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Updated: Aug 1, 2025
Development of an Electrochemical DNA Biosensor to Detect a Foodborne Pathogen
Published on: June 3, 2018
Intelligent convolution neural network-assisted SERS to realize highly accurate identification of six pathogenic
Hui Yu1, Zhilan Yang1, Shiying Fu1
1College 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.
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.
