Targeted Surface-Enhanced Raman Scattering for Highly Accurate Identification of Bacterial Species and Finding Spectral Signatures with Explainable Artificial Intelligence

. Using a deep neural network model, the targeted SERS approach with mannose-modified AuNPs under 532 nm excitation achieved a high classification accuracy (96.1%) for 14 bacterial species. In addition, we propose a framework designed to explain how AI algorithms accurately interpret Raman spectra. The normalized positive values of Shapley additive explanations (npSHAP) were utilized to identify the top five peaks as an AI-selected spectral barcode. The spectral signatures identified by the proposed framework are presented in a manner that is both intuitive and straightforward, facilitating a clear and precise interpretation of the bacterial classification process.