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), 2026)
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.