INTEGRATING COUPLED GC–MS/GC–FID AND DATA ANALYTICS FOR PREDICTIVE AUTHENTICATION OF AQUILARIA SPECIES

Authors

DOI:

https://doi.org/10.11113/jurnalteknologi.v88.24590

Keywords:

Aquilaria species, agarwood authentication, GC-MS coupled with GC-FID, stepwise regression, chemical compounds

Abstract

Agarwood (oud) is a highly valuable resinous wood derived from Aquilaria species, widely used in perfumery, incense, and traditional medicine. Traditional sensory-based classification methods are often subjective and susceptible to fraud and misidentification. This study applies gas chromatography–mass spectrometry (GC-MS) coupled with gas chromatography–flame ionization detection (GC-FID) integrated with statistical modelling to establish a predictive framework for authenticating Aquilaria species. Stepwise regression identified three chemical markers (γ-eudesmol, δ-guaiene, and 10-epi-γ-eudesmol) as significant differentiators among species. The optimized model achieved high predictive accuracy (R² = 0.990), demonstrating the reliability of a chemometric approach for species classification. The proposed framework provides a scientific basis for standardizing agarwood authentication, reducing market adulteration, and supporting regulatory enforcement and conservation of endangered Aquilaria resources. Future work will expand chemical datasets and incorporate machine-learning algorithms to further enhance predictive precision and industrial scalability.

Author Biographies

  • Nur Athirah Syafiqah Noramli, Advanced Signal Processing Research Interest Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Nur Athirah Syafiqah Noramli received her Bachelor of Science (Hons.) in Computer Science from Universiti Teknologi MARA (UiTM) Cawangan Melaka, Kampus Jasin. She is currently a postgraduate student at the School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia. Her research focuses on advanced signal processing, machine learning, and deep learning, with a particular interest in their applications across various domains, including image and speech processing. She is passionate about leveraging artificial intelligence (AI) techniques to enhance real-world problem-solving, particularly in data-driven environments. Throughout her academic journey, she has been actively involved in research projects and won award in exhibition. Her expertise includes data analysis, neural networks and predictive modelling.

  • Noor Aida Syakira Ahmad Sabri, Advanced Signal Processing Research Interest Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Noor Aida Syakira Ahmad Sabri  received her Bachelor of Engineering (Hons) in Electronic Engineering from Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia, in 2022. Currently, she is pursuing postgraduate studies at the School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia. Her research interests focus on advanced signal processing and machine learning.

    Email: aidasyakiraaa01@gmail.com

  • Muhammad Ikhsan Roslan, Advanced Signal Processing Research Interest Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Muhammad Ikhsan Roslan  earned his Master of Science in Electronic Systems Design Engineering from Universiti Sains Malaysia (USM), Penang, Malaysia, in 2022 with first-class honors. He is currently a server validation engineer specializing in IP-level validation at UST (M) Sdn. Bhd, while also pursuing full-time postgraduate studies at the School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia. With a strong passion for research in engineering, particularly in artificial intelligence, he combines academic excellence with practical experience, showcasing a dedicated commitment to advancing the field.

    Email: muhammadikhsanroslan@gmail.com

  • Nurlaila Ismail, Advanced Signal Processing Research Interest Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Assoc. Prof. Ir. Ts. Dr. Nurlaila Ismail     received her PhD in Electrical Engineering from Universiti Teknologi MARA, Malaysia. She is currently a senior lecturer at School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam, Malaysia. Her research interests include advanced signal processing and artificial intelligence.

    Email: nurlaila0583@uitm.edu.my

  • Zakiah Mohd Yusoff, Advanced Signal Processing Research Interest Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Assoc. Prof. Ts. Dr. Zakiah Mohd Yusoff     received her Bachelor’s Degree in Electrical Engineering and PhD in Electrical Engineering from Universiti Teknologi MARA Shah Alam, in 2009 and 2014, respectively. She is a senior lecturer who is currently working at the School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM) Shah Alam, Malaysia. In Mei 2014, she joined Universiti Teknologi MARA as a teaching staff. Her major interests include process control, system identification, and essential oil extraction systems.

