Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy
Language
en
Article de revue
This item was published in
Spectrochimica Acta Part B: Atomic Spectroscopy. 2013-02-01, vol. 78-79, p. 51-57
Elsevier
English 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 ...Read more >
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.Read less <
English Keywords
Laser-induced breakdown spectroscopy (LIBS)
Soil
Quantitative analysis
Artificial neural network
Origin
Hal imported