A new method for simultaneous determination of Ra and uranium in aqueous samples by liquid scintillation using chemometrics

In this work, simultaneous determination of low levels of Ra and uranium in aqueous samples were performed by alpha-liquid scintillation counting (LSC) in conjunction with artificial neural network (ANN) and partial least squares (PLS). The counting rates at 73 channels, which were selected by genetic algorithm, were used for training. A PLS model with four latent variables and a principle component ANN model (4-4-2) with linear transfer function after hidden and output layers were created. Total relative error of prediction for PLS and ANN in synthetic mixtures was 18.05% and 24.78%, respectively. The matrix effect was studied by spiking the real samples with radium and uranium. Laser induced fluorescence was used for assessment of uranium prediction results in real samples.