    Email: zakiah9018@uitm.edu.my

  • Mohd Nasir Taib, Advanced Signal Processing Research Interest Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Prof. Ir. Ts. Dr. Haji Mohd Nasir Taib  received the degree in electrical engineering from the University of Tasmania, Hobart, Australia, the M.Sc. degree in control engineering from Sheffield University, UK, and the Ph.D. degree in instrumentation from the University of Manchester Institute of Science and Technology, UK. He is currently an Honorary Professor at Universiti Teknologi MARA (UiTM), Malaysia. He heads the Advanced Signal Processing Research Group at the School of Electrical Engineering, College of Engineering, UiTM. He has been a very active researcher and over the years had author and/or co-author many papers published in refereed journals and conferences.

    Email: dr.nasir@uitm.edu.my 

References

[1] D. Jia and S. Yi, “Classification of Hoi-An and Sin-Chew agarwood by components analysis of VOCs released in heat-treated agarwood using TD-GCMS and chemometric methods,” Bioresources, Vol. 13, No. 2, pp. 2916–2931, 2018.

[2] P. Shivanand, N. F. Arbie, S. Krishnamoorthy, and N. Ahmad, “Agarwood—The Fragrant Molecules of a Wounded Tree,” Molecules, Vol. 27, No. 11, p. 3386, May 2022, DOI: https:// 10.3390/molecules27113386.

[3] T. T. Kim Ngan et al., “Chemical Components of Agarwood (Aquilaria crassna) Essential Oils Grown in Various Regions of Asia,” Asian Journal of Chemistry, Vol. 32, No. 1, pp. 36–40, Nov. 2019, DOI: https:// 10.14233/ajchem.2020.22177.

[4] M.-R. Wang, W. Li, S. Luo, X. Zhao, C.-H. Ma, and S.-X. Liu, “GC-MS Study of the Chemical Components of Different Aquilaria sinensis (Lour.) Gilgorgans and Agarwood from Different Asian Countries,” Molecules, Vol. 23, No. 9, p. 2168, Aug. 2018, DOI: https:// 10.3390/molecules23092168.

[5] Z.-Q. Xie, J.-Y. Xu, M. Rafiq, and C.-S. Cheng, “An analysis of Agarwood trade patterns, historical perspectives, and species identification challenges: repercussions for importing nations,” TMR Modern Herbal Medicine, Vol. 7, No. 1, pp. 1–10, 2024, DOI: https:// 10.53388/MHM2024001.

[6] M. N. Azah, N. Ismail, J. Mailina, M. N. Taib, M. Rahiman, and Z. M. Hafizi, “Chemometric Study of Selected Agarwood Oils by Gas Chromatography-Mass Spectrometry,” Journal of Tropical Forest Science, Vol. 26, No. 3, pp. 382–388, 2014.

[7] Y. Zh-y Hashim, N. Ismail, and P. Abbas, “Analysis of Chemical Compounds of Agarwood Oil from Different Species by Gas Chromatography Mass Spectrometry (GCMS),” Hashim et al. IIUM Engineering Journal, Vol. 15, No. 1, pp. 1–6, 2014, DOI: https://doi.org/10.31436/iiumej.v15i1.469.

[8] H. E. Shaian, C. M. Aizal, and S. N. Tajuddin, “Chemical Composition of Agarwood Essential Oil (Aquilaria malaccensis) Upon Exposure Towards Heat Condition,” Malaysian Journal of Chemistry, Vol. 23, No. 2, pp. 199–204, 2021.

[9] S. Ismail et al., “Discriminative Analysis of Different Grades of Gaharu (Aquilaria malaccensis Lamk.) via 1H-NMR-Based Metabolomics Using PLS-DA and Random Forests Classification Models,” Molecules, Vol. 22, No. 10, p. 1612, Sep. 2017, DOI: https:// 10.3390/molecules22101612.

