Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants

Establishing standardized methods for a consistent analysis of spectral data remains a largely underexplored aspect in surface-enhanced Raman spectroscopy (SERS), particularly applied to biological and biomedical research. Here we propose an effective machine learning classification of protein species with closely resembled spectral profiles by a mixed data processing based on principal component analysis (PCA) applied to multipeak fitting on SERS spectra. This strategy simultaneously assures a successful discrimination of proteins and a thorough characterization of the chemostructural differences among them, ultimately opening up new routes for SERS evolution toward sensing applications and diagnostics of interest in life sciences.

Automated summary

The article proposes an effective machine learning classification method for protein species with similar spectral profiles, based on principal component analysis (PCA) applied to multipeak fitting on SERS spectra. This strategy ensures successful discrimination of proteins and thorough characterization of their chemostructural differences, ultimately paving the way for new applications and diagnostics in the field of life sciences.