Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants
- Authors
- Barucci, Andrea; D'Andrea, Cristiano; Farnesi, Edoardo; Banchelli, Martina; Amicucci, Chiara; de Angelis, Marella; Hwang, Byungil; Matteini, Paolo
- Issue Date
- Jan-2021
- Publisher
- ROYAL SOC CHEMISTRY
- Citation
- ANALYST, v.146, no.2, pp 674 - 682
- Pages
- 9
- Journal Title
- ANALYST
- Volume
- 146
- Number
- 2
- Start Page
- 674
- End Page
- 682
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47636
- DOI
- 10.1039/d0an02137g
- ISSN
- 0003-2654
1364-5528
- 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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Collections - College of ICT Engineering > School of Integrative Engineering > 1. Journal Articles
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