Abstract
Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.
Highlights
Sensory evaluation plays an important role in the quality control of food productions
Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain
This paper outlines literature of intelligent sensory modelling on sensory data analysis
Summary
Sensory evaluation plays an important role in the quality control of food productions.
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