Intelligent convolution neural network-assisted SERS to realize highly accurate identification of six pathogenic .

Journal: Chemical communications (Cambridge, England)
Published Date:

Abstract

Based on label-free SERS technology, the relationship between the Raman signals of pathogenic microorganisms and purine metabolites was analyzed in detail. A deep learning CNN model was successfully developed, achieving a high accuracy rate of 99.7% in the identification of six typical pathogenic species within 15 minutes, providing a new method for pathogen identification.

Authors

  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.
  • Zhilan Yang
    College of Materials, State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. xinwang@xmu.edu.cn.
  • Shiying Fu
    College of Materials, State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. xinwang@xmu.edu.cn.
  • Yuejiao Zhang
    College of Materials, State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, College of Energy, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. xinwang@xmu.edu.cn.
  • Rajapandiyan Panneerselvamc
    Department of Chemistry, SRM University AP, Amaravati, Andhra Pradesh 522502, India.
  • Baoqiang Li
    State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China. Zhanglin_zju@aliyun.com.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Zehui Chen
    Xiamen City Center for Disease Control and Prevention, Xiamen 361005, China. 3846172@qq.com.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Jianfeng Li
    Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, No. 100 Pingleiyuan, Chaoyang District, Beijing, China.