Título:	Laser-induced breakdown spectroscopy as an analytical tool for total carbon quantification in tropical and subtropical soils: evaluation of calibration algorithms.
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.
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.
Ano de publicação:	2024
Referência:	Frontiers in Soil Science, v. 3, 1242647, 2024.
Páginas:	14 p.
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.
Palavras-chave:	Artificial neural network Tropical soil Matrix-matching calibration
Digital Object Identifier:	10.3389/fsoil.2023.1242647
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPDIA)