清喉利咽颗粒的UPLC指纹图谱建立及主成分分析
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篇名: | 清喉利咽颗粒的UPLC指纹图谱建立及主成分分析 |
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摘要: | 目的:建立清喉利咽颗粒的超高效液相色谱(UPLC)指纹图谱,并结合主成分分析法(PCA)为其质量控制提供参考。方法:采用UPLC法。色谱柱为ACQUITY UPLC BEH C18,流动相为乙腈(A)-0.1%磷酸水溶液(B)(梯度洗脱),流速为0.4 mL/min,检测波长为280 nm,柱温为30 ℃,进样量为2 μL。以鞣花酸为参照物,分析12批清喉利咽颗粒样品,采用“中药指纹图谱相似度评价系统”软件进行相似度分析,并根据对照品对其中的共有峰进行指认,同时对共有峰进行主成分分析。 结果:12批清喉利咽颗粒指纹图谱标定了18个共有峰,指认了6个主要色谱峰(没食子酸、鞣花酸、柚皮苷、橙皮苷、新橙皮苷、黄芩苷);12批样品与对照指纹图谱相似度均≥0.984。经主成分分析,18个共有峰可综合为3个主成分,累计方差贡献率为78.277%;黄芩苷和16号峰为清喉利咽颗粒指纹图谱中影响比较大的指标。结论:该方法可为清喉利咽颗粒的质量评价提供参考。 |
ABSTRACT: | OBJECTIVE: To establish the UPLC fingerprint for Qinghou liyan granule, and provide reference for its quality control by combining with principal component analysis (PCA). METHODS: UPLC was performed on the column of ACQUITY UPLC BEH C18 with mobile phase of acetonitrile (A)-0.1% aqueous phosphoric acid solution (B) (gradient elution) at a flow rate of 0.4 mL/min, the detection wavelength was 280 nm, column temperature was 30 ℃, and injection volume was 2 μL. Using ellagic acid as a reference, 12 batches of samples were analyzed, “Similarity Evaluation Software for Chromatographic Fingerprint of Traditional Chinese Medicine” was used for the similarity analysis and identification of the common peaks, and the PCA was used for common peaks. RESULTS: There were 18 common peaks in the fingerprints of 12 batches of samples, and 6 principal peaks (gallic acid, ellagic acid, naringin, hesperidin, neohesperidin and baicalin) were identified; the similarity degree of 12 batches and reference fingerprints were no less than 0.984. According to PCA, the 18 peaks can be integrated into 3 principal components, with cumulative contribution rate of principal component of 78.277%; baicalin and 16 peaks were the discriminating factors of the fingerprint of Qinghou liyan granule. CONCLUSIONS: The method can provide reference for the quality control of Qinghou liyan granule. |
期刊: | 2017年第28卷第6期 |
作者: | 牛小花,陈洪源 |
AUTHORS: | NIU Xiaohua,CHEN Hongyuan |
关键字: | 清喉利咽颗粒;超高效液相色谱法;指纹图谱;主成分分析 |
KEYWORDS: | Qinghou liyan granule; UPLC; Fingerprint; Principal component analysis |
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