Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants. Issue 2 (19th November 2020) - Barucci, Andrea; D'Andrea, Cristiano; Farnesi, Edoardo; Banchelli, Martina; Amicucci, Chiara; de Angelis, Marella; Hwang, Byungil; Matteini, Paolo
Abstract : We implement a machine learning classification of similar proteins by PCA mixed with multipeak fitting on SERS spectra for effective discrimination based on valid biological differences. Abstract : 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.