Chemometric-assisted Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) for agarwood quality assessment
Main Article Content
Abstract
Abstract. Kuek TZC, Wong LS, Samling B, Sim SF. 2026. Chemometric-assisted Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) for agarwood quality assessment. Asian J Nat Prod Biochem 24 (1): f240101. https://doi.org/10.13057/biofar/f240101. Agarwood, the rare and fragrant resinous heartwood of Aquilaria species, is highly valued for medicinal, cultural, and perfumery uses, yet its quality is typically assessed through subjective sensory evaluation. This study aimed to establish an objective, data-driven approach for agarwood quality differentiation using large-scale chemical profiling combined with chemometric analysis. In this study, 304 agarwood samples from a local collector were analyzed using Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC/MS). The resulting large-scale profiling detected 1,036 chemical compounds, providing a database on the chemical characteristics of wild agarwood. Hierarchical clustering, supported by the Calinski-Harabasz Index and silhouette analysis, identified three optimal groups. After filtering for prevalent compounds, Principal Component Analysis (PCA) showed partial overlap among groups, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved an average of 90.31% classification accuracy across 100 training-test splits. Variable Importance in Projection (VIP) scores highlighted key discriminatory compounds, including α-agarofuran, γ-eudesmol, (-)-aristolene, allo-khusiol, β-maaliene, and β-dihydroagarofuran. These findings demonstrate that integrating chemical profiling with multivariate analysis enables objective differentiation of agarwood samples. Although detailed species identity and provenance information were unavailable due to reliance on private collections, the results demonstrate that integrating HS-SPME-GC-MS with multivariate analysis enables reliable, relative classification of agarwood based on chemical composition. The study also establishes a valuable chemical reference for wild agarwood, offering insights relevant to cultivated agarwood production and aiding efforts to optimize induction methods and improve industry practices.
Article Details
Issue
Section

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Abdulah L, Susanti R, Rahajoe JS et al. 2022. Feasibility of agarwood cultivation in Indonesia: Dynamic system modeling approach. Forests 13 (11): 1869. https://doi.org/10.3390/f13111869.
Adam AZ, Lee SY, Mohamed R. 2017. Pharmacological properties of agarwood tea derived from Aquilaria (Thymelaeaceae) leaves: An emerging contemporary herbal drink. J Herb Med 10: 37-44. https://doi.org/10.1016/j.hermed.2017.06.002.
Ahmaed DT, Mohammed M, Masaad AM, Tajuddin SN. 2017. Investigation of agarwood compounds in Aquilaria malaccensis and Aquilaria rostrata chipwood by using solid phase microextraction. Biomed J Sci Tech Res 1 (6): 1609-1616. https://doi.org/10.26717/BJSTR.2017.01.000499.
Ahmaed DT, Osman FAY, Masaad AM, Tajuddin SN. 2022. Phytochemical screening and characterization of agarwood (Aquilaria malaccensis) chips wood grade as incense headspace volatile compounds by GC-MS. Ms. Q. TOF, SPME. Omdurman J Pharm Sci 2: 85-104. https://doi.org/10.52981/ojps.v2i2.2206.
Bellioua S, Polito F, Dilagui I, Benrazzouk K, De Feo V, Bekkouche K, Larhsini M, Markouk M. 2024. Chemical composition, antioxidant, antimicrobial, and antibiofilm activities of essential oil from the Moroccan endemic plant, Calendula maroccana (Ball) BD Jacks. J Essent Oil Bear Plants 27 (3): 678-692. https://doi.org/10.1080/0972060X.2024.2338174.
Bi Y, Wu J, Zhai X, Shen S, Tang L, Huang K, Zhang D. 2021. Application of partial least squares-discriminate analysis model based on water chemical compositions in identifying water inrush sources from multiple aquifers in mines. Geofluids 2021: 6663827. https://doi.org/10.1155/2021/6663827.
Charikar M, Chatziafratis V, Niazadeh R, Yaroslavtsev G. 2019. Hierarchical clustering for Euclidean data. PMLR 89: 2721-2730.
