A new design strategy with stochastic optimization on the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA)

In this study, a new design strategy with a systematic optimization process is proposed for the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA) by using the concentration of magnetite nanoparticle, glutaraldehyde and tyrosinase enzyme as design variables. A comprehensive study on multiple non-linear neuroregression analysis has been performed as a compelling alternative to the insufficient approaches on modelingdesign-optimization of MCLTA. For this aim, the experimental process has been modeled with 13 candidate functional structures by using a hybrid method to test the accuracy of their predictions. R-training(2), R-testing(2) values, and boundedness of the functions have been checked to reveal the realistic ones. Then four different design approaches in terms of three distinct scenarios have been used to optimize the process. The results show that, all models define the process well, depending on R-training(2). However, only five and nine models are appropriate based on R-testing(2) for the first use activity and residual activity, respectively. On the other hand, depending on to be a realistic value, model TON best describes the first use activity, while the best one is FONT for residual activity. It is also concluded that the scenario types and selection of constraints for design variables affect the optimization results.