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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
Ismail, Nurlaila
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Chemometric classification and authentication of four Aquilaria species from essential oil profiles using GC-MS/GC-FID and ANN Noramli, Nur Athirah Syafiqah; Ahmad Sabri, Noor Aida Syakira; Roslan, Muhammad Ikhsan; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Taib, Mohd Nasir
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2141

Abstract

Agarwood, derived from the Aquilaria species, is among the most valuable aromatic resources, yet frequent species misidentification hampers conservation efforts and compliance with trade regulations. This study applied a chemometric ANN framework to classify four Aquilaria species (A. malaccensis, A. beccariana, A. subintegra, and A. crassna) based on essential oil composition. A total of 720 samples (180 per species, each analyzed in triplicate) were extracted by hydrodistillation and profiled using GC–MS coupled to GC–FID. Six compounds were consistently detected, and three (δ-guaiene, 10-epi-γ-eudesmol, γ-eudesmol) were retained for classification based on ≥95% detection frequency and >0.2% relative abundance. Pearson correlation guided feature selection, and ANN models were trained using both a 70:15:15 train–validation–test split and stratified 5-fold cross-validation with 1000 bootstrap resamples. The optimized network achieved near-perfect performance, with a mean accuracy of ~99.8% (95% CI: 99.6–100.0), and precision, recall, and F1 scores all exceeding 99.5%. In comparison, bootstrapped confidence intervals were tightly bounded at 100%, confirming robustness against data leakage. These findings demonstrate that correlation-guided feature selection combined with ANN modeling enables reproducible and highly accurate species authentication, offering a practical framework for integration into agarwood quality control, conservation monitoring, and international trade compliance.
Co-Authors Abdullah, Hasnain Ahmad Sabri, Noor Aida Syakira Ahmad, Azizah Almisreb, Ali Abd Bakar, Zahari Abu Che Hassan, Mohammad Arif Fahmi Haron, Mohamad Hushni Haron, Mohamad Hushnie Hasnu Al-Hadi, Anis Hazirah 'Izzati Mohamad Noor, Norhayati Mohd Huzir, Siti Mariatul Hazwa Mohd Kasim, Linda Mohd Nasir Taib Mohd Yusoff, Zakiah Nik Kamaruzaman, Nik Fasha Edora Noramli, Nur Athirah Syafiqah Nordin, Siti Aminah Othman, Norhalida Roslan, Muhammad Ikhsan Syafiqah Noramli, Nur Athirah Tajuddin, Saiful Nizam Yassin, Ahmad Ihsan Mohd Yusoff, Zakiah Mohd Zaidi, Amir Hussairi