Development if intelligent classifier and estimator for tualang honey purity

Honey is a natural substance well-known as supplement for maintaining good health. It is also useful as an ingredient in medicine. However, the market price of pure honey is expensive, causing irresponsible parties to adulterate pure honey by adding various sugar substances. It is very challenging t...

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Main Author:	Norazian, Subari
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/12076/1/NORAZIAN%20BINTI%20SUBARI.PDF
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id	my-ump-ir.12076
record_format	uketd_dc
spelling	my-ump-ir.120762021-08-24T02:16:07Z Development if intelligent classifier and estimator for tualang honey purity 2014-08 Norazian, Subari QA76 Computer software Honey is a natural substance well-known as supplement for maintaining good health. It is also useful as an ingredient in medicine. However, the market price of pure honey is expensive, causing irresponsible parties to adulterate pure honey by adding various sugar substances. It is very challenging to come out with a suitable method to prove the presence of adulterants in honey products. Most previous studies involved close data observation from experts that is time-consuming. This research proposes the development of intelligent classifier to aid the task of differentiating pure honey from adulterated ones. Besides intelligent classifier, this research has also developed an intelligent estimator for the purpose of giving a percentage estimation of pure honey that exists in adulterated honey sample. The pure honey classifier and estimator are developed using Artificial Neural Network (ANN) approach. Ten types of pure honey from different brands and sugar compounds have been used to prepare various pure and adulterated honey (at different percentages of pure honey) samples. Electronic nose (E-Nose) and Fourier Transform Infrared Spectroscopy (FTIR) raw data have been gathered from various honey samples. The B-Nose, FTIR low level Fusion data of B-Nose and FTIR of raw and normalized data have been used to train a number of ANNs to produce intelligent classifiers (pure or adulterated honey) and estimators (fraction of pure honey). The research results showed that intelligent pure honey classifiers developed using E-Nose, FTIR and Fusion data gives classification accuracies of 100% with 0.390 seconds training time, 99.72% with 0.359 seconds training time and 100% with 0.094 seconds training time, respectively. The result comparison show that the intelligent pure honey classifier gives the best performance is the one trained with Fusion data. The developed intelligent pure honey fraction estimator systems gave average absolute errors of 16.55%, 6.11% and 4.88% for E-Nose, FTIR and Fusion data, respectively. The research result has revealed that intelligent pure honey classifier and estimator developed based on raw E-Nose and FTIR low level Fusion data is better than those using single E-Nose or FTIR data. However, correct pure honey fraction estimation of 30.9% suggests that the performance of intelligent pure honey fraction estimator still needs to be improved. Overall, this research has shown that ANN has the potential to aid the tasks of differentiating pure honey from adulterated ones and estimate the fraction of pure honey in adulterated honey samples. 2014-08 Thesis http://umpir.ump.edu.my/id/eprint/12076/ http://umpir.ump.edu.my/id/eprint/12076/1/NORAZIAN%20BINTI%20SUBARI.PDF application/pdf en public masters Universiti Sains Malaysia Universiti Sains Malaysia
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Norazian, Subari Development if intelligent classifier and estimator for tualang honey purity
description	Honey is a natural substance well-known as supplement for maintaining good health. It is also useful as an ingredient in medicine. However, the market price of pure honey is expensive, causing irresponsible parties to adulterate pure honey by adding various sugar substances. It is very challenging to come out with a suitable method to prove the presence of adulterants in honey products. Most previous studies involved close data observation from experts that is time-consuming. This research proposes the development of intelligent classifier to aid the task of differentiating pure honey from adulterated ones. Besides intelligent classifier, this research has also developed an intelligent estimator for the purpose of giving a percentage estimation of pure honey that exists in adulterated honey sample. The pure honey classifier and estimator are developed using Artificial Neural Network (ANN) approach. Ten types of pure honey from different brands and sugar compounds have been used to prepare various pure and adulterated honey (at different percentages of pure honey) samples. Electronic nose (E-Nose) and Fourier Transform Infrared Spectroscopy (FTIR) raw data have been gathered from various honey samples. The B-Nose, FTIR low level Fusion data of B-Nose and FTIR of raw and normalized data have been used to train a number of ANNs to produce intelligent classifiers (pure or adulterated honey) and estimators (fraction of pure honey). The research results showed that intelligent pure honey classifiers developed using E-Nose, FTIR and Fusion data gives classification accuracies of 100% with 0.390 seconds training time, 99.72% with 0.359 seconds training time and 100% with 0.094 seconds training time, respectively. The result comparison show that the intelligent pure honey classifier gives the best performance is the one trained with Fusion data. The developed intelligent pure honey fraction estimator systems gave average absolute errors of 16.55%, 6.11% and 4.88% for E-Nose, FTIR and Fusion data, respectively. The research result has revealed that intelligent pure honey classifier and estimator developed based on raw E-Nose and FTIR low level Fusion data is better than those using single E-Nose or FTIR data. However, correct pure honey fraction estimation of 30.9% suggests that the performance of intelligent pure honey fraction estimator still needs to be improved. Overall, this research has shown that ANN has the potential to aid the tasks of differentiating pure honey from adulterated ones and estimate the fraction of pure honey in adulterated honey samples.
format	Thesis
qualification_level	Master's degree
author	Norazian, Subari
author_facet	Norazian, Subari
author_sort	Norazian, Subari
title	Development if intelligent classifier and estimator for tualang honey purity
title_short	Development if intelligent classifier and estimator for tualang honey purity
title_full	Development if intelligent classifier and estimator for tualang honey purity
title_fullStr	Development if intelligent classifier and estimator for tualang honey purity
title_full_unstemmed	Development if intelligent classifier and estimator for tualang honey purity
title_sort	development if intelligent classifier and estimator for tualang honey purity
granting_institution	Universiti Sains Malaysia
granting_department	Universiti Sains Malaysia
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/12076/1/NORAZIAN%20BINTI%20SUBARI.PDF
_version_	1783731961567641600

Development if intelligent classifier and estimator for tualang honey purity

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