Prediction of abnormal wine fermentations using computational intelligent techniques

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...

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Autores principales: Hernández, Gonzalo, León, Roberto, Urtubia, Alejandra
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/44718
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr15-1.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-44718
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
spellingShingle Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
Hernández, Gonzalo
León, Roberto
Urtubia, Alejandra
Prediction of abnormal wine fermentations using computational intelligent techniques
topic_facet Ciencias Informáticas
support vector machines
Fermentación
Neural nets
Vino
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.
format Articulo
Articulo
author Hernández, Gonzalo
León, Roberto
Urtubia, Alejandra
author_facet Hernández, Gonzalo
León, Roberto
Urtubia, Alejandra
author_sort Hernández, Gonzalo
title Prediction of abnormal wine fermentations using computational intelligent techniques
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
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/44718
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr15-1.pdf
work_keys_str_mv AT hernandezgonzalo predictionofabnormalwinefermentationsusingcomputationalintelligenttechniques
AT leonroberto predictionofabnormalwinefermentationsusingcomputationalintelligenttechniques
AT urtubiaalejandra predictionofabnormalwinefermentationsusingcomputationalintelligenttechniques
bdutipo_str Repositorios
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