Milk fraud by the addition of whey using an artificial neural network

Detalhes bibliográficos
Autor(a) principal: Condé,Vitor Augusto
Data de Publicação: 2020
Outros Autores: Valente,Gerson de Freitas Silva, Minighin,Eliene Carvalho
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000700453
Resumo: ABSTRACT: The adulteration of milk by the addition of whey is a problem that concerns national and international authorities. The objective of this research was to quantify the whey content in adulterated milk samples using artificial neural networks, employing routine analyses of dairy milk samples. The analyses were performed with different concentrations of whey (0, 5, 10, and 20%), and samples were analyzed for fat, non-fat solids, density, protein, lactose, minerals, and freezing point, totaling 164 assays, of which 60% were used for network training, 20% for network validation, and 20% for neural network testing. The Garson method was used to determine the importance of the variables. The neural network technique for the determination of milk fraud by the addition of whey proved to be efficient. Among the variables of highest relevance were fat content and density.
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spelling Milk fraud by the addition of whey using an artificial neural networkartificial intelligenceglycomacropeptideANNwheyadulterationfraudABSTRACT: The adulteration of milk by the addition of whey is a problem that concerns national and international authorities. The objective of this research was to quantify the whey content in adulterated milk samples using artificial neural networks, employing routine analyses of dairy milk samples. The analyses were performed with different concentrations of whey (0, 5, 10, and 20%), and samples were analyzed for fat, non-fat solids, density, protein, lactose, minerals, and freezing point, totaling 164 assays, of which 60% were used for network training, 20% for network validation, and 20% for neural network testing. The Garson method was used to determine the importance of the variables. The neural network technique for the determination of milk fraud by the addition of whey proved to be efficient. Among the variables of highest relevance were fat content and density.Universidade Federal de Santa Maria2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000700453Ciência Rural v.50 n.7 2020reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20190312info:eu-repo/semantics/openAccessCondé,Vitor AugustoValente,Gerson de Freitas SilvaMinighin,Eliene Carvalhoeng2020-06-02T00:00:00ZRevista
dc.title.none.fl_str_mv Milk fraud by the addition of whey using an artificial neural network
title Milk fraud by the addition of whey using an artificial neural network
spellingShingle Milk fraud by the addition of whey using an artificial neural network
Condé,Vitor Augusto
artificial intelligence
glycomacropeptide
ANN
whey
adulteration
fraud
title_short Milk fraud by the addition of whey using an artificial neural network
title_full Milk fraud by the addition of whey using an artificial neural network
title_fullStr Milk fraud by the addition of whey using an artificial neural network
title_full_unstemmed Milk fraud by the addition of whey using an artificial neural network
title_sort Milk fraud by the addition of whey using an artificial neural network
author Condé,Vitor Augusto
author_facet Condé,Vitor Augusto
Valente,Gerson de Freitas Silva
Minighin,Eliene Carvalho
author_role author
author2 Valente,Gerson de Freitas Silva
Minighin,Eliene Carvalho
author2_role author
author
dc.contributor.author.fl_str_mv Condé,Vitor Augusto
Valente,Gerson de Freitas Silva
Minighin,Eliene Carvalho
dc.subject.por.fl_str_mv artificial intelligence
glycomacropeptide
ANN
whey
adulteration
fraud
topic artificial intelligence
glycomacropeptide
ANN
whey
adulteration
fraud
description ABSTRACT: The adulteration of milk by the addition of whey is a problem that concerns national and international authorities. The objective of this research was to quantify the whey content in adulterated milk samples using artificial neural networks, employing routine analyses of dairy milk samples. The analyses were performed with different concentrations of whey (0, 5, 10, and 20%), and samples were analyzed for fat, non-fat solids, density, protein, lactose, minerals, and freezing point, totaling 164 assays, of which 60% were used for network training, 20% for network validation, and 20% for neural network testing. The Garson method was used to determine the importance of the variables. The neural network technique for the determination of milk fraud by the addition of whey proved to be efficient. Among the variables of highest relevance were fat content and density.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
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=S0103-84782020000700453
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000700453
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20190312
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 Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Rural v.50 n.7 2020
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
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