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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
15/02/2022 |
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Data da última atualização: |
16/02/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MORAIS, P. A. de O.; SOUZA, D. M. de; MADARI, B. E.; OLIVEIRA, A. E. de. |
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Afiliação: |
PEDRO AUGUSTO DE OLIVEIRA MORAIS, UFG; DIEGO MENDES DE SOUZA, CNPAF; BEATA EMOKE MADARI, CNPAF; ANSELMO ELCANA DE OLIVEIRA, UFG. |
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Título: |
Predicting silicon, aluminum, and iron oxides contents in soil using computer vision and infrared. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Microchemical Journal, v. 170, 106669, Nov. 2021. |
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ISSN: |
0026-265X |
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DOI: |
https://doi.org/10.1016/j.microc.2021.106669 |
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Idioma: |
Inglês |
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Conteúdo: |
Silicon, aluminum, and iron oxides are very abundant in soil. Their quantification is important for soil classification, which is a relevant information for the sustainable use and management of soils. In soil laboratories the determination of these oxides, using standard methods, is destructive, costly, laborious, and time consuming. This article presents two analytical methods to quantify SiO2, Al2O3, and Fe2O3 in soil samples using computer vision (COMPVIS) and mid-infrared spectroscopy (MIR). These two methods were developed using 52 soil samples from four states of Brazil. Digital images and MIR spectra were correlated with oxides contents quantified by atomic absorption spectroscopy (AAS) after acid digestion using three multivariate calibration methods: PLS, SPA-MLR, and LS-SVM. This the first time that soil image data has been correlated to silicon and aluminum oxides and the proposed method found excellent correlation values ( ranging from 0.95 to 0.99). With the exception of SiO2, MIR resulted in similar predictions to the COMPVIS method?s. LS-SVM presented higher than 0.95 for all oxides estimates. The developed analyses are low cost, fast, and environmentally sustainables. |
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Palavras-Chave: |
MIA; SVM. |
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Thesagro: |
Química do Solo. |
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Thesaurus Nal: |
Aluminum; Environmental sustainability; Green chemistry; Hematite; Iron oxides; Silicon; Soil chemistry. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02075naa a2200301 a 4500
001 2140072
005 2022-02-16
008 2021 bl uuuu u00u1 u #d
022 $a0026-265X
024 7 $ahttps://doi.org/10.1016/j.microc.2021.106669$2DOI
100 1 $aMORAIS, P. A. de O.
245 $aPredicting silicon, aluminum, and iron oxides contents in soil using computer vision and infrared.$h[electronic resource]
260 $c2021
520 $aSilicon, aluminum, and iron oxides are very abundant in soil. Their quantification is important for soil classification, which is a relevant information for the sustainable use and management of soils. In soil laboratories the determination of these oxides, using standard methods, is destructive, costly, laborious, and time consuming. This article presents two analytical methods to quantify SiO2, Al2O3, and Fe2O3 in soil samples using computer vision (COMPVIS) and mid-infrared spectroscopy (MIR). These two methods were developed using 52 soil samples from four states of Brazil. Digital images and MIR spectra were correlated with oxides contents quantified by atomic absorption spectroscopy (AAS) after acid digestion using three multivariate calibration methods: PLS, SPA-MLR, and LS-SVM. This the first time that soil image data has been correlated to silicon and aluminum oxides and the proposed method found excellent correlation values ( ranging from 0.95 to 0.99). With the exception of SiO2, MIR resulted in similar predictions to the COMPVIS method?s. LS-SVM presented higher than 0.95 for all oxides estimates. The developed analyses are low cost, fast, and environmentally sustainables.
650 $aAluminum
650 $aEnvironmental sustainability
650 $aGreen chemistry
650 $aHematite
650 $aIron oxides
650 $aSilicon
650 $aSoil chemistry
650 $aQuímica do Solo
653 $aMIA
653 $aSVM
700 1 $aSOUZA, D. M. de
700 1 $aMADARI, B. E.
700 1 $aOLIVEIRA, A. E. de
773 $tMicrochemical Journal$gv. 170, 106669, Nov. 2021.
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| 1. |  | CHACHAR, M. J. d'A.; ANJOS, J. R. N.; SILVA, W. A. M.; SILVA, M. S.; MICHALSKI, M. V. Detecção de fungos em sementes de genótipos de Panicum maximum. Fitopatologia Brasileira, Brasília, DF, v. 32, S223-S224, ago. 2007. Suplemento. Trabalho apresentado no 49. Congresso Brasileiro de Fitopatologia, 2007, Maringá, PR.| Tipo: Resumo em Anais de Congresso | Circulação/Nível: -- - -- |
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