Multidimensional surface-enhanced Raman scattering biosensor integrated convolutional neural networks for accurate bacteria identification

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful technique for bacterial detection, offering high sensitivity and molecular-level specificity. However, conventional label-free SERS methods relie on the spontaneous adsorption of limited chemical components onto the SERS substrate. Here we developed a multidimensional SERS biosensor capable of capturing more comprehensive information through substrate surface modifications. By employing molecular modifiers with distinct chemical characteristics, we modulated the selective adsorption behaviors of bacterial components, enhancing the diversity of physicochemical interactions at the sensing interface. The physicochemical properties of the nanomaterials were characterized using UV-vis spectroscopy, scanning electron microscopy (SEM), dynamic light scattering (DLS), and zeta potential analysis. A database comprising 119,000 SERS profiles from 17 bacterial strains across seven dimensions was constructed. The 1D-convolutional neural network (1D-CNN) model was utilized to analyze 127 dimensional combinations, achieving a maximum accuracy of 99.29 %. The results demonstrate the capability of the multidimensional SERS biosensor to enhance bacterial identification accuracy by leveraging the rich biochemical diversity captured across multiple dimensions. Nevertheless, optimization of the dimensionality is necessary to mitigate problems such as redundancy and overfitting during data processing.