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Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981450/ https://www.ncbi.nlm.nih.gov/pubmed/36864313 http://dx.doi.org/10.1007/s00216-023-04620-y |
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author | dos Santos, Diego P. Sena, Marcelo M. Almeida, Mariana R. Mazali, Italo O. Olivieri, Alejandro C. Villa, Javier E. L. |
author_facet | dos Santos, Diego P. Sena, Marcelo M. Almeida, Mariana R. Mazali, Italo O. Olivieri, Alejandro C. Villa, Javier E. L. |
author_sort | dos Santos, Diego P. |
collection | PubMed |
description | Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-9981450 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-99814502023-03-03 Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends dos Santos, Diego P. Sena, Marcelo M. Almeida, Mariana R. Mazali, Italo O. Olivieri, Alejandro C. Villa, Javier E. L. Anal Bioanal Chem Critical Review Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2023-03-03 /pmc/articles/PMC9981450/ /pubmed/36864313 http://dx.doi.org/10.1007/s00216-023-04620-y Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Critical Review dos Santos, Diego P. Sena, Marcelo M. Almeida, Mariana R. Mazali, Italo O. Olivieri, Alejandro C. Villa, Javier E. L. Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
title | Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
title_full | Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
title_fullStr | Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
title_full_unstemmed | Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
title_short | Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
title_sort | unraveling surface-enhanced raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
topic | Critical Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981450/ https://www.ncbi.nlm.nih.gov/pubmed/36864313 http://dx.doi.org/10.1007/s00216-023-04620-y |
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