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Modelling the sporulation of some fungi associated with cheese, at different temperature and water activity regimes

The objectives of this study were to determine, in-vitro, the influence of temperature (T; 10–30 °C, step 5°), water activity (aw, 0.83–0.99; step 0.04) and time on sporulation (SPO) of some cheese-related fungi belonging to Penicillium spp. and A. versicolor. Overall, sporulation started rapidly (8...

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Bibliographic Details
Published in:International journal of food microbiology 2018-08, Vol.278, p.52-60
Main Authors: Camardo Leggieri, Marco, Decontardi, Simone, Battilani, Paola
Format: Article
Language:English
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Summary:The objectives of this study were to determine, in-vitro, the influence of temperature (T; 10–30 °C, step 5°), water activity (aw, 0.83–0.99; step 0.04) and time on sporulation (SPO) of some cheese-related fungi belonging to Penicillium spp. and A. versicolor. Overall, sporulation started rapidly (8 h in optimal conditions); it was significantly influenced by T and aw and the fungi studied were clearly distinguished based on their thermo-hydro adaptation. Boundary conditions for sporulation were defined for all the fungi considered and the sporulation rate was successfully modelled, especially based on T and time regimes. Penicillium crustosum, P. nordicum and P. verrucosum showed optimum for SPO at T between 20 and 25 °C and their sporulation continued up to aw = 0.87 (aw = 0.83 for P. nordicum). They resulted the fungi best adapted to the environmental conditions of ripening grana cheese storehouses; therefore, it is expected they dominate on the grana cheese surface. Studies on cheese are necessary to validate these results obtained on artificial media and without fungi co-occurrence. •Mycotoxin producing fungi can sporulate during grana cheese ripening.•Sporulation started after 8 h in optimal conditions.•P. crustosum, P. nordicum and P. verrucosum are expected to be the dominant.•Modelling sporulation as function of T and time was successful.•This research is a crucial contribute to develop a mechanistic predictive model.
ISSN:0168-1605
1879-3460
DOI:10.1016/j.ijfoodmicro.2018.04.023