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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Yusoff, Zakiah
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Identification of chemical markers for species differentiation in Aquilaria essential oils using self-organizing maps Noramli, Nur Athirah Syafiqah; Roslan, Muhammad Ikhsan; Ahmad Sabri, Noor Aida Syakira; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Taib, Mohd Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1339-1348

Abstract

This study analyzes the chemical diversity of essential oils from four Aquilaria species, A. beccariana, A. malaccensis, A. crassna, and A. subintegra, which are important sources of agarwood used in perfumery and traditional medicine. Despite their economic and ecological value, the chemical profiles of these species remain insufficiently characterized, hindering accurate species differentiation and resource management. This research aims to identify distinctive chemical patterns to improve species classification. Self-organizing maps (SOMs) were employed to analyze complex chemical composition data and to identify significant compounds responsible for species separation. The analysis revealed several compounds with strong discriminatory power and species-specific distribution patterns, with compounds C, D, and E identified as the most significant markers. These findings demonstrate substantial biochemical diversity among Aquilaria species and confirm the effectiveness of SOM for essential oil profiling. The results support improved species identification and have important implications for ecological conservation, sustainable agarwood management, and pharmacological development.
Applications of artificial intelligence in analyzing Aquilaria essential oils: a review of current machine learning techniques Ahmad Sabri, Noor Aida Syakira; Noramli, Nur Athirah Syafiqah; Roslan, Muhammad Ikhsan; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Almisreb, Ali Abd; Taib, Mohd Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1087-1096

Abstract

This study explores the application of machine learning (ML) techniques in the classification of agarwood oil, focusing on the use of various algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), random forest (RF), and artificial neural networks (ANN). Since 2013, ML has played a pivotal role in analyzing agarwood oil, particularly by leveraging data from a variety of chemical compounds found in the Aquilaria genus. Through a systematic review and bibliometric analysis using the SCOPUS database, this study compiles and highlights recent works that have successfully employed ML techniques for the quality assessment of agarwood oil. These studies utilize chemical data, such as gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR), for the classification and detection of different oil grades. The review reveals a broad range of ML applications, demonstrating their growing importance in the field of essential oil analysis. By systematically presenting the findings from recent research, this work emphasizes the potential for further exploration of ML in the standardization and improvement of agarwood oil classification techniques.
Co-Authors Ahmad Sabri, Noor Aida Syakira Almisreb, Ali Abd Aminah Nordin, Siti Che Hassan, Mohammad Arif Fahmi Fawwaz Mohd Amidon, Aqib Haron, Mohamad Hushni Haron, Mohamad Hushnie Hasnu Al-Hadi, Anis Hazirah 'Izzati Ismail, Nurlaila Khairul Anuar, Noor Hafizah Khamis @ Subari, Mohamad Fadzli Mariatul Hazwa Mohd Huzir, Siti Mohd Amidon, Aqib Fawwaz Mohd Huzir, Siti Mariatul Hazwa Mohd Nasir Taib Muhamad, Nur Amalina Munira Markom, Arni Nadia Mohammad, Nurul Nasir Taib, Mohd Noramli, Nur Athirah Syafiqah Othman, Norhalida Roslan, Muhammad Ikhsan Tajuddin, Saiful Nizam Zaidi, Amir Hussairi