Identification model of Raman spectra for Escherichia coli and Shigella species based on deep learning

Objective To explore the use of surface-enhanced Raman scattering (SERS) combined with deep learning for identification of Escherichia coli and Shigella species. Methods A total of 10 Escherichia coli strains and 10 Shigella strains were collected from the Affiliated Hospital of Xuzhou Medical University to establish a Raman spectroscopy database.Through the convolutional neural network (CNN), the classification models of the two bacteria were constructed. Results In the current study, highly similar Escherichia coli and Shigella species were precisely distinguished through SERS combined with deep learning, with an identification accuracy of 100%. Compared with the traditional bacterial identification methods, the identification period was significantly shortened. Conclusions SERS combined with deep learning is greatly useful in rapid identification of Escherichia coli and Shigella species.