Partial Least Squares Multivariate Regression as an Alternative To Handle Interferences of Fe on the Determination of Trace Cr in Water by Electrothermal Atomic Absorption Spectrometry

Current chromium concentrations in natural waters amount to only a few micrograms per liter. Electrothermal atomic absorption spectrometry (ETAAS) is one of the most suitable techniques for its determination. However, it was found that high iron concentrations (3-4 orders of magnitude) may cause serious atomic signal enhancement in Cr analysis. It is discussed that Fe may cause either spectral or chemical (or both) interferences on Cr determination. The goal of this paper is to develop multivariate calibration models not affected by such interferences. Three multivariate regression methods were applied. One of them was linear in its nature (partial least squares, PLS), and two were nonlinear (polynomial PLS and locally weighted regression). PLS was revealed to be a suitable and convenient alternative to simplify current laboratory workload in order to cope with chemical and spectral interferences caused by a major metal (Fe) when determining another trace metal (Cr) by ETAAS. Three effects, which modify the atomic peak shape, due to the aging of the atomizers were simulated to evaluate to what extent they affect the predictions, namely, peak-shift, peak enhancement (depletion), and increased random noise. The ETAAS-PLS methodology was satisfactorily tested with water samples and two CRMs.