A two steps method: non linear regression and pruning neural network for analyzing multicomponent mixtures

This work deals with the use of pruning ANNs in conjunction with genetic algorithms for resolving nonlinear multicomponent systems based on oscillating chemical reactions. The singular analytical response provides by this chemical system after its perturbation was tted to a gaussian curve by least-square regression and the estimates were used as inputs to the ANNs. The proposed methodology was validated by the simultaneous determination of pyrogallol and gallic acid (two strong related phenol derivatives) in mixtures on the basis of their perturbation e ects on the classical Belousov-Zhabotinskii reaction. The trained network estimates concentrations of pyrogallol and gallic acid with a standard error of prediction for the testing set of ca. 4% and 5.7% respectively or 4.4%, 9% for di erent sets of train/test patterns. This result is much smaller than those provided by a classical parametric method such as non-linear regression.