Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob

Agarwood oil is a highly traded essential oil with a high price tag. The oil is extracted from the stems of resin-impregnated agarwood trees. Agarwood essential oil has been used in various applications, including incense, perfumery, medicinal purposes and religious ceremonies. Conventionally, agarw...

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Main Author:	Mahabob, Noratikah Zawani
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Aromatic plants Aromatherapy Including essences and essential oils
Online Access:	https://ir.uitm.edu.my/id/eprint/75214/1/75214.pdf
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id	my-uitm-ir.75214
record_format	uketd_dc
spelling	my-uitm-ir.752142023-05-29T06:29:09Z Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob 2022 Mahabob, Noratikah Zawani Aromatic plants. Aromatherapy. Including essences and essential oils Agarwood oil is a highly traded essential oil with a high price tag. The oil is extracted from the stems of resin-impregnated agarwood trees. Agarwood essential oil has been used in various applications, including incense, perfumery, medicinal purposes and religious ceremonies. Conventionally, agarwood essential oil is graded manually by a human expert sensory panel based on its physical properties such as odor, color intensity and infection level. The method for obtaining the quality of agarwood oil is time consuming due to the large number of test samples. Furthermore, humans are prone to fatigue as a result of continuously handling the samples. This research proposes an intelligent technique for grading agarwood essential oil based on its chemical properties using the artificial neural network (ANN) technique. Initially, the sample data of chemical compounds of selected agarwood oil were measured using gas chromatography-mass spectrometry (GC-MS) and then pre-processed in sequence by three pre-processing techniques, namely Z-score, PCA and stepwise regression; the number of chemical compounds was reduced from more than ninety to only seven, three and four, respectively. These significant chemical compounds from each preprocessing technique, were used as the input to ANN. To classify the agarwood essential oil into high and low qualities, this research applied a three backpropagation training algorithms—scaled conjugate gradient (SCG), Levenberg Marquardt (LM) and resilient backpropagation (RBP). The training and validation of the ANN was based on optimization of its training parameters and guided by the convergence of the mean squared errors (MSE). The result revealed that the ANN performance had the highest accuracy when training using the LM training algorithm. The ANN with the seven inputs (seven significant compounds from Z-score pre-processing technique) trained by one hidden neuron of LM algorithm provided the best performance with 100% for accuracy, specificity, sensitivity and precision as well as minimum convergence epoch. 2022 Thesis https://ir.uitm.edu.my/id/eprint/75214/ https://ir.uitm.edu.my/id/eprint/75214/1/75214.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) College of Engineering Mohd Yusoff, Zakiah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohd Yusoff, Zakiah
topic	Aromatic plants Aromatherapy Including essences and essential oils
spellingShingle	Aromatic plants Aromatherapy Including essences and essential oils Mahabob, Noratikah Zawani Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
description	Agarwood oil is a highly traded essential oil with a high price tag. The oil is extracted from the stems of resin-impregnated agarwood trees. Agarwood essential oil has been used in various applications, including incense, perfumery, medicinal purposes and religious ceremonies. Conventionally, agarwood essential oil is graded manually by a human expert sensory panel based on its physical properties such as odor, color intensity and infection level. The method for obtaining the quality of agarwood oil is time consuming due to the large number of test samples. Furthermore, humans are prone to fatigue as a result of continuously handling the samples. This research proposes an intelligent technique for grading agarwood essential oil based on its chemical properties using the artificial neural network (ANN) technique. Initially, the sample data of chemical compounds of selected agarwood oil were measured using gas chromatography-mass spectrometry (GC-MS) and then pre-processed in sequence by three pre-processing techniques, namely Z-score, PCA and stepwise regression; the number of chemical compounds was reduced from more than ninety to only seven, three and four, respectively. These significant chemical compounds from each preprocessing technique, were used as the input to ANN. To classify the agarwood essential oil into high and low qualities, this research applied a three backpropagation training algorithms—scaled conjugate gradient (SCG), Levenberg Marquardt (LM) and resilient backpropagation (RBP). The training and validation of the ANN was based on optimization of its training parameters and guided by the convergence of the mean squared errors (MSE). The result revealed that the ANN performance had the highest accuracy when training using the LM training algorithm. The ANN with the seven inputs (seven significant compounds from Z-score pre-processing technique) trained by one hidden neuron of LM algorithm provided the best performance with 100% for accuracy, specificity, sensitivity and precision as well as minimum convergence epoch.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mahabob, Noratikah Zawani
author_facet	Mahabob, Noratikah Zawani
author_sort	Mahabob, Noratikah Zawani
title	Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
title_short	Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
title_full	Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
title_fullStr	Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
title_full_unstemmed	Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob
title_sort	intelligent grading of agarwood essential oil quality using artificial neural network (ann) / noratikah zawani mahabob
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Engineering
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/75214/1/75214.pdf
_version_	1783736039661109248

Intelligent grading of agarwood essential oil quality using artificial neural network (ANN) / Noratikah Zawani Mahabob

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