Wenying Zhou: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Xue Han: Department of Neurology, Affiliated Hospital of Chengde Medical University, Chengde, 067000, Hebei, China.
Yanjun Wu: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Guochao Shi: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Shiqi Xu: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Mingli Wang: State Key Laboratory of Metastable Materials Science and Technology, Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao, 066004, China.
Wenzhi Yuan: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Jiahao Cui: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Zelong Li: Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China.
Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 10 and the limit of detection (LOD) was as low as 1 × 10 M. Then, SERS technology combined with support vector machine (SVM), decision tree (DT), Naive Bayes (NB) and other machine learning algorithms were used to classify and identify the three TCM decoctions, and the classification accuracy rate was as high as 97.78 %. In summary, it is expected that the proposed method combining SERS and machine learning method will have a high development in the practical application of multi-component analytes in TCM.
