Non-targeted metabolomic analysis of variation of volatile fractions of ginseng from different habitats by HS-SPME-GC-MS coupled with chemometrics. Issue 36 (31st August 2022)

• Record Type: Journal Article
• Authors: Gu, Wen-Ting; Li, Lin-Yuan; Rui, Wen-Jing; Diao, Zhong-Wen; Zhuang, Guo-Dong; Chen, Xiao-Mei; Qian, Zheng-Ming; Wang, Shu-Mei; Tang, Dan; Ma, Hong-Yan
• Is Part Of: Analytical methods. Volume 14:Issue 36(2022)
• Page Start: 3583
• Page End: 3597
• Publication Date: 2022-08-31
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d2ay01060g
• Languages: English
• ISSNs: 1759-9660

Abstract

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. 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.