Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , |
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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Engenharia Agrícola |
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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 |
_version_ |
1752126270732238848 |