Chen ST, Rao YK. 2022. An overview of agarwood, phytochemical constituents, pharmacological activities, and analyses. Tradit Med 3 (1): 1-71. https://doi.org/10.35702/Trad.10008.
Chhipa H, Chowdhary K, Kaushik N. 2017. Artificial production of agarwood oil in Aquilaria sp. by fungi: A review. Phytochem Rev 16: 835-860. https://doi.org/10.1007/s11101-017-9492-6.
Choudhary MI, Musharraf SG, Nawaz SA, Anjum S, Parvez M, Fun HK. 2005. Microbial transformation of (-)-isolongifolol and butyrylcholinesterase inhibitory activity of transformed products. Bioorg Med Chem 13: 1939-1944. https://doi.org/10.1016/j.bmc.2005.01.015.
da Silva GS, Pozzatti P, Rigatti F, Hörner R, Alves SH, Mallmann CA, Heinzmann BM. 2015. Antimicrobial evaluation of sesquiterpene α-curcumene and its synergism with imipenem. J Microbiol Biotechnol Food Sci 4 (5): 434-436. https://doi.org/10.15414/jmbfs.2015.4.5.434-436.
Fei N, Gao Y, Lu Z, Xiang T. 2021. Z-Score Normalization, Hubness, and Few-Shot Learning. Proc IEEE Intl Conf Comput Vision 2021: 142-151. https://doi.org/10.1109/ICCV48922.2021.00021.
Gutiérrez S, Overmans S, Wellman GB, Samaras VG, Oviedo C, Gede M, Szekely G, Lauersen KJ. 2024. A synthetic biology and green bioprocess approach to recreate agarwood sesquiterpenoid mixtures. Green Chem 26: 2577-2591. https://doi.org/10.1039/D3GC03708H.
Haron MH. 2020. Agarwood Oil Quality Grading Model using Self-Organizing Map (SOM). [Dissertation]. Universiti Teknologi MARA, Shah Alam.
Hidayat W, Shakaff AYM, Ahmad MN, Adom AH. 2010. Classification of agarwood oil using an electronic nose. Sensors 10 (5): 4675-4685. https://doi.org/10.3390/s100504675.
Hung CH, Lee CY, Yang CL, Lee MR. 2014. Classification and differentiation of agarwoods by using non-targeted HS-SPME-GC/MS and multivariate analysis. Anal Method 6: 7449-7456. https://doi.org/10.1039/C4AY01151A.
Ismail N, Azah MAN, Jamil M, Rahiman MHF, Tajuddin SN, Taib MN. 2013. Analysis of high-quality agarwood oil chemical compounds by means of SPME/GC-MS and Z-score technique. Malays J Anal Sci 17: 403-413
Ismail SN, Maulidiani M, Akhtar MT, Abas F, Ismail IS, Khatib A, Ali NAM, Shaari K. 2017. Discriminative analysis of different grades of gaharu (Aquilaria malaccensis Lamk.) via 1H-NMR-based metabolomics using PLS-DA and random forests classification models. Molecules 22 (10): 1612. https://doi.org/10.3390/molecules22101612.
Januzaj Y, Beqiri E, Luma A. 2023. Determining the optimal number of clusters using the silhouette score as a data mining technique. Intl J Online Biomed Eng 19: 174-182. https://doi.org/10.3991/ijoe.v19i04.37059.
Jolliffe IT, Cadima J. 2016. Principal component analysis: A review and recent developments. Philos Trans A Math Phys Eng Sci 374: 20150202. https://doi.org/10.1098/rsta.2015.0202.
Kalra R, Kaushik N. 2017. A review of chemistry, quality, and analysis of the infected agarwood tree (Aquilaria sp.). Phytochem Rev 16: 1045-1079. https://doi.org/10.1007/s11101-017-9518-0.
