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| Registros recuperados : 769 | |
| Registros recuperados : 769 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
08/04/2024 |
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Data da última atualização: |
08/04/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 5 |
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Autoria: |
BABOS, D. V.; GUEDES, W. N.; FREITAS, V. S.; SILVA, F. P.; TOZO, M. L. L.; VILLAS-BOAS, P. R.; MARTIN NETO, L.; MILORI, D. M. B. P. |
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Afiliação: |
Brazilian Agricultural Research Corporation (Embrapa), Embrapa Instrumentation; Brazilian Agricultural Research Corporation (Embrapa), Embrapa Instrumentation; Brazilian Agricultural Research Corporation (Embrapa), Embrapa Instrumentation; Institute of Chemistry of São Carlos, University of São Paulo; Center for Exact Sciences and Technology, Federal University of São Carlos; PAULINO RIBEIRO VILLAS BOAS, CNPDIA; LADISLAU MARTIN NETO, CNPDIA; DEBORA MARCONDES BASTOS PEREIRA, CNPDIA. |
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Título: |
Laser-induced breakdown spectroscopy as an analytical tool for total carbon quantification in tropical and subtropical soils: evaluation of calibration algorithms. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Frontiers in Soil Science, v. 3, 1242647, 2024. |
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Páginas: |
14 p. |
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DOI: |
10.3389/fsoil.2023.1242647 |
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Idioma: |
Inglês |
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Conteúdo: |
The demand for efficient, accurate, and cost-effective methods of measuring soil carbon (C) in agriculture is growing. Traditional approaches are time consuming and expensive, highlighting the need for alternatives. This study tackles the challenge of utilizing laser-induced breakdown spectroscopy (LIBS) as a more economical method while managing its potential accuracy issues due to physical–chemical matrix effects. A set of 1,019 soil samples from 11 Brazilian farms was analyzed using various univariate and multivariate calibration strategies. The artificial neural network (ANN) demonstrated the best performance with the lowest root mean square error of prediction (RMSEP) of 0.48 wt% C, a 28% reduction compared to the following best calibration method (matrix-matching calibration – MMC inverse regression and multiple linear regression – MLR at 0.67 wt% C). Furthermore, the study revealed a strong correlation between total C determined by LIBS and the elemental CHNS analyzer for soils samples in nine farms (R² ≥ 0.73). The proposed method offers a reliable, rapid, and cost-efficient means of measuring total soil C content, showing that LIBS and ANN modeling can significantly reduce errors compared to other calibration methods. This research fills the knowledge gap in utilizing LIBS for soil C measurement in agriculture, potentially benefiting producers and the soil C credit market. Specific recommendations include further exploration of ANN modeling for broader applications, ensuring that agricultural soil management becomes more accessible and efficient. MenosThe demand for efficient, accurate, and cost-effective methods of measuring soil carbon (C) in agriculture is growing. Traditional approaches are time consuming and expensive, highlighting the need for alternatives. This study tackles the challenge of utilizing laser-induced breakdown spectroscopy (LIBS) as a more economical method while managing its potential accuracy issues due to physical–chemical matrix effects. A set of 1,019 soil samples from 11 Brazilian farms was analyzed using various univariate and multivariate calibration strategies. The artificial neural network (ANN) demonstrated the best performance with the lowest root mean square error of prediction (RMSEP) of 0.48 wt% C, a 28% reduction compared to the following best calibration method (matrix-matching calibration – MMC inverse regression and multiple linear regression – MLR at 0.67 wt% C). Furthermore, the study revealed a strong correlation between total C determined by LIBS and the elemental CHNS analyzer for soils samples in nine farms (R² ≥ 0.73). The proposed method offers a reliable, rapid, and cost-efficient means of measuring total soil C content, showing that LIBS and ANN modeling can significantly reduce errors compared to other calibration methods. This research fills the knowledge gap in utilizing LIBS for soil C measurement in agriculture, potentially benefiting producers and the soil C credit market. Specific recommendations include further exploration of ANN modeling for broader applications,... Mostrar Tudo |
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Palavras-Chave: |
Artificial neural network; Matrix-matching calibration; Tropical soil. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163375/1/P-Laser-induced-breakdown-spectroscopy-as-an-analytical.pdf
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Marc: |
LEADER 02456naa a2200265 a 4500
001 2163375
005 2024-04-08
008 2024 bl uuuu u00u1 u #d
024 7 $a10.3389/fsoil.2023.1242647$2DOI
100 1 $aBABOS, D. V.
245 $aLaser-induced breakdown spectroscopy as an analytical tool for total carbon quantification in tropical and subtropical soils$bevaluation of calibration algorithms.$h[electronic resource]
260 $c2024
300 $a14 p.
520 $aThe demand for efficient, accurate, and cost-effective methods of measuring soil carbon (C) in agriculture is growing. Traditional approaches are time consuming and expensive, highlighting the need for alternatives. This study tackles the challenge of utilizing laser-induced breakdown spectroscopy (LIBS) as a more economical method while managing its potential accuracy issues due to physical–chemical matrix effects. A set of 1,019 soil samples from 11 Brazilian farms was analyzed using various univariate and multivariate calibration strategies. The artificial neural network (ANN) demonstrated the best performance with the lowest root mean square error of prediction (RMSEP) of 0.48 wt% C, a 28% reduction compared to the following best calibration method (matrix-matching calibration – MMC inverse regression and multiple linear regression – MLR at 0.67 wt% C). Furthermore, the study revealed a strong correlation between total C determined by LIBS and the elemental CHNS analyzer for soils samples in nine farms (R² ≥ 0.73). The proposed method offers a reliable, rapid, and cost-efficient means of measuring total soil C content, showing that LIBS and ANN modeling can significantly reduce errors compared to other calibration methods. This research fills the knowledge gap in utilizing LIBS for soil C measurement in agriculture, potentially benefiting producers and the soil C credit market. Specific recommendations include further exploration of ANN modeling for broader applications, ensuring that agricultural soil management becomes more accessible and efficient.
653 $aArtificial neural network
653 $aMatrix-matching calibration
653 $aTropical soil
700 1 $aGUEDES, W. N.
700 1 $aFREITAS, V. S.
700 1 $aSILVA, F. P.
700 1 $aTOZO, M. L. L.
700 1 $aVILLAS-BOAS, P. R.
700 1 $aMARTIN NETO, L.
700 1 $aMILORI, D. M. B. P.
773 $tFrontiers in Soil Science$gv. 3, 1242647, 2024.
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Embrapa Instrumentação (CNPDIA) |
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