Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Tipo de documento: | Artigo |
Título da fonte: | Repositório da Produção Científica e Intelectual da Unicamp |
Texto Completo: | https://hdl.handle.net/20.500.12733/476 |
Resumo: | Abstract: The performance of different chemometric approaches to discriminate artisanal and industrial pork sausages using traditional physicochemical parameters was investigated. A total of 90 samples of sausages marketed in various supermarkets and open-markets in Rio de Janeiro, Brazil were analyzed for their content of moisture, protein, fat, nitrite, sodium and calcium. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used as exploratory methods, while linear and non-linear classification methods, such as k-nearest neighbors (k-NN), soft independent modeling of class analogy (SIMCA), partial least square discriminant analysis (PLSDA) and artificial neural networks (ANN) were used for assessing the data. Different behaviors for all parameters were analyzed between the classes. Principal component analysis and hierarchical cluster analysis did not show a complete discrimination of the samples. KNN and ANN results showed excellent performance for both categories with 100% correct prediction while SIMCA and PLSDA presented performance of 100% and 85.7% for inspected and artisanal sausages, respectively. According to the SIMCA, PLSDA and ANN, the contents of moisture and fat showed the highest discriminative power. Overall, the findings emphasize the use of multivariate techniques to evaluate the quality of processed foods, as pork sausages |
id |
CAMP_0aab33d58d4d32e7b32e08b044179d48 |
---|---|
oai_identifier_str |
oai:https://www.repositorio.unicamp.br/:1231645 |
network_acronym_str |
CAMP |
network_name_str |
Repositório da Produção Científica e Intelectual da Unicamp |
repository_id_str |
|
spelling |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methodsReconhecimento de padrõesPattern recognitionChemometric methodsClassificationArtificial neural networksSausageArtigo originalAbstract: The performance of different chemometric approaches to discriminate artisanal and industrial pork sausages using traditional physicochemical parameters was investigated. A total of 90 samples of sausages marketed in various supermarkets and open-markets in Rio de Janeiro, Brazil were analyzed for their content of moisture, protein, fat, nitrite, sodium and calcium. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used as exploratory methods, while linear and non-linear classification methods, such as k-nearest neighbors (k-NN), soft independent modeling of class analogy (SIMCA), partial least square discriminant analysis (PLSDA) and artificial neural networks (ANN) were used for assessing the data. Different behaviors for all parameters were analyzed between the classes. Principal component analysis and hierarchical cluster analysis did not show a complete discrimination of the samples. KNN and ANN results showed excellent performance for both categories with 100% correct prediction while SIMCA and PLSDA presented performance of 100% and 85.7% for inspected and artisanal sausages, respectively. According to the SIMCA, PLSDA and ANN, the contents of moisture and fat showed the highest discriminative power. Overall, the findings emphasize the use of multivariate techniques to evaluate the quality of processed foods, as pork sausagesFechadoUNIVERSIDADE ESTADUAL DE CAMPINASCavalcanti, Rodrigo Nunes, 1979-2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.12733/476CAVALCANTI, Rodrigo Nunes. Discrimination of brazilian artisanal and inspected pork sausages: application of unsupervised, linear and non-linear supervised chemometric methods. Food research international. Oxford : Elsevier, 2014. Vol. 64 (Oct., 2014), p. 380-386. Disponível em: https://hdl.handle.net/20.500.12733/476. Acesso em: 24 mai. 2023.Inglêshttps://repositorio.unicamp.br/acervo/detalhe/1231645reponame:Repositório da Produção Científica e Intelectual da Unicampinstname:Universidade Estadual de Campinas (UNICAMP)instacron:UNICAMPinfo:eu-repo/semantics/openAccess2021-11-12T11:51:16Zoai:https://www.repositorio.unicamp.br/:1231645Repositório InstitucionalPUBhttp://repositorio.unicamp.br/oai/requestreposip@unicamp.bropendoar:2021-11-12T11:51:16Repositório da Produção Científica e Intelectual da Unicamp - Universidade Estadual de Campinas (UNICAMP)false |
