质量分析

基于GC指纹图谱结合化学计量学对麝香品质的评价研究*

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  • 四川省中医药科学院, 成都 610041
第一作者 Tel: 13982220266; E-mail:26525157@qq.com
** Tel: 18919561371; E-mail: luohmail666@163.com

收稿日期: 2022-08-02

  网络出版日期: 2024-06-21

基金资助

* 四川省科技厅省级科研院所基本科研业务专项项目(2021JDKY0013)

Quality evaluation of musk based on GC characteristic chromatogram and chemometrics*

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  • Sichuan Academy of Chinese Medicine Sciences, Center of Instrumental Analysis, Chengdu 610041, China

Received date: 2022-08-02

  Online published: 2024-06-21

摘要

目的:建立GC指纹图谱,并结合化学计量学方法对麝香化学成分进行相关性分析。方法:采用Agilent HP-5气相色谱柱 (30 m×0.25 mm,0.25 μm),进样口温度为230 ℃,不分流模式,FID检测器温度为280 ℃,程序升温。以麝香酮峰为参照,利用“中药色谱指纹图谱相似度评价系统(2012版)”建立16批麝香样品的指纹图谱,确定共有峰并进行相似度评价。基于指纹图谱共有峰峰面积测定结果,采用聚类分析(HCA)、主成分分析(PCA)和偏最小二乘法-判别分析(OPLS-DA)对麝的品种、取香年龄、养殖地与麝香化学成分进行相关性评价。结果:建立麝香GC 指纹图谱,确定17个共有峰;16批样品与对照指纹图谱相似度在0.89~0.996,3种化学识别模式分析方法可将16批样品分为2类,并筛选出导致不同年龄阶段质量差异的 5个差异标志物。结论:本研究建立的指纹图谱分析方法简单、可靠,精密度、稳定性、重复性良好,结合化学计量学方法,能够较好地辨认影响麝香质量差异的标志性成分,为麝香的质量控制和品质评价提供科学依据。

本文引用格式

汤燕, 罗恒, 沈立, 杨安东 . 基于GC指纹图谱结合化学计量学对麝香品质的评价研究*[J]. 药物分析杂志, 2023 , 43(11) : 1965 -1973 . DOI: 10.16155/j.0254-1793.2023.11.20

Abstract

Objective: To establish a quality evaluation method for musk based on GC fingerprints and use chemometrics to perform correlation analysis of chemical components of musk. Methods: The chromatographic column was Agilent HP-5 (30 m×0.25 mm, 0.25 μm) with temperature programming, the sample inlet temperature was set at 230 ℃, FID detector temperature was set at 280 ℃. Using Muscone as a reference, the GC fingerprint of 16 batches of musk samples was established, the common peaks were defined by “similarity evaluation system for the chromatographic fingerprint of Tradition Chinese medicine”(2012 Version), and the similarity was calculated. The chemometrics methods such as cluster analysis (HCA),principal component analysis (PCA) and partial least squares analysis (OPLS-DA) were used to evaluate the quality of different batches of musk based on the results of fingerprint common peak area, and analyze the correlation between musk deer species, musk deer age, breeding place, and musk chemical composition. Results: The GC fingerprint of musk was established and 17 common peaks were identified. The similarities between the 16 batches of samples and the reference fingerprint were 0.89-0.996. Three chemical pattern recognition analysis methods divided the samples into two categories, and 5 main components that caused the quality difference in different ages were screened out. Conclusion: The GC characteristic chromatogram established in this study is simple and reliable, with good precision, stability, and repeatability. Based on chemical pattern recognition technology, the marker peaks that may affect the difference of musk are better identified. This method can provide a scientific basis for quality control and quality evaluation of M musk。

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