Most Aconitum species, also known as aconite, are extremely poisonous, so it must be identified carefully. Differentiation of Aconitum species is challenging because of their similar appearance and chemical components. In this study, a universal strategy to discover chemical markers was developed for effective authentication of three commonly used aconite roots. The major procedures include: (1) chemical profiling and structural assignment of herbs by liquid chromatography with mass spectrometry (LC-MS), (2) quantification of major components by LC-MS, (3) probabilistic neural network (PNN) model to calculate contributions of components toward species classification, (4) discovery of minimized number of chemical markers for quality control. The MS fragmentation pathways of diester-, monoester-, and alkyloyamine-diterpenoid alkaloids were compared. Using these rules, 42 aconite alkaloids were identified in aconite roots. Subsequently, 11 characteristic compounds were quantified. A component-species modeling by PNN was then established combining the 11 analytes and 26-batch samples from three aconite species. The contribution of each analyte to species classification was calculated. Selection of fuziline, benzoylhypaconine, and talatizamine, or a combination of more compounds based on a contribution order, can be used for successful categorization of the three aconite species. Collectively, the proposed strategy is beneficial to selection of rational chemical markers for the species classification and quality control of herbal medicines.
Keywords: Alkaloids; Chemical marker; High performance liquid chromatography; Probabilistic neural network; Processed aconite roots; Species classification.
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