Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo
Ano de defesa: | 2021 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Ciências Rurais |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Medicina Veterinária
|
Departamento: |
Medicina Veterinária
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/23198 |
Resumo: | Cereal grain quality can be altered by the presence of fungi, which may produce mycotoxins that have the potential to cause harm to human and animal health. It is thus essential to monitor the levels of such substances in products intended for consumption. Despite being efficient, current analytical methodologies are time consuming and require the use of varied reagents and instruments, so the Industry demands the development of fast and reliable methods to expedite decision making. In this setting, the present work employs near infrared reflectance spectroscopy (NIRS) in association with chemometric methods for quantification and classification to build multivariate models for predicting mycotoxins. Four studies were performed in the following matrices: corn; corn distiller’s dried grains with solubles (DDGS); and wheat flour. The analyzed mycotoxins were aflatoxin B1 (AFB1), fumonisin B1 (FB1) + fumonisin B2 (FB2) (total fumonisins, FBs), deoxynivalenol (DON) and zearalenone (ZEA). Spectral data were processed through partial least squares and the number of principal components of the models was determined by cross-validation. Liquid chromatography coupled to tandem mass spectrometry was used as the reference methodology. The first study developed prediction curves for FBs and ZEA in corn. Correlation coefficient (R), determination coefficient and residual prediction deviation (RPD) for FBs and ZEA were, respectively: 0.809 and 0.991; 0.899 and 0.984; and 3.33 and 2.71. The second study assessed mycotoxicological prevalence and chemical composition (water activity, crude protein, ether extract, starch and apparent metabolisable energy in poultry) in 8,854 spectra of corn originating from Argentina, Bolivia, Brazil (stratified per regions), Colombia and Peru in 2020. FBs showed the greatest prevalence in South American as well as in Brazilian samples: 91.6% and 92.6%, respectively. Crude protein ranged from 6.7% in Colombia to 8.4% in Bolivia in relation to the mean (7.4%). The chemical composition of the samples from the Southeast region of Brazil presented the largest positive variability in relation to the means. The third study was conducted in DDGS and elaborated prediction curves for FB1 and FB2. One hundred ninety samples were used to build the models, being 132 for calibration and 58 for external validation. The results of R and RPD for FB1 and FB2 were, respectively: 0.90 and 0.88; and 2.16 and 2.06. The fourth study evaluated DONcontaminated wheat flour samples using partial least-squares discriminant analysis (PLS-DA) and principal components-linear discriminant analysis (PC-LDA). The samples were classified according to the maximum tolerated limit (MTL) for DON in Brazil, 750 μg.kg-¹, and two groups were established for the calibration set: category A (≤ 450 μg kg-¹), non-contaminated or below the MTL; and category B (> 450 μg kg-¹), contaminated or above the MTL. Validation samples analyzed via PLS-DA showed correct classification rates between 85 and 87.5%; for PC-LDA, the hit rate was over 85%. Both methods presented a 10-15% error. The results achieved through these studies evidence the potential of the alternative technology NIRS to be used in the Industry, providing agility to the analytical process of the ingredients. Therefore, decisions can be made assertively and thus ensure food quality and safety. |
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2021-12-09T11:13:44Z2021-12-09T11:13:44Z2021-09-01http://repositorio.ufsm.br/handle/1/23198Cereal grain quality can be altered by the presence of fungi, which may produce mycotoxins that have the potential to cause harm to human and animal health. It is thus essential to monitor the levels of such substances in products intended for consumption. Despite being efficient, current analytical methodologies are time consuming and require the use of varied reagents and instruments, so the Industry demands the development of fast and reliable methods to expedite decision making. In this setting, the present work employs near infrared reflectance spectroscopy (NIRS) in association with chemometric methods for quantification and classification to build multivariate models for predicting mycotoxins. Four studies were performed in the following matrices: corn; corn distiller’s dried grains with solubles (DDGS); and wheat flour. The analyzed mycotoxins were aflatoxin B1 (AFB1), fumonisin B1 (FB1) + fumonisin B2 (FB2) (total fumonisins, FBs), deoxynivalenol (DON) and zearalenone (ZEA). Spectral data were processed through partial least