ArticleBMC plant biology2026
Widely targeted metabolomics of Forsythia suspensa for geographical origin authentication by UPLC-MS/MS combined with chemometrics.
Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundQingqiao (Forsythia suspensa) is a valuable traditional Chinese medicine with considerable industrial potential owing to its rich profile of nutritional and functional components. However, systematic characterization of its metabolite composition across different growing regions remains limited, constraining effective quality control and origin-based standardization for industrial applications.
resultsIn this study, a comprehensive UPLC-MS/MS-based metabolomics approach combined with chemometric analysis was employed to compare Qingqiao samples from 3 distinct geographical origins: the Loess Plateau (LP), the Guanzhong Plain (GZP), and the Qinling Mountains (QM). 3,052 metabolites were identified, comprising 1,114 primary and 1,938 secondary metabolites. Distinct metabolic profiles were observed among the 3 regions: LP samples were abundant in organic acids, alkaloids, lignans, coumarins, flavonoids, quinones, amino acids, and derivatives; QM samples exhibited higher levels of lipids, steroids, terpenoids, and tannins; while GZP samples were enriched in phenolic acids, nucleotides, and their derivatives. Combined content-function analysis indicated that GZP1, GZP2, GZP3, QM3, QM4, LP1, LP2, LP5, and LP6 possessed distinctive metabolite profiles for their potential in developing functional products. Comparative analysis revealed 610, 555, and 610 differentially accumulated metabolites between GZP-vs-LP, GZP-vs-QM, and QM-vs-LP, respectively, which were significantly enriched in 8 key metabolic pathways. Furthermore, 14 differential metabolites were identified as potential origin-specific biomarkers. Under internal site‑level split validation, a newly established OPLS-DA model demonstrated high discriminative accuracy at both the replicate level (95.24%) and the site level (85.71%). Moreover, the AUC values derived from random forest analysis further confirmed the suitability of the OPLS-DA model for geographical authentication within the current dataset.
conclusionsThis study elucidated the region-specific variations in nutritional and functional components of Qingqiao, and proposed a metabolomics-based framework with preliminary potential for geographical authentication, offering critical insights for quality control, functional application, and industrial standardization of Qingqiao.
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