Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm

The agarwood oil quality has been divided into four grades, including low, medium-low, medium-high, and high, and has been thoroughly examined in this manuscript. Recently, there has been a high demand for agarwood oil but the current grading method is based on conventional techniques that rely on v...

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Published in:IEEE ACCESS
Main Authors: Huzir, Siti Mariatul Hazwa Mohd; Al-Hadi, Anis Hazirah 'Izzati Hasnu; Yusoff, Zakiah Mohd; Ismail, Nurlaila; Taib, Mohd Nasir
Format: Article
Language:English
Published: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001204926900001
author Huzir
Siti Mariatul Hazwa Mohd; Al-Hadi
Anis Hazirah 'Izzati Hasnu; Yusoff
Zakiah Mohd; Ismail
Nurlaila; Taib
Mohd Nasir
spellingShingle Huzir
Siti Mariatul Hazwa Mohd; Al-Hadi
Anis Hazirah 'Izzati Hasnu; Yusoff
Zakiah Mohd; Ismail
Nurlaila; Taib
Mohd Nasir
Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
Computer Science; Engineering; Telecommunications
author_facet Huzir
Siti Mariatul Hazwa Mohd; Al-Hadi
Anis Hazirah 'Izzati Hasnu; Yusoff
Zakiah Mohd; Ismail
Nurlaila; Taib
Mohd Nasir
author_sort Huzir
spelling Huzir, Siti Mariatul Hazwa Mohd; Al-Hadi, Anis Hazirah 'Izzati Hasnu; Yusoff, Zakiah Mohd; Ismail, Nurlaila; Taib, Mohd Nasir
Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
IEEE ACCESS
English
Article
The agarwood oil quality has been divided into four grades, including low, medium-low, medium-high, and high, and has been thoroughly examined in this manuscript. Recently, there has been a high demand for agarwood oil but the current grading method is based on conventional techniques that rely on visual inspection of various characteristics such as intensity, smell, texture, and weight. However, this method is not standardized, making it difficult to grade agarwood oil accurately. Therefore, the use of artificial neural networks (ANN) in artificial intelligence (AI) was employed to develop a system for identifying agarwood oil quality using the Levenberg-Marquardt (LM) algorithm. Data from 660 samples of chemical compounds extracted from agarwood oil were used to train the ANN. To enhance the accuracy of agarwood oil quality identification with LM performance, the data was split into 70% for validation, 15% for training, and 15% for testing. The results showed that the ANN with the eleven inputs (10-epi- gamma -eudesmol, alpha -agarofuran, gamma -eudesmol, beta -agarofuran, ar-curcumene, valerianol, beta -dihydro agarofuran, alpha -guaiene, allo aromadendrene epoxide and Upsilon -cadinene) trained by ten hidden neurons of LM algorithm provided the best performance with 100% for accuracy, specificity, sensitivity and precision as well as minimum convergence epoch. The experimental implementation of the model was done using the MATLAB version R2015a platform. This study will help to standardize agarwood oil quality determination using intelligent modeling techniques and serve as a guide for future research in the essential oil industry.
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2169-3536

2024
12

10.1109/ACCESS.2024.3381627
Computer Science; Engineering; Telecommunications

WOS:001204926900001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001204926900001
title Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
title_short Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
title_full Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
title_fullStr Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
title_full_unstemmed Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
title_sort Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm
container_title IEEE ACCESS
language English
format Article
description The agarwood oil quality has been divided into four grades, including low, medium-low, medium-high, and high, and has been thoroughly examined in this manuscript. Recently, there has been a high demand for agarwood oil but the current grading method is based on conventional techniques that rely on visual inspection of various characteristics such as intensity, smell, texture, and weight. However, this method is not standardized, making it difficult to grade agarwood oil accurately. Therefore, the use of artificial neural networks (ANN) in artificial intelligence (AI) was employed to develop a system for identifying agarwood oil quality using the Levenberg-Marquardt (LM) algorithm. Data from 660 samples of chemical compounds extracted from agarwood oil were used to train the ANN. To enhance the accuracy of agarwood oil quality identification with LM performance, the data was split into 70% for validation, 15% for training, and 15% for testing. The results showed that the ANN with the eleven inputs (10-epi- gamma -eudesmol, alpha -agarofuran, gamma -eudesmol, beta -agarofuran, ar-curcumene, valerianol, beta -dihydro agarofuran, alpha -guaiene, allo aromadendrene epoxide and Upsilon -cadinene) trained by ten hidden neurons of LM algorithm provided the best performance with 100% for accuracy, specificity, sensitivity and precision as well as minimum convergence epoch. The experimental implementation of the model was done using the MATLAB version R2015a platform. This study will help to standardize agarwood oil quality determination using intelligent modeling techniques and serve as a guide for future research in the essential oil industry.
publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
issn 2169-3536

publishDate 2024
container_volume 12
container_issue
doi_str_mv 10.1109/ACCESS.2024.3381627
topic Computer Science; Engineering; Telecommunications
topic_facet Computer Science; Engineering; Telecommunications
accesstype
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url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001204926900001
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