[10] A. Porrello, A. Vaglica, D. Savoca, M. Bruno, and F. Sottile, “Variability in Crithmum maritimum L. Essential Oils’ Chemical Composition: PCA Analysis, Food Safety, and Sustainability,” Sustainability (Switzerland) , Vol. 16, No. 6, Mar. 2024, DOI: https://10.3390/su16062541.

[11] Z. Zhang, “Variable selection with stepwise and best subset approaches,” Ann Transl Med, Vol. 4, No. 7, pp. 136–136, Apr. 2016, DOI: https://10.21037/atm.2016.03.35.

[12] M. G. Kim, “Geometric aspects of deletion diagnostics in multivariate regression,” J Appl Stat, Vol. 42, No. 10, pp. 2073–2079, Oct. 2015, DOI: https://10.1080/02664763.2015.1016411.

[13] P. Pandit, P. Dey, and K. N. Krishnamurthy, “Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models,” SN Comput Sci, Vol. 2, No. 2, p. 76, Apr. 2021, DOI: https://10.1007/s42979-021-00473-3.

[14] A. E. Goodenough, A. G. Hart, and R. Stafford, “Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets,” PLoS One, Vol. 7, No. 3, pp. 1–10, Mar. 2012, DOI: https://10.1371/journal.pone.0034338.

[15] Aqib Fawwaz Mohd Amidon, Zakiah Mohd Yusoff, Nurlaila Ismail, and Mohd Nasir Taib, “KNN Euclidean Distance Model Performance on Aquilaria Malaccensis Oil Qualities,” Journal of Advanced Research in Applied Sciences and Engineering Technology, Vol. 48, No. 2, pp. 1–13, Jul. 2024, DOI: https://10.37934/araset.48.2.1628.

[16] N. Z. Mahabob, Z. M. Yusoff, A. F. M. Amidon, N. Ismail, and M. N. Taib, “Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification,” International Journal of Electrical and Computer Engineering, Vol. 11, No. 6, pp. 5505–5514, Dec. 2021, DOI: https://10.11591/ijece.v11i6.pp5505-5514.

[17] N. Zawani Mahabob, A. F. Mohd Amidon, Z. Mohd Yusoff, N. Ismail, and S. N. Tajjudin, “Comparison of ANN Performance Towards Agarwood Oil Compounds Pre-processing Based on Principal Component Analysis (PCA) and Stepwise Regression Selection Method,” Journal of Electrical & Electronic Systems Research, Vol. 19, No. OCT2021, pp. 51–55, Oct. 2021, DOI: https://10.24191/jeesr.v19i1.007.

[18] M. A. Haqmi Abas, “Agarwood Oil Quality Classification using Support Vector Classifier and Grid Search Cross Validation Hyperparameter Tuning,” International Journal of Emerging Trends in Engineering Research, Vol. 8, No. 6, pp. 2551–2556, Jun. 2020, DOI: https://10.30534/ijeter/2020/55862020.

[19] M. A. Aiman Ngadilan, N. Ismail, M. H. F. Rahiman, M. N. Taib, N. A. Mohd Ali, and S. N. Tajuddin, “Radial basis function (RBF) tuned kernel parameter of agarwood oil compound for quality classification using support vector machine (SVM),” 2018 9th IEEE Control and System Graduate Research Colloquium, ICSGRC 2018 - Proceeding, pp. 64–68, Jul. 2018, DOI: https://10.1109/ICSGRC.2018.8657524.

[20] N. A. Syafiqah Noramli, N. A. S. Ahmad Sabri, M. I. Roslan, N. Ismail, Z. M. Yusoff, and M. N. Taib, “Unraveling the relationships among essential oil compounds in Aquilaria species using GC-MS and GC-FID techniques,” Indonesian Journal of Electrical Engineering and Computer Science, Vol. 39, No. 1, p. 167, Jul. 2025, DOI: https://10.11591/ijeecs.v39.i1.pp167-177.

