Siti Mariatul Hazwa, Mohd Huzir and Saiful Nizam, Tajuddin and Zakiah, Mohd Yusoff and Nurlaila F., Ismail and Abd Almisreb, Ali Abd and Mohd Nasir, Taib (2024) Agarwood oil quality identification using artificial neural network modelling for five grades. International Journal of Electrical and Computer Engineering, 14 (2). pp. 2254-2261. ISSN 2088-8708. (Published)
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Abstract
Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass γ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-γ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt-artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Aquilaria Malaccensis; Chemical compounds; Lavenberg-Marquardt; Machine learning; Neural network |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor Q Science > Q Science (General) T Technology > T Technology (General) |
| Faculty/Division: | Faculty of Industrial Sciences And Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 28 May 2024 08:10 |
| Last Modified: | 28 May 2024 08:10 |
| URI: | http://umpir.ump.edu.my/id/eprint/40959 |
| Download Statistic: | View Download Statistics |
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