Quality Evaluation of Dayuanyin Based on Fingerprint and Chemical Pattern Recognition
Objective To establish the ultra-high performance liquid chromatography(UPLC)fingerprint of Dayuanyin,and to evaluate its quality by combining chemical pattern recognition.Methods The chromatographic column was an Acquity UPLC BEH C18 column(50 mm × 2.1 mm,1.7 μm),the mobile phase was acetonitrile-0.1％phosphoric acid solution(gradient elution),the flow rate was 0.5 mL/min,the column temperature was 30 ℃,the detection wavelength was 190-400 nm,the detection wavelength was 240 nm,and the injection volume was 2 µL.The UPLC fingerprints of eighteen batches of Dayuanyin samples were established,and the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine(Version 2012)was used for similarity evaluation.The common peaks were assigned and identified.The fingerprints were evaluated by the hierarchical cluster analysis(HCA),principal component analysis(PCA),and orthogonal partial least squares-discriminant analysis(OPLS-DA).Results The fingerprints of eighteen batches of samples showed good similarity(0.988-0.999),with thirty common peaks calibrated and assigned,and eight chromatographic peaks identified,namely peak 7(mangiferin),peak 9(paeoniflorin),peak 11(liquiritin),peak 17(baicalin),peak 24(baicalein),peak 27(monoammonium glycyrrhizinate),peak 29(honokiol),and peak 30(magnolol).The eighteen batches of samples could be divided into three categories by the HCA and PCA methods,and four differential components,namely peak 17,peak 22,peak 23(from Scutellariae Radix),and peak 3(from Anemarrhenae Rhizoma),were judged by the variable importance in projection(VIP)value.Conclusion The established UPLC fingerprint and chemical pattern recognition evaluation method for Dayuanyin are stable and feasible,which can be used for quality control and evaluation of Dayuanyin.The quality of Scutellariae Radix and Anemarrhenae Rhizoma decoction pieces in the formula has a significant impact on the fingerprint of Dayuanyin.
DayuanyinUPLCfingerprintsprincipal component analysishierarchical cluster analysisorthogonal partial least squares-discriminant analysis
万方数据
维普
