Computational neural networks for resolving nonlinear multicomponent systems based on chemiluminescence methods

This paper proves that computational neural networks are reliable, effective tools for resolving nonlinear multicomponent systems involving synergistic effects by using chemiluminescence-based methods developed by continuous addition of reagent technique. Computational neural networks (CNNs) were implemented using a preprocessing of data by principal component analysis; the principal components to be used as input to the CNN were selected on the basis of a heuristic method. The leave-one-out method was applied on the basis of theoretical considerations in order to reduce sample size with no detriment to the prediction capacity of the network. The proposed approach was used to resolve trimeprazine/methotrimeprazine mixtures with a classical peroxyoxalate chemiluminescent system, such as the reaction between bis(2,4,6-trichlorophenyl)oxalate and hydrogen peroxide. The optimum network design, 9:5s:2l, allowed the resolution of mixtures of the two analytes in concentration ratios from 1:10 to 10:1 with very small (less than 5%) relative errors.