Explainable Machine Learning to Unveil Detection Mechanisms with Au Nanoisland-Based Surface-Enhanced Raman Scattering for SARS-CoV-2 Antigen Detection

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Santa Catarina (UFSC)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual de Mato Grosso do Sul (UEMS)-
Autor(es): dc.contributorFederal Institute of São Paulo (IFSP)-
Autor(es): dc.creatorPazin, Wallance Moreira-
Autor(es): dc.creatorFurini, Leonardo Negri-
Autor(es): dc.creatorBraz, Daniel C.-
Autor(es): dc.creatorPopolin-Neto, Mário-
Autor(es): dc.creatorFernandes, José Diego-
Autor(es): dc.creatorLeopoldo Constantino, Carlos J.-
Autor(es): dc.creatorOliveira, Osvaldo N.-
Data de aceite: dc.date.accessioned2025-08-21T23:46:24Z-
Data de disponibilização: dc.date.available2025-08-21T23:46:24Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-25-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1021/acsanm.3c05848-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298155-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298155-
Descrição: dc.descriptionIn 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.-
Descrição: dc.descriptionDepartment of Physics and Meteorology School of Sciences São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionDepartment of Physics Federal University of Santa Catarina, Santa Catarina-
Descrição: dc.descriptionSão Carlos Institute of Physics University of São Paulo (USP), São Paulo-
Descrição: dc.descriptionMato Grosso do Sul State University (UEMS), Mato Grosso do Sul-
Descrição: dc.descriptionFederal Institute of São Paulo (IFSP), São Paulo-
Descrição: dc.descriptionInstitute of Mathematics and Computer Sciences (ICMC) University of São Paulo (USP), São Paulo-
Descrição: dc.descriptionDepartment of Physics School of Sciences and Technology São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionDepartment of Physics and Meteorology School of Sciences São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionDepartment of Physics School of Sciences and Technology São Paulo State University (UNESP), São Paulo-
Formato: dc.format2335-2342-
Idioma: dc.languageen-
Relação: dc.relationACS Applied Nano Materials-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectbiosensors-
Palavras-chave: dc.subjectclinical diagnosis-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectnanobiosensor-
Palavras-chave: dc.subjectSARS-CoV-2-
Palavras-chave: dc.subjectsurface-enhanced Raman scattering-
Título: dc.titleExplainable Machine Learning to Unveil Detection Mechanisms with Au Nanoisland-Based Surface-Enhanced Raman Scattering for SARS-CoV-2 Antigen Detection-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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