Chemometric alternatives for resolution of classical analytical problems Spectrophotometric determination of lanthanide mixtures

The simultaneous determination of lanthanide family elements is one of the greatest problems in analytical chemistry, due to the close similarity of their chemical properties. Spectrophotometric methods are generally of limited use, due to the various mutual spectral interferences involved. By using multivariate calibration methods (partial least-squares regression, PLSR), it was possible to obtain a model that adjusts itself perfectly to the values of the mixture concentrations used in the calibration. The model used absorption spectra in the 290-800 nm range for a set of 20 different mixtures of Ce, Pr, Nd and Sm, and made possible the determination of Ce, Pr and Nd concentrations of a commercial rare-earth product, with significantly greater precision than the conventional univariate calibration method. Determination of the Sm concentrations was not possible, since its concentration was below the concentrations used in the model definition.

Elimination of iron interference in the molecular spectrophotometric determination of aluminum in soil extracts using artificial neural networks

An artificial neural network (ANN) calibration model was developed to determine aluminum in the presence of iron in soil extracts, using xylenol orange as chromogenic reagent. The spectral data of synthetic mixtures of Al(3+) and Fe(3+) as well as of the soil extracts, were recorded in the range between 410 and 580 nm. Method validation was carried out using 18 soil extracts. The results gave good linear correlations between the ANN model and the ICP OES measurements for both species.