LAUSR

Source authentication of herbal medicines was essential for ensuring their safety, efficacy and quality consistency, especially those with multiple botanical origins. This study proposed a metabolomics strategy for species discrimination and source recognition. Uncariae Rammulus Cum Uncis, officially stipulating the stems with hooks of five Uncaria species as its origins, was taken as a case study. Firstly, an untargeted MSE method was developed by ultra-high performance liquid chromatography hyphenated with quadrupole time-of-flight mass spectrometry for global metabolite characterization. Subsequently, data pretreatment was conducted by using a Progenesis QI software and screening rules. The obtained metabolite features were defined as variables for statistical analyses. Principal component analysis and chemical fingerprinting spectra suggested that five official species were differentiated from each other except for Uncaria hirsuta and Uncaria sinensis. Furthermore, orthogonal partial least squares discrimination analysis was performed to discriminate confused two species, and resulted in the discovery of nine contributing markers. Ultimately, a Support Vector Machine model was developed to recognize five species and predict origins of commercial materials. The study demonstrated that the developed strategy was effective in discrimination and recognition of confused species, and promising in tracking botanical origins of commercial materials. This article is protected by copyright. All rights reserved.