Simultaneous determination of lead and sulfur by energy-dispersive x-ray spectrometry. Comparison between artificial neural networks and other multivariate calibration methods

The need for mathematical methods to model data in energy-dispersive x-ray fluorescence (EDXRF) spectrometry is common owing to the overlapping of intense spectral lines in complex samples. This overlapping generally produces a large amount of scatter in the analytical curve, preventing simultaneous direct determinations of some elements without data treatment. This work demonstrates the performance of artificial neural networks (ANN) and other methods of multivariate calibration (linear or not) for the simultaneous determination of sulfur and lead, when overlapping of the sulfur Kα spectral line (2.308 keV) and the lead Mα line (2.346 keV) is observed. The performance of neural networks was compared by the f-test with five other data treatment methods: PLS (partial least squares), POLYPLS (polynomial partial least squares), NNPLS (partial least square neural networks), LR (linear regression) and CI (corrected intensity). It was verified that the ANN produces better predictions than the other methods, for both sulfur and lead, allowing their simultaneous determination in solid samples with good accuracy.