EL HADDAD, Josette; VILLOT-KADRI, M.; ISMAEL, Amina; GALLOU, G.; MICHEL, Karine; BRUYÈRE, Delphine; LAPERCHE, Valérie; CANIONI, Lionel; BOUSQUET, Bruno

doi:10.1016/j.sab.2012.11.007

Metadatos

Mostrar el registro completo del ítem

Licencia de uso del documento

http://creativecommons.org/licenses/by-nc/

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

VILLOT-KADRI, M.
IVEA Solution

ISMAEL, Amina
IVEA Solution

Idioma

Article de revue

Este ítem está publicado en

Spectrochimica Acta Part B: Atomic Spectroscopy. 2013-02-01, vol. 78-79, p. 51-57

Elsevier

Resumen en inglés

Nowadays, due to environmental concerns, fast on-site quantitative analyses of soils are required. Laser induced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi-elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this approach and consequently emphasizes the advantage of using artificial neural networks well suited for non-linear and multi-variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on-site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, copper and iron demonstrate the good efficiency of the artificial neural networks for on-site quantitative LIBS of soils.< Leer menos

Palabras clave en inglés

Laser-induced breakdown spectroscopy (LIBS)

Soil

Quantitative analysis

Artificial neural network

Metadatos

Licencia de uso del documento

Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

Idioma

Este ítem está publicado en

Resumen en inglés

Palabras clave en inglés

DOI

Orígen

Centros de investigación