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UPLC指纹图谱快速评价银杏叶提取物及制剂的质量 被引量:12

Rapid Evaluation on Quality Control of Ginkgo Leaves Extract and Preparations by UPLC Fingerprint
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摘要 目的:建立银杏叶固体制剂、提取物的超高效液相色谱(UPLC)指纹图谱方法,为建立快速而高效的整体质量控制方法提供实验依据。方法:采用Agilent UPLC色谱柱(2.1 mm×100 mm,1.8μm),以乙腈-0.1%甲酸水溶液梯度洗脱,流速0.2 m L·min^(-1),检测波长360 nm,柱温30℃;利用化学计量学方法对色谱数据进行相似度分析、聚类分析和主成分分析。结果:在16 min内完成指纹图谱分析,标出14个共有峰,对4个色谱峰进行了归属;以对照药材、对照提取物生成的对照图谱为参照图谱,相似度考察结果可将样品分成两类,一类为相似度0.312~0.585;一类为相似度0.978~0.993;聚类分析、主成分分析的分类结果与相似度考察结果一致。结论:所建立的UPLC指纹图谱和模式识别方法为银杏叶制剂和提取物的质量控制提供更全面的参考。 Objective: To establish the ultra performance liquid chromatography(UPLC) fingerprint method for effective quality control of Ginkgo leaves extract and Ginkgo leaves solid preparation. Method: The chromatographic fingerprint was obtained with Agilent UPLC column(2. 1 mm × 100 mm,1. 8 μm),with acetonitrile-0. 1% formic acid solution as the mobile phase for gradient elution. The flow rate was 0. 2 m L·min^-1,and the column temperature was maintained at 30 ℃. The detection wavelength was set at 360 nm. The fingerprints of samples were further evaluated by chemometrics methods including similarity analysis(SA),hierarchical clustering analysis(HCA) and principal component analysis(PCA). Result: The fingerprint was obtained within16 minutes. There were 14 common peaks in the fingerprints,and 4 peaks were identified. With the fingerprint of control herb and control extract as the reference fingerprint,the samples can be divided into two types according to the similarity results: one type with similarity of 0. 312-0. 585,and the other type with similarity of 0. 978-0. 993. The results of HCA and PCA were consistent with those of SA. Conclusion: The method is fast and efficient. It can be used for the quality evaluation of Ginkgo leaves extract and Ginkgo leaves solid preparation.
出处 《中国实验方剂学杂志》 CAS CSCD 北大核心 2017年第1期64-68,共5页 Chinese Journal of Experimental Traditional Medical Formulae
关键词 银杏叶提取物 银杏叶固体制剂 超高效液相色谱法 指纹图谱 相似度分析 聚类分析 主成分分析 Ginkgo leaves extract Ginkgo leaves solid preparation UPLC fingerprint similarity analysis hierarchical clustering analysis principal component analysis
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