Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/23356 |
Resumo: | In the last decade, there has been an increasing demand for wild-captured fish, which attains higher prices compared to farmed species, thus being prone to mislabeling practices. In this work, fatty acid composition coupled to advanced chemometrics was used to discriminate wild from farmed salmon. The lipids extracted from salmon muscles of different production methods and origins (26 wild from Canada, 25 farmed from Canada, 24 farmed from Chile and 25 farmed from Norway) were analyzed by gas chromatography with flame ionization detector (GC-FID). All the tested chemometric approaches, namely principal components analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and seven machine learning classifiers, namely k-nearest neighbors (kNN), decision tree, support vector machine (SVM), random forest, artificial neural networks (ANN), naïve Bayes and AdaBoost, allowed for differentiation between farmed and wild salmons using the 17 features obtained from chemical analysis. PCA did not allow clear distinguishing between salmon geographical origin since farmed samples from Canada and Chile overlapped. Nevertheless, using the 17 features in the models, six out of the seven tested machine learning classifiers allowed a classification accuracy of ≥99%, with ANN, naïve Bayes, random forest, SVM and kNN presenting 100% accuracy on the test dataset. The classification models were also assayed using only the best features selected by a reduction algorithm and the best input features mapped by t-SNE. The classifier kNN provided the best discrimination results because it correctly classified all samples according to production method and origin, ultimately using only the three most important features (16:0, 18:2n6c and 20:3n3 + 20:4n6). In general, the classifiers presented good generalization with the herein proposed approach being simple and presenting the advantage of requiring only common equipment existing in most labs. |
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Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmonAuthenticityChemometricsFatty acidsFishMachine learningMislabelingSalmo salar LIn the last decade, there has been an increasing demand for wild-captured fish, which attains higher prices compared to farmed species, thus being prone to mislabeling practices. In this work, fatty acid composition coupled to advanced chemometrics was used to discriminate wild from farmed salmon. The lipids extracted from salmon muscles of different production methods and origins (26 wild from Canada, 25 farmed from Canada, 24 farmed from Chile and 25 farmed from Norway) were analyzed by gas chromatography with flame ionization detector (GC-FID). All the tested chemometric approaches, namely principal components analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and seven machine learning classifiers, namely k-nearest neighbors (kNN), decision tree, support vector machine (SVM), random forest, artificial neural networks (ANN), naïve Bayes and AdaBoost, allowed for differentiation between farmed and wild salmons using the 17 features obtained from chemical analysis. PCA did not allow clear distinguishing between salmon geographical origin since farmed samples from Canada and Chile overlapped. Nevertheless, using the 17 features in the models, six out of the seven tested machine learning classifiers allowed a classification accuracy of ≥99%, with ANN, naïve Bayes, random forest, SVM and kNN presenting 100% accuracy on the test dataset. The classification models were also assayed using only the best features selected by a reduction algorithm and the best input features mapped by t-SNE. The classifier kNN provided the best discrimination results because it correctly classified all samples according to production method and origin, ultimately using only the three most important features (16:0, 18:2n6c and 20:3n3 + 20:4n6). In general, the classifiers presented good generalization with the herein proposed approach being simple and presenting the advantage of requiring only common equipment existing in most labs.This work was supported by the European project FOODINTEGRITY (FP7-KBBE-2013-single-stage, under grant agreement No 613688) and FCT (Fundação para a Ciência e Tecnologia, Portugal) under the Partnership Agreements UIDB 50006/2020, UIDB 00690/2020 (CIMO) and UIDB/5757/2020 (CeDRI). L. Grazina and M.A. Nunes acknowledge the FCT grant SFRH/BD/132462/2017 and SFRH/BD/130131/2017 financed by POPH-QREN (subsidised by FSE and MCTES).Biblioteca Digital do IPBGrazina, LilianaRodrigues, Pedro JoãoIgrejas, GetúlioNunes, Maria A.Mafra, IsabelArlorio, MarcoOliveira, BeatrizAmaral, Joana S.2018-01-19T10:00:00Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/23356engGrazina, Liliana; Rodrigues, P. J.; Igrejas, Getúlio; Nunes, Maria A.; Mafra, Isabel; Arlorio, Marco; Oliveira, M. Beatriz; Amaral, Joana S. (2020). Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon. Foods. ISSN 2304-8158. 9:11, p. 1-1510.3390/foods9111622info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T10:52:13Zoai:bibliotecadigital.ipb.pt:10198/23356Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:14:22.061256Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