Kandsi F, Abdnim R, Benkhaira N, Zahra Lafdil F, Bnouham M, Yamani B, Naceiri Mrabti H, Wondmie GF, Bin Jardan YA, Ibenmoussa S, El Hachlafi N. 2024. Integrated assessment of phytochemicals, antilipase, hemoglobin antiglycation, antihyperglycemic, antifungal, and antibacterial properties of Vetiveria zizanioides (L.) Nash. Intl J Food Prop 27 (1): 1150-1166. https://doi.org/10.1080/10942912.2024.2387435.
Kao WY, Hsiang CY, Ho SC, Ho TY, Lee KT. 2018. Chemical profiles of incense smoke ingredients from agarwood by headspace gas chromatography-tandem mass spectrometry. Molecules 23 (11): 2969. https://doi.org/10.3390/molecules23112969.
Karna A, Gibert K. 2022. Automatic identification of the number of clusters in hierarchical clustering. Neural Comput Appl 34: 119-134. https://doi.org/10.1007/s00521-021-05873-3.
Korada RR, Naskar S, Mukherjee A, Jayaprakas CA. 2010. Management of sweet potato weevil, Cylas formicarius: A world review. J Root Crop 36: 14-26.
Kristanti AN, Tanjung M, Aminah NS. 2018. Secondary metabolites of Aquilaria, a Thymelaeaceae genus. Mini-Rev Org Chem 15: 36-55. https://doi.org/10.2174/1570193X14666170721143041.
Lasalvia M, Capozzi V, Perna G. 2022. A comparison of PCA-LDA and PLS-DA techniques for classification of vibrational spectra. Appl Sci 12: 5345. https://doi.org/10.3390/app12115345.
Lever J, Krzywinski M, Altman N. 2017. Points of significance: Principal component analysis. Nat Method 14: 641-642. https://doi.org/10.1038/nmeth.4346.
Ma S, Fu Y, Li Y, Wei P, Liu Z. 2021. The formation and quality evaluation of agarwood induced by the fungi in Aquilaria sinensis. Ind Crop Prod 173: 114129. https://doi.org/10.1016/j.indcrop.2021.114129.
Ma X. 2024. Utilizing principal component analysis to enhance machine learning in bankruptcy prediction: A comparative investigation. Appl Comput Eng 68: 304-310. https://doi.org/10.54254/2755-2721/68/20241500.
Marcomini EK, Rezende PS, Gomes J, Leite CR, de Oliveira KM, Pomini AM, Svidzinski TI, Negri M. 2025. 2-ethyl-1-hexanol: A promising molecule against priority fungal pathogens. J Appl Microbiol 136 (7): lxaf165. https://doi.org/10.1093/jambio/lxaf165.
Monggoot S, Popluechai S, Gentekaki E, Pripdeevech P. 2017. Fungal endophytes: An alternative source for production of volatile compounds from agarwood oil of Aquilaria subintegra. Microb Ecol 74: 54-61. https://doi.org/10.1007/s00248-016-0908-4.
Naziz PS, Das R, Sen S. 2019. The scent of stress: Evidence from the unique fragrance of agarwood. Front Plant Sci 10: 840. https://doi.org/10.3389/fpls.2019.00840.
Ntana F, Bhat WW, Johnson SR, Jørgensen HJ, Collinge DB, Jensen B, Hamberger B. 2021. A sesquiterpene synthase from the endophytic fungus Serendipita indica catalyzes the formation of viridiflorol. Biomolecules 11 (6): 898. https://doi.org/10.3390/biom11060898.
Obasi DC, Ogugua VN. 2021. GC-MS analysis, pH, and antioxidant effect of Ruzu herbal bitters on alloxan-induced diabetic rats. Biochem Biophys Rep 27: 101057. https://doi.org/10.1016/j.bbrep.2021.101057.
Ogbuabor G, Ugwoke FN. 2018. Clustering algorithm for a healthcare dataset using the silhouette score value. Intl J Comput Sci Inf Technol 10: 27-37. https://doi.org/10.56899/151.05.07.
