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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite; Embrapa Instrumentação. |
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Data corrente: |
28/09/2022 |
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Data da última atualização: |
28/09/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
COATRINI-SOARES, A.; COATRINI-SOARES, J.; POPOLIN NETO, M.; MELLO, S. S. de; PINTO, D. S. C.; CARVALHO, W. A.; GILMORE, M. S.; PIAZZETTA, M. H. O.; GOBBI, A. L.; BRANDÃO, H. M.; PAULOVICH, F. V.; OLIVEIRA JR, O. N.; MATTOSO, L. H. C. |
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Afiliação: |
LUIZ HENRIQUE CAPPARELLI MATTOSO, CNPDIA. |
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Título: |
Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Chemical Engineering Journal, v. 451, e138523, 2022. |
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Páginas: |
1 - 9 |
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ISSN: |
1385-8947 |
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DOI: |
https://doi.org/10.1016/j.cej.2022.138523 |
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Idioma: |
Inglês |
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Conteúdo: |
We report an electronic tongue based on impedance spectroscopy to detect Staphylococcus aureus and diagnose bovine mastitis in milk samples. This was achieved with optimized sensing units made with layer-by-layer films and by treating the capacitance data with machine learning algorithms employing decision trees models. These films were made with chitosan, chondroitin sulfate, sericin and gold nanoparticles /sericin, whose molecularlevel interaction with S.aureus depended on the architecture according to PM-IRRAS measurements. The limit of detection in blank milk varied from 3.41 to 2.01 CFU/mL depending on the sensing unit. This sensitivity was complemented with the selectivity provided by combining the electrical responses of the four sensing units. Indeed, with machine learning it was possible to determine multidimensional calibration spaces (MCS) that could generate rules to explain how the milk samples could be discriminated. With a 7-dimension MCS, distinct S. aureus concentrations could be distinguished from possible interferents with a 100 % accuracy. In crude milk samples, 94 % accuracy was obtained with a 6-dimension MCS in multiclass classification for milk from different udders of a mastitis infected cow, including samples diluted 50-fold, in addition to milk from an infected cow treated with Bronopol and from a healthy cow. It is significant that in a ternary classification with these crude milk samples, a 2-dimension MCS could distinguish between milk from an infected cow, treated with Bronopol and from a healthy cow with 100 % accuracy. The combination of electronic tongues and machine learning ? as in this proof-of-concept study - is promising for diagnosis of mastitis at a low cost. MenosWe report an electronic tongue based on impedance spectroscopy to detect Staphylococcus aureus and diagnose bovine mastitis in milk samples. This was achieved with optimized sensing units made with layer-by-layer films and by treating the capacitance data with machine learning algorithms employing decision trees models. These films were made with chitosan, chondroitin sulfate, sericin and gold nanoparticles /sericin, whose molecularlevel interaction with S.aureus depended on the architecture according to PM-IRRAS measurements. The limit of detection in blank milk varied from 3.41 to 2.01 CFU/mL depending on the sensing unit. This sensitivity was complemented with the selectivity provided by combining the electrical responses of the four sensing units. Indeed, with machine learning it was possible to determine multidimensional calibration spaces (MCS) that could generate rules to explain how the milk samples could be discriminated. With a 7-dimension MCS, distinct S. aureus concentrations could be distinguished from possible interferents with a 100 % accuracy. In crude milk samples, 94 % accuracy was obtained with a 6-dimension MCS in multiclass classification for milk from different udders of a mastitis infected cow, including samples diluted 50-fold, in addition to milk from an infected cow treated with Bronopol and from a healthy cow. It is significant that in a ternary classification with these crude milk samples, a 2-dimension MCS could distinguish between milk from an i... Mostrar Tudo |
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Palavras-Chave: |
Impedance spectroscopy; Machine learning; Mastite; Multidimensional calibration space; S aureus; Sensors. |
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Thesagro: |
Bovino; Doença Animal; Gado Leiteiro. |
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Categoria do assunto: |
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L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02936naa a2200409 a 4500
001 2146958
005 2022-09-28
008 2022 bl uuuu u00u1 u #d
022 $a1385-8947
024 7 $ahttps://doi.org/10.1016/j.cej.2022.138523$2DOI
100 1 $aCOATRINI-SOARES, A.
