Prediction of abnormal wine fermentations using computational intelligent techniques

Autores
Hernández, Gonzalo; León, Roberto; Urtubia, Alejandra
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The early detection abnormal fermentations (sluggish and stuck) is one of the main problems that appear in wine production, due to the signi cant impacts in wine quality and utility. This situation is specially important in Chile, which is one of the top ten worldwide wine production countries. In last years, two di erent methods coming from Computational Intelligence have been applied to solve this problem: Arti cial Neural Networks and Support Vector Machines. In this work we present the main results that have been obtained to detect abnormal wine fermentations applying these approaches. The Support Vector Machine method with radial basis kernel present the best results for the time cuto s considered (72 [hr] and 96 [hr]) over all the techniques studied with respect to prediction rates and number of the training sets.
Facultad de Informática
Materia
Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/44718
Acceder
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network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Prediction of abnormal wine fermentations using computational intelligent techniquesHernández, GonzaloLeón, RobertoUrtubia, AlejandraCiencias Informáticassupport vector machinesFermentaciónNeural netsVinoThe early detection abnormal fermentations (sluggish and stuck) is one of the main problems that appear in wine production, due to the signi cant impacts in wine quality and utility. This situation is specially important in Chile, which is one of the top ten worldwide wine production countries. In last years, two di erent methods coming from Computational Intelligence have been applied to solve this problem: Arti cial Neural Networks and Support Vector Machines. In this work we present the main results that have been obtained to detect abnormal wine fermentations applying these approaches. The Support Vector Machine method with radial basis kernel present the best results for the time cuto s considered (72 [hr] and 96 [hr]) over all the techniques studied with respect to prediction rates and number of the training sets.Facultad de Informática2015-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1-7http://sedici.unlp.edu.ar/handle/10915/44718enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr15-1.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:43:38Zoai:sedici.unlp.edu.ar:10915/44718Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:43:39.067SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Prediction of abnormal wine fermentations using computational intelligent techniques
title Prediction of abnormal wine fermentations using computational intelligent techniques
spellingShingle Prediction of abnormal wine fermentations using computational intelligent techniques
Hernández, Gonzalo
Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
title_short Prediction of abnormal wine fermentations using computational intelligent techniques
title_full Prediction of abnormal wine fermentations using computational intelligent techniques
title_fullStr Prediction of abnormal wine fermentations using computational intelligent techniques
title_full_unstemmed Prediction of abnormal wine fermentations using computational intelligent techniques
title_sort Prediction of abnormal wine fermentations using computational intelligent techniques
dc.creator.none.fl_str_mv Hernández, Gonzalo
León, Roberto
Urtubia, Alejandra
author Hernández, Gonzalo
author_facet Hernández, Gonzalo
León, Roberto
Urtubia, Alejandra
author_role author
author2 León, Roberto
Urtubia, Alejandra
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
topic Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
dc.description.none.fl_txt_mv The early detection abnormal fermentations (sluggish and stuck) is one of the main problems that appear in wine production, due to the signi cant impacts in wine quality and utility. This situation is specially important in Chile, which is one of the top ten worldwide wine production countries. In last years, two di erent methods coming from Computational Intelligence have been applied to solve this problem: Arti cial Neural Networks and Support Vector Machines. In this work we present the main results that have been obtained to detect abnormal wine fermentations applying these approaches. The Support Vector Machine method with radial basis kernel present the best results for the time cuto s considered (72 [hr] and 96 [hr]) over all the techniques studied with respect to prediction rates and number of the training sets.
Facultad de Informática
description The early detection abnormal fermentations (sluggish and stuck) is one of the main problems that appear in wine production, due to the signi cant impacts in wine quality and utility. This situation is specially important in Chile, which is one of the top ten worldwide wine production countries. In last years, two di erent methods coming from Computational Intelligence have been applied to solve this problem: Arti cial Neural Networks and Support Vector Machines. In this work we present the main results that have been obtained to detect abnormal wine fermentations applying these approaches. The Support Vector Machine method with radial basis kernel present the best results for the time cuto s considered (72 [hr] and 96 [hr]) over all the techniques studied with respect to prediction rates and number of the training sets.
publishDate 2015
dc.date.none.fl_str_mv 2015-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/44718
url http://sedici.unlp.edu.ar/handle/10915/44718
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
1-7
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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