摘要
目的建立中药三七的HPLC化学模式识别方法,用于三七不同药用部位(主根、剪口、支根)的区分,为其质量评价提供可靠依据。方法利用HPLC获取35批不同部位三七样品的化学信息,采用中药色谱指纹图谱相似度评价系统(2012版)计算相似度,运用主成分分析(PCA)和偏最小二乘法-判别分析(PLS-DA)分别进行模式识别研究。结果建立了35批三七样品的HPLC指纹图谱共有模式(相似度>0.994);所建立的PCA模型可区分剪口,PLS-DA可将三者完全区分,并发现人参皂苷Rg1、人参皂苷Rb1等7个特征峰是导致三七不同部位间差异的标志物。结论本实验所建立的化学模式识别模型,能准确有效的区分三七不同部位,为三七的质量控制及评价提供参考依据。
OBJECTIVE To establish a chemical pattern recognition method for Panax notoginseng(P.notoginseng)and classify the main root,rhizome,and rootlet.METHODS The fingerprints of P.notoginseng samples in three different parts were established based on HPLC method.The similarity was calculated by the Similarity Evaluation System of Chromatographic Fingerprints of Traditional Chinese Medicine(2012 edition).The pattern recognition were carried out by principal component analysis(PCA)and partial least squares discriminant analysis(PLS-DA).RESULTS The HPLC fingerprint common pattern of 35 batches of P.notoginseng samples was established.Similarity values were in the range of 0.994 to 1.000,which indicated that similarity analysis could not classify them.The established PCA model could only identify the rhizome,besides PLS-DA can completely identify the three parts of P.notoginseng.Seven characteristic peaks,such as ginsenoside Rg1,ginsenosides Rb1 were screened as biomarkers.CONCLUSION The combination of HPLC fingerprint and chemical pattern recognition could provide a comprehensive reference for the quality control and quality evaluation of P.notoginseng.
作者
孟哲
王丽君
黄洋
江坤
王珏
殷果
王铁杰
MENG Zhe;WANG Li-jun;HUANG Yang;JIANG Kun;WANG Jue;YIN Guo;WANG Tie-jie(School of Pharmacy,Shenyang Pharmaceutical University,Shenyang 110016,China;Shenzhen Institute for Drug Control,Shenzhen 518057,China;Shenzhen Key Laboratory of Drug Quality Standard Research,Shenzhen 518057,China)
出处
《中国药学杂志》
CAS
CSCD
北大核心
2020年第7期504-509,共6页
Chinese Pharmaceutical Journal
基金
深圳市知识创新计划基础研究项目资助(JCYJ20170817141452019)。
关键词
三七
HPLC指纹图谱
化学模式识别
主成分分析
偏最小二乘法-判别分析
Panax notoginseng
HPLC fingerprint
chemical pattern recognition
principal component analysis
partial least squares-discriminant analysis