Integration of a virtual database-based precursor ion list data acquisition strategy combined with untargeted metabolomics for characterizing and distinguishing triterpenoids and flavonoids in Astragalus membranaceus var. mongholicus and Astragalus membranaceus

Systematic characterization of natural product components and identification of chemical markers are important prerequisites for quality control and product development. Although data dependent acquisition (DDA) methods are widely used for the identification of natural product components, they have the limitation of potentially missing key active ingredients present at low abundance. This study proposes a comprehensive analytical strategy that integrates a virtual database-based precursor ion list (PIL) acquisition method with untargeted metabolomics to deeply characterize triterpenoids and flavonoids in the leaves of Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao (AMM) and Astragalus membranaceus (Fisch.) Bge. (AM). This method established a virtual database comprising 20160 triterpenoids and 7560 flavonoids by systematically enumerating and combining aglycones, substituents, and glycosyl groups, thereby significantly expanding the compound detection range. In addition, the virtual database serves as a template for matching precursor ions, enabling rapid screening of potential target compounds. Compared to the traditional DDA method, the PIL-based DDA approach identifies a greater number of triterpenoids and flavonoids, with 706 compounds identified by the traditional method versus 788 by the PIL-based method. Untargeted metabolomics was used to identify potential chemical biomarkers in AMM and AM leaves. As a result, 788 triterpenoids and flavonoids were identified in the leaves of AMM and AM, including 443 putatively annotated unknown compounds. Among these, 42 compounds were recognized as candidate discriminatory features, comprising 35 triterpenoids and 7 flavonoids. This comprehensive analytical strategy broadens the scope of triterpenoid and flavonoid identification in AMM and AM leaves, significantly enhancing compound coverage. Moreover, it offers a powerful tool for the systematic characterization of key components in complex natural products.