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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Muhammad Ikhsan Roslan
Universiti Teknologi MARA
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 Nur Athirah Syafiqah Noramli; Muhammad Ikhsan Roslan; Noor Aida Syakira Ahmad Sabri; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohd Nasir Taib
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 Noor Aida Syakira Ahmad Sabri; Nur Athirah Syafiqah Noramli; Muhammad Ikhsan Roslan; Nurlaila Ismail; Zakiah Mohd Yusoff; Ali Abd Almisreb; Mohd Nasir Taib
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.
Data-driven modelling of Aquilaria essential oils via dual GC profiling and multicollinearity diagnostics Nur Athirah Syafiqah Noramli; Noor Aida Syakira Ahmad Sabri; Muhammad Ikhsan Roslan; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohd Nasir Taib
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10870

Abstract

Aquilaria-derived essential oils are chemically diverse and hold significant value in pharmaceuticals, fragrances, and traditional medicine. However, the complexity of their chemical composition presents challenges in statistical modelling, particularly due to multicollinearity among biosynthetically related compounds. This study investigates the extent of multicollinearity in Aquilaria essential oil data using multiple linear regression (MLR) and variance inflation factor (VIF) analysis. A regression model was constructed using three compounds, ? -guaiene, 10-epi ? -eudesmol, and ? -eudesmol, across 360 samples, with VIF and collinearity diagnostics applied to assess model validity. The model explained 93% of the variance in species classification, which is substantially higher than values typically reported in earlier chemometric studies of Aquilaria oils. This demonstrates that even a limited number of carefully selected compounds, when supported by diagnostic safeguards, can achieve strong classification accuracy. These findings emphasize the importance of applying multicollinearity diagnostics to improve the interpretability and reliability of chemometric analyses. The study contributes a robust analytical framework for future research and practical applications in species authentication, essential oil quality control, and conservation of Aquilaria resources.
Co-Authors Ali Abd Almisreb Mohd Nasir Taib Noor Aida Syakira Ahmad Sabri Nur Athirah Syafiqah Noramli Nurlaila Ismail Zakiah Mohd Yusoff