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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
25/06/2024 |
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Data da última atualização: |
31/07/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
RIBEIRO, M. C.; CABRAL, J.; NICOLODELLI, G.; SENESI, G. S.; CAIRES, A. R. L.; GONÇALVES, D. A.; MENEGATTI, C.; MILORI, D. M. B. P.; CENA, C.; MARAGONI, B. |
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Afiliação: |
UFMS – UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; UNIVERSIDADE FEDERAL DE UBERLÂNDIA; UFSC – UNIVERSIDADE FEDERAL DE SANTA CATARINA; CNR-ISTITUTO PER LA SCIENZA E TECNOLOGIA DEI PLASMI (ISTP); UFMS – UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; UNIVERSIDADE FEDERAL DA GRANDE DOURADOS (UFGD); UNIVERSIDADE DE SÃO PAULO; DEBORA MARCONDES BASTOS PEREIRA, CNPDIA; UFMS – UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; UFMS – UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL. |
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Título: |
Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Microchemical Journal, v. 203, 110898, 2024. |
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Páginas: |
1 - 11 |
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ISSN: |
0026-265X |
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DOI: |
https://doi.org/10.1016/j.microc.2024.110898 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT Background: In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Results: Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. Conclusions: These results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize. MenosABSTRACT Background: In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Results: Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combi... Mostrar Tudo |
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Palavras-Chave: |
Chemometric analysis; LIBS; Machine learning algorithms; Non-transgenic maize; Transgenic maize. |
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Categoria do assunto: |
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Marc: |
LEADER 02825naa a2200325 a 4500
001 2165093
005 2024-07-31
008 2024 bl uuuu u00u1 u #d
022 $a0026-265X
024 7 $ahttps://doi.org/10.1016/j.microc.2024.110898$2DOI
100 1 $aRIBEIRO, M. C.
245 $aDiscrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms.$h[electronic resource]
260 $c2024
300 $a1 - 11
520 $aABSTRACT Background: In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Results: Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. Conclusions: These results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize.
653 $aChemometric analysis
653 $aLIBS
653 $aMachine learning algorithms
653 $aNon-transgenic maize
653 $aTransgenic maize
700 1 $aCABRAL, J.
700 1 $aNICOLODELLI, G.
700 1 $aSENESI, G. S.
700 1 $aCAIRES, A. R. L.
700 1 $aGONÇALVES, D. A.
700 1 $aMENEGATTI, C.
700 1 $aMILORI, D. M. B. P.
700 1 $aCENA, C.
700 1 $aMARAGONI, B.
773 $tMicrochemical Journal$gv. 203, 110898, 2024.
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| 1. |  | RODRIGUES, A. C. P. S.; FERNANDES, R. C.; ARAÚJO, D. A. de O.; CARDOSO, F. N.; ARAÚJO, T. M. H.; PINTO, D. R.; RODRIGUES, P. de F. M.; QUEZADO-DUVAL, A. M.; RODRIGUES, T. T. M. S. Severidade da mancha bacteriana em tomateiro industrial com uso de acibenzolar-s-metil e estrobilurina, em Januária, MG. In: SIMPÓSIO DE INICIAÇÃO CIENTÍFICA , 5., MOSTRA DE TRABALHOS CIENTÍFICOS DO IFNMG, 4., Arinos, 2015. 3 p.| Tipo: Artigo em Anais de Congresso |
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