Tham, Heng Jin and Tang, S. Y and Loh, S. P (2016) A review on intelligent sensory modelling. In: International Conference on Chemical Engineering and Bioprocess Engineering, 25-26 October 2016, Jeddah, Saudi Arabia.
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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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keyword: | Sensory evaluation, Artificial neural network, Fuzzy Logic, intelligent modelling techniques |
| Subjects: | T Technology > TX Home economics > TX1-1110 Home economics > TX341-641 Nutrition. Foods and food supply |
| Department: | FACULTY > Faculty of Engineering |
| Depositing User: | MUNIRA BINTI MARASAN - |
| Date Deposited: | 18 Feb 2018 19:56 |
| Last Modified: | 18 Feb 2018 19:56 |
| URI: | https://eprints.ums.edu.my/id/eprint/18790 |
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