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In enzyme kinetic studies, linear transformations of the Michaelis–Menten equation, such as the Lineweaver–Burk double-reciprocal transformation, present some constraints. The linear transformation distorts the experimental error and the relationship between x and y axes; consequently, linear regression of transformed data is less accurate when compared with methodologies that use nonlinear regression. However, linear transformations are widely used. Explanations for this are the facility to determine model parameters by hand calculations, and until recently, the use of nonlinear regression was difficult as specialized software was not readily available to most scientists and students. Because utilization of personal computers is widespread, these constraints are no longer applicable. This work describes how to perform nonlinear regression with the Solver supplement of Microsoft Office Excel. It is easy to use and to view the results graphically. The F-test was applied to discriminate between models. These methodologies are important in any biochemistry syllabus and can be used to create an active-learning environment where students discriminate between different kinetic models and explore their own experimental results based on several hypotheses.

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In this study, chestnut shells (CNS), a recalcitrant and low-value agro-industrial waste obtained during the peeling of Castanea sativa fruits, were subjected to solid-state fermentation by six white-rot fungal strains (Irpex lacteus, Ganoderma resinaceum, Phlebia rufa, Bjerkandera adusta and two Trametes isolates). After being fermented, CNS was subjected to hydrolysis by a commercial enzymatic mix to evaluate the effect of fermentation in saccharification yield. After 48 h hydrolysis with 10 CMCase U mL−1 enzymatic mix, CNS fermented with both Trametes strains was recorded with higher saccharification yield (around 253 mg g−1 fermented CNS), representing 25% w/w increase in reducing sugars as compared to non-fermented controls. To clarify the relationships and general mechanisms of fungal fermentation and its impacts on substrate saccharification, the effects of some independent or explanatory variables in the production of reducing sugars were estimated by general predictive saccharification models. The variables considered were lignocellulolytic activities in fungal fermentation, CNS hydrolysis time, and concentration of enzymatic hydrolysis mix. Multiple linear regression analysis revealed a very high significant effect (p < 0.0001) of fungal laccase and xylanase activities in the saccharification models, thus proving the key potential of these enzymes in CNS solid-state fermentation.