基于FTIR的黄芪药材质量评价研究
FTIR quantum fingerprints of Radix astragali and quality control
郝彩琴 1周莹 2刘明嘉 3孙国祥3
作者信息
- 1. 宁夏职业技术学院生命科技学院,银川 750002
- 2. 抚顺市药品检验检测所,辽宁 抚顺 113006
- 3. 沈阳药科大学药学院,沈阳 110016
- 折叠
摘要
目的 建立黄芪药材的红外原始指纹图谱与量子指纹图谱,为其质量控制提供参考.方法 采用傅里叶变换红外光谱仪采集35批黄芪药材的红外指纹图谱,经数据处理建立黄芪的红外量子指纹图谱,采用系统指纹定量法以及t检验比较两种指纹图谱是否存在差异,并对量子指纹图谱的共有量子峰面积进行系统聚类分析.结果 黄芪药材的红外指纹图谱与量子指纹图谱间并不存在显著性差异,系统指纹定量法将35批黄芪分为8个质量等级,系统聚类分析表明35批黄芪的质量可聚为两类.结论 红外量子指纹图谱能够提供大量特征信息,系统指纹定量法能够全面、准确、直观地将黄芪药材分为8个质量等级,两者结合可以实现对黄芪等中药材质量的全面评价.
Abstract
Objective To establish the infrared original fingerprints and quantum fingerprints of Radix astragali to provide reference for its quality control.Methods The infrared fingerprints of 35 batches of Radix astragali were collected by Fourier transform infrared spectroscopy,and the infrared quantum fingerprints of Radix astragali were established by data processing.The systematically quantified fingerprint method was applied and t-test was used to compare whether the two fingerprints differed from each other,and the area of the common quantum peaks of the quantum fingerprints was systematically determined by clustering analysis.Results There was no significant difference between the infrared fingerprints and quantum fingerprints of Radix astragali.The systematic fingerprint quantification method divided the 35 batches of Radix astragali into 8 quality classes,and the cluster analysis showed that the quality of the 35 batches of Radix astragali could be clustered into two groups.Conclusion The infrared quantum fingerprints can provide a large amount of characteristic information,and the systematic fingerprint quantitative method can comprehensively,accurately and intuitively classify astragalus herbs into 8 quality grades.Combination of both can comprehensively evaluate the quality of Radix astragali and other Chinese herbal medicines.
关键词
黄芪/傅里叶变换红外光谱/量子指纹图谱/系统指纹定量法Key words
Radix astragali/FTIR/quantum fingerprint/systematically quantified fingerprint method引用本文复制引用
基金项目
宁夏中药材开发与利用人才小高地项目(宁人社函[2019]463号)
宁夏回族自治区高教项目(NGY2020144)
出版年
2024
NETL
NSTL
万方数据