Modelling of different enzyme productions by solid-state fermentation on several agro-industrial residues

Abstract

A simple kinetic model, with only three fitting parameters, for several enzymes production in Petri dishes by solid state fermentation is proposed in this paper, which may be a valuable tool for simulation of this type of processes. Basically, the model is able to predict temporal fungal enzymes production by solid state fermentation on complex substrates, maximum enzyme activity expected and time at which these maximums are reached. In this work several fermentations in solid state were performed in Petri dishes, using four filamentous fungi grown on different agro-industrial residues, measuring xylanase, exopoligalacturonase, cellulose and laccase activities over time. Regression coefficients after fitting experimental data to the proposed model turned out to be quite high in all cases. In fact, these results are very interesting considering on the one hand, the simplicity of the model, and on the other hand, that enzyme activities correspond to different enzymes, produced by different fungi on different substrates.

Modelling of solid‐state fermentation over wide operational range for application in process optimization

“…A cardinal temperature model with inflection was used to interpolate the growth and product formation parameters across the experimental temperature conditions. Diaz et al [18] used the same modelling as Saithi et al, [12] but included an inactivation term to the enzymatic activity, the term Àk Di P i in Equation (6). In Equation (6), the parameters a and b are the growth-related and non-growth-related product yields, respectively, P is the product of interest, and i is the i th product evaluated.…”

“…This new model is a combination of the models presented in the Theoretical Background section, which has been developed in order to better represent the experimental observations. The new proposed model was written in the form of Equations (11)(12)(13)(14)(15)(16)(17)(18). Based on this phenomenon, a secondary growth phase, structurally identical to the first phase, but with distinct growth capability, was added to the model.…”

“…Therefore, a biomass inactivation restriction was added to the model in order to express the way that temperature variations affect the living cells and subsequent growth, as shown in Equation (18). Therefore, a biomass inactivation restriction was added to the model in order to express the way that temperature variations affect the living cells and subsequent growth, as shown in Equation (18).…”

“…The mathematical model, defined in Equations (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), was used with parameters as described in the Parameter interpolation section. The mathematical model, defined in Equations (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), was used with parameters as described in the Parameter interpolation section.…”

“…In Equation (6), the parameters a and b are the growth-related and non-growth-related product yields, respectively, P is the product of interest, and i is the i th product evaluated. Diaz et al [18] used the same modelling as Saithi et al, [12] but included an inactivation term to the enzymatic activity, the term Àk Di P i in Equation (6). This approach allowed them to calculate the maximum enzymatic production for several different processes, with distinct microorganisms and substrates involved.…”