squares and the number of principal components of the models was determined by cross-validation. Liquid chromatography coupled to tandem mass spectrometry was used as the reference methodology. The first study developed prediction curves for FBs and ZEA in corn. Correlation coefficient (R), determination coefficient and residual prediction deviation (RPD) for FBs and ZEA were, respectively: 0.809 and 0.991; 0.899 and 0.984; and 3.33 and 2.71. The second study assessed mycotoxicological prevalence and chemical composition (water activity, crude protein, ether extract, starch and apparent metabolisable energy in poultry) in 8,854 spectra of corn originating from Argentina, Bolivia, Brazil (stratified per regions), Colombia and Peru in 2020. FBs showed the greatest prevalence in South American as well as in Brazilian samples: 91.6% and 92.6%, respectively. Crude protein ranged from 6.7% in Colombia to 8.4% in Bolivia in relation to the mean (7.4%). The chemical composition of the samples from the Southeast region of Brazil presented the largest positive variability in relation to the means. The third study was conducted in DDGS and elaborated prediction curves for FB1 and FB2. One hundred ninety samples were used to build the models, being 132 for calibration and 58 for external validation. The results of R and RPD for FB1 and FB2 were, respectively: 0.90 and 0.88; and 2.16 and 2.06. The fourth study evaluated DONcontaminated wheat flour samples using partial least-squares discriminant analysis (PLS-DA) and principal components-linear discriminant analysis (PC-LDA). The samples were classified according to the maximum tolerated limit (MTL) for DON in Brazil, 750 μg.kg-¹, and two groups were established for the calibration set: category A (≤ 450 μg kg-¹), non-contaminated or below the MTL; and category B (> 450 μg kg-¹), contaminated or above the MTL. Validation samples analyzed via PLS-DA showed correct classification rates between 85 and 87.5%; for PC-LDA, the hit rate was over 85%. Both methods presented a 10-15% error. The results achieved through these studies evidence the potential of the alternative technology NIRS to be used in the Industry, providing agility to the analytical process of the ingredients. Therefore, decisions can be made assertively and thus ensure food quality and safety.A qualidade do grão de cereal pode ser alterada pela presença de fungos, os quais podem produzir micotoxinas com potencial de causar danos à saúde humana e animal. Portanto, faz-se necessário monitorar os níveis dessas substâncias em produtos destinados ao consumo. Apesar de eficientes, as metodologias analíticas atualmente utilizadas são morosas e requerem o uso de diversos reagentes e instrumentos, sendo o desenvolvimento de métodos rápidos e confiáveis uma demanda da Indústria para agilizar a tomada de decisão. Nesse contexto, o presente trabalho emprega a espectroscopia de refletância no infravermelho próximo (NIRS) associada a métodos quimiométricos para quantificação e classificação na construção de modelos multivariados para a predição de micotoxinas. Quatro estudos foram desenvolvidos nas seguintes matrizes: milho; resíduo seco de destilaria com solúveis (DDGS) de milho; e farinha de trigo. As micotoxinas analisadas foram: aflatoxina B1 (AFB1), fumonisina B1 (FB1) + fumonisina B2 (FB2) (fumonisinas totais, FBs), deoxinivalenol (DON) e zearalenona (ZEA). Os dados espectrais foram processados pelo método de mínimos quadrados parciais e o número de componentes principais dos modelos foi determinado por validação cruzada. A cromatografia líquida acoplada à espectrometria de massas em tandem foi usada como metodologia de referência. O primeiro estudo desenvolveu curvas de predição para FBs e ZEA em milho. O coeficiente de correlação (R), o coeficiente de determinação e a relação de desempenho do desvio (RPD) para FBs e ZEA foram, respectivamente: 0,809 e 0,991; 0,899 e 0,984; e 3,33 e 2,71. O segundo estudo analisou a prevalência micotoxicológica e a composição nutricional (atividade de água, proteína bruta, extrato etéreo, amido e energia metabolizável aparente para aves) em 8.854 espectros de milho proveniente da Argentina, Bolívia, Brasil (estratificado por região), Colômbia e Peru em 2020. As FBs apresentaram a maior prevalência nas amostras latino-americanas assim como nas brasileiras: 91,6% e 92,6%, respectivamente. A proteína bruta variou de 6,7% na Colômbia a 8,4% na Bolívia em relação à média (7,4%). A composição química das amostras originárias da região Sudeste do Brasil mostrou a maior variabilidade positiva em relação às médias. O terceiro estudo foi realizado em DDGS e elaborou curvas de predição para FB1 e FB2. Os modelos foram construídos a partir de 190 amostras, sendo 132 para calibração e 58 para validação externa. Os resultados de R e RPD para FB1 e FB2 foram, respectivamente: 0,90 e 0,88; e 2,16 e 2,06. O quarto