Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

Detalhes bibliográficos
Autor(a) principal: Carvalho,Naiara Barbosa
Data de Publicação: 2013
Outros Autores: Minim,Valéria Paula Rodrigues, Silva,Rita de Cássia dos Santos Navarro, Della Lucia,Suzana Maria, Minim,Luis Aantonio
Tipo de documento: Artigo
Idioma: por
Título da fonte: Food Science and Technology (Campinas)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000400018
Resumo: The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.
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spelling Artificial neural networks (ANN): prediction of sensory measurements from instrumental dataartificial neural networkquantitative descriptive analysistextureThe objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2013-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000400018Food Science and Technology v.33 n.4 2013reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/S0101-20612013000400018info:eu-repo/semantics/openAccessCarvalho,Naiara BarbosaMinim,Valéria Paula RodriguesSilva,Rita de Cássia dos Santos NavarroDella Lucia,Suzana MariaMinim,Luis Aantoniopor2014-02-12T00:00:00Zoai:scielo:S0101-20612013000400018Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2014-02-12T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false
dc.title.none.fl_str_mv Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
title Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
spellingShingle Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
Carvalho,Naiara Barbosa
artificial neural network
quantitative descriptive analysis
texture
title_short Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
title_full Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
title_fullStr Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
title_full_unstemmed Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
title_sort Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
author Carvalho,Naiara Barbosa
author_facet Carvalho,Naiara Barbosa
Minim,Valéria Paula Rodrigues
Silva,Rita de Cássia dos Santos Navarro
Della Lucia,Suzana Maria
Minim,Luis Aantonio
author_role author
author2 Minim,Valéria Paula Rodrigues
Silva,Rita de Cássia dos Santos Navarro
Della Lucia,Suzana Maria
Minim,Luis Aantonio
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Carvalho,Naiara Barbosa
Minim,Valéria Paula Rodrigues
Silva,Rita de Cássia dos Santos Navarro
Della Lucia,Suzana Maria
Minim,Luis Aantonio
dc.subject.por.fl_str_mv artificial neural network
quantitative descriptive analysis
texture
topic artificial neural network
quantitative descriptive analysis
texture
description The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.
publishDate 2013
dc.date.none.fl_str_mv 2013-12-01
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=S0101-20612013000400018
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000400018
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv 10.1590/S0101-20612013000400018
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 Sociedade Brasileira de Ciência e Tecnologia de Alimentos
publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
dc.source.none.fl_str_mv Food Science and Technology v.33 n.4 2013
reponame:Food Science and Technology (Campinas)
instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
instacron:SBCTA
instname_str Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
instacron_str SBCTA
institution SBCTA
reponame_str Food Science and Technology (Campinas)
collection Food Science and Technology (Campinas)
repository.name.fl_str_mv Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
repository.mail.fl_str_mv ||revista@sbcta.org.br
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