ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail

This thesis presents a new ANN modelling in discriminating agarwood oil quality using selected significant chemical compounds of the oil. In order to accomplish the work, the analyses have been carried out in two categories. The first category is the abundances pattern of odor chemical compounds obs...

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Main Author:	Ismail, Nurlaila
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Electricity Electricity and magnetism
Online Access:	http://ir.uitm.edu.my/id/eprint/27935/ http://ir.uitm.edu.my/id/eprint/27935/1/TP_NURLAILA%20ISMAIL%20EE%2014_5.pdf

id	uitm-27935
recordtype	eprints
spelling	uitm-279352020-01-30T05:37:46Z http://ir.uitm.edu.my/id/eprint/27935/ ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail Ismail, Nurlaila Electricity Electricity and magnetism This thesis presents a new ANN modelling in discriminating agarwood oil quality using selected significant chemical compounds of the oil. In order to accomplish the work, the analyses have been carried out in two categories. The first category is the abundances pattern of odor chemical compounds observation and investigation. The extraction of odor chemical compounds is done by solid phase micro-extraction (SPME). In this work two types of SPME fibers were used; divinylbenzene-carboxenpolydimethylsiloxane( DVB-CAR-PDMS) and polydimethylsiloxane(PDMS) to analyze the odor compounds under three different sampling temperature conditions; 40°C, 60°C and 80°C. A consistent abundances pattern of five significant odor chemical compounds as highlighted by Z-score were revealed. The compounds are 10-epi-γ-eudesmol, aromadendrane, β-agarofiiran, α-agarofuran and T-eudesmol. These odor chemical compounds are important as they contributed to the odor of high quality agarwood oils. Then the second category was performed by the extraction of the agarwood oil chemical compounds using gas chromatography-mass spectrometry (GC-MS). The identified compounds from SPME were used as marker compounds for agarwood oil quality discrimination using GC-MS data. In this category, Z-score highlightedseven significant chemical compounds; β-agarofuran, α-agarofuran, 10-epi-γ-eudesmol, γ-eudesmol, longifolol, hexadecanol and eudesmol. Their abundances has been used as input to k-nearest neighbor (k-NN) and artificial neural network (ANN) applications. In this study, all the agarwood oil samples were obtained from two institution; Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP). The experiments were carried out using k-NN and ANN modeling. The study showed that the k-NN classification accuracy is within 81-86% for k=1 to k=5 and 100% accuracy for the classification of ANN modeling. 2014 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/27935/1/TP_NURLAILA%20ISMAIL%20EE%2014_5.pdf Ismail, Nurlaila (2014) ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail. PhD thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Electricity Electricity and magnetism
spellingShingle	Electricity Electricity and magnetism Ismail, Nurlaila ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail
description	This thesis presents a new ANN modelling in discriminating agarwood oil quality using selected significant chemical compounds of the oil. In order to accomplish the work, the analyses have been carried out in two categories. The first category is the abundances pattern of odor chemical compounds observation and investigation. The extraction of odor chemical compounds is done by solid phase micro-extraction (SPME). In this work two types of SPME fibers were used; divinylbenzene-carboxenpolydimethylsiloxane( DVB-CAR-PDMS) and polydimethylsiloxane(PDMS) to analyze the odor compounds under three different sampling temperature conditions; 40°C, 60°C and 80°C. A consistent abundances pattern of five significant odor chemical compounds as highlighted by Z-score were revealed. The compounds are 10-epi-γ-eudesmol, aromadendrane, β-agarofiiran, α-agarofuran and T-eudesmol. These odor chemical compounds are important as they contributed to the odor of high quality agarwood oils. Then the second category was performed by the extraction of the agarwood oil chemical compounds using gas chromatography-mass spectrometry (GC-MS). The identified compounds from SPME were used as marker compounds for agarwood oil quality discrimination using GC-MS data. In this category, Z-score highlightedseven significant chemical compounds; β-agarofuran, α-agarofuran, 10-epi-γ-eudesmol, γ-eudesmol, longifolol, hexadecanol and eudesmol. Their abundances has been used as input to k-nearest neighbor (k-NN) and artificial neural network (ANN) applications. In this study, all the agarwood oil samples were obtained from two institution; Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP). The experiments were carried out using k-NN and ANN modeling. The study showed that the k-NN classification accuracy is within 81-86% for k=1 to k=5 and 100% accuracy for the classification of ANN modeling.
format	Thesis
author	Ismail, Nurlaila
author_facet	Ismail, Nurlaila
author_sort	Ismail, Nurlaila
title	ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail
title_short	ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail
title_full	ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail
title_fullStr	ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail
title_full_unstemmed	ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail
title_sort	ann modelling of agarwood oil significant chemical compounds for quality discrimination / nurlaila ismail
publishDate	2014
url	http://ir.uitm.edu.my/id/eprint/27935/ http://ir.uitm.edu.my/id/eprint/27935/1/TP_NURLAILA%20ISMAIL%20EE%2014_5.pdf
first_indexed	2023-09-18T23:19:17Z
last_indexed	2023-09-18T23:19:17Z
_version_	1777419322217463808

ANN modelling of agarwood oil significant chemical compounds for quality discrimination / Nurlaila Ismail

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