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

• Published in: Analyst (London) Vol. 146; no. 2; pp. 674 - 682
• Main Authors: Barucci, Andrea, D'Andrea, Cristiano, Farnesi, Edoardo, Banchelli, Martina, Amicucci, Chiara, de Angelis, Marella, Hwang, Byungil, Matteini, Paolo
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 21.01.2021
• Subjects: Data processing; Machine Learning; Models, Molecular; Nanowires - chemistry; Principal components analysis; Protein Conformation; Proteins; Raman spectroscopy; Silver - chemistry; Species classification; Spectra; Spectrum analysis; Spectrum Analysis, Raman - methods
• ISSN: 0003-2654; 1364-5528; 1364-5528
• DOI: 10.1039/d0an02137g

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. 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.