期刊
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
卷 79-80, 期 -, 页码 51-57出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.sab.2012.11.007
关键词
Laser-induced breakdown spectroscopy (LIBS); Soil; Quantitative analysis; Artificial neural network
类别
资金
- French Environment and Energy Management Agency (ADEME)
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. (C) 2012 Published by Elsevier B.V.
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