HPLC指纹图谱结合化学模式识别评价天冬的质量
Quality evaluation of Asparagi Radix by HPLC fingerprinting with chemical pattern recognition
姚诚 1张宝林 1罗东玲 1李凤鸣 1罗誓言 1母芹 1张敬梅 1罗敏1
作者信息
- 1. 内江市食品药品检验检测中心四川省药品监督管理局化学药品质量控制及评价重点实验室,四川内江 641100
- 折叠
摘要
目的 采用指纹图谱结合化学模式识别评价不同产地天冬的质量.方法 采用中谱红ODS-H C18色谱柱(250 mm×4.6 mm,5 μm),流动相为乙腈-甲醇-0.1％磷酸水,梯度洗脱,流速1.0 mL·min-1,柱温30 ℃,检测波长215 nm.采用SIMCA 14.1软件,用化学模式识别分析21批不同产地天冬样品的共有峰.结果 建立了不同产地天冬的HPLC指纹图谱,标定了11个共有峰,并指认了2个成分,相似度为0.712～0.951;聚类分析、主成分分析及偏最小二乘判别分析均将21批天冬分为4类,结合投影重要性指标(VIP)＞1,筛选出峰1、峰5、峰6、峰8、峰9、峰11等6个成分可能是不同产地天冬的差异性特征成分.结论 所用方法稳定可靠、专属性强,可为综合评价不同产地天冬药材的质量提供参考.
Abstract
OBJECTIVE To evaluate the quality of Asparagi Radix from different growing regions using the fingerprinting and chemical pattern recognition method.METHODS Medium red ODS-H C18 column(250 mm×4.6 mm,5 µm)was adopted,the acetonitrile-methanol-0.1％phosphoric acid water was used as mobile phase for gradient elution,the volume flow rate was 1.0 mL·min-1,the column temperature was 30 ℃,and the detection wavelength was 215 nm.SIMCA 14.1 software was used for chemical pattern recognition analysis of common peaks of 21 batches of Asparagi Radix samples from growing regions.RESULTS HPLC fingerprints of Asparagi Radix from four cultivation areas were established.A total of 11 common peaks were calibrated and two components were identified;The similarity was 0.712-0.951.Cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis divided 21 batches of Asparagi Radix into 4 categories.Six components including Peak 1,Peak 5,Peak 6,Peak 8,Peak 9 and Peak 11 were selected according to the projection importance index VIP value,which may be the different characteristic components of Asparagi Radix from different cultivation areas.CONCLUSION The method is stable,reliable and highly specific,and can provide a reference for the comprehensive evaluation of the quality of Asparagi Radix from different growing regions.
关键词
天冬/产地/HPLC指纹图谱/相似度/聚类分析/主成分分析/偏最小二乘判别分析/差异性特征成分/质量评价Key words
Asparagi Radix/Cultivation areas/HPLC fingerprint/Similarity/Cluster analysis/Principal component analysis/Orthogonal partial least squares discriminant analysis/Different characteristic components/Quality evaluation引用本文复制引用
基金项目
四川省药品监督管理局药品科技计划项目(2021011)
出版年
2024
NETL
NSTL
万方数据