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/ http://umpir.ump.edu.my/id/eprint/12088/ http://umpir.ump.edu.my/id/eprint/12088/1/MUHAMMAD%20SHARFI%20BIN%20NAJIB.PDF

id	ump-12088
recordtype	eprints
spelling	ump-120882017-11-03T02:14:37Z http://umpir.ump.edu.my/id/eprint/12088/ Agarwood classification based on odor profile using intelligent signal processing technique 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 NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12088/1/MUHAMMAD%20SHARFI%20BIN%20NAJIB.PDF M. S., Najib (2012) Agarwood classification based on odor profile using intelligent signal processing technique. PhD thesis, Universiti Teknologi Mara. http://iportal.ump.edu.my/lib/item?id=chamo:87615&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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
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
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/12088/ http://umpir.ump.edu.my/id/eprint/12088/ http://umpir.ump.edu.my/id/eprint/12088/1/MUHAMMAD%20SHARFI%20BIN%20NAJIB.PDF
first_indexed	2023-09-18T22:13:20Z
last_indexed	2023-09-18T22:13:20Z
_version_	1777415173923930112

Agarwood classification based on odor profile using intelligent signal processing technique

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