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network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, Elsevier, 2013Elsevier, , 78-79, pp.51-57. 10.1016Elsevier, /j.sab.2012 Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy Nowadays, due to environmental concerns, fast on site quantitative analyses of soils are required. Laser in duced 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 ap proach 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, cop per and iron demonstrate the good efficiency of the artificial neural networks for on site quantitative LIBS of soils.

Time-saving, low-cost analyses of soil contamination are required to ensure fast and efficient pollution removal and remedial operations. In this work, laser-induced breakdown spectroscopy (LIBS) has been successfully applied to in situ analyses of polluted soils, providing direct semi-quantitative information about the extent of pollution. A field campaign has been carried out in Brittany (France) on a site presenting high levels of heavy metal concentrations. Results on iron as a major component as well as on lead and copper as minor components are reported. Soil samples were dried and prepared as pressed pellets to minimize the effects of moisture and density on the results. LIBS analyses were performed with a Nd:YAG laser operating at 1064 nm, 60 mJ per 10 ns pulse, at a repetition rate of 10 Hz with a diameter of 500 μm on the sample surface. Good correlations were obtained between the LIBS signals and the values of concentrations deduced from inductively coupled plasma atomic emission spectroscopy (ICP-AES). This result proves that LIBS is an efficient method for optimizing sampling operations. Indeed, "LIBS maps" were established directly on-site, providing valuable assistance in optimizing the selection of the most relevant samples for future expensive and time-consuming laboratory analysis and avoiding useless analyses of very similar samples. Finally, it is emphasized that in situ LIBS is not described here as an alternative quantitative analytical method to the usual laboratory measurements but simply as an efficient time-saving tool to optimize sampling operations and to drastically reduce the number of soil samples to be analyzed, thus reducing costs. The detection limits of 200 ppm for lead and 80 ppm for copper reported here are compatible with the thresholds of toxicity; thus, this in situ LIBS campaign was fully validated for these two elements. Consequently, further experiments are planned to extend this study to other chemical elements and other matrices of soils.

International audienceArtificial 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

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