Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks

This work aims to use artificial neural networks for mathematical modeling of correcting the biosynthetic activity of yeast during storage with natural minerals. The object of the research is the brewer's seed yeast of the C34 race. Artificial neural networks were created on the basis of experimental data, which were used to solve the problems of regression and prediction of output values. Four networks were created: the first one modeled the values of all output variables, while the second, the third and the fourth modeled the number of yeast cells with glycogen, budding cells and dead cells respectively. Next, the optimal yeast storage parameters were determined. As a result of the study, the possibility of using artificial neural networks for mathematical modeling of undesirable deviations in the physiological parameters of brewer's seed yeast during storage with natural minerals was proved.