title |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
spellingShingle |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon Grazina, Liliana Authenticity Chemometrics Fatty acids Fish Machine learning Mislabeling Salmo salar L |
title_short |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
title_full |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
title_fullStr |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
title_full_unstemmed |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
title_sort |
Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon |
author |
Grazina, Liliana |
author_facet |
Grazina, Liliana Rodrigues, Pedro João Igrejas, Getúlio Nunes, Maria A. Mafra, Isabel Arlorio, Marco Oliveira, Beatriz Amaral, Joana S. |
author_role |
author |
author2 |
Rodrigues, Pedro João Igrejas, Getúlio Nunes, Maria A. Mafra, Isabel Arlorio, Marco Oliveira, Beatriz Amaral, Joana S. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Grazina, Liliana Rodrigues, Pedro João Igrejas, Getúlio Nunes, Maria A. Mafra, Isabel Arlorio, Marco Oliveira, Beatriz Amaral, Joana S. |
dc.subject.por.fl_str_mv |
Authenticity Chemometrics Fatty acids Fish Machine learning Mislabeling Salmo salar L |
topic |
Authenticity Chemometrics Fatty acids Fish Machine learning Mislabeling Salmo salar L |
description |
In the last decade, there has been an increasing demand for wild-captured fish, which attains higher prices compared to farmed species, thus being prone to mislabeling practices. In this work, fatty acid composition coupled to advanced chemometrics was used to discriminate wild from farmed salmon. The lipids extracted from salmon muscles of different production methods and origins (26 wild from Canada, 25 farmed from Canada, 24 farmed from Chile and 25 farmed from Norway) were analyzed by gas chromatography with flame ionization detector (GC-FID). All the tested chemometric approaches, namely principal components analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and seven machine learning classifiers, namely k-nearest neighbors (kNN), decision tree, support vector machine (SVM), random forest, artificial neural networks (ANN), naïve Bayes and AdaBoost, allowed for differentiation between farmed and wild salmons using the 17 features obtained from chemical analysis. PCA did not allow clear distinguishing between salmon geographical origin since farmed samples from Canada and Chile overlapped. Nevertheless, using the 17 features in the models, six out of the seven tested machine learning classifiers allowed a classification accuracy of ≥99%, with ANN, naïve Bayes, random forest, SVM and kNN presenting 100% accuracy on the test dataset. The classification models were also assayed using only the best features selected by a reduction algorithm and the best input features mapped by t-SNE. The classifier kNN provided the best discrimination results because it correctly classified all samples according to production method and origin, ultimately using only the three most important features (16:0, 18:2n6c and 20:3n3 + 20:4n6). In general, the classifiers presented good generalization with the herein proposed approach being simple and presenting the advantage of requiring only common equipment existing in most labs. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-19T10:00:00Z 2020 2020-01-01T00:00:00Z |
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 |
http://hdl.handle.net/10198/23356 |
url |
http://hdl.handle.net/10198/23356 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Grazina, Liliana; Rodrigues, P. J.; Igrejas, Getúlio; Nunes, Maria A.; Mafra, Isabel; Arlorio, Marco; Oliveira, M. Beatriz; Amaral, Joana S. (2020). Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon. Foods. ISSN 2304-8158. 9:11, p. 1-15 10.3390/foods9111622 |
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 Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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