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

被引：59

作者：

Barucci, Andrea ^{[1
]}

D'Andrea, Cristiano ^{[1
]}

Farnesi, Edoardo ^{[1
]}

Banchelli, Martina ^{[1
]}

Amicucci, Chiara ^{[1
]}

de Angelis, Marella ^{[1
]}

Hwang, Byungil ^{[2
]}

Matteini, Paolo ^{[1
]}

机构：

[1] Italian Natl Res Council, Inst Appl Phys Nello Carrara, Via Madonna Piano 10, I-50019 Sesto Fiorentino, Italy

[2] Chung Ang Univ, Sch Integrat Engn, Seoul 06974, South Korea

来源：

ANALYST | 2021年 / 146卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

ENHANCED RAMAN-SPECTROSCOPY; SCATTERING;

D O I：

10.1039/d0an02137g

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页码：674 / 682

页数：9

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