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基于伪靶向代谢组学和化学计量学方法的黄芪质量评价。

Quality assessment of Astragali Radix based on pseudo-targeted metabolomics and chemometric approach.

机构信息

Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P. R. China.

Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University, Taiyuan, P. R. China.

出版信息

J Sep Sci. 2023 Jun;46(11):e2200985. doi: 10.1002/jssc.202200985. Epub 2023 Apr 6.

DOI:10.1002/jssc.202200985
PMID:36965089
Abstract

Astragali Radix is widely used because of its dual use in medicine and food, and its quality evaluation is of great importance. In this study, a pseudo-targeted metabolomics approach based on scheduled multiple reaction monitoring was developed, and a total of 114 compounds with good linearity, sensitivity, and reproducibility were selected for relative quantification, and the chemical differences between Astragali Radix of different growth patterns were further compared by chemometric analysis. With the help of multivariate and univariate analysis, 26 differential compounds between wild/semi-wild Astragali Radix and cultivated Astragali Radix were determined. Then five marker compounds were screened out by lasso regression, and further verified by systematic clustering, random forest, support vector machine, and logistic regression. In addition, malonyl-substituted flavonoids showed relatively higher content in wild/semi-wild Astragali Radix. Thus, the malonyl substitution was characteristic for flavonoids in wild/semi-wild Astragali Radix. In conclusion, the application of pseudo-targeted metabolomics and various statistical methods could offer multi-dimensional information for the holistic quality evaluation of Astragali Radix.

摘要

黄芪由于兼具药用和食用双重功能而被广泛使用,其质量评价非常重要。本研究建立了一种基于预定多重反应监测的伪靶向代谢组学方法,共选择了 114 种具有良好线性、灵敏度和重现性的化合物进行相对定量,并通过化学计量学分析进一步比较了不同生长模式黄芪之间的化学差异。借助多元和单变量分析,确定了野生/半野生黄芪与栽培黄芪之间的 26 种差异化合物。然后通过套索回归筛选出 5 个标记化合物,并通过系统聚类、随机森林、支持向量机和逻辑回归进一步验证。此外,半野生/野生黄芪中存在相对较高含量的丙二酰化黄酮类化合物。因此,丙二酰化是野生/半野生黄芪黄酮类化合物的特征。总之,伪靶向代谢组学和各种统计方法的应用可以为黄芪的整体质量评价提供多维信息。

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