Phys.org March 4, 2022
Researchers in South Korea have demonstrated a markedly simpler, faster, and effective route to classify signals of two common bacteria E. coli and S. epidermidis and their resident media without any separation procedures by using surface-enhanced Raman spectroscopy (SERS) analysis boosted with a newly proposed deep learning model named dual-branch wide-kernel network (DualWKNet). With outstanding classification accuracies up to 98%, the synergistic combination of SERS and deep learning serves as an effective platform for “separation-free“ detection of bacteria in arbitrary media with short data acquisition times and small amounts of training data. Universal and fast bacterial detection technology is imperative for food safety analyses and diagnosis of infectious diseases…read more. TECHNICAL ARTICLEÂ
Schematics of the general process of Raman data collection and analysis…. Credit: The Korea Advanced Institute of Science and Technology (KAIST)