EL HADDAD, Josette; BRUYÈRE, Delphine; ISMAËL, Amina; GALLOU, G.; LAPERCHE, Valérie; MICHEL, Karine; CANIONI, Lionel; BOUSQUET, Bruno

doi:10.1016/j.sab.2014.04.014

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EL HADDAD, Josette
Laboratoire Ondes et Matière d'Aquitaine [LOMA]

BRUYÈRE, Delphine
Bureau de Recherches Géologiques et Minières [BRGM]

ISMAËL, Amina
IVEA Solution

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Spectrochimica Acta Part B: Atomic Spectroscopy. 2014-07-01, vol. 97, p. 57-64

Elsevier

Résumé en anglais

Artificial neural networks were applied to process data from on-site LIBS analysis of soil samples. A first artificial neural network allowed retrieving the relative amounts of silicate, calcareous and ores matrices into soils. As a consequence, each soil sample was correctly located inside the ternary diagram characterized by these three matrices, as verified by ICP-AES. Then a series of artificial neural networks were applied to quantify lead into soil samples. More precisely, two models were designed for classification purpose according to both the type of matrix and the range of lead concentrations. Then, three quantitative models were locally applied to three data subsets. This complete approach allowed reaching a relative error of prediction close to 20%, considered as satisfying in the case of on-site analysis.< Réduire

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Artificial neural network

LIBS

Soil

Lead

Matrix

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Application of a series of artificial neural networks to on-site quantitative analysis of lead into real soil samples by laser induced breakdown spectroscopy

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