Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , |
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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Food Science and Technology (Campinas) |
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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 |
_version_ |
1752126318180302848 |