A review on intelligent sensory modelling
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) me...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2016
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/18790/1/A%20review%20on%20intelligent%20sensory%20modelling.pdf |
| _version_ | 1825713285371002880 |
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| author | Tham, Heng Jin Tang, S. Y Loh, S. P |
| author_facet | Tham, Heng Jin Tang, S. Y Loh, S. P |
| author_sort | Tham, Heng Jin |
| collection | UMS |
| description | 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. |
| first_indexed | 2024-03-06T02:54:11Z |
| format | Conference or Workshop Item |
| id | ums.eprints-18790 |
| institution | Universiti Malaysia Sabah |
| language | English |
| last_indexed | 2024-03-06T02:54:11Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ums.eprints-187902018-02-18T11:56:11Z https://eprints.ums.edu.my/id/eprint/18790/ A review on intelligent sensory modelling Tham, Heng Jin Tang, S. Y Loh, S. P TX341-641 Nutrition. Foods and food supply 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. 2016 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/18790/1/A%20review%20on%20intelligent%20sensory%20modelling.pdf 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. http://iopscience.iop.org/article/10.1088/1755-1315/36/1/012065/meta |
| spellingShingle | TX341-641 Nutrition. Foods and food supply Tham, Heng Jin Tang, S. Y Loh, S. P A review on intelligent sensory modelling |
| title | A review on intelligent sensory modelling |
| title_full | A review on intelligent sensory modelling |
| title_fullStr | A review on intelligent sensory modelling |
| title_full_unstemmed | A review on intelligent sensory modelling |
| title_short | A review on intelligent sensory modelling |
| title_sort | review on intelligent sensory modelling |
| topic | TX341-641 Nutrition. Foods and food supply |
| url | https://eprints.ums.edu.my/id/eprint/18790/1/A%20review%20on%20intelligent%20sensory%20modelling.pdf |
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