Machine Learning-Enhanced SERS on Silicon-Gold Sensors for Ultra-High-Throughput Pathogen Detection

Surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) has emerged as a powerful strategy for intelligent and automated biosensing. In this study, we present an in-house fabricated and data-driven SERS platform for automated discrimination of multiple experimental states within a nanostructured biosensor system. The sensing architecture was developed using reproducible gold nanoparticles-coated silicon substrates (Au NPs), synthesized and assembled in-house, followed by functionalization with a Raman reporter molecule (4-mercaptobenzoic acid, 4-MBA) or three biologically relevant targets: Escherichia coli, OC43 coronavirus, and Shiga toxin 2. The classification task was formulated as a six-class problem corresponding to distinct experimental sensor states: bare silicon substrate, Au NP-coated plasmonic substrate, 4-MBA-plasmonic functionalized surface, and plasmonic substrates functionalized with each biological target. Raman spectral data were processed through an automated analytical pipeline and evaluated using principal component analysis, linear discriminant analysis, support vector machines, random forest, and a one-dimensional convolutional neural network (1D-CNN). Among the evaluated models, the 1D-CNN achieved superior performance, providing the highest classification accuracy and robust discrimination across all six experimental classes. The results demonstrate that deep learning applied directly to normalized spectral vectors enhances feature extraction and class separability compared to conventional approaches. This work highlights the potential of integrating in-house engineered SERS nanoplatforms with automated ML frameworks for comprehensive sensor-state discrimination and next-generation intelligent biosensor development.