Separation-free bacterial identification in arbitrary media via deep neural network-based SERS analysis.

Universal and fast bacterial detection technology is imperative for food safety analyses and diagnosis of infectious diseases. Although surface-enhanced Raman spectroscopy (SERS) has recently emerged as a powerful solution for detecting diverse microorganisms, its widespread application has been hampered by strong signals from surrounding media that overwhelm target signals and require time-consuming and tedious bacterial separation steps. By using SERS analysis boosted with a newly proposed deep learning model named dual-branch wide-kernel network (DualWKNet), a markedly simpler, faster, and effective route to classify signals of two common bacteria E.