期刊
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
卷 97, 期 -, 页码 57-64出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.sab.2014.04.014
关键词
LIBS; Artificial neural network; Matrix; Lead; Soil
类别
资金
- French Environment and Energy Management Agency (ADEME)
- IVEA SAS
Artificial 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. (C) 2014 Elsevier B.V. All rights reserved.
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