SONG Yi, WANG Quan
Objective: To establish a method for evaluating quality difference of Farfarae Flos from different habitats by quantitative analysis of multicomponents by single marker (QAMS) method combined with chemical pattern recognition, and weighted technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA) fusion model, and to improve the quality control level. Methods: High performance liquid chromatography (HPLC) was applied with a gradient elution of acetonitrile (A) and 0.1% phosphoric acid aqueous solution (B) as the mobile phase. The detection wavelengths were 256 nm (adenosine, rutin, and isoquercitrin), 326 nm (neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, caffeic acid, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C), and 220 nm (tussilagone). The flow rate was 1.0 mL · min-1 and the column temperature was 30 ℃. The contents of adenosine, neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, caffeic acid, rutin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C and tussilagone in 19 batches of Farfarae Flos were determined by QAMS using isochlorogenic acid B as the internal reference substance. The extracts were determined according to Chinese Pharmacopoeia (2020 edition). Chemical pattern recognition was used to analyze the quality differences of samples from different habitats and reveal the characteristic components that caused the quality differences. The quality of 19 batches of Farfarae Flos was ranked, and the quality difference of Farfarae Flos from different habitats was evaluated by weighted TOPSIS and GRA fusion model. Results: There was no significant difference between the results determined by QAMS and external standard method (ESM). The contents of adenosine, neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, caffeic acid, rutin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C and tussilagone in 19 batches of Farfarae Flos were 0.106%-0.192%, 0.021%-0.061%, 0.622%-1.247%, 0.041%-0.103%, 0.004%-0.017%, 0.069%-0.248%, 0.027%-0.075%, 0.596%-1.443%, 0.504%-0.968%, 0.314%-0.781% and 0.045%-0.109%, respectively. Through chemical pattern recognition, 19 batches of samples were grouped into 3 categories, and the characteristic components affecting the quality of Farfarae Flos were chlorogenic acid, isochlorogenic acid A, tussilagone and isoquercitrin. In the weighted TOPSIS and GRA fusion model, the relative closeness of 19 batches of samples was 0.221 1-0.761 8, and there were quality differences in the Farfarae Flos samples from different habitats. The quality ranking from best to worst was: samples from Gansu, Shanxi, Hebei, Henan and Neimenggu. Conclusion: The established method of QAMS combined with chemical pattern recognition, and weighted TOPSIS and GRA fusion model can evaluate the quality of Farfarae Flos comprehensively and objectively, and the method provides a basis for the quality control and regional difference research.