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藏药斑花黄堇HPLC指纹图谱研究 被引量:1

Study on the HPLC fingerprint of Tibet medicine Corydalis conspersa Maxim
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摘要 目的:建立藏药斑花黄堇HPLC指纹图谱评价体系。方法:采用HPLC法,Agilent ZORBAX SB-C18柱(250mm×4.6mm,5μm);流动相:甲醇-水(1%冰乙酸),非线性梯度洗脱;柱温:30℃;流速:1mL/min;检测波长:360nm;进样量:20μL。采用"中药色谱指纹图谱相似度评价系统(2004A版)"对11批斑花黄堇指纹图谱进行相似度评价分析,并结合系统聚类分析和主成分分析进行模式识别研究。结果:建立了有15个共有峰的斑花黄堇HPLC指纹图谱,相似度在0.906~0.989,系统聚类分析和主成分分析均将样品分为两大类。结论:所建HPLC指纹图谱可为斑花黄堇的质量评价提供参考。 Objective: To establish an evaluation system for HPLC fingerprint of Corydalis conspersa Maxim.. Methods: HPLC method was adopted. Chromatographic column Agilent ZORBAX SB-C18 column(250 mm×4.6 mm, 5μm)was used with methanol-water(1%glacial acetic acid) in gradient elution mode. The column temperature was 30℃, the flow rate was 1.0 mL/min, the detection wavelength was 360 nm and the injection volume was 20μL. The ’Chinese Medicine Chromatographic Fingerprint Smilarity Evaluation System(2004 A version)’ was used to analyze the fingerprints of 11 batches of Corydalis conspersa Maxim. fingerprints, and combined with systematic clustering analysis and principal component analysis for pattern recognition. Results: The HPLC fingerprint of Corydalis conspersa Maxim. with 15 common peaks was established, the similarity was between 0.906 and 0.989, both the systematic clustering analysis and principal component analysis divided the samples into 2 categories. Conclusion: The established fingerprint can provide a reference for the quality evaluation of Corydalis conspersa Maxim..
作者 吴江 王志波 马如媛 WU Jiang;WANG Zhi-bo;MA Ru-yuan(School of Chemistry and Chemical Engineering,Qinghai Nationalities University,Xining 810007,China;School of Pharmacy,Qinghai Nationalities University,Qinghai Provincial Key Laboratory for Chemistry of Plant Resources of Qinghai-Tibet Plateau,Xining 810007,China)
出处 《中华中医药杂志》 CAS CSCD 北大核心 2021年第3期1603-1606,共4页 China Journal of Traditional Chinese Medicine and Pharmacy
基金 青海省自然科学基金项目(No.2016-ZJ-903)。
关键词 藏药 斑花黄堇 高效液相色谱法 指纹图谱 系统聚类分析 主成分分析 质量控制 相似度评价分析 Tibet medicine Corydalis conspersa Maxim. HPLC Fingerprint System cluster analysis Principal component analysis Quality control Similarity evaluation analysis
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