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| 3. |  | LIMA, F. B. de; PASSOS, A. M. A. dos; SILVA, J. B. da; ALVES, C. Z.; NETTO, D. A. M.; COTRIM, M. F. Accelerated aging in sorghum genotypes. Bioscience Journal, v. 35, n. 2, p. 450-458, 2019 .| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | OLIVEIRA, I. C.; FRANCA, T.; NICOLODELLI, G.; MORAIS, C. P.; MARANGONI, B.; BACCHETTA, G.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C. Fast and accurate discrimination of Brachiaria brizantha (A.Rich.) stapf seeds by molecular spectroscopy and machine learning. ACS Agricultural Science & Technology, v. 1, 2021. 443?448| Biblioteca(s): Embrapa Instrumentação. |
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| 5. | | 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.| Biblioteca(s): Embrapa Instrumentação. |
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| 6. | | LARIOS, G. S.; NICOLODELLI, G.; SENESI, G. S.; RIBEIRO, M. C. S.; XAVIER, A. A. P.; MILORI, D. M. B. P.; ALVES, C. Z.; MARANGONI, B. S.; CENA, C. Laser-induced breakdown spectroscopy as a powerful tool for distinguishing high- and low-vigor soybean seed lots. Food Analytical Methods, v. 13, 1691?1698, 2020. 1691 - 1698| Biblioteca(s): Embrapa Instrumentação. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
02/12/2021 |
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Data da última atualização: |
24/11/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, I. C.; FRANCA, T.; NICOLODELLI, G.; MORAIS, C. P.; MARANGONI, B.; BACCHETTA, G.; MILORI, D. M. B. P.; ALVES, C. Z.; CENA, C. |
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Afiliação: |
DEBORA MARCONDES BASTOS PEREIRA, CNPDIA. |
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Título: |
Fast and accurate discrimination of Brachiaria brizantha (A.Rich.) stapf seeds by molecular spectroscopy and machine learning. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
ACS Agricultural Science & Technology, v. 1, 2021. |
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Páginas: |
443?448 |
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DOI: |
https://doi.org/10.1021/acsagscitech.1c00067 |
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Idioma: |
Inglês |
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Conteúdo: |
: Brachiaria brizantha is the most common forage plant used in cattle pastures. The field plant population is the main parameter for cattle nutrition; it is mainly determined by the genotype and physiological quality of the seed, that is, seed vigor. Seed vigor standard tests are considered time-consuming and laborious. Fourier transformed infrared (FTIR) spectroscopy was used to classify the seed cultivar and vigor from two different genotypes of B. brizantha, namely, Marandu and Paiaguas cultivars. Two ́ batches from each group were used for classification by applying FTIR and machine learning algorithms. The algorithms with a higher overall accuracy in the leave-one-out cross-validation were also validated by an external validation using a dedicated set of samples exclusively separated for this purpose. The results indicate that molecular spectroscopy combined with machine learning analysis presents great potential for the classification of B. brizantha seeds. |
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Palavras-Chave: |
FTIR spectroscopy; Machine learning; Seed vigor. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01826naa a2200277 a 4500
001 2137033
005 2022-11-24
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1021/acsagscitech.1c00067$2DOI
100 1 $aOLIVEIRA, I. C.
245 $aFast and accurate discrimination of Brachiaria brizantha (A.Rich.) stapf seeds by molecular spectroscopy and machine learning.$h[electronic resource]
260 $c2021
300 $a443?448
520 $a: Brachiaria brizantha is the most common forage plant used in cattle pastures. The field plant population is the main parameter for cattle nutrition; it is mainly determined by the genotype and physiological quality of the seed, that is, seed vigor. Seed vigor standard tests are considered time-consuming and laborious. Fourier transformed infrared (FTIR) spectroscopy was used to classify the seed cultivar and vigor from two different genotypes of B. brizantha, namely, Marandu and Paiaguas cultivars. Two ́ batches from each group were used for classification by applying FTIR and machine learning algorithms. The algorithms with a higher overall accuracy in the leave-one-out cross-validation were also validated by an external validation using a dedicated set of samples exclusively separated for this purpose. The results indicate that molecular spectroscopy combined with machine learning analysis presents great potential for the classification of B. brizantha seeds.
653 $aFTIR spectroscopy
653 $aMachine learning
653 $aSeed vigor
700 1 $aFRANCA, T.
700 1 $aNICOLODELLI, G.
700 1 $aMORAIS, C. P.
700 1 $aMARANGONI, B.
700 1 $aBACCHETTA, G.
700 1 $aMILORI, D. M. B. P.
700 1 $aALVES, C. Z.
700 1 $aCENA, C.
773 $tACS Agricultural Science & Technology$gv. 1, 2021.
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