Bio-inspired sensor data fusion for herbal tea flavour assessment

Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the int...

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Format:	Thesis
Language:	English
Subjects:	Multisensor data fusion Natural computation Herbal teas
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/6/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/3/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/5/Declaration%20form.pdf
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id	my-unimap-77315
record_format	uketd_dc
spelling	my-unimap-773152022-11-30T04:56:22Z Bio-inspired sensor data fusion for herbal tea flavour assessment Ali Yeon, Md. Shakaff, Prof. Dr. Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One of famous herbal-based product is herbal tea. This thesis investigates bio-inspired flavour assessments in a data fusion framework involving an E-nose and E-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion (LLDF) and intermediate level data fusion (ILDF). Four classification approaches; Fisher Linear Data Analysis (FDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Probability Neural Network (PNN) were examined in search of the best classifier in order to achieve the research objectives. In order to evaluate the classifiers‘ performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC/MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC/MS TIC data varies in different application. Since KNN provide the highest classification performance, automatic grading system was developed based on this technique. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77315 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/4/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/6/Page%201-24.pdf 1e8a5f5d797f2ac21a000d77441f0e38 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/3/Full%20text.pdf c861206e41af5833d53b097e8a0c50f6 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/5/Declaration%20form.pdf 9505cfe0a897ce5a735d37af024460f7 Universiti Malaysia Perlis (UniMAP) Multisensor data fusion Natural computation Herbal teas School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Ali Yeon, Md. Shakaff, Prof. Dr.
topic	Multisensor data fusion Natural computation Herbal teas
spellingShingle	Multisensor data fusion Natural computation Herbal teas Bio-inspired sensor data fusion for herbal tea flavour assessment
description	Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One of famous herbal-based product is herbal tea. This thesis investigates bio-inspired flavour assessments in a data fusion framework involving an E-nose and E-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion (LLDF) and intermediate level data fusion (ILDF). Four classification approaches; Fisher Linear Data Analysis (FDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Probability Neural Network (PNN) were examined in search of the best classifier in order to achieve the research objectives. In order to evaluate the classifiers‘ performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC/MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC/MS TIC data varies in different application. Since KNN provide the highest classification performance, automatic grading system was developed based on this technique.
format	Thesis
title	Bio-inspired sensor data fusion for herbal tea flavour assessment
title_short	Bio-inspired sensor data fusion for herbal tea flavour assessment
title_full	Bio-inspired sensor data fusion for herbal tea flavour assessment
title_fullStr	Bio-inspired sensor data fusion for herbal tea flavour assessment
title_full_unstemmed	Bio-inspired sensor data fusion for herbal tea flavour assessment
title_sort	bio-inspired sensor data fusion for herbal tea flavour assessment
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/6/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/3/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77315/5/Declaration%20form.pdf
_version_	1776104250944782336

Bio-inspired sensor data fusion for herbal tea flavour assessment

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