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A radial basis function (RBF) neural network was developed and compared against a quadratic response surface (RS) model for predicting the specific growth rates of the biotechnologically important basidiomycetous fungi, Physisporinus vitreus and Neolentinus lepideus, under three environmental conditions: temperature (10-30 degrees C), water activity (0.950-9.998), and pH (4-6). Both the RBF network and polynomial RS model were mathematically evaluated against experimental data using graphical plots and several statistical indices. The evaluation showed that both models gave reasonably good predictions, but the performance of the RBF neural network was superior to that of the classical statistical method for all three data sets used (training, testing, full). Sensitivity analysis revealed that of the three experimental factors the most influential on the growth rate of P. vitreus was water activity, followed by temperature and pH to a lesser extent. In contrast, temperature in particular and then water activity were the key determinants of the development of N. lepideus. RBF neural networks could be a powerful technique for modeling fungal growth behavior under certain parameters and an alternative to time-consuming, traditional microbiological techniques.

Aim:  To evaluate the influence of water activity (aw), temperature and pH on the radial growth and lag phase of Physisporinus vitreus (E‐642), a basidiomycete was used in the biotechnological process of bioincising. Methods and Results:  Radial growth was monitored for 20 days on malt extract agar medium. Five levels of aw (0·998, 0·982, 0·955, 0·928, 0·892) were combined with three incubation temperatures (10, 15, 20°C) and three pH values (4, 5, 6). Data analyses showed a highly significant effect of aw and temperature (P < 0·0001) and a significant effect of pH (P < 0·05). The radial growth rate and lag phase of P. vitreus were very sensitive to aw reduction. Although P. vitreus was able to grow at all the selected temperatures and pH values, the lag phase increased with decreasing aw and growth became inhibited at aw = 0·955. Optimal conditions for growth of P. vitreus were aw = 0·998, 20°C and pH 5. The response surface model provided reliable estimates of these growth parameters and confirmed a greater dependence on aw than on temperature or pH under in vitro conditions. Conclusions:  Low levels of aw can prevent growth of P. vitreus, so wood moisture content should be adjusted accordingly. Significance and Impact of the Study:  Implementation of these results should contribute towards the optimization and efficiency of bioincising.

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