dc.title.none.fl_str_mv |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
title |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
spellingShingle |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods Cavalcanti, Rodrigo Nunes, 1979- Reconhecimento de padrões Pattern recognition Chemometric methods Classification Artificial neural networks Sausage Artigo original |
title_short |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
title_full |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
title_fullStr |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
title_full_unstemmed |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
title_sort |
Discrimination of brazilian artisanal and inspected pork sausages : application of unsupervised, linear and non-linear supervised chemometric methods |
author |
Cavalcanti, Rodrigo Nunes, 1979- |
author_facet |
Cavalcanti, Rodrigo Nunes, 1979- |
author_role |
author |
dc.contributor.none.fl_str_mv |
UNIVERSIDADE ESTADUAL DE CAMPINAS |
dc.contributor.author.fl_str_mv |
Cavalcanti, Rodrigo Nunes, 1979- |
dc.subject.por.fl_str_mv |
Reconhecimento de padrões Pattern recognition Chemometric methods Classification Artificial neural networks Sausage Artigo original |
topic |
Reconhecimento de padrões Pattern recognition Chemometric methods Classification Artificial neural networks Sausage Artigo original |
description |
Abstract: The performance of different chemometric approaches to discriminate artisanal and industrial pork sausages using traditional physicochemical parameters was investigated. A total of 90 samples of sausages marketed in various supermarkets and open-markets in Rio de Janeiro, Brazil were analyzed for their content of moisture, protein, fat, nitrite, sodium and calcium. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used as exploratory methods, while linear and non-linear classification methods, such as k-nearest neighbors (k-NN), soft independent modeling of class analogy (SIMCA), partial least square discriminant analysis (PLSDA) and artificial neural networks (ANN) were used for assessing the data. Different behaviors for all parameters were analyzed between the classes. Principal component analysis and hierarchical cluster analysis did not show a complete discrimination of the samples. KNN and ANN results showed excellent performance for both categories with 100% correct prediction while SIMCA and PLSDA presented performance of 100% and 85.7% for inspected and artisanal sausages, respectively. According to the SIMCA, PLSDA and ANN, the contents of moisture and fat showed the highest discriminative power. Overall, the findings emphasize the use of multivariate techniques to evaluate the quality of processed foods, as pork sausages |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/20.500.12733/476 CAVALCANTI, Rodrigo Nunes. Discrimination of brazilian artisanal and inspected pork sausages: application of unsupervised, linear and non-linear supervised chemometric methods. Food research international. Oxford : Elsevier, 2014. Vol. 64 (Oct., 2014), p. 380-386. Disponível em: https://hdl.handle.net/20.500.12733/476. Acesso em: 24 mai. 2023. |
url |
https://hdl.handle.net/20.500.12733/476 |
identifier_str_mv |
CAVALCANTI, Rodrigo Nunes. Discrimination of brazilian artisanal and inspected pork sausages: application of unsupervised, linear and non-linear supervised chemometric methods. Food research international. Oxford : Elsevier, 2014. Vol. 64 (Oct., 2014), p. 380-386. Disponível em: https://hdl.handle.net/20.500.12733/476. Acesso em: 24 mai. 2023. |
dc.language.iso.fl_str_mv |
Inglês |
language_invalid_str_mv |
Inglês |
dc.relation.none.fl_str_mv |
https://repositorio.unicamp.br/acervo/detalhe/1231645 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório da Produção Científica e Intelectual da Unicamp instname:Universidade Estadual de Campinas (UNICAMP) instacron:UNICAMP |
instname_str |
Universidade Estadual de Campinas (UNICAMP) |
instacron_str |
UNICAMP |
institution |
UNICAMP |
reponame_str |
Repositório da Produção Científica e Intelectual da Unicamp |
collection |
Repositório da Produção Científica e Intelectual da Unicamp |
repository.name.fl_str_mv |
Repositório da Produção Científica e Intelectual da Unicamp - Universidade Estadual de Campinas (UNICAMP) |
repository.mail.fl_str_mv |
reposip@unicamp.br |
_version_ |
1766887321872891904 |