Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are subm...

Descripción completa

Detalles Bibliográficos
Autores: Roselló, Adam, Serrano i Plana, Núria, Díaz Cruz, José Manuel, Ariño Blasco, Cristina
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/176679
Acceso en línea:https://hdl.handle.net/2445/176679
Access Level:acceso abierto
Palabra clave:Voltametria
Cervesa
Xarxes neuronals (Informàtica)
Voltammetry
Beer
Neural networks (Computer science)
id ES_ebf6e6eab8b9d38ea7e570b0dce662a2
oai_identifier_str oai:recercat.cat:2445/176679
network_acronym_str ES
network_name_str España
repository_id_str
spelling Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrodeRoselló, AdamSerrano i Plana, NúriaDíaz Cruz, José ManuelAriño Blasco, CristinaVoltametriaCervesaXarxes neuronals (Informàtica)VoltammetryBeerNeural networks (Computer science)A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.Wiley-VCH2021202220212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion9 p.application/pdfhttps://hdl.handle.net/2445/176679Articles publicats en revistes (Enginyeria Química i Química Analítica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1002/elan.202060515Electroanalysis, 2021, vol. 33, num. 4, p. 864 -872https://doi.org/10.1002/elan.202060515(c) Wiley-VCH, 2021info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1766792026-05-29T05:05:01Z
dc.title.none.fl_str_mv Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
title Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
spellingShingle Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
Roselló, Adam
Voltametria
Cervesa
Xarxes neuronals (Informàtica)
Voltammetry
Beer
Neural networks (Computer science)
title_short Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
title_full Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
title_fullStr Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
title_full_unstemmed Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
title_sort Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
dc.creator.none.fl_str_mv Roselló, Adam
Serrano i Plana, Núria
Díaz Cruz, José Manuel
Ariño Blasco, Cristina
author Roselló, Adam
author_facet Roselló, Adam
Serrano i Plana, Núria
Díaz Cruz, José Manuel
Ariño Blasco, Cristina
author_role author
author2 Serrano i Plana, Núria
Díaz Cruz, José Manuel
Ariño Blasco, Cristina
author2_role author
author
author
dc.subject.none.fl_str_mv Voltametria
Cervesa
Xarxes neuronals (Informàtica)
Voltammetry
Beer
Neural networks (Computer science)
topic Voltametria
Cervesa
Xarxes neuronals (Informàtica)
Voltammetry
Beer
Neural networks (Computer science)
description A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/176679
url https://hdl.handle.net/2445/176679
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1002/elan.202060515
Electroanalysis, 2021, vol. 33, num. 4, p. 864 -872
https://doi.org/10.1002/elan.202060515
dc.rights.none.fl_str_mv (c) Wiley-VCH, 2021
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Wiley-VCH, 2021
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9 p.
application/pdf
dc.publisher.none.fl_str_mv Wiley-VCH
publisher.none.fl_str_mv Wiley-VCH
dc.source.none.fl_str_mv Articles publicats en revistes (Enginyeria Química i Química Analítica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869423273993830400
score 15.811543

Similares en LA Referencia