High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction
Wenying Zhou,
Xue Han,
Yanjun Wu,
Guochao Shi,
Shiqi Xu,
Mingli Wang,
Wenzhi Yuan,
Jiahao Cui,
Zelong Li
Affiliations
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; Corresponding author.
Shiqi Xu
Hebei International Research Center for Medical-Engineering, Chengde Medical University, Chengde, 067000, Hebei, China; Corresponding author.
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; Corresponding author.
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 × 107 and the limit of detection (LOD) was as low as 1 × 10−10 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.