estudo avaliou amostras de farinha de trigo contaminadas com DON utilizando os métodos de análise discriminante por mínimos quadrados parciais (PLS-DA) e análise discriminante linear de componentes principais (PC-LDA). A classificação das amostras baseou-se no limite máximo tolerado (LMT) para DON no Brasil, 750 μg kg-¹, sendo dois grupos estabelecidos para o conjunto de calibração: categoria A (≤ 450 μg kg-¹), não contaminado ou inferior ao LMT; e categoria B (> 450 μg kg-¹), contaminado ou acima do LMT. As amostras de validação analisadas por PLS-DA apresentaram taxas de classificação correta entre 85 e 87,5%; para PC-LDA, a taxa de acerto foi superior a 85%. Nos dois casos, o erro foi de 10 a 15%. Os resultados obtidos através desses estudos indicam o potencial da tecnologia alternativa NIRS para uso na Indústria, conferindo agilidade ao processo analítico dos ingredientes. Dessa forma, as decisões podem ser tomadas de forma assertiva e assim assegurar a qualidade e a segurança alimentar.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Medicina VeterináriaUFSMBrasilMedicina VeterináriaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessMicotoxinasEspectroscopia de reflectância no infravermelho próximoQuimiometriaMínimos quadrados parciaisMétodos classificatóriosMycotoxinsNear Infrared reflectance spectroscopyChemometricsPartial least squaresClassification methodsCNPQ::CIENCIAS AGRARIAS::MEDICINA VETERINARIAPredição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximoPrediction of mycotoxins in cereals and by-products via near infrared reflectance spectroscopyinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisMallmann, Carlos Augustohttp://lattes.cnpq.br/5193771213666058Vogel, Fernanda Silveira Flôreshttp://lattes.cnpq.br/9676833435314493Almeida, Carlos Alberto Araujo deNascimento, Paulo Cícero doKrabbe, Everton LuisSá, Luciano Moraeshttp://lattes.cnpq.br/1346776288727019Tyska, Denize500500000007600600600600600600600600f8b7bb02-50a2-468a-91b2-0a75e5374e2ffec2e6d6-10e4-4f5a-9b36-857b0b2bbc2987a417ae-60eb-4318-9a6c-e38e8b3da5d65690e3d3-e4ff-47ee-8a96-dcb3c75bd4474b484079-716a-42c6-95ca-2c152d0f628f03ccf2ea-12df-4622-bfeb-f88b759332b9e32c8dae-5338-4416-ad0f-bfe6339c29fcreponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGMV_2021_TYSKA_DENIZE.pdfTES_PPGMV_2021_TYSKA_DENIZE.pdfTese de doutoradoapplication/pdf2763501http://repositorio.ufsm.br/bitstream/1/23198/1/TES_PPGMV_2021_TYSKA_DENIZE.pdfde867094a3ac36f27835adc55ae55298MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
dc.title.alternative.eng.fl_str_mv |
Prediction of mycotoxins in cereals and by-products via near infrared reflectance spectroscopy |
title |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
spellingShingle |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo Tyska, Denize Micotoxinas Espectroscopia de reflectância no infravermelho próximo Quimiometria Mínimos quadrados parciais Métodos classificatórios Mycotoxins Near Infrared reflectance spectroscopy Chemometrics Partial least squares Classification methods CNPQ::CIENCIAS AGRARIAS::MEDICINA VETERINARIA |
title_short |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
title_full |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
title_fullStr |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
title_full_unstemmed |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
title_sort |
Predição de micotoxinas em cereais e subprodutos via espectroscopia de reflectância no infravermelho próximo |
author |
Tyska, Denize |
author_facet |
Tyska, Denize |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Mallmann, Carlos Augusto |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/5193771213666058 |
dc.contributor.advisor-co1.fl_str_mv |
Vogel, Fernanda Silveira Flôres |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/9676833435314493 |
dc.contributor.referee1.fl_str_mv |
Almeida, Carlos Alberto Araujo de |
dc.contributor.referee2.fl_str_mv |
Nascimento, Paulo Cícero do |
dc.contributor.referee3.fl_str_mv |
Krabbe, Everton Luis |
dc.contributor.referee4.fl_str_mv |
Sá, Luciano Moraes |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1346776288727019 |
dc.contributor.author.fl_str_mv |
Tyska, Denize |
contributor_str_mv |
Mallmann, Carlos Augusto Vogel, Fernanda Silveira Flôres Almeida, Carlos Alberto Araujo de Nascimento, Paulo Cícero do Krabbe, Everton Luis Sá, Luciano Moraes |
dc.subject.por.fl_str_mv |
Micotoxinas Espectroscopia de reflectância no infravermelho próximo Quimiometria Mínimos quadrados parciais Métodos classificatórios |
topic |
Micotoxinas Espectroscopia de reflectância no infravermelho próximo Quimiometria Mínimos quadrados parciais Métodos classificatórios Mycotoxins Near Infrared reflectance spectroscopy Chemometrics Partial least squares Classification methods CNPQ::CIENCIAS AGRARIAS::MEDICINA VETERINARIA |
dc.subject.eng.fl_str_mv |
