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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
28/11/2022 |
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Data da última atualização: |
22/01/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CIOCCIA, G.; MORAIS, C. P. de; BABOS, D. V.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C.; NICOLODELLI, G.; MARANGONI, B. S. |
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Afiliação: |
DEBORA MARCONDES BASTOS PEREIRA, CNPDIA. |
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Título: |
Laser-induced breakdown spectroscopy associated with the design of experiments and machine learning for discrimination of Brachiaria brizantha seed vigor. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Sensors, v. 22, a5067, 2022. |
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Páginas: |
12 p. |
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DOI: |
https://doi.org/10.3390/s22145067 |
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Idioma: |
Inglês |
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Conteúdo: |
Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor seeds. A 23 factorial design was used to optimize the LIBS experimental parameters for spectral analysis. A total of 120 samples from two distinct cultivars of Brachiaria brizantha seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-oneout cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination,regardless of the cultivar or the classification algorithm. |
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Palavras-Chave: |
Design of experiments; Discriminating; LIBS; Machine learning. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1148856/1/P-Laser-Induced-Breakdown-Spectroscopy-Associated-with-the.pdf
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Marc: |
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100 1 $aCIOCCIA, G.
245 $aLaser-induced breakdown spectroscopy associated with the design of experiments and machine learning for discrimination of Brachiaria brizantha seed vigor.$h[electronic resource]
260 $c2022
300 $a12 p.
520 $aLaser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor seeds. A 23 factorial design was used to optimize the LIBS experimental parameters for spectral analysis. A total of 120 samples from two distinct cultivars of Brachiaria brizantha seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-oneout cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination,regardless of the cultivar or the classification algorithm.
653 $aDesign of experiments
653 $aDiscriminating
653 $aLIBS
653 $aMachine learning
700 1 $aMORAIS, C. P. de
700 1 $aBABOS, D. V.
700 1 $aMILORI, D. M. B. P.
700 1 $aALVES, C. Z.
700 1 $aCENA, C.
700 1 $aNICOLODELLI, G.
700 1 $aMARANGONI, B. S.
773 $tSensors$gv. 22, a5067, 2022.
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Embrapa Instrumentação (CNPDIA) |
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| 1. |  | CIOCCIA, G.; MORAIS, C. P. de; BABOS, D. V.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C.; NICOLODELLI, G.; MARANGONI, B. S. Laser-induced breakdown spectroscopy associated with the design of experiments and machine learning for discrimination of Brachiaria brizantha seed vigor. Sensors, v. 22, a5067, 2022. 12 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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