人工智能
拉曼散射
机器学习
支持向量机
计算机科学
线性判别分析
生物传感器
特征选择
监督学习
随机森林
校准
特征向量
纳米技术
模式识别(心理学)
材料科学
拉曼光谱
物理
光学
人工神经网络
量子力学
作者
Wallance Moreira Pazin,Leonardo N. Furini,Daniel Cesar Braz,Mário Popolin-Neto,José Diego Fernandes,Carlos J. L. Constantino,Osvaldo N. Oliveira
出处
期刊:ACS applied nano materials
[American Chemical Society]
日期:2024-01-05
卷期号:7 (2): 2335-2342
标识
DOI:10.1021/acsanm.3c05848
摘要
In this study, we introduce a simplified surface-enhanced Raman scattering (SERS) nanobiosensor for precise detection of a SARS-CoV-2 antigen, leveraging supervised machine learning approaches. The biosensor was made with Au nanoislands conjugated with a 4-aminothiophenol Raman reporter and an anti-SARS-CoV-2 antibody. Through the integration of feature selection and learning algorithms, namely, logistic regression, linear discriminant analysis, and support vector machine, we achieved high accuracies ranging from 96 to 100% in antigen detection. Furthermore, we identified the underlying detection mechanisms by employing the concept of multidimensional calibration space, which is based on decision trees and random forest algorithms. This analysis with explainable machine learning allowed us to gain insights into the reasons why our simplified nanobiosensor exhibits lower sensitivity compared with that of the previous sandwich-type immunosensors for SARS-CoV-2. The results presented here emphasize the potential of supervised machine learning in SERS biosensing, which can be applied to any type of diagnostics.
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