This article provides an analysis of experimental designs simplex centroid mixtures with binomial and Poisson responses using a Generalized Linear Model. Throughout a simulation process, in which a model was assumed known, it was compared accuracy and coverage of the confidence intervals around the expected average obtained under a model based on normal theory with that provided by a Generalized Linear Model. Simulations were carried out using specialized software, and random perturbation to the true average expected in each experimental point was introduced repeatedly. The new response vectors were analyzed in each of the two models. Accuracy and coverage were reported in each case depending on the magnitude of the induced disturbance and were used as a measure of comparison of estimation uncertainty and to evaluate the performance of both models. In an experimental design with mixtures including no normal reponse, a Generalized Linear Model produced better results in terms of accuracy and coverage than an Ordinary Least Squares based on a transformation.