Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks

Autores
Cuellas, Anahí V.; Oddone, Sebastián; Mammarella, Enrique José; Rubiolo, Amelia Catalina
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network (ANN) to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial beta-galactosidase either free or immobilized in a chitosan support was used. The neural network developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysis
Fil: Cuellas, Anahí V.. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Área Ingeniería en Alimentos; Argentina
Fil: Oddone, Sebastián. Universidad Argentina de la Empresa. Facultad de Ingeniería y Ciencias Exactas; Argentina
Fil: Mammarella, Enrique José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Materia
CHESSE WHEY
BETA-GALACTOSIDASE
LACTOSE HYDROLYSIS
ARTIFICIAL NEURAL NETWORK
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/8578
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural NetworksCuellas, Anahí V.Oddone, SebastiánMammarella, Enrique JoséRubiolo, Amelia CatalinaCHESSE WHEYBETA-GALACTOSIDASELACTOSE HYDROLYSISARTIFICIAL NEURAL NETWORKhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network (ANN) to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial beta-galactosidase either free or immobilized in a chitosan support was used. The neural network developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysisFil: Cuellas, Anahí V.. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Área Ingeniería en Alimentos; ArgentinaFil: Oddone, Sebastián. Universidad Argentina de la Empresa. Facultad de Ingeniería y Ciencias Exactas; ArgentinaFil: Mammarella, Enrique José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaDavid Publishing2013-10-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/8578Cuellas, Anahí V.; Oddone, Sebastián; Mammarella, Enrique José; Rubiolo, Amelia Catalina; Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks; David Publishing; Journal of Agricultural Science and Technology A; 2013; 10; 20-10-2013; 811-8181939-1250enginfo:eu-repo/semantics/altIdentifier/url/http://www.davidpublisher.org/Article/index?id=14643.html#Abstractinfo:eu-repo/semantics/altIdentifier/doi/10.17265/2161-6256/2013.10A.008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:31Zoai:ri.conicet.gov.ar:11336/8578instacron: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:34982025-09-03 10:03:32.102CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
title Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
spellingShingle Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
Cuellas, Anahí V.
CHESSE WHEY
BETA-GALACTOSIDASE
LACTOSE HYDROLYSIS
ARTIFICIAL NEURAL NETWORK
title_short Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
title_full Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
title_fullStr Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
title_full_unstemmed Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
title_sort Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks
dc.creator.none.fl_str_mv Cuellas, Anahí V.
Oddone, Sebastián
Mammarella, Enrique José
Rubiolo, Amelia Catalina
author Cuellas, Anahí V.
author_facet Cuellas, Anahí V.
Oddone, Sebastián
Mammarella, Enrique José
Rubiolo, Amelia Catalina
author_role author
author2 Oddone, Sebastián
Mammarella, Enrique José
Rubiolo, Amelia Catalina
author2_role author
author
author
dc.subject.none.fl_str_mv CHESSE WHEY
BETA-GALACTOSIDASE
LACTOSE HYDROLYSIS
ARTIFICIAL NEURAL NETWORK
topic CHESSE WHEY
BETA-GALACTOSIDASE
LACTOSE HYDROLYSIS
ARTIFICIAL NEURAL NETWORK
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network (ANN) to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial beta-galactosidase either free or immobilized in a chitosan support was used. The neural network developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysis
Fil: Cuellas, Anahí V.. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Área Ingeniería en Alimentos; Argentina
Fil: Oddone, Sebastián. Universidad Argentina de la Empresa. Facultad de Ingeniería y Ciencias Exactas; Argentina
Fil: Mammarella, Enrique José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
description The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network (ANN) to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial beta-galactosidase either free or immobilized in a chitosan support was used. The neural network developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysis
publishDate 2013
dc.date.none.fl_str_mv 2013-10-20
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/11336/8578
Cuellas, Anahí V.; Oddone, Sebastián; Mammarella, Enrique José; Rubiolo, Amelia Catalina; Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks; David Publishing; Journal of Agricultural Science and Technology A; 2013; 10; 20-10-2013; 811-818
1939-1250
url http://hdl.handle.net/11336/8578
identifier_str_mv Cuellas, Anahí V.; Oddone, Sebastián; Mammarella, Enrique José; Rubiolo, Amelia Catalina; Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks; David Publishing; Journal of Agricultural Science and Technology A; 2013; 10; 20-10-2013; 811-818
1939-1250
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.davidpublisher.org/Article/index?id=14643.html#Abstract
info:eu-repo/semantics/altIdentifier/doi/10.17265/2161-6256/2013.10A.008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv David Publishing
publisher.none.fl_str_mv David Publishing
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 13.13397

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