Agarwood classification based on odor profile using intelligent signal processing technique

This thesis presents the classification of Agarwood from Malaysia and Indonesia regions based on signal processing technique. Signal processing for the Agarwood classification is a new area and has yet been actively implemented. In this thesis, the Agarwood has been pre-identified by experts using...

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Main Author:	M. S., Najib
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QP Physiology
Online Access:	http://umpir.ump.edu.my/id/eprint/12088/1/MUHAMMAD%20SHARFI%20BIN%20NAJIB.PDF
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id	my-ump-ir.12088
record_format	uketd_dc
spelling	my-ump-ir.120882021-08-24T02:08:13Z Agarwood classification based on odor profile using intelligent signal processing technique 2012-06 M. S., Najib QP Physiology This thesis presents the classification of Agarwood from Malaysia and Indonesia regions based on signal processing technique. Signal processing for the Agarwood classification is a new area and has yet been actively implemented. In this thesis, the Agarwood has been pre-identified by experts using 32 sensor arrays to measure the Agarwood odor profile. General Agarwood pattern has been plot in 2D diagram. The odor profile from different samples have been normalized and pre-processed and visualized in 3D and 2D plot to find unique patterns. The variation of patterns that has been visualized has been marked as different group samples. From 32 data sensor arrays, several significant data sensor array have been pre-processed using principal component analysis (PCA) as data reduction process. The selected data from PCA are applied as input to compute sensor centroid for k-NN and ANN model design. To test the robustness of the classification techniques, the data sets are randomized for both k-NN classifier and ANN model. The classification results of the k-NN classifier and the ANN model utilizing significant sensor centroid new features for Agarwood grades and regions. It was found that the k-NN classifier and the ANN model is able to classify 100% of Agarwood grade and region. 2012-06 Thesis http://umpir.ump.edu.my/id/eprint/12088/ http://umpir.ump.edu.my/id/eprint/12088/1/MUHAMMAD%20SHARFI%20BIN%20NAJIB.PDF application/pdf en public phd doctoral Universiti Teknologi Mara Faculty of Electrical Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	QP Physiology
spellingShingle	QP Physiology M. S., Najib Agarwood classification based on odor profile using intelligent signal processing technique
description	This thesis presents the classification of Agarwood from Malaysia and Indonesia regions based on signal processing technique. Signal processing for the Agarwood classification is a new area and has yet been actively implemented. In this thesis, the Agarwood has been pre-identified by experts using 32 sensor arrays to measure the Agarwood odor profile. General Agarwood pattern has been plot in 2D diagram. The odor profile from different samples have been normalized and pre-processed and visualized in 3D and 2D plot to find unique patterns. The variation of patterns that has been visualized has been marked as different group samples. From 32 data sensor arrays, several significant data sensor array have been pre-processed using principal component analysis (PCA) as data reduction process. The selected data from PCA are applied as input to compute sensor centroid for k-NN and ANN model design. To test the robustness of the classification techniques, the data sets are randomized for both k-NN classifier and ANN model. The classification results of the k-NN classifier and the ANN model utilizing significant sensor centroid new features for Agarwood grades and regions. It was found that the k-NN classifier and the ANN model is able to classify 100% of Agarwood grade and region.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	M. S., Najib
author_facet	M. S., Najib
author_sort	M. S., Najib
title	Agarwood classification based on odor profile using intelligent signal processing technique
title_short	Agarwood classification based on odor profile using intelligent signal processing technique
title_full	Agarwood classification based on odor profile using intelligent signal processing technique
title_fullStr	Agarwood classification based on odor profile using intelligent signal processing technique
title_full_unstemmed	Agarwood classification based on odor profile using intelligent signal processing technique
title_sort	agarwood classification based on odor profile using intelligent signal processing technique
granting_institution	Universiti Teknologi Mara
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/12088/1/MUHAMMAD%20SHARFI%20BIN%20NAJIB.PDF
_version_	1783731964056961024

Agarwood classification based on odor profile using intelligent signal processing technique

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