Machine learning toward high-performance electrochemical sensors
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s00216-023-04514-z http://hdl.handle.net/11449/246639 |
Resumo: | The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications. Graphical Abstract: [Figure not available: see fulltext.]. |
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Machine learning toward high-performance electrochemical sensorsAccuracyArtificial intelligenceClassificationData treatmentRegressionThe so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications. Graphical Abstract: [Figure not available: see fulltext.].Brazilian Nanotechnology National Laboratory Brazilian Center for Research in Energy and Materials, São PauloInstitute of Chemistry University of Campinas, São PauloCenter for Natural and Human Sciences Federal University of ABC, São PauloSão Carlos Institute of Chemistry University of São Paulo, São PauloSchool of Sciences São Paulo State University, São PauloSchool of Sciences São Paulo State University, São PauloBrazilian Center for Research in Energy and MaterialsUniversidade Estadual de Campinas (UNICAMP)Federal University of ABCUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Giordano, Gabriela F.Ferreira, Larissa F.Bezerra, Ítalo R. S.Barbosa, Júlia A.Costa, Juliana N. Y.Pimentel, Gabriel J. C. [UNESP]Lima, Renato S.2023-07-29T12:46:29Z2023-07-29T12:46:29Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s00216-023-04514-zAnalytical and Bioanalytical Chemistry.1618-26501618-2642http://hdl.handle.net/11449/24663910.1007/s00216-023-04514-z2-s2.0-85146226952Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnalytical and Bioanalytical Chemistryinfo:eu-repo/semantics/openAccess2023-07-29T12:46:29Zoai:repositorio.unesp.br:11449/246639Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:43:05.573632Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine learning toward high-performance electrochemical sensors |
title |
Machine learning toward high-performance electrochemical sensors |
spellingShingle |
Machine learning toward high-performance electrochemical sensors Giordano, Gabriela F. Accuracy Artificial intelligence Classification Data treatment Regression |
title_short |
Machine learning toward high-performance electrochemical sensors |
title_full |
Machine learning toward high-performance electrochemical sensors |
title_fullStr |
Machine learning toward high-performance electrochemical sensors |
title_full_unstemmed |
Machine learning toward high-performance electrochemical sensors |
title_sort |
Machine learning toward high-performance electrochemical sensors |
author |
Giordano, Gabriela F. |
author_facet |
Giordano, Gabriela F. Ferreira, Larissa F. Bezerra, Ítalo R. S. Barbosa, Júlia A. Costa, Juliana N. Y. Pimentel, Gabriel J. C. [UNESP] Lima, Renato S. |
author_role |
author |
author2 |
Ferreira, Larissa F. Bezerra, Ítalo R. S. Barbosa, Júlia A. Costa, Juliana N. Y. Pimentel, Gabriel J. C. [UNESP] Lima, Renato S. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Brazilian Center for Research in Energy and Materials Universidade Estadual de Campinas (UNICAMP) Federal University of ABC Universidade de São Paulo (USP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Giordano, Gabriela F. Ferreira, Larissa F. Bezerra, Ítalo R. S. Barbosa, Júlia A. Costa, Juliana N. Y. Pimentel, Gabriel J. C. [UNESP] Lima, Renato S. |
dc.subject.por.fl_str_mv |
Accuracy Artificial intelligence Classification Data treatment Regression |
topic |
Accuracy Artificial intelligence Classification Data treatment Regression |
description |
The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications. Graphical Abstract: [Figure not available: see fulltext.]. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T12:46:29Z 2023-07-29T12:46:29Z 2023-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s00216-023-04514-z Analytical and Bioanalytical Chemistry. 1618-2650 1618-2642 http://hdl.handle.net/11449/246639 10.1007/s00216-023-04514-z 2-s2.0-85146226952 |
url |
http://dx.doi.org/10.1007/s00216-023-04514-z http://hdl.handle.net/11449/246639 |
identifier_str_mv |
Analytical and Bioanalytical Chemistry. 1618-2650 1618-2642 10.1007/s00216-023-04514-z 2-s2.0-85146226952 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Analytical and Bioanalytical Chemistry |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128969340354560 |