Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

Detalhes bibliográficos
Autor(a) principal: Messias,José A. T.
Data de Publicação: 2012
Outros Autores: Melo,Evandro de C., Lacerda Filho,Adílio F. de, Braga,José L., Cecon,Paulo R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000200015
Resumo: The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.
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spelling Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural networkcoffee qualitychemical analysisclassificationcomputer systemsThe present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.Associação Brasileira de Engenharia Agrícola2012-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000200015Engenharia Agrícola v.32 n.2 2012reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/S0100-69162012000200015info:eu-repo/semantics/openAccessMessias,José A. T.Melo,Evandro de C.Lacerda Filho,Adílio F. deBraga,José L.Cecon,Paulo R.eng2012-07-16T00:00:00Zoai:scielo:S0100-69162012000200015Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2012-07-16T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
title Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
spellingShingle Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
Messias,José A. T.
coffee quality
chemical analysis
classification
computer systems
title_short Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
title_full Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
title_fullStr Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
title_full_unstemmed Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
title_sort Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
author Messias,José A. T.
author_facet Messias,José A. T.
Melo,Evandro de C.
Lacerda Filho,Adílio F. de
Braga,José L.
Cecon,Paulo R.
author_role author
author2 Melo,Evandro de C.
Lacerda Filho,Adílio F. de
Braga,José L.
Cecon,Paulo R.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Messias,José A. T.
Melo,Evandro de C.
Lacerda Filho,Adílio F. de
Braga,José L.
Cecon,Paulo R.
dc.subject.por.fl_str_mv coffee quality
chemical analysis
classification
computer systems
topic coffee quality
chemical analysis
classification
computer systems
description The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.
publishDate 2012
dc.date.none.fl_str_mv 2012-04-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000200015
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162012000200015
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0100-69162012000200015
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
dc.source.none.fl_str_mv Engenharia Agrícola v.32 n.2 2012
reponame:Engenharia Agrícola
instname:Associação Brasileira de Engenharia Agrícola (SBEA)
instacron:SBEA
instname_str Associação Brasileira de Engenharia Agrícola (SBEA)
instacron_str SBEA
institution SBEA
reponame_str Engenharia Agrícola
collection Engenharia Agrícola
repository.name.fl_str_mv Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)
repository.mail.fl_str_mv revistasbea@sbea.org.br||sbea@sbea.org.br
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