Laser induced break down spectro-analysis an d characterization of environmental matrices utilizing multivariate chemo metrics
Mukhono, P. M .
Angeyo, K. H.
Dehaye, A .
Kaduki, K. A .
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We exploited multivariate chemometric methods to reduce the spectral complexity and to retrieve heavy trace metal analyte signatures directly from the LIBS spectra as well as to extract latent trace meta l profile characteristics in two important envi ronmental samples (soils, rocks) sampled from a geothermal field associated with a high background radiation area (HBRA). As, Cr, Cu, P band Ti were modeled for direct trace (quantitative) analysis using partial least squares (PLS) and artificial neural networks (ANN) with regression coefficients ( R2 ) ~ 0. 99. PLS performed better in soils than in rocks; the use of ANN improved the accuracies in rocks because ANNs are more robust than PLS at correcting matrix effects and modeling spectral non - linearities. The predicted trace metal profiles together with LI BS atomic and molecular signatures acquired using single ablation in the 200 – 5 45 nm spectral range were utilized to successfully classify and identify the soils and rocks with regard to whether they were derived from (i ) a high background radiation area (HB RA) - geothermal, (ii) HBRA - non - geothermal or (ii) non - HBRA - geothermal field using principal components analysis (PCA) and soft in dependent modeling of class analogy (SIMCA)
Citation7 th Laser Induced Break down Spectroscopy Conference, September 29 - October 4, 2012, Luxor, Egypt
School of Physical Sciences