Modelling fungal growth using radial basis function neural networks: the case of the ascomycetous fungus Monascus ruber van Tieghem.
A radial basis function (RBF) neural network was developed and evaluated against a quadratic response surface model to predict the maximum specific growth rate of the ascomycetous fungus Monascus ruber in relation to temperature (20-40 degrees C), water activity (0.937-0.970) and pH (3.5-5.0), based on the data of Panagou et al. [Panagou, E.Z., Skandamis, P.N., Nychas, G.-J.E., 2003. Modelling the combined effect of temperature, pH and aw on the growth rate of M. ruber, a heat-resistant fungus isolated from green table olives. J. Appl. Microbiol. 94, 146-156]. Both RBF network and polynomial model were compared against the experimental data using five statistical indices namely, coefficient of determination (R(2)), root mean square error (RMSE), standard error of prediction (SEP), bias (B(f)) and accuracy (A(f)) factors. Graphical plots were also used for model comparison. For training data set the RBF network predictions outperformed the classical statistical model, whereas in the case of test data set the network gave reasonably good predictions, considering its performance for unseen data. 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.
Panagou
E Z
EZ
National Agricultural Research Foundation, Institute of Technology of Agricultural Products, Lycovrissi, GR-141 23, Greece. stathispanagou@aua.gr
Kodogiannis
V
V
Nychas
G J-E
GJ
D019540:Area Under Curve; D015169:Colony Count, Microbial; D005506:Food Contamination; D005516:Food Microbiology; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D032741:Monascus / Q000254:growth & development*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D013696:Temperature; D014867:Water / Q000378:metabolism
D014867:Water