Comparing radial basis function and feed-forward neural networks assisted by linear discriminant or principal component analysis for simultaneous spectrophotometric quantification of mercury and copper

Copper(II) and mercury(II) were analyzed simultaneously employing a spectrophotometric method based on application of murexide solution as a chromogenic reagent. A full factorial six level design was used for the construction of calibration and prediction data sets consisting of absorption spectra recorded in 350-700 nm range from solution mixtures. A control data set, from a random design, was applied for validation of the calibration models. The presence of non-linearities was confirmed by a recently discussed methodology based on augmented partial residual plots (APaRPs). Combinations of principal component analysis (PCA) or linear discriminant analysis (LDA) with radial basis function networks (RBFNs) or feed-forward neural networks (FFNNs) were built and investigated, as four calibration models. Number of inputs and hidden nodes for each of the networks were optimized. Performances of methods were tested with relative standard error of prediction (RSEP%), using synthetic solutions of two metal ions as prediction set. Linear discriminant analysis assisted networks (LDRBNN) resulted in preferred models, using only one latent variable for each of the analytes. All of the methods were applied for the analysis of a number of synthetic samples and a dental alloy sample and satisfactory results were obtained.