Abstract
Laser-induced breakdown spectroscopy (LIBS) has been applied to the analysis of three chromium-doped soils. Two chemometric techniques, principal components analysis (PCA) and neural networks analysis (NNA), were used to discriminate the soils on the basis of their LIBS spectra. An excellent rate of correct classification was achieved and a better ability of neural networks to cope with real-world, noisy spectra was demonstrated. Neural networks were then used for measuring chromium concentration in one of the soils. We performed a detailed optimization of the inputs of the network so as to improve its predictive performances and we studied the effect of the presence of matrix-specific information in the inputs examined. Finally the inputs of the network—the spectral intensities—were replaced by the line areas. This provided the best results with a prediction accuracy and precision of about 5% in the determination of chromium concentration and a significant reduction of the data, too.
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Acknowledgements
We gratefully thank Jean-Pierre Dubost (Université Bordeaux 2, France) for the fruitful discussions on chemometrics. This research is financially supported by ANR, ADEME and Conseil Régional d’Aquitaine.
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Awarded a poster prize on the occasion of the Euro-Mediterranean Symposium on Laser-induced Breakdown Spectroscopy (EMSLIBS 2005), Aachen, Germany, 6–9 September 2005.
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Sirven, JB., Bousquet, B., Canioni, L. et al. Qualitative and quantitative investigation of chromium-polluted soils by laser-induced breakdown spectroscopy combined with neural networks analysis. Anal Bioanal Chem 385, 256–262 (2006). https://doi.org/10.1007/s00216-006-0322-8
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DOI: https://doi.org/10.1007/s00216-006-0322-8