摘要
目的:通过建立凉粉草药材的高效液相(HPLC)指纹图谱,为科学区分及评价不同来源的凉粉草药材提供参考。方法:采用高效液相色谱仪对凉粉草提取溶液进行梯度洗脱,对比不同来源的凉粉草溶液所得色谱图,选取其中9个特征峰,对其峰面积数据采用相似度分析、主成分分析等进行数据处理。结果:建立凉粉草的指纹图谱,20批次的凉粉草相似度较高,基本大于0.9,统计学分析结果基本一致。结论:来自广东平远上举镇、泗水镇、仁居镇等地的凉粉草质量较好;指纹图谱能较好的区分不同产地的凉粉草,为其质量控制提供科学依据。
Obejective:To provide reference for scientific differentiation and evaluation of Mesona chinensis from different sources by establishing high performance liquid phase(HPLC)fingerprints of Mesona chinensis.Method:Gradient elution was performed on the extract solution of Mesona chinensis by high performance liquid chromatograph.The chromatograms obtained from the solution of Mesona chinensis from different sources were compared,and 9 characteristic peaks were selected,and the peak area data were analyzed by similarity and principal component analysis perform data processing.Results:The fingerprint of Mesona chinensis was established,and the similarity of 20 batches of Mesona chinensis was relatively high,basically greater than 0.9,and the statistical analysis results were basically the same.Conclusion:The quality of the jelly grass from Shangju Town,Sishui Town,Renju Town,Pingyuan,Guangdong Province are better than others;the fingerprint can better distinguish the Mesona chinensis from different origins,and provide a scientific basis for its quality control.
作者
赖志明
宋晓娟
魏星任
莫穗芬
卢晓莹
曾唯雅
严萍
詹若挺
Lai Zhiming;Song Xiaojuan;Wei Xingren;Mo Suifen;Lu Xiaoying;Zeng Weiya;Yan Ping;Zhan Ruoting(Guangdong Nanling Pharmaceutical Co.,Ltd.,Pingyuan 514600;Research Center of Chinese Herbal Resource Science and Engineering,Guangzhou University of Traditional Chinese Medicine,Joint Laboratory of National Engineering Research Center for the Pharmaceutics of Traditional Chinese Medicines,Guangzhou 510006,China)
出处
《广东化工》
CAS
2022年第17期187-189,共3页
Guangdong Chemical Industry
基金
广东省普通高校“服务乡村振兴计划”重点领域专项—基于广东乡村振兴的南药产业科技服务体系构建(2019KZDZX2017)
广东省省级乡村振兴战略专项—广东省现代南药产业技术体系创新团队(2020KJ148)
广州中医药大学2021年国家级大学生创新创业训练计划项目(X202110572197)。
关键词
凉粉草
质量评价
HPLC指纹图谱
聚类分析
主成分分析
Mesona chinensis
estimation of quality
HPLC Fingerprint
Principal Components Analysts(PCA)
ClusterAnalysis(CA)