Modeling germination of fungal spores at constant and fluctuating temperature conditions

The germination of Penicillium expansum and Aspergillus niger spores was monitored microscopically on malt extract agar at isothermal conditions ranging from 0 to 33 °C and 5 to 41.5 °C, respectively. The obtained germination data, expressed as percentage of germination (% P) versus time, were fitted to the modified Gompertz equation for the estimation of the germination kinetic parameters (lag time, λg, and germination rate, μg), which were further modeled as a function of temperature via the use of Cardinal Models with Inflection (CMI). The effect of temperature on these parameters was similar with that previously reported for mycelium growth kinetics of the tested isolates. The germination of spores was also studied at various dynamic time–temperature conditions including single or sequential temperature shifts. The germination of spores at fluctuating temperatures was predicted using the modified Gompertz equation in conjunction with the CMI models for λg and μg and based on the assumptions that i) a temperature shift does not result in any additional λg and, thus, the total lag can be calculated by adding relative parts of the lag time, and ii) after a temperature shift the germination rate μg adapts instantaneously to the new temperature. The comparison between predicted and observed data showed that the germination of spores is strongly affected by the extent of the temperature shift, the percentage of germinated spores at the time of the shift and the fungal species. Apart from the scientific interest in understanding the dynamics of spores' germination, the models developed in this study can be used as tools in effective quality management systems for fungi control in foods.

► A detailed kinetic study of the germination of fungal spores was conducted.
► Models for the effect of temperature on fungal spore germination were developed.
► The models were validated under dynamic temperature conditions.
► Spore germination was significantly affected by both the time and the extent of the temperature shift.
► The models can be used as effective tools for predicting fungal spoilage of foods.