membership vect or or fuzzy histogram of response, which gives an overall panel response fr ee of the unverifiable assumptions implied in conventional approaches. Neur al 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 majorit y opinion. Two applications in meat quality evaluation are used to demonstr ate 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.