Classification and differentiation of agarwoods by using non-targeted HS-SPME-GC/MS and multivariate analysis

Abstract

Agarwood and its related products are important, useful and valuable for many applications, such as medicine, incense, and perfume. In general, the grading method of agarwood is based on its physical properties, which is inefficient, time consuming and lacks repeatability. In this study, non-targeted headspace solid-phase microextraction (HS-SPME) combined with gas chromatography/mass spectrometry (GC/MS) and multivariate analysis was developed to classify and differentiate agarwoods based on their aromatic characteristics. Five samples from Indonesia and Vietnam were extracted with polydimethylsiloxane (PDMS) fiber and analyzed by HS-SPME-GC/MS. GC/MS data were processed using MZmine for statistical purposes. Principle component analysis (PCA) was applied to establish the relationship between samples and aromatic characteristics. In PCA results, samples were classified successfully according to the source, price, and types. A total of 17 markers were adopted and identified by GC/MS, and also confirmed. This result demonstrates that the proposed method is efficient, simple, and useful for grading of agarwoods.