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生菟丝子、盐菟丝子的高效液相色谱特征图谱和5种主要成分含量测定 被引量:2

Specific Chromatogram of Chinese Dodder Seed Unprocessed or Processed with Salt Solution on High-performance Liquid Chromatography and Content Determination of Five Main Components
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摘要 目的建立生菟丝子与盐菟丝子样品的高效液相色谱特征图谱及多成分含量测定方法。方法采用高效液相色谱法建立生菟丝子和盐菟丝子的特征图谱并进行5种成分的含量测定,通过层次聚类分析、主成分分析、正交偏最小二乘-判别分析和皮尔逊相关性分析评价不同批次菟丝子生品、盐炙饮片的质量差异,找寻质量差异性的主要成分。结果生菟丝子、盐菟丝子的高效液相色谱特征图谱中有18个共有峰,指认出绿原酸、芦丁、金丝桃苷、紫云英苷、异鼠李素5个共有峰,峰面积占比>70%。相似度分析结果显示,生菟丝子、盐菟丝子的相似度>0.9。聚类分析可将两者聚为2类;主成分分析结果显示,前3个主成分可区分生菟丝子与盐菟丝子;正交偏最小二乘-判别分析结合变量投影重要性值>1的原则,筛选出金丝桃苷、紫云英苷、异鼠李素3种与菟丝子生品、盐炙饮片质量相关性较强的化学成分。含量测定结果表明,生菟丝子、盐菟丝子中的金丝桃苷、紫云英苷、异鼠李素含量差异有统计学意义(P<0.05)。皮尔逊相关性分析显示,金丝桃苷与紫云英苷、异鼠李素含量呈显著负相关。结论基于化学计量学方法建立的生菟丝子与盐菟丝子高效液相色谱特征图谱及含量测定方法简便准确,可为菟丝子生品、盐炙饮片质量评价提供依据。 Objective To establish the specific chromatogram of Chinese dodder seed unprocessed or processed with salt solution on high-performance liquid chromatography(HPLC)and the method for content determination of multiple components.Methods HPLC was performed to establish the specific chromatogram of Chinese dodder seed unprocessed or processed with salt solution and determine the content of five components.Hierarchical cluster analysis(HCA),principal component analysis(PCA),orthogonal partial least squares-discriminant analysis(OPLS-DA),and Pearson correlation analysis were used to investigate quality differences between different batches of unprocessed product and salt-processed decoction pieces of Chinese dodder seed,and the main components leading to quality differences were identified.Results There were 18 common peaks on the specific chromatogram of Chinese dodder seed unprocessed or processed with salt solution on HPLC,and 5 common peaks were identified,i.e.,chlorogenic acid,rutin,hyperoside,astragalin,and isorhamnetin,with a peak area accounting for more than 70%.The similarity analysis showed a similarity of>0.9 between unprocessed Chinese dodder seed and Chinese dodder seed processed with salt solution.Chinese dodder seed unprocessed or processed with salt solution was clustered into two groups based on HCA;PCA showed that the first three principal components could distinguish unprocessed Chinese dodder seed from Chinese dodder seed processed with salt solution;based on the principle of variable importance in projection>1 by OPLS-DA,three chemical components(hyperoside,astragalin,and isorhamnetin)were strongly associated with the quality of unprocessed Chinese dodder seed and Chinese dodder seed processed with salt solution.Content determination showed that there were significant differences in the content of hyperoside,astragalin,and isorhamnetin between unprocessed Chinese dodder seed and Chinese dodder seed processed with salt solution(P<0.05).The Pearson correlation analysis showed that the content of hyperoside was negatively correlated with that of astragalin and isorhamnetin.Conclusion Established based on the chemometric method,HPLC specific chromatograms and content determination methods for Chinese dodder seed unprocessed or processed with salt solution is simple and accurate and can provide a basis for the quality assessment of the unprocessed product and salt-processed decoction pieces of Chinese dodder seed.
作者 徐白杨 丁西萌 金传山 吴德玲 朋汤义 汪小莉 XU Baiyang;DING Ximeng;JIN Chuanshan;WU Deling;PENG Tangyi;WANG Xiaoli(School of Pharmacy,Anhui University of Chinese Medicine,Anhui Hefei 230012,China;Anhui Provincial Key Laboratory of New Manufacturing Technology for Traditional Chinese Medicine Decoction Pieces,Anhui Hefei 230012,China;Traditional Chinese Medicine Processing and Inheritance Base,State Administration of Traditional Chinese Medicine,Anhui University of Chinese Medicine,Anhui Hefei 230012,China;The First Affiliated Hospital of Anhui University of Chinese Medicine,Anhui Hefei 230031,China)
出处 《安徽中医药大学学报》 CAS 2023年第2期95-100,共6页 Journal of Anhui University of Chinese Medicine
基金 国家自然科学基金项目(82204623) 安徽中医药大学青年科技英才培育项目(2021qnyc04) 安徽中医药大学2019-2020人才支持计划项目(2019rcyb002)。
关键词 菟丝子 盐炙 特征图谱 含量测定 聚类分析 主成分分析 判别分析 Chinese dodder seed Processing with salt solution Specific chromatogram Content determination Cluster analysis Principal component analysis Discriminant analysis
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