Chemometric-assisted Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) for agarwood quality assessment - Thomas Kuek, Zuo Chen; WONG, LEONG SENG; Benedict, Samling; Sim, Siong Fong (Smujo International, 2026)
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 βdihydroagarofuran. 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.