Predicting the grades of Astragali radix using mass spectrometry-based metabolomics and machine learning / 药物分析学报

ABSTRACT

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.