Multilogistic regression by evolutionary neural network as a classification tool to discriminate highly overlapping signals: Qualitative investigation of volatile organic compounds in polluted waters by using headspace-mass spectrometric analysis

This work investigates the ability of multilogistic regression models including nonlinear effects of the covariates as a multi-class pattern recognition technique to discriminate highly overlapping analytical signals using a very short number of input covariates. For this purpose, three methodologies recently reported by us were applied based on the combination of linear and nonlinear terms which are transformations of the linear ones by using evolutionary product unit neural networks. To test this approach, drinking water samples contaminated with volatile organic compounds such as benzene, toluene, xylene and their mixtures were classified in seven classes through the very close data provided by their headspace-mass spectrometric analysis. Instead of using the total ion current profile provided by the MS detector as input covariates, the three-parameter Gaussian curve associated to it was used as linear covariates for the standard multilogistic regression model, whereas the product unit basic functions or their combination with the linear covariates were used for the nonlinear models. The hybrid nonlinear model, pruned by a backward stepwise method, provided the best classification results with a correctly classified rate for the training and generalization sets of 100% and 76.2%, respectively. The reduced dimensions of the proposed model: only three terms, namely one initial covariate and two basis product units, enabled to infer interesting interpretations from a chemical point of view.