Combining prior knowledge with input selection algorithms for quantitative analysis using neural networks in laser induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for the analysis of rocks and mineral samples. Artificial neural networks (ANNs) have been used to estimate the concentration of minerals in samples from LIBS spectra. These spectra are very high dimensional data, and it is known that only specific wavelengths have information on the atomic and molecular features of the sample under investigation. This work presents a systematic methodology based on the Akaike information criterion (AIC) for selecting the wavelengths of LIBS spectra as well as the ANN model complexity, by combining prior knowledge and variable selection algorithms. Several variable selection algorithms are compared within the proposed methodology, namely KBest, a least absolute shrinkage and selection operator (LASSO) regularization, principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS). As an illustrative example, the estimation of copper, iron and arsenic concentrations in pelletized mineral samples is performed. A dataset of LIBS emission spectra with 12 287 wavelengths in the range of 185-1049 nm obtained from 131 samples of copper concentrates is used for regression analysis. An ANN is then trained considering the selected reduced wavelength data. The results are satisfactory using LASSO and CARS algorithms along with prior knowledge, showing that the proposed methodology is very effective for selecting wavelengths and model complexity in quantitative analyses based on ANNs and LIBS.

Oxidative degradation of sulfathiazole by Fenton and photo-Fenton reactions.

This article presents experimental results on 47 µmol L(-1) sulfathiazole (STZ) degradation by Fenton and photo-Fenton reactions using multivariate analysis. The optimal experimental conditions for reactions were obtained by Response Surface Methodology (RSM). In the case of the Fenton reactions there were 192 µmol L(-1) ferrous ions (Fe(II)) and 1856 µmol L(-1) hydrogen peroxide (H2O2), as compared with 157 µmol L(-1) (Fe(II)) and 1219 µmol L(-1) (H2O2) for photo-Fenton reactions. Under these conditions, around 90% of STZ degradation were achieved after 8 minutes treatment by Fenton and photo-Fenton reactions, respectively. Moreover, a marked difference was observed in the total organic carbon (TOC) removal after 60-min treatment, achieving 30% and 75% for the Fenton and photo-Fenton reactions, respectively. Acetic, maleic, succinic and oxamic acids could be identified as main Fenton oxidation intermediates. A similar pattern was found in the case of photo-Fenton reaction, including the presence of oxalic acid and ammonia at short periods of irradiation with UV-A. The calculated values of Average Oxidation State (AOS) corroborate the formation of oxidized products from the initial steps of the reaction.