Determination of Shigella spp. via label-free SERS spectra coupled with deep learning

Abstract

Accurate discrimination of Shigella spp. sits in the core of shigellosis prevention and control. As a label-free method, surface enhanced Raman spectroscopy (SERS) is being intensively investigated for bacterial diagnostics. In this study, we developed a novel method for rapid and accurate discrimination of Shigella spp. via label-free SERS coupling with multiscale deep-learning method. In particular, SERS spectral deconvolution was used to generate unique barcodes, revealing subtle differences in molecular composition between Shigella spp. Four supervised learning models based on Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and One-Dimensional Multi-Scale CNN (1DMSCNN) were constructed and assessed for their predictive capacities of Shigella spp. The results showed that 1DMSCNN achieved the best performance, which could quickly distinguish four Shigella spp. accurately. Finally, we built a software embedded with 1DMSCNN model to predict raw SERS spectra of Shigella spp., which is freely available at https://github.com/4forfull/1DMSCNN_RAMAN_SHIGELLA.