指纹图谱和化学模式识别研究黄芪的不同基原和生长方式
Fingerprint and Chemical Pattern Recognition Study on Astragali Radix of Different Botanical Origins and Growth Patterns
赵颖 1尚奥丽 1李亚栋 1杨若泓 1陈君1
作者信息
- 1. 中国药科大学 中药学院,南京 211100
- 折叠
摘要
目的:基于化学组分信息探究不同基原、生长方式的黄芪药材的质量差异.方法:采用高效液相色谱-蒸发光散射检测技术(HPLC-ELSD)建立指纹图谱分析方法,对收集的28批不同基原、不同生长方式的黄芪样品进行相似度评价和化学模式识别分析.结果:建立的黄芪HPLC指纹图谱共鉴定出11个共有峰,聚类分析将28批样品分为蒙古黄芪和膜荚黄芪两类,其中蒙古黄芪明显分为野生/仿野生黄芪和移栽黄芪两类.正交偏最小二乘法判别分析(OPLS-DA)筛选出5个野生/仿野生黄芪与移栽黄芪的差异性成分.结论:本研究建立的黄芪HPLC-ELSD指纹图谱结合化学模式识别方法,较为全面地反映出黄芪药材化学成分的总体质量特征,可区分不同基原和不同生长方式的黄芪药材,可为黄芪药材质量控制提供参考.
Abstract
Objective:Based on the information of chemical components,the differences of Astragali Radix samples form different botanical origins and growth patterns were studied.Methods:A fingerprint analysis method was established by high performance liquid chromatography with evaporative light scattering detection(HPLC-ELSD),and similarity evaluation and chemical pattern recognition analysis were carried out on 28 batches of Astragali Radix samples from different botanical origins and growth patterns.Results:The HPLC fingerprints of Astragali Radix were established,and 11 common peaks were identified.Cluster analysis divided the 28 batches of samples into two categories:Astragalus mongholicus and Astragalus membranaceus,among which the Astragalus mongholicus samples were obviously divided into wild/wild-sim-ulated and cultivated.Five components differensiating wild/wild-simulated Astragali Radix and cultivated Astragali Radix were selected by orthogonal partial least squares discriminant analysis(OPLS-DA).Conclu-sion:The HPLC-ELSD fingerprints of Astragali Radix combined with the chemical pattern recognition method established in this study can comprehensively reflect the overall quality characteristics of the chem-ical components of Astragali Radix,and can distinguish Astragali Radix from different botanical origins and growth patterns,which can provide reference for the quality control of Astragali Radix.
关键词
黄芪/基原/生长方式/指纹图谱/化学模式识别Key words
Astragali Radix/Botanical origin/Growth pattern/Fingerprint/Chemical pattern recognition analysis引用本文复制引用
基金项目
国家重点研发计划(2022YFC3501604)
出版年
2024
NETL
NSTL
万方数据