AI-DRIVEN SURFACE-ENHANCED RAMAN SPECTROSCOPY USING HYBRID PLASMONIC NANOSTRUCTURES FOR INTERPRETABLE

Abstract

Surface-Enhanced Raman Spectroscopy (SERS) has emerged as a highly sensitive analytical technique capable of detecting trace-level biomolecular signatures through localized surface plasmon resonance (LSPR) effects. This work presents an end-to- end              implementation   framework combining SERS with deep learning–based feature extraction and classification to identify and discriminate biomolecular patterns in multi-omics datasets. Metal and hybrid nanostructures, including silver (Ag), gold (Au), titanium nitrate (TiN), graphene oxide (GO), and perovskite nanocrystals, were fabricated as plasmonic substrates to enhance Raman signal intensity. The system architecture integrates experimental Raman spectral acquisition with computational modeling using convolutional neural networks (CNNs) and explainable AI (XAI) modules such as SHAP and Grad-CAM to interpret spectral features and correlate them with molecular vibrations. Experimental results demonstrate that optimized nanostructure geometries significantly amplify the Raman scattering cross-section, achieving enhancement factors up to 10⁶–10⁸. The trained CNN models achieved classification accuracies exceeding 97% across multiple biomolecular datasets, confirming the robustness of the approach for omics-scale analysis. Furthermore, the integration of deep learning enables automated preprocessing, denoising, and spectral segmentation, reducing manual intervention while improving reproducibility. The proposed implementation provides a scalable, AI-assisted analytical pipeline for real-time SERS applications in biomedical diagnostics, environmental monitoring, and precision agriculture.