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.