Quality Evaluation of Solanum Nigrum Based on HPLC Fingerprint and Chemical Pattern Recognition
Objective To establish a high-performance liquid chromatography(HPLC)fingerprint and chemical pattern recognition method for evaluating the quality of Solanum nigrum.Methods The chromatographic column was Waters C18 column(250 mm × 4.6 mm,5 μm),the mobile phase was acetonitrile-0.3％phosphoric acid solution(gradient elution),and the flow rate was 1.0 mL/min,the detection wavelength was 205 nm,the column temperature was 26 ℃,and the injection volume was 10 μL.HPLC overlay fingerprints of 13 batches of samples were established,and the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine(Version 2004A)was used for similarity evaluation to determine and identify common peaks,and the content of identified components was determined.The different batches of samples were clustered by the hierarchical cluster analysis(HCA),principal component analysis(PC A),and orthogonal partial least squares-discriminant analysis(OPLS-DA).Results Nine common peaks were calibrated for 13 batches of samples,with similarity ranging from 0.701 to 0.984.Two components were identified,namely solanen(peak 6)and solamargine(peak 7),with the linear range of 3.9-500 μg/mL(R2 ≥ 0.995 0,n=5).The content of solanenin in Vietnamese-produced Solanum photeinocarpum(895.57 mg/kg)was significantly higher than that in Guangxi Nanning-produced Solanum photeinocarpum(68.05 mg/kg,it was the highest content in China).However,the content of solamargine in Vietnamese-produced Solanum photeinocarpum(68.05 mg/kg)was only 7.85％of that in Guangxi Nanning-produced Solanum photeinocarpum(867.42 mg/kg).The HCA,PCA,OPLS-DA results showed that the 13 batches of Solanum nigrum samples were divided into two categories based on their origins,one group was Solanum nigrum,and the other group was Solanum photeinocarpum.Conclusion The method is simple,accurate,which can quickly,scientifically,and accurately evaluate the quality of Solanum nigrum.
万方数据
维普