Mycotoxins Near Infrared reflectance spectroscopy Chemometrics Partial least squares Classification methods |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::MEDICINA VETERINARIA |
description |
Cereal grain quality can be altered by the presence of fungi, which may produce mycotoxins that have the potential to cause harm to human and animal health. It is thus essential to monitor the levels of such substances in products intended for consumption. Despite being efficient, current analytical methodologies are time consuming and require the use of varied reagents and instruments, so the Industry demands the development of fast and reliable methods to expedite decision making. In this setting, the present work employs near infrared reflectance spectroscopy (NIRS) in association with chemometric methods for quantification and classification to build multivariate models for predicting mycotoxins. Four studies were performed in the following matrices: corn; corn distiller’s dried grains with solubles (DDGS); and wheat flour. The analyzed mycotoxins were aflatoxin B1 (AFB1), fumonisin B1 (FB1) + fumonisin B2 (FB2) (total fumonisins, FBs), deoxynivalenol (DON) and zearalenone (ZEA). Spectral data were processed through partial least squares and the number of principal components of the models was determined by cross-validation. Liquid chromatography coupled to tandem mass spectrometry was used as the reference methodology. The first study developed prediction curves for FBs and ZEA in corn. Correlation coefficient (R), determination coefficient and residual prediction deviation (RPD) for FBs and ZEA were, respectively: 0.809 and 0.991; 0.899 and 0.984; and 3.33 and 2.71. The second study assessed mycotoxicological prevalence and chemical composition (water activity, crude protein, ether extract, starch and apparent metabolisable energy in poultry) in 8,854 spectra of corn originating from Argentina, Bolivia, Brazil (stratified per regions), Colombia and Peru in 2020. FBs showed the greatest prevalence in South American as well as in Brazilian samples: 91.6% and 92.6%, respectively. Crude protein ranged from 6.7% in Colombia to 8.4% in Bolivia in relation to the mean (7.4%). The chemical composition of the samples from the Southeast region of Brazil presented the largest positive variability in relation to the means. The third study was conducted in DDGS and elaborated prediction curves for FB1 and FB2. One hundred ninety samples were used to build the models, being 132 for calibration and 58 for external validation. The results of R and RPD for FB1 and FB2 were, respectively: 0.90 and 0.88; and 2.16 and 2.06. The fourth study evaluated DONcontaminated wheat flour samples using partial least-squares discriminant analysis (PLS-DA) and principal components-linear discriminant analysis (PC-LDA). The samples were classified according to the maximum tolerated limit (MTL) for DON in Brazil, 750 μg.kg-¹, and two groups were established for the calibration set: category A (≤ 450 μg kg-¹), non-contaminated or below the MTL; and category B (> 450 μg kg-¹), contaminated or above the MTL. Validation samples analyzed via PLS-DA showed correct classification rates between 85 and 87.5%; for PC-LDA, the hit rate was over 85%. Both methods presented a 10-15% error. The results achieved through these studies evidence the potential of the alternative technology NIRS to be used in the Industry, providing agility to the analytical process of the ingredients. Therefore, decisions can be made assertively and thus ensure food quality and safety. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-12-09T11:13:44Z |
dc.date.available.fl_str_mv |
2021-12-09T11:13:44Z |
dc.date.issued.fl_str_mv |
2021-09-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
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publishedVersion |
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http://repositorio.ufsm.br/handle/1/23198 |
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http://repositorio.ufsm.br/handle/1/23198 |
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por |
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por |
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eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Medicina Veterinária |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Medicina Veterinária |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
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Universidade Federal de Santa Maria (UFSM) |
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UFSM |
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UFSM |
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Biblioteca Digital de Teses e Dissertações do UFSM |
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atendimento.sib@ufsm.br||tedebc@gmail.com |
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