Fusarium graminearum and zearalenone in wheat: a water activity–temperature model

Abstract

Zearalenone (ZEN) is a nonsteroidal estrogenic mycotoxin produced primarily by Fusarium graminearum, posing significant threats to agricultural grain production. When ZEN levels exceed regulatory limits, grains face rejection, and its harmful effects on the female reproductive system raise health concerns. Despite its importance, there is a lack of information on the ecophysiological conditions that promote F. graminearum colonisation and ZEN production in wheat grains. This study aimed to develop and validate predictive models for the growth of F.graminearum and ZEN accumulation in wheat. For this purpose, two strains isolated from wheat were inoculated in agar wheat-based medium supplemented with glycerol to adjust the water activity (aw) to five different values of 0.88, 0.91, 0.94, 0.97 and 0.99. The cultures were incubated at 4, 6, 8.5, 15, 20, 25, 30 and 35 °C, the colony growth was measured daily, and ZEN accumulation assessed at day 10, 20 and 30. To analyse the growth kinetics of F. graminearum, the fungal growth rate (μ) and lag time (λ) were calculated, applying the Cardinal/Rosso, Davey, and Gibson models. These techniques, commonly used in secondary modelling, were enhanced through variable transformation, with the square root transformation yielding optimal results in the Cardinal models. The outcome showed probabilistic model accuracy for growth ranging 65–79 % and ZEN production ranging 45–77 % on internal and external data set. Optimum temperature for ZEN production was 25–30 °C in media and wheat. In wheat, a higher aW was required for both growing (0.92 aw) and ZEN production compared to media (0.90 aw). Probabilities of growth over 80 % were predicted in the range of 0.90–0.95 aw at 16–34 °C after 30 days. In conclusion, to avoid mycotoxin contamination in wheat an aw < 0.89 should be maintained, and temperatures in the range 18–31 °C should be avoided (P < 0.5). The integration of predictive models into decision support systems could assist farmers in identifying pre-harvest contamination risks and in optimising harvesting and drying practices to minimise post-harvest contamination. This study highlights the importance of understanding the ecophysiological profiles of mycotoxigenic species like F. graminearum to mitigate contamination risks and optimise storage conditions in wheat.