Modeling of starch hydrolysis by commercial $$\alpha$$-amylases with enzyme deactivation

The use of $$\alpha$$ -amylase in the food, feed, and industrial sectors is constantly increasing, especially in the context of sustainable development. Modeling starch hydrolysis by commercial $$\alpha$$ -amylase with enzyme deactivation was performed for initial concentration of $${20\%}$$ , $${30\%}$$ and $${40\%}$$ at 333 K. However, earlier in this study, need to determine the parameters of the starch hydrolysis process using commercial $$\alpha$$ -amylase from Bacillus spp. The analysis focused on determining the deactivation energy $${E_{{\text{d}}}}$$ in starch hydrolysis while simultaneously deactivating commercial $$\alpha$$ -amylases from Bacillus subtillis and Bacillus licheniformis. The mathematical model applied assumes that the changes in starch concentrations and the deactivation of commercially bacterial $$\alpha$$ -amylase are first-order reactions with respect to enzyme concentration. The calculated activation energies $${E_{{\text{a}}}}$$ ranged from $${22.08 \pm 6.96}$$ kJ $$\text {mol}^{-1}$$ to $${70.35 \pm 15.24}$$ kJ $$\text {mol}^{-1}$$ , and the deactivation energies $${E_{{\text{d}}}}$$ ranged from $${21.30 \pm 1.05}$$ kJ $$\text {mol}^{-1}$$ to $${163.66 \pm 6.57}$$ kJ $$\text {mol}^{-1}$$ . The results obtained in this study were used to model the conversion of starch hydrolysis by commercial $$\alpha$$ -amylase from Bacillus spp. Additionally, the obtained $${E_{\text{a}}}$$ and $${E_{\text{d}}}$$ values from the activity versus temperature were compared to the $${E_{{{\text{a}}}}}$$ and $${E_{{{\text{d}}}}}$$ values obtained using the Arrhenius methods. The obtained activation energies $${E_{{{\text{a}}}}}$$ and deactivation energies $${E_{{{\text{d}}}}}$$ allow to modeling hydrolysis of starch by $$\alpha$$ -amylase from Bacillus spp. Presented modeling of starch hydrolysis with commercial $$\alpha$$ -amylase deactivation is essential to optimize the use of commercial $$\alpha$$ -amylase, improve process efficiency and minimize costs in large-scale applications.