[21] N. A. S. Noramli, N. A. S. A. Sabri, M. I. Roslan, N. Ismail, Z. M. Yusoff, and M. N. Taib, “Analytical Characterization of Aquilaria Species Essential Oils Using Advanced Chromatographic Techniques Based on GC-MS and GC-FID,” in 2025 21st IEEE International Colloquium on Signal Processing & Its Applications (CSPA), IEEE, Feb. 2025, pp. 10–15. DOI: https://10.1109/CSPA64953.2025.10933407.

[22] Z. M. Yusoff and N. Ismail, “Datasets of chemical compounds in three different species of Aquilaria using GC-MS coupled with GC-FID analysis,” Data Brief, Vol. 53, p. 110209, Apr. 2024, DOI: https://10.1016/j.dib.2024.110209.

[23] N. Muhamad Sarih, N. Ismail, N. Akhtar, and S. N. Tajuddin, “Analysis of GC-FID and GC-MS Microwave-Assisted Hydrodistillation Extraction (MAHD) of Agarwood Chips,” The International Journal of Integrated Engineering, Vol. 13, No. 6, Aug. 2021, DOI: https://10.30880/ijie.2021.13.06.017.

[24] A. Shuttleworth and S. D. Johnson, “GC-MS/FID/EAD: A method for combining mass spectrometry with gas chromatography-electroantennographic detection,” Front Ecol Evol, Vol. 10, Dec. 2022, DOI: https://10.3389/fevo.2022.1042732.

[25] A. P. Joshi and B. V. Patel, “Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process,” Oriental journal of computer science and technology, Vol. 13, No. 0203, pp. 78–81, Jan. 2021, DOI: https://10.13005/ojcst13.0203.03.

[26] K. Gibert, M. Sànchez–Marrè, and J. Izquierdo, “A survey on pre-processing techniques: Relevant issues in the context of environmental data mining,” AI Communications, Vol. 29, No. 6, pp. 627–663, Dec. 2016, DOI: https://10.3233/AIC-160710.

[27] B. B. Xia, X. F. Zhao, and D. G. Feng, “Improve Steganalysis by MWM Feature Selection,” in Watermarking - Volume 2, Vol. 2, InTech, 2012, pp. 243–258. DOI: https://10.5772/37988.

[28] K. P. Vatcheva and M. Lee, “Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies,” Epidemiology: Open Access, Vol. 6, No. 2, pp. 1–9, 2016, DOI: https://10.4172/2161-1165.1000227.

[29] J. H. Kim, “Multicollinearity and misleading statistical results,” Korean J Anesthesiol, Vol. 72, No. 6, pp. 558–569, Dec. 2019, DOI: https://10.4097/kja.19087.

[30] S. H. Huang, “Supervised feature selection: A tutorial,” Artif Intell Res, Vol. 4, No. 2, pp. 22–37, Apr. 2015, DOI: https://10.5430/air.v4n2p22.

[31] H. J. Zainodin, G. Khuneswari, A. Noraini, and F. A. A. Haider, “Selected Model Systematic Sequence via Variance Inflationary Factor,” International Journal of Applied Physics and Mathematics, Vol. 5, No. 2, pp. 105–114, 2015, DOI: https://10.17706/ijapm.2015.5.2.105-114.

[32] M. O. Akinwande, H. G. Dikko, and A. Samson, “Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis,” Open J Stat, Vol. 05, No. 07, pp. 754–767, 2015, DOI: https://10.4236/ojs.2015.57075.

[33] Y. Kang, P. Liu, F. Lv, Y. Zhang, Y. Yang, and J. Wei, “Genetic relationship and source species identification of 58 Qi-Nan germplasms of Aquilaria species in China that easily form agarwood,” PLoS One, Vol. 17, No. 6, p. e0270167, Jun. 2022, DOI: https://10.1371/journal.pone.0270167.

