Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants†
Andrea
Barucci, 
‡a
Cristiano
D'Andrea,
‡a
Martina
Banchelli,
a
Marella
de Angelis,
a
Byungil
Hwang
b
and
Paolo
Matteini
*a
‡a
Cristiano
D'Andrea,
‡a
Martina
Banchelli,
a
Marella
de Angelis,
a
Byungil
Hwang
b
and
Paolo
Matteini
*a
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.
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