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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.
Boxplot analysis of 4 grade agarwood essential oil for various grades Hasnu Al-Hadi, Anis Hazirah 'Izzati; Mohd Amidon, Aqib Fawwaz; Mohd Huzir, Siti Mariatul Hazwa; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp238-244

Abstract

Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, β-agarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies.
The significance of artificial intelligent technique in classifying various grades of agarwood oil Fawwaz Mohd Amidon, Aqib; Mariatul Hazwa Mohd Huzir, Siti; Mohd Yusoff, Zakiah; Ismail, Nurlaila; Nasir Taib, Mohd
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp261-269

Abstract

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.
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 Abdullah, Hasnain Ahmad Sabri, Noor Aida Syakira Ahmad, Azizah Almisreb, Ali Abd Bakar, Zahari Abu Che Hassan, Mohammad Arif Fahmi Fawwaz Mohd Amidon, Aqib Haron, Mohamad Hushni Haron, Mohamad Hushnie Hasnu Al-Hadi, Anis Hazirah 'Izzati Mariatul Hazwa Mohd Huzir, Siti Mohamad Noor, Norhayati Mohd Amidon, Aqib Fawwaz Mohd Huzir, Siti Mariatul Hazwa Mohd Kasim, Linda Mohd Nasir Taib Mohd Yusoff, Zakiah Nasir Taib, Mohd 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