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双定性双定量相似度法评价麻黄真实质量 被引量:8

Assessment of authentical quality of herba ephedrae by dual qualitative and dual quantitative similarities
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摘要 目的应用双定性双定量相似度法评价麻黄药材质量。方法采用反相高效液相色谱法,以CenturySIL C18BDS(200 mm×4.6 mm,5μm)色谱柱,以色谱指纹图谱信息量指数I为目标函数优化选择指纹图谱检测条件,确定以0.1%磷酸水-甲醇溶剂系统为流动相,线性梯度洗脱,流速为1.0 mL.min-1,柱温(30.00±0.15)℃,进样量10μL,紫外检测波长210 nm。以双定量相似度为参量通过对10批麻黄进行聚类分析并进行质量评价。结果以麻黄碱峰为参照物峰,确定了41个共有峰,建立了麻黄HPLC指纹图谱,用双定性双定量相似度法评价出3批质量完全合格,3批含量明显偏高,3批含量偏低,1批化学成分分布比例与含量不合格。结论所建立HPLC指纹图谱稳定性和特征性较强,用双定性双定量相似度法可从宏观角度定性定量鉴别麻黄质量。 Objective To use dual qualitative and dual quantitative similarities (DODOS) to evaluate the quality of herba ephedrae. Methods Index of fingerprint information amount (I) was used to optimize the CFP conditions. The best conditions were obtained by injecting 10 μL of the sample solution each time on a CenturySIL BDS column (200 min× 4. 6 mm, 5μm) with the gradient elution solvent system of phosphoric acid (0. 1%) -water-methanol. The flow rate was 1.0 mL · min^-1 , the column temperature was maintained at (30.00±0. 15)℃, and the detection wavelength was 210 nm. The 10 batches of herba ephedrae were identified with hierarchical clustering analysis using dual quantitative similarities as variables. Results CFPs containing 41 co-possessing peaks were obtained by choosing ephedrine peak as the reference peak. Among the 10 batches of herba ephedrae, 3 batches were completely qualified. The contents of 3 batches were obviously higher while another 3 batches were lower, and the chemical constituent distribution proportion in 1 batch was not qualified. Conclusion This method with good precision and reproducibility can be perfectly applied in the quality control of herba ephedrae. DQDQS method can effectively and authentically control the quality of herba ephedrae.
出处 《中南药学》 CAS 2009年第1期59-62,共4页 Central South Pharmacy
基金 国家自然科学基金重大研究计划课题(90612002)
关键词 麻黄 HPLC指纹图谱 色谱指纹图谱信息量指数I 双定性双定量相似度法 聚类分析 herba ephedrae HPLC fingerprint index of fingerprint information amount (I) dual qualitative similarities (SF and SF') and dual quantitative similarities (C and P) hierarchical cluster analysis
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