Modeling the kinetics of isobaric-isothermal inactivation of Bacillus subtilis α-amylase with artificial neural networks

Abstract During the isobaric-isothermal Inactivation of the enzyme α-amylase a simple first order inactivation kinetic is observed. However, the temperature and pressure dependence of the inactivation rate constant k (min −1 ) is more complex. A non-synergetic (cumulative) model inspired by the Arrhenius law is proposed as a non-linear description. Significant model deficiency is observed at non-intermediate values of pressure and temperature. Due to the lack of sufficient knowledge of the underlying biological and physical mechanisms, in this paper a black box modeling approach is made using artificial neural networks. The resulting artificial neural network structure is able to predict the combined effect of pressure and temperature on the inactivation rate constant k (min −1 ) of α-amylase without significant increase of the model complexity as compared to the Arrhenius type model. The accuracy of the ANN model parameters is evaluated using the concept of joint confidence regions.

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