Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selecti...

Autores
Franco, Vanina Gisela; Perín, Juan C.; Mantovani, Victor Eduardo; Goicoechea, Hector Casimiro
Año de publicación
2006
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determinationwas calculated by analysing spiked real fermentation samples (recoveries ca. 115%).
Fil: Franco, Vanina Gisela. Universidad Nacional del Litoral; Argentina
Fil: Perín, Juan C.. Universidad Nacional del Litoral; Argentina
Fil: Mantovani, Victor Eduardo. Universidad Nacional del Litoral; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina
Materia
BIOPROCESS
MULTIVARIATE
CALIBRATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/106574
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selectionFranco, Vanina GiselaPerín, Juan C.Mantovani, Victor EduardoGoicoechea, Hector CasimiroBIOPROCESSMULTIVARIATECALIBRATIONhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determinationwas calculated by analysing spiked real fermentation samples (recoveries ca. 115%).Fil: Franco, Vanina Gisela. Universidad Nacional del Litoral; ArgentinaFil: Perín, Juan C.. Universidad Nacional del Litoral; ArgentinaFil: Mantovani, Victor Eduardo. Universidad Nacional del Litoral; ArgentinaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; ArgentinaElsevier Science2006-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/106574Franco, Vanina Gisela; Perín, Juan C.; Mantovani, Victor Eduardo; Goicoechea, Hector Casimiro; Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection; Elsevier Science; Talanta; 68; 3; 1-2006; 1005-10120039-9140CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.talanta.2005.07.003info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:52:57Zoai:ri.conicet.gov.ar:11336/106574instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:52:57.58CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
title Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
spellingShingle Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
Franco, Vanina Gisela
BIOPROCESS
MULTIVARIATE
CALIBRATION
title_short Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
title_full Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
title_fullStr Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
title_full_unstemmed Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
title_sort Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection
dc.creator.none.fl_str_mv Franco, Vanina Gisela
Perín, Juan C.
Mantovani, Victor Eduardo
Goicoechea, Hector Casimiro
author Franco, Vanina Gisela
author_facet Franco, Vanina Gisela
Perín, Juan C.
Mantovani, Victor Eduardo
Goicoechea, Hector Casimiro
author_role author
author2 Perín, Juan C.
Mantovani, Victor Eduardo
Goicoechea, Hector Casimiro
author2_role author
author
author
dc.subject.none.fl_str_mv BIOPROCESS
MULTIVARIATE
CALIBRATION
topic BIOPROCESS
MULTIVARIATE
CALIBRATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determinationwas calculated by analysing spiked real fermentation samples (recoveries ca. 115%).
Fil: Franco, Vanina Gisela. Universidad Nacional del Litoral; Argentina
Fil: Perín, Juan C.. Universidad Nacional del Litoral; Argentina
Fil: Mantovani, Victor Eduardo. Universidad Nacional del Litoral; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina
description An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determinationwas calculated by analysing spiked real fermentation samples (recoveries ca. 115%).
publishDate 2006
dc.date.none.fl_str_mv 2006-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/106574
Franco, Vanina Gisela; Perín, Juan C.; Mantovani, Victor Eduardo; Goicoechea, Hector Casimiro; Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection; Elsevier Science; Talanta; 68; 3; 1-2006; 1005-1012
0039-9140
CONICET Digital
CONICET
url http://hdl.handle.net/11336/106574
identifier_str_mv Franco, Vanina Gisela; Perín, Juan C.; Mantovani, Victor Eduardo; Goicoechea, Hector Casimiro; Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection; Elsevier Science; Talanta; 68; 3; 1-2006; 1005-1012
0039-9140
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.talanta.2005.07.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.24909

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