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FUZZY SET THEORY APPLIED TO PRODUCT CLASSIFICATION BY A SENSORY PANEL 1
Author(s) -
WESTENBERG H. W. LINCKLAEN.,
De JONG S.,
MEEL D. A.,
QUADT J.F.A.,
BACKER E.,
DUIN R.P.W.
Publication year - 1989
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.1989.tb00457.x
Subject(s) - class (philosophy) , fuzzy set , fuzzy logic , set (abstract data type) , mathematics , artificial intelligence , sensory system , fuzzy classification , computer science , product (mathematics) , pattern recognition (psychology) , inference , data mining , machine learning , psychology , cognitive psychology , programming language , geometry
. It is frequently impossible to meet the assumptions underlying the statistical approach to classification of food products by a sensory panel. To find an alternative, we have investigated the applicability of the fuzzy set theory. Within a fuzzy set framework it is acceptable that a product belongs to several classes simultaneously and no assumptions regarding the distribution of sensory properties for a product class are made. Fuzzy classification models can be constructed from a set of training objects by linking the soft class labels to the sensory attributes applying an inference procedure based on fuzzy logic. A number of fuzzy inference procedures has been evaluated using a number of attribute sets. A satisfactory classification has been found using a very simple implication rule and a set of three attributes.