Application of neural networks as a non-linear modelling technique in food mycology

Fungal growth leads to spoilage of food and animal feeds and to formation of mycotoxins and potentially allergenic spores. There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different micro-organisms however the nature of neural networks, as highly non-linear approximator schemes, considers them as an alternative methodology. The application of neural networks in predictive microbiology is presented in this paper. This technique was used to build up a model of the joint effect of water activity, pH level and temperature to predict the maximum specific growth rate of the ascomycetous fungus Monascus ruber. Neural network and polynomial models were compared against the experimental data using six statistical indices namely, coefficient of determination (R²), root mean square error (RMSE), mean relative percentage error (MRPE), mean absolute percentage error (MAPE), standard error of prediction (SEP), bias (B_f) and accuracy (A_f) factors. Graphical plots were also used for model comparison. The performance of the learning-based systems provide encouraging results while sensitivity analysis showed that from the three environmental factors the most influential on fungal growth was temperature, followed by water activity and pH to a lesser extend. Neural networks offer an alternative and powerful technique to model microbial kinetic parameters and could thus become an additional tool in predictive mycology.