245 $aMicrofluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models.$h[electronic resource]
260 $c2022
300 $a1 - 9
520 $aWe report an electronic tongue based on impedance spectroscopy to detect Staphylococcus aureus and diagnose bovine mastitis in milk samples. This was achieved with optimized sensing units made with layer-by-layer films and by treating the capacitance data with machine learning algorithms employing decision trees models. These films were made with chitosan, chondroitin sulfate, sericin and gold nanoparticles /sericin, whose molecularlevel interaction with S.aureus depended on the architecture according to PM-IRRAS measurements. The limit of detection in blank milk varied from 3.41 to 2.01 CFU/mL depending on the sensing unit. This sensitivity was complemented with the selectivity provided by combining the electrical responses of the four sensing units. Indeed, with machine learning it was possible to determine multidimensional calibration spaces (MCS) that could generate rules to explain how the milk samples could be discriminated. With a 7-dimension MCS, distinct S. aureus concentrations could be distinguished from possible interferents with a 100 % accuracy. In crude milk samples, 94 % accuracy was obtained with a 6-dimension MCS in multiclass classification for milk from different udders of a mastitis infected cow, including samples diluted 50-fold, in addition to milk from an infected cow treated with Bronopol and from a healthy cow. It is significant that in a ternary classification with these crude milk samples, a 2-dimension MCS could distinguish between milk from an infected cow, treated with Bronopol and from a healthy cow with 100 % accuracy. The combination of electronic tongues and machine learning ? as in this proof-of-concept study - is promising for diagnosis of mastitis at a low cost.
650 $aBovino
650 $aDoença Animal
650 $aGado Leiteiro
653 $aImpedance spectroscopy
653 $aMachine learning
653 $aMastite
653 $aMultidimensional calibration space
653 $aS aureus
653 $aSensors
700 1 $aCOATRINI-SOARES, J.
700 1 $aPOPOLIN NETO, M.
700 1 $aMELLO, S. S. de
700 1 $aPINTO, D. S. C.
700 1 $aCARVALHO, W. A.
700 1 $aGILMORE, M. S.
700 1 $aPIAZZETTA, M. H. O.
700 1 $aGOBBI, A. L.
700 1 $aBRANDÃO, H. M.
700 1 $aPAULOVICH, F. V.
700 1 $aOLIVEIRA JR, O. N.
700 1 $aMATTOSO, L. H. C.
773 $tChemical Engineering Journal$gv. 451, e138523, 2022.
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Registro original: |
Embrapa Instrumentação (CNPDIA) |
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| Registros recuperados : 9 | |
| 3. |  | PINTO, D. S.; TOMAZ, R. S.; GLASENAPP, J. S.; ESTOPA, R. A.; RESENDE, M. D. V. de; CRUZ, C. D. Pacote pedigreemm e sua utilização no melhoramento vegetal. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 7., 2013, Uberlândia. Variedade melhorada: a força da nossa agricultura: anais. Viçosa, MG: SBMP, 2013. p. 422-425.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 4. | | FELIPE, J. P.; EL-HUSNY, J. C.; PINTO, D. S.; FARIAS NETO, J. T. de; PEREIRA, M. Z. Interação genótipos por ambientes no desempenho de linhagens de soja no estado do Pará. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 6. | | NASCIMENTO, M.; CRUZ, C. D.; PETERNELLI, L. A.; CAMPANA, A. C. M.; PINTO, D. S.; FERREIRA, R. de P. Teste dos sinais para tendência: uma aplicação em melhoramento de plantas. Revista Brasileira Biometria, v. 26, n. 4, p. 19-30, 2008. metas 2009| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 7. | | NASCIMENTO, M.; ROCHA, G. S. da; PINTO, D. S.; BARROSO, L. M. A.; NASCIMENTO, A. C. C.; FERREIRA, R. de P.; SILVA, F. F. e. Correlação de Spearman aplicada ao estudo de adaptabilidade e estabilidade em genótipos de alfafa. Investigacion Agrária, v. 15, n. 2, p. 83-90, 2013.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 8. | | ESTEVES, G. B.; ZANETTE, R. S. S.; PINTO, D. S. C.; MONGELLI, M. S.; LOURES, M. D. de A.; BARDUCCI, R. S.; BRANDAO, H. de M. Estudo de dose-resposta de beta-glucana em bezerros: resultados iniciais. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 27., 2023, Juiz de Fora. Anais... Juiz de Fora: Embrapa Gado de Leite, 2024. p. 68-72. (Embrapa Gado de Leite. Eventos Técnicos & Científicos, 2). Pibic/CNPq.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 9. | | COATRINI-SOARES, A.; COATRINI-SOARES, J.; POPOLIN NETO, M.; MELLO, S. S. de; PINTO, D. S. C.; CARVALHO, W. A.; GILMORE, M. S.; PIAZZETTA, M. H. O.; GOBBI, A. L.; BRANDÃO, H. M.; PAULOVICH, F. V.; OLIVEIRA JR, O. N.; MATTOSO, L. H. C. Microfluidic E-tongue to diagnose bovine mastitis with milk samples using machine learning with decision tree models. Chemical Engineering Journal, v. 451, e138523, 2022. 1 - 9| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Gado de Leite; Embrapa Instrumentação. |
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| Registros recuperados : 9 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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