Sequential Learning of Raman Signatures From Attenuated Spectral Inputs Using Deep Neural Networks

Abstract

Raman spectroscopy suffers from inherently weak scattering, especially in organic and biological samples, limiting its utility for low‐concentration analysis. Despite improvements in instrumentation, achieving an adequate signal‐to‐noise ratio in dilute aqueous systems remains challenging. To overcome this limitation, a deep learning (DL) framework was developed that performs spectral‐to‐spectral (S2S) regression rather than relying on classification or qualitative analysis. The framework reconstructs feature‐rich Raman spectra from weak, noise‐dominated inputs by learning concentration‐dependent spectral transformations across the full frequency range, thereby preserving peak integrity and enhancing signal quality. Five chemically diverse compounds spanning 1 nM to 2 M concentrations were used to train and evaluate multiple architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs). RNNs variant, BiLSTM demonstrated the highest accuracy in capturing long‐range spectral dependencies, achieving superior R2, RMSE, and SSIM metrics. Robustness was validated using Monte Carlo uncertainty quantification and a leave‐one‐sample‐out (LOSO) strategy to assess generalization across unseen samples. This study presents a cost‐effective and computation‐driven strategy to simulate the rich Raman spectral features where conventional measurements fail. Beyond spectral reconstruction, the proposed approach can be extended to real‐time biomedical diagnostics and the development of virtual spectral databases for ultra‐low yield biologics and related molecules.