Milk fraud by the addition of whey using an artificial neural network
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , |
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. |
id |
UFSM-2_389a62407de2e2ec7278a70d53a7a94d |
---|---|
oai_identifier_str |
oai:scielo:S0103-84782020000700453 |
network_acronym_str |
UFSM-2 |
network_name_str |
Ciência rural (Online) |
repository_id_str |
|
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 |
|
_version_ |
1749140554815373312 |