Modelling the growth of Leuconostoc mesenteroides by Artificial Neural Networks

Abstract

The combined effect of temperature (10.5 to 24.5 °C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the predicted specific growth rate ( G r), lag-time (Lag) and maximum population density ( y End) of Leuconostoc mesenteroides under aerobic and anaerobic conditions, was studied using an Artificial Neural Network-based model (ANN) in comparison with Response Surface Methodology (RS). For both aerobic and anaerobic conditions, two types of ANN model were elaborated, unidimensional for each of the growth parameters, and multidimensional in which the three parameters G r, Lag, and y End are combined. Although in general no significant statistical differences were observed between both types of model, we opted for the unidimensional model, because it obtained the lowest mean value for the standard error of prediction for generalisation. The ANN models developed provided reliable estimates for the three kinetic parameters studied; the SEP values in aerobic conditions ranged from between 2.82% for G r, 6.05% for Lag and 10% for y End, a higher degree accuracy than those of the RS model ( G r: 9.54%; Lag: 8.89%; y End: 10.27%). Similar results were observed for anaerobic conditions. During external validation, a higher degree of accuracy ( A f) and bias ( B f) were observed for the ANN model compared with the RS model. ANN predictive growth models are a valuable tool, enabling swift determination of L. mesenteroides growth parameters.