首页|Predicting the grades of Astragali radix using mass spectrometry-based metabolomics and machine learning

Predicting the grades of Astragali radix using mass spectrometry-based metabolomics and machine learning

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Astragali radix(AR,the dried root of Astragalus)is a popular herbal remedy in both China and the United States.The commercially available AR is commonly classified into premium graded(PG)and ungraded(UG)ones only according to the appearance.To uncover novel sensitive and specific markers for AR grading,we took the integrated mass spectrometry-based untargeted and targeted metabolomics ap-proaches to characterize chemical features of PG and UG samples in a discovery set(n=16 batches).A series of five differential compounds were screened out by univariate statistical analysis,including arginine,calycosin,ononin,formononetin,and astragaloside Ⅳ,most of which were observed to be accumulated in PG samples except for astragaloside Ⅳ.Then,we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model.Finally,the external validation in an independent validation set of AR(n=20 batches)verified that the five com-pounds,as well as the model,had strong capability to distinguish the two grades of AR,with the pre-diction accuracy＞90％.Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading.

Astragali radixMetabolomicsMachine learningQuality markersPrediction model

Xinyue Yu、Jingxue Nai、Huimin Guo、Xuping Yang、Xiaoying Deng、Xia Yuan、Yunfei Hua、Yuan Tian、Fengguo Xu、Zunjian Zhang、Yin Huang

展开 >

Key Laboratory of Drug Quality Control and Pharmacovigilance,China Pharmaceutical University,Ministry of Education,Nanjing,210009,China

Department of Pharmaceutical Analysis,School of Pharmacy,China Pharmaceutical University,Nanjing,210009,China

Center for Biological Technology,Anhui Agricultural University,Hefei,230036,China

This work was supported by the National Science and Tech-nology Major Project of ChinaOpen Project Program of MOE Key Laboratory of Drug Quality Control and PharmacovigilanceOpen Project Program of MOE Key Laboratory of Drug Quality Control and Pharmacovigilance

2017ZX09101001DQCP2017MS02China

2021

药物分析学报(英文)
西安交通大学

药物分析学报(英文)

SCI
影响因子：0.244
ISSN：2095-1779
年,卷(期)：2021.11(5)
  • 2
  • 32