[34] N. Roustaei, “Application and interpretation of linear-regression analysis,” Medical hypothesis discovery and innovation in ophthalmology, Vol. 13, No. 3, pp. 151–159, Oct. 2024, DOI: https://10.51329/mehdiophthal1506.

[35] N. Shrestha, “Detecting Multicollinearity in Regression Analysis,” Am J Appl Math Stat, Vol. 8, No. 2, pp. 39–42, Jun. 2020, DOI: https://10.12691/ajams-8-2-1.

[36] S. M. H. Mohd Huzir, S. N. Tajuddin, Z. Mohd Yusoff, N. Ismail, A. A. Almisreb, and M. N. Taib, “Agarwood oil quality identification using artificial neural network modelling for five grades,” International Journal of Electrical and Computer Engineering (IJECE), Vol. 14, No. 2, p. 2254, Apr. 2024, DOI: https://10.11591/ijece.v14i2.pp2254-2261.

[37] A. Fawwaz Mohd Amidon, S. Mariatul Hazwa Mohd Huzir, Z. Mohd Yusoff, N. Ismail, and M. Nasir Taib, “The significance of artificial intelligent technique in classifying various grades of agarwood oil,” Indonesian Journal of Electrical Engineering and Computer Science, Vol. 29, No. 1, pp. 261–269, Jan. 2022, DOI: https://10.11591/ijeecs.v29.i1.pp261-269.

[38] A. H. ‘Izzati H. Al-Hadi et al., “Boxplot analysis of 4 grade agarwood essential oil for various grades,” Indonesian Journal of Electrical Engineering and Computer Science, Vol. 29, No. 1, pp. 238–244, Jan. 2023, DOI: https://10.11591/ijeecs.v29.i1.pp238-244.

[39] X. Zhang et al., “Sesquiterpenoids in Agarwood: Biosynthesis, Microbial Induction, and Pharmacological Activities,” J Agric Food Chem, Vol. 72, No. 42, pp. 23039–23052, Oct. 2024, DOI: https://10.1021/acs.jafc.4c06383.

[40] S. M. H. M. Huzir et al., “Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation,” Indonesian Journal of Electrical Engineering and Computer Science, Vol. 29, No. 2, p. 735, Feb. 2023, DOI: https://10.11591/ijeecs.v29.i2.pp735-741.

[41] S. R. Adhikari, K. Pokhrel, and S. D. Baral, “Economic Value of Agarwood and Its Prospects of Cultivation,” Int J Appl Sci Biotechnol, Vol. 9, No. 1, pp. 23–31, Mar. 2021, DOI: https://10.3126/ijasbt.v9i1.35984.

[42] N. Cebi, M. Arici, and O. Sagdic, “The famous Turkish rose essential oil: Characterization and authenticity monitoring by FTIR, Raman and GC–MS techniques combined with chemometrics,” Food Chem, Vol. 354, Aug. 2021, DOI: https://10.1016/j.foodchem.2021.129495.

[43] E. Al-Sayed, H. A. Gad, and D. M. El-Kersh, “Characterization of Four Piper Essential Oils (GC/MS and ATR-IR) Coupled to Chemometrics and Their anti- Helicobacter pylori Activity,” ACS Omega, Vol. 6, No. 39, pp. 25652–25663, Oct. 2021, DOI: https://10.1021/acsomega.1c03777.

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Published

01-11-2026

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Vol. 88 No. 6 (2026): November 2026

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Science and Engineering

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How to Cite

INTEGRATING COUPLED GC–MS/GC–FID AND DATA ANALYTICS FOR PREDICTIVE AUTHENTICATION OF AQUILARIA SPECIES. (2026). Jurnal Teknologi (Sciences & Engineering), 88(6), 271–279. https://doi.org/10.11113/jurnalteknologi.v88.24590