Peiró-Vila P, Pérez-Gracia C, Baeza-Baeza JJ, García-Alvarez-Coque MC, Torres-Lapasió JR. 2024. Analysis and classification of tea varieties using high-performance liquid chromatography and global retention models. J Chromatogr A 1730: 465128. https://doi.org/10.1016/j.chroma.2024.465128.
Qian SZ, Jiang YM, Yan QL, Wu DH, Zhang WX, Chung JP. 2025. Visualization of OPLS class models of GC-MS-based metabolomics data for identifying agarwood essential oil extracted by hydro-distillation. Sci Rep 15: 5421. https://doi.org/10.1038/s41598-025-85976-2.
Rachwał A, Popławska E, Gorgol I, Cieplak T, Pliszczuk D, Skowron Ł, Rymarczyk T. 2023. Determining the quality of a dataset in clustering terms. Appl Sci 13: 2942. https://doi.org/10.3390/app13052942.
Randriamihamison N, Vialaneix N, Neuvial P. 2021. Applicability and interpretability of Ward's hierarchical agglomerative clustering with or without contiguity constraints. J Classif 38: 363-389. https://doi.org/10.1007/s00357-020-09377-y.
Rastegar-Moghaddam SH, Amirahmadi S, Akbarian M, Sharizina M, Beheshti F, Rajabian A, Ghalibaf MH, Azimi M, Mahmoudabady M, Hosseini M. 2024. Cardioprotective effect of cedrol in a systemic inflammation model induced by lipopolysaccharide: Biochemical and histological verification. J Cardiovasc Thorac Res 16 (2): 120-128. https://doi.org/10.34172/jcvtr.33112.
Rubio‐Sánchez R, Ríos‐Reina R, Ubeda C. 2023. Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS‐SPME‐GC‐MS and chemometrics. Thorac Cancer 14 (36): 3522-3529. https://doi.org/10.1111/1759-7714.15154.
Sen S, Dehingia M, Talukdar NC, Khan M. 2017. Chemometric analysis reveals links in the formation of fragrant bio-molecules during agarwood (Aquilaria malaccensis) and fungal interactions. Sci Rep 7: 44406. https://doi.org/10.1038/srep44406.
Shalini K, Guleria S, Salaria D, Rolta R, Fadare OA, Mehta J, Awofisayo O, Mandyal P, Shandilya P, Kaushik N, Choi EH, Chandel SR, Kaushik NK. 2024. Antimicrobial potential of phytocompounds of Acorus calamus: An in silico approach. J Biomol Struct Dyn 42 (5): 2726-2737. https://doi.org/10.1080/07391102.2023.2209653.
Shao H, Mei WL, Kong FD, Dong WH, Gai CJ, Li W, Zhu GP, Dai HF. 2016. Sesquiterpenes of agarwood from Gyrinops salicifolia. Fitoterapia 113: 182-187. https://doi.org/10.1016/j.fitote.2016.07.015.
Shi X, Song J, Wang H, Lv X, Zhu Y, Zhang W, Bu W, Zeng L. 2023. Improving soil organic matter estimation accuracy by combining optimal spectral preprocessing and feature selection methods based on pXRF and vis-NIR data fusion. Geoderma 430: 116301. https://doi.org/10.1016/j.geoderma.2022.116301.
Sundaraj S, Mediani A, Rodriques KF, Baharum SN. 2023. GC-MS olfactometry reveals that sesquiterpenes α-humulene and δ-cadinene significantly influence the aroma of treated Aquilaria malaccensis essential oil. Aust J Crop Sci 17: 893-901. https://doi.org/10.21475/ajcs.23.17.12.p3916.
Tan CS, Isa NM, Ismail I, Zainal Z. 2019. Agarwood induction: Current developments and future perspectives. Front Plant Sci 10: 122. https://doi.org/10.3389/fpls.2019.00122.
