Objective: To establish the UPLC-MS/MS method for simultaneous determination of 9 components (nicotinic acid, kaempferol, swertisin, quercetin, luteolin, rutin, vitexin, spinosin, salicylic acid) in Desmodium caudatum (Thunb.) DC. and construct a back propagation(BP) neural network model to predict the origin of Desmodium caudatum (Thunb.) DC. from different habitats. Methods: The chromatographic separation was achieved on an Agilent Zorbax SB C18 column (50 mm×3.0 mm,1.8 μm). The mobile phase consisted of methanol-0.1% acctic acid (containing 0.02 mol·L-1 ammonium acetate) at a flow rate of 0.3 mL·min-1 with gradient elution, the MS analysis were performed by multiple reaction monitoring (MRM) under ESI+ and ESI. A correlation analysis was conducted on the contents of each component, and a BP neural network model was constructed to distinguish Desmodium caudatum (Thunb.) DC. from different habitats. Results: Under the optimized conditions, 9 components(nicotinic acid, kaempferol, swertisin, quercetin, luteolin, rutin, vitexin, spinosin, salicylic acid) showed good linear relationships in the ranges of 0.388 8-38.88 ng·mL-1, 10.07-1 006.6 ng·mL-1, 34.22-34 221.6 ng·mL-1, 3.944-394.4 ng·mL-1, 2.124-212.4 ng·mL-1, 4.344-434.4 ng·mL-1, 46.50-4 650.1 ng·mL-1, 1.649-164.9 ng·mL-1, 4.880-488.0 ng·mL-1, respectively (r>0.995 1), whose average recoveries were 96.9%-103.9% (RSDs<1.9%). The contents of the above nine components in 40 batches of Desmodium caudatum (Thunb.) DC. were 1.657-7.407 μg·g-1, 15.801-64.488 μg·g-1, 1 068.348-4 270.780 μg·g-1, 10.608-123.228 μg·g-1, 3.897-16.802 μg·g-1, 1.269-97.834 μg·g-1, 405.285-1 955.796 μg·g-1, 13.614-36.124 μg·g-1, 4.417-87.509 μg·g-1, respectively. According to correlation analysis, four components (swertisin, rutin, spinosin, and luteolin) in Desmodium caudatum (Thunb.) DC. showed a highly linear positive correlation, indicating that these four components had a certain synergistic effect in Desmodium caudatum (Thunb.) DC.. The BP neural network model was constructed to predict Desmodium caudatum (Thunb.) DC. from different habitats, and the accuracy of the test set reached 92.3%. Conclusion: The method is simple, sensitive and efficient, and can be used for the rapid determination of the components in Desmodium caudatum (Thunb.) DC.. Using the BP neural network model to predict the habitats plays a significant role in tracing the origin of Desmodium caudatum (Thunb.) DC..
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