Surface-Enhanced Raman Spectroscopy Semi-Quantitative Molecular Profiling with a Convolutional Neural Network.

ABSTRACT: Surface-enhanced Raman scattering (SERS) spectroscopy represents a powerful analytical platform that combines non-destructive, label-free molecular identification with exceptional sensitivity for trace-level detection. Its capacity to generate information-rich spectral fingerprints makes SERS particularly advantageous for simultaneous multi-analyte analysis across diverse sample matrices, including complex biological systems. This study addresses the analytical challenges associated with identifying and quantifying multiple molecular species in complex environments by integrating SERS with advanced machine learning methodologies. We developed a hierarchical analytical framework that leverages the complementary strengths of deep learning and regression techniques: A multi-label convolutiona

SUBMITTER: Lebrun A 

PROVIDER: S-EPMC12717294 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature