Mould germination: Data treatment and modelling

The objectives of this study were i/ to examine germination data sets over a range of environmental conditions (water activity, temperature) for eight food spoilage moulds, ii/ to compare the ability of the Gompertz equation and logistic function to fit the experimental plots, iii/ to simulate germination by assessing various distributions of the latent period for germination amongst a population of spores. Data sets (percentage germination, P (%), versus time, t) of Aspergillus carbonarius, Aspergillus ochraceus, Fusarium verticillioides, Fusarium proliferatum, Gibberella zeae, Mucor racemosus, Penicillium chrysogenum and Penicillium verrucosum were analysed. No correlation, or relationship between the mean percentage [mean (P)] and the variance [var (P)] was found. Therefore no transformation of the germination data was required. Experimental data were fitted by using the Gompertz equation P = A exp (− exp [μm e / A (δ − t) + 1]) and the logistic function P = Pmax / (1 + exp (k (τ − t))). Based on the residual mean square error (RMSE), no model performed better than the other one. However, model parameters were generally determined more precisely with the logistic model than with the Gompertz one. The time course of fungal spore germination curves was simulated assuming different distributions of the latent period for germination, lag, amongst a population of spores. The growth rate of germ tubes was calculated by means of the relationship: lag · rate = k. For normal Gaussian distributions, germination curves were symmetrical with respect to the inflection point and should be modelled with the logistic function. Skewed distributions were capable of simulating an asymmetric germination curve that was fitted by the Gompertz model. Future studies should be conducted for assessing whether the distributions assumed in this paper are in accordance with the experimental distributions that are still unknown.