Issue 36, 2022

Non-targeted metabolomic analysis of variation of volatile fractions of ginseng from different habitats by HS-SPME-GC-MS coupled with chemometrics

Abstract

Cultivated ginseng (CG), transplanted ginseng (TG) and mountain cultivated ginseng (MCG) classified by the habitat type all belong to Panax ginseng and were reported to have similar types of secondary metabolites. Nonetheless, owing to the distinctly diverse habitats in which these ginseng types grow, their pharmacological effects differ. In the present study, an emerging analytical approach involving headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC–MS) was established to effectively distinguish among CG, TG and MCG. First, the volatile components were analysed and identified by using the NIST library combined with measured retention indices (Kovats', RI), and a total of 78 volatile components were finally characterized, which included terpenes, alcohols, esters, aldehydes and alkynols. Furthermore, multivariate statistical approaches, principal component analysis (PCA) and orthogonal partial least-squares discrimination analysis (OPLS-DA) were subsequently utilized to screen for compounds of significance. Under optimized HS-SPME-GC–MS conditions, 12, 16, and 16 differential markers were screened in the CG-TG, CG-MCG and TG-MCG groups, respectively. Our study suggested that HS-SPME-GC–MS analysis combined with metabolomic analytical methods and chemometric techniques can be applied as potent tools to identify chemical marker candidates to distinguish CG, TG and MCG.

Graphical abstract: Non-targeted metabolomic analysis of variation of volatile fractions of ginseng from different habitats by HS-SPME-GC-MS coupled with chemometrics

Article information

Article type
Paper
Submitted
30 Jun 2022
Accepted
11 Aug 2022
First published
12 Aug 2022

Anal. Methods, 2022,14, 3583-3597

Non-targeted metabolomic analysis of variation of volatile fractions of ginseng from different habitats by HS-SPME-GC-MS coupled with chemometrics

W. Gu, L. Li, W. Rui, Z. Diao, G. Zhuang, X. Chen, Z. Qian, S. Wang, D. Tang and H. Ma, Anal. Methods, 2022, 14, 3583 DOI: 10.1039/D2AY01060G

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