金橘药材的UPLC指纹图谱建立、聚类分析及主成分分析 点击下载
论文标题: 金橘药材的UPLC指纹图谱建立、聚类分析及主成分分析
英文标题:
中文摘要: 目的:建立金橘药材的超高效液相色谱(UPLC)指纹图谱,并进行聚类分析和主成分分析。方法:采用UPLC法,色谱柱为Waters Acquity UPLC BEH C18,流动相为乙腈-0.1%磷酸水溶液(梯度洗脱),流速为0.3 mL/min,检测波长为330 nm,进样量为2 μL。以金柑苷峰为参照,绘制8批药材样品的UPLC指纹图谱;采用《中药色谱指纹图谱相似度评价系统》(2012 版)进行相似度评价,确定共有峰;采用SPSS 24.0软件对8批药材样品进行聚类分析和主成分分析。结果:8批药材样品的UPLC指纹图谱有24个共有峰,相似度均大于0.97。聚类分析结果显示,8批药材样品可聚为两类,S1~S4、S6~S8聚为一类,S5聚为一类。经主成分分析,3个主成分因子的累计方差贡献率为81.366%。结论:所建UPLC指纹图谱及聚类分析和主成分分析结果可为金橘药材的质量控制提供参考。
英文摘要: OBJECTIVE: To establish UPLC fingerprint of Fortunella margarita, and to conduct its cluster analysis and principal component analysis. METHODS: UPLC method was adopted. The determination was performed on Waters Acquity UPLC BEH C18 column with mobile phase consisted of acetonitrile-0.1% phosphoric acid solution (gradient elution) at the flow rate of 0.3 mL/min. The detection wavelength was set at 330 nm, and sample size was 2 μL. Using fortunellin as reference, UPLC fingerprints of 8 batches of F. margarita were determined. The similarity of 8 batches of samples was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System(2012 edition) to confirm common peak. Cluster analysis and principal component analysis were performed by using SPSS 24.0 software. RESULTS: There were 24 common peaks in UPLC fingerprints of 8 batches of sample,the similarity of which was higher than 0.97. Cluster analysis showed that 8 batches of samples were clustered into 2 categories. S1, S2, S3, S4, S6, S7 and S8 were clustered into one category; S5 was clustered into the other category. By principal component analysis, the accumulative contribution rate of three main components was 81.366%. CONCLUSIONS: Established UPLC fingerprint, the results of cluster analysis and principal component analysis can provide reference for quality control of F. margarita.
期刊: 2019年第30卷第12期
作者: 黄华花,王明军,黄鸣清,吕诗诗,王圣江
英文作者: HUANG Huahua,WANG Mingjun,HUANG Mingqing,LYU Shishi,WANG Shengjiang
关键字: 金橘;超高效液相色谱法;指纹图谱;相似度评价;聚类分析;主成分分析
KEYWORDS: Fortunella margarita; UPLC; Fingerprint; Similarity evaluation; Cluster analysis; Principal component analysis
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