Tao T, Ye B, Xu Y, Wang Y, Zhu Y, Tian Y. 2022. β-Patchoulene preconditioning protects mice against hepatic ischemia-reperfusion injury by regulating Nrf2/HO-1 signaling pathway. J Surg Res 275: 161-171. https://doi.org/10.1016/j.jss.2022.02.001.
Tian CP, Yao XD, Lu JH, Shen LQ, Wu AQ. 2021. GC-MS fingerprints of essential oils from agarwood grown in wild and artificial environments. Trees 35: 2105-2117. https://doi.org/10.1007/s00468-021-02177-w.
Tufariello M, Pati S, Palombi L, Grieco F, Losito I. 2022. Use of multivariate statistics in the processing of data on wine volatile compounds obtained by HS-SPME-GC-MS. Foods 11 (7): 910. https://doi.org/10.3390/foods11070910.
Varsha KK, Devendra L, Shilpa G, Priya S, Pandey A, Nampoothiri KM. 2015. 2, 4-Di-tert-butyl phenol as the antifungal, antioxidant bioactive purified from a newly isolated Lactococcus sp. Intl J Food Microbiol 211: 44-50. https://doi.org/10.1016/j.ijfoodmicro.2015.06.025.
Villareal JF, Abasolo WP, Mendoza RC. 2022. Fiber morphology and extractive content of Aquilaria cumingiana (Decne.) Ridl. wood from Davao Oriental, Philippines. Philipp J Sci 151 (5): 1623-1631. https://doi.org/10.56899/151.05.07.
Wang Y, Hussain M, Jiang Z, Wang Z, Gao J, Ye F, Mao R, Li H. 2021. Aquilaria species (Thymelaeaceae) distribution, volatile and non-volatile phytochemicals, pharmacological uses, agarwood grading system, and induction methods. Molecules 26 (24): 7708. https://doi.org/10.3390/molecules26247708.
Wang Y, Lin S, Zhao L, Sun X, He W, Zhang Y, Dai YC. 2019. Lasiodiplodia spp. associated with Aquilaria crassna in Laos. Mycol Prog 18: 683-701. https://doi.org/10.1007/s11557-019-01481-7.
Ward Jr JH. 1963. Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58: 236-244. https://doi.org/10.2307/2282967.
Xu Z, Fang X, Zhi Y, Xiao X, Yang J, Hu J, Zhu H, Chen F, Cheng W, Liu T, Lu L. 2025. Systematic identification of terpene synthases from sacred lotus (Nelumbo nucifera) and heterologous biosynthesis of the insecticidal and antimicrobial compound γ-eudesmol. Hortic Res 12 (10): uhaf191. https://doi.org/10.1093/hr/uhaf191.
Xue BX, Liu SX, Oduro PK, Mireku-Gyimah NA, Zhang LH, Wang Q, Wu HH. 2023. Vasodilatory constituents of essential oil from Nardostachys jatamansi DC.: Virtual screening, experimental validation, and the potential molecular mechanisms. Arab J Chem 16: 104911. https://doi.org/10.1016/j.arabjc.2023.104911.
Zhang SY, Tibpromma S, Karunarathna SC, Ye S, Mapook A, Wang YH, Xu JC. 2022. Endophytic fungi of agarwood and their chemical compounds: A review. Fungal Biotech 2: 16-35.
Zhang X, Wang LX, Hao R, Huang JJ, Zargar M, Chen MX, Zhu FY, Dai HF. 2024. Sesquiterpenoids in agarwood: Biosynthesis, microbial induction, and pharmacological activities. J Agric Food Chem 72: 23039-23052. https://doi.org/10.1021/acs.jafc.4c06383.
Zhang Y, Wu S, Zhang B, Zhou X, Zhou W, Zhang W, Gao X, Chen X. 2025. Determination of antitumor active ingredients in agarwood essential oil by Gas Chromatography-Mass Spectrometry (GC-MS) and grey relational analysis. Anal Lett 58 (2): 341-363. https://doi.org/10.1080/00032719.2024.2325570.