Memetic algorithms-based artificial multiplicative neural models selection for resolving multi-component mixtures based on dynamic responses

Abstract: The potential of the product unit neural networks built by using different memetic evolutionary algorithms for the simultaneous determination of mixtures of analytes based on dynamic responses was investigated. For this purpose, three methodologies for obtaining the structure and weights of neural networks were proposed, based on the combination of the evolutionary programming algorithm, a clustering process and a local improvement procedure carried out by the Levenberg–Marquardt algorithm. To test these approaches, two phenol derivatives, pyrogallol and gallic acid, were quantified in mixtures based on their perturbation effect in a classical oscillating chemical system, namely, the Belousov–Zhabotinskyi reaction. The four-parameter Weibull curve associated with the profile of perturbation response estimated by the Levenberg–Marquardt method was used as input data for the models. Straightforward network topologies 4:3:1 (13 weights) and 4:2:1 (9 weights) for pyrogallol and gallic acid, respectively, allowed the analytes to be quantified with great accuracy (mean standard error of prediction for the generalization test) and precision (standard deviation) of 2.45% and 0.21 for pyrogallol and 7.61% and 1.63 for gallic acid. The selected model can be easily implemented via software by using simple quantification equations, from which significant chemical remarks can be inferred. Finally, the product unit neural network modelling offered better results when compared with sigmoidal neural networks and response surface analysis. [Copyright &y& Elsevier]