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Bio-inspired sensor data fusion for herbal tea flavour assessment

Master of Science in Mechatronic Engineering

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Bibliographic Details
Main Author: Nur Zawatil Isqi, Zakaria
Other Authors: Ali Yeon, Md. Shakaff, Prof. Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Multisensor data fusion
Natural computation
Herbal teas
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77315
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spelling my.unimap-773152022-11-30T04:56:22Z Bio-inspired sensor data fusion for herbal tea flavour assessment Nur Zawatil Isqi, Zakaria Ali Yeon, Md. Shakaff, Prof. Dr. Multisensor data fusion Natural computation Herbal teas Master of Science in Mechatronic Engineering 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. 2017 2022-11-30T04:50:49Z 2022-11-30T04:50:49Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77315 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Multisensor data fusion
Natural computation
Herbal teas
spellingShingle Multisensor data fusion
Natural computation
Herbal teas
Nur Zawatil Isqi, Zakaria
Bio-inspired sensor data fusion for herbal tea flavour assessment
description Master of Science in Mechatronic Engineering
author2 Ali Yeon, Md. Shakaff, Prof. Dr.
author_facet Ali Yeon, Md. Shakaff, Prof. Dr.
Nur Zawatil Isqi, Zakaria
format Thesis
author Nur Zawatil Isqi, Zakaria
author_sort Nur Zawatil Isqi, Zakaria
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
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77315
_version_ 1753972996064673792
score 13.222552

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