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

Abstract : 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.
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Spectrochimica Acta Part B: Atomic Spectroscopy, Elsevier, 2013, 78-79, pp.51-57. 〈10.1016/j.sab.2012.11.007〉
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Josette El Haddad, M. Villot-Kadri, Amina Ismael, G. Gallou, Karine Michel, et al.. Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, Elsevier, 2013, 78-79, pp.51-57. 〈10.1016/j.sab.2012.11.007〉. 〈hal-00788952〉

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