摘要
目的建立评价黑果腺肋花楸果实质量的HPLC指纹图谱分析方法,联合聚类分析方法,为黑果腺肋花楸果实质量控制提供一定的参考依据。方法采用Agilent TC-C_(18)(250 mm×4.6 mm,5μm)色谱柱,流动相乙腈-0.5%甲酸水溶液进行梯度洗脱,检测波长为280 nm,柱温30℃,流速1.0 mL·min^(-1),进样量10μL。对11批黑果腺肋花楸进行指纹图谱研究,通过相似度评价、聚类分析对其进行质量评价。结果建立黑果腺肋花楸果实的HPLC指纹图谱,以绿原酸为参照峰,标定17个共有峰,11批样品相似度均在0.90以上,方法学考察均在范围内,聚类分析分为两类。结论通过指纹图谱、聚类分析方法全面综合评价不同产地的黑果腺肋花楸的质量,此法简单、高效、重复性高,可有效用于黑果腺肋花楸的质量评价,为黑果腺肋花楸的质量评价提供了有效的参考依据。
Objective A HPLC fingerprint analysis method for evaluating the quality of S.melanocarpa fruit was established,combined with a cluster analysis method,to provide a certain reference for the quality control of S.melanocarpa fruit.Methods Separation was performed on a Agilent TC-C_(18)(250 mm×4.6 mm,5μm)column and the mobile phase was acetonitrile-0.5%formic acid aqueous solution for gradient elution.The detection wavelength was 280 nm,the column temperature was 30℃,and the flow rate was 1.0 mL·min^(-1).The injection volume is 10μL.The fingerprints of 11 batches of Sorbus rubus were studied and its quality was evaluated by similarity evaluation and cluster analysis.Results The HPLC fingerprint of Sorbus melanocarpa fruit was established.With chlorogenic acid as the reference peak,17 common peaks were calibrated.The similarity of 11 batches of samples were all above 0.90.The methodological investigations were all within the range.The cluster analysis was divided into Two types.Conclusion Comprehensive evaluation of the quality of Sorbus melanocarpa from different origins through fingerprint and cluster analysis methods.This method is simple,efficient and highly repeatable.It can be effectively used for the quality evaluation of Sorbus melanocarpa.The quality evaluation of Sorbus glandularis provides an effective reference basis.
作者
史锐
吴鹏
刘苗苗
丛龙娇
SHI Rui;WU Peng;LIU Miaomiao;CONG Longjiao(College of Pharmacy,Liaoning University of Chinese Medicine,Dalian 110000,Liaoning,China)
出处
《中华中医药学刊》
CAS
北大核心
2021年第12期185-189,I0046,共6页
Chinese Archives of Traditional Chinese Medicine
基金
辽宁省教育厅项目(L201938)。
关键词
黑果腺肋花楸果实
指纹图谱
高效液相色谱法
聚类分析
Aronia melanocarpa fruit
fingerprint
high performance liquid chromatography
cluster analysis