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

Hui Yu; Zhilan Yang; Shiying Fu; Yuejiao Zhang; Rajapandiyan Panneerselvamc; Baoqiang Li; Lin Zhang; Zehui Chen; Xin Wang; Jianfeng Li

doi:10.1039/D3CC01129A

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

Hui Yu,^a Zhilan Yang,^a Shiying Fu,^a Yuejiao Zhang,^a Rajapandiyan Panneerselvamc,

^b Baoqiang Li,

^c Lin Zhang,*^c Zehui Chen,*^d Xin Wang*^a and Jianfeng Li

*^a

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* Corresponding authors

^a 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
E-mail: xinwang@xmu.edu.cn, li@xmu.edu.cn

^b Department of Chemistry, SRM University AP, Amaravati, Andhra Pradesh 522502, India

^c State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China
E-mail: Zhanglin_zju@aliyun.com

^d Xiamen City Center for Disease Control and Prevention, Xiamen 361005, China
E-mail: 3846172@qq.com

Abstract

Based on label-free SERS technology, the relationship between the Raman signals of pathogenic Vibrio 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 Vibrio species within 15 minutes, providing a new method for pathogen identification.

Chemical Communications

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

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

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