Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The compreh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/14/11/523 |
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| author | Thais de Andrade Silva Gabriel Fernandes Souza dos Santos Adilson Ribeiro Prado Daniel Cruz Cavalieri Arnaldo Gomes Leal Junior Flávio Garcia Pereira Camilo A. R. Díaz Marco Cesar Cunegundes Guimarães Servio Túlio Alves Cassini Jairo Pinto de Oliveira |
| author_facet | Thais de Andrade Silva Gabriel Fernandes Souza dos Santos Adilson Ribeiro Prado Daniel Cruz Cavalieri Arnaldo Gomes Leal Junior Flávio Garcia Pereira Camilo A. R. Díaz Marco Cesar Cunegundes Guimarães Servio Túlio Alves Cassini Jairo Pinto de Oliveira |
| author_sort | Thais de Andrade Silva |
| collection | DOAJ |
| description | This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research. |
| format | Article |
| id | doaj-art-ab805b3e37714db09e30c413f6130c67 |
| institution | Kabale University |
| issn | 2079-6374 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj-art-ab805b3e37714db09e30c413f6130c672024-11-26T17:54:32ZengMDPI AGBiosensors2079-63742024-10-01141152310.3390/bios14110523Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgGThais de Andrade Silva0Gabriel Fernandes Souza dos Santos1Adilson Ribeiro Prado2Daniel Cruz Cavalieri3Arnaldo Gomes Leal Junior4Flávio Garcia Pereira5Camilo A. R. Díaz6Marco Cesar Cunegundes Guimarães7Servio Túlio Alves Cassini8Jairo Pinto de Oliveira9Morphology Department, Federal University of Espirito Santo, Av Marechal Campos, 1468, Vitória 29040-090, ES, BrazilMorphology Department, Federal University of Espirito Santo, Av Marechal Campos, 1468, Vitória 29040-090, ES, BrazilFederal Institute of Espírito Santo, Campus Serra, Serra 29173-087, ES, BrazilFederal Institute of Espírito Santo, Campus Serra, Serra 29173-087, ES, BrazilTelecommunications Laboratory, Electrical Engineering Department, Federal University of Espírito Santo (UFES), Av Fernando Ferrari 514, Vitória 29075-910, ES, BrazilFederal Institute of Espírito Santo, Campus Serra, Serra 29173-087, ES, BrazilTelecommunications Laboratory, Electrical Engineering Department, Federal University of Espírito Santo (UFES), Av Fernando Ferrari 514, Vitória 29075-910, ES, BrazilMorphology Department, Federal University of Espirito Santo, Av Marechal Campos, 1468, Vitória 29040-090, ES, BrazilCenter of Research, Innovation and Development of Espirito Santo, Ladeira Eliezer Batista, Cariacica 29140-130, ES, BrazilMorphology Department, Federal University of Espirito Santo, Av Marechal Campos, 1468, Vitória 29040-090, ES, BrazilThis work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.https://www.mdpi.com/2079-6374/14/11/523gold nanoparticlesmultivariate analysisSERSmachine learning |
| spellingShingle | Thais de Andrade Silva Gabriel Fernandes Souza dos Santos Adilson Ribeiro Prado Daniel Cruz Cavalieri Arnaldo Gomes Leal Junior Flávio Garcia Pereira Camilo A. R. Díaz Marco Cesar Cunegundes Guimarães Servio Túlio Alves Cassini Jairo Pinto de Oliveira Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG Biosensors gold nanoparticles multivariate analysis SERS machine learning |
| title | Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG |
| title_full | Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG |
| title_fullStr | Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG |
| title_full_unstemmed | Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG |
| title_short | Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG |
| title_sort | surface enhanced raman scattering combined with machine learning for rapid and sensitive detection of anti sars cov 2 igg |
| topic | gold nanoparticles multivariate analysis SERS machine learning |
| url | https://www.mdpi.com/2079-6374/14/11/523 |
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