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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
04/07/2024 |
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Data da última atualização: |
30/09/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
NEIVA, D. K.; GUEDES, W. N.; MARTIN NETO, L.; VILLAS BOAS, P. R. |
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Afiliação: |
UNIVERSIDADE DE SÃO PAULO; WESLEY NASCIMENTO GUEDES, UNIVERSIDADE ESTADUAL PAULISTA JÚLIO DE MESQUITA FILHO (UNESP); LADISLAU MARTIN NETO, CNPDIA; PAULINO RIBEIRO VILLAS BOAS, CNPDIA. |
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Título: |
Enhancing elemental quantification in LIBS with SHAP-guided emission line analysis: A soil carbon study. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Spectrochimica Acta Part B: Atomic Spectroscopy, v. 217, 106971, 2024. |
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Páginas: |
1 - 8 |
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ISSN: |
0584-8547 |
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DOI: |
https://doi.org/10.1016/j.sab.2024.106971 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT In laser-induced breakdown spectroscopy (LIBS), identifying key emission lines for accurate elemental quantification has long posed a challenge. Traditional methods rely on experimental knowledge, atomic databases, and intricate spectral analyses. Although machine learning techniques – such as boosting algorithms and neural networks – offer efficient processing for large datasets, the complexity of these techniques often compromises interpretability. To address this issue, our study integrates the SHapley Additive exPlanations (SHAP) algorithm with gradient boosting models in order to interpret the most important spectral features, thus enhancing our understanding of how specific emission lines contribute to the carbon (C) concentration predictions in soils. Deployed on a large dataset of 1019 soil samples, a wrapper method with a random forest regressor reduced the initial spectral intensity features from 13,748 to 1098. Subsequent application of a LightGBM regression model calibrated via the Optuna framework yielded R2 of 0.98 and 0.77, and RMSE values of 1.55 and 4.54 g kg− 1 for training and validation sets, respectively. The SHAP summary plot showed that C emission lines influenced the model's predictions positively, as anticipated, whereas silicon (Si) emission lines produced a negative impact, suggesting a lower C concentration in sandy soils. Our findings not only validate the efficacy of SHAP in improving LIBS-based soil C quantification, but they also offer a sophisticated framework for decoding the complex interplay between emission lines and target elemental concentrations. MenosABSTRACT In laser-induced breakdown spectroscopy (LIBS), identifying key emission lines for accurate elemental quantification has long posed a challenge. Traditional methods rely on experimental knowledge, atomic databases, and intricate spectral analyses. Although machine learning techniques – such as boosting algorithms and neural networks – offer efficient processing for large datasets, the complexity of these techniques often compromises interpretability. To address this issue, our study integrates the SHapley Additive exPlanations (SHAP) algorithm with gradient boosting models in order to interpret the most important spectral features, thus enhancing our understanding of how specific emission lines contribute to the carbon (C) concentration predictions in soils. Deployed on a large dataset of 1019 soil samples, a wrapper method with a random forest regressor reduced the initial spectral intensity features from 13,748 to 1098. Subsequent application of a LightGBM regression model calibrated via the Optuna framework yielded R2 of 0.98 and 0.77, and RMSE values of 1.55 and 4.54 g kg− 1 for training and validation sets, respectively. The SHAP summary plot showed that C emission lines influenced the model's predictions positively, as anticipated, whereas silicon (Si) emission lines produced a negative impact, suggesting a lower C concentration in sandy soils. Our findings not only validate the efficacy of SHAP in improving LIBS-based soil C quantification, but they also offe... Mostrar Tudo |
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Palavras-Chave: |
Gradient boosting model; Identification of emission lines; LIBS; Machine learning; SHAP method; Soil carbon quantification. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02469naa a2200265 a 4500
001 2165437
005 2024-09-30
008 2024 bl uuuu u00u1 u #d
022 $a0584-8547
024 7 $ahttps://doi.org/10.1016/j.sab.2024.106971$2DOI
100 1 $aNEIVA, D. K.
245 $aEnhancing elemental quantification in LIBS with SHAP-guided emission line analysis$bA soil carbon study.$h[electronic resource]
260 $c2024
300 $a1 - 8
520 $aABSTRACT In laser-induced breakdown spectroscopy (LIBS), identifying key emission lines for accurate elemental quantification has long posed a challenge. Traditional methods rely on experimental knowledge, atomic databases, and intricate spectral analyses. Although machine learning techniques – such as boosting algorithms and neural networks – offer efficient processing for large datasets, the complexity of these techniques often compromises interpretability. To address this issue, our study integrates the SHapley Additive exPlanations (SHAP) algorithm with gradient boosting models in order to interpret the most important spectral features, thus enhancing our understanding of how specific emission lines contribute to the carbon (C) concentration predictions in soils. Deployed on a large dataset of 1019 soil samples, a wrapper method with a random forest regressor reduced the initial spectral intensity features from 13,748 to 1098. Subsequent application of a LightGBM regression model calibrated via the Optuna framework yielded R2 of 0.98 and 0.77, and RMSE values of 1.55 and 4.54 g kg− 1 for training and validation sets, respectively. The SHAP summary plot showed that C emission lines influenced the model's predictions positively, as anticipated, whereas silicon (Si) emission lines produced a negative impact, suggesting a lower C concentration in sandy soils. Our findings not only validate the efficacy of SHAP in improving LIBS-based soil C quantification, but they also offer a sophisticated framework for decoding the complex interplay between emission lines and target elemental concentrations.
653 $aGradient boosting model
653 $aIdentification of emission lines
653 $aLIBS
653 $aMachine learning
653 $aSHAP method
653 $aSoil carbon quantification
700 1 $aGUEDES, W. N.
700 1 $aMARTIN NETO, L.
700 1 $aVILLAS BOAS, P. R.
773 $tSpectrochimica Acta Part B: Atomic Spectroscopy$gv. 217, 106971, 2024.
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