Ochratoxin A in grapes: development of a prototype predictive model.

Aspergillus carbonarius is considered the main responsible for ochratoxin A (OTA) contamination in grapes while A. niger and A. tubingensis were confirmed as dominant on berries. Ochratoxin A is a nephrotoxin and possible carcinogen fungal metabolite. The European Commission set 2 µg/Kg as maximum OTA content allowed in wine, must and grape juice being wine the second most important source of OTA in the human diet, after cereals. For these reasons it is crucial for stakeholders in the grape-wine chain to predict OTA risk related to the geographic area and year. Thus, the aim of this study was to develop a mechanistic predictive model (OTA-grapes) based on the infection cycle of A. carbonarius on grapes. A relational diagram of A. carbonarius infection cycle was drawn. Data available in literature were used to develop mathematical functions for each step of the infection cycle (A. carbonarius sub-model), in relation to grape growth stages (grapes sub-model). Regarding sporulation and infection, few quantitative data were available and only limit conditions for their occurrence were established, while fungal germination, growth and OTA production were modelled based on temperature and water activity regimes (Bete and linear regression equation, respectively). Regarding OTA production, wide variability was noticed between strains, i.e. different combinations of optimal temperature and incubation time to optimize the production, apparently not related to their geographic origin. Therefore, two different functions were developed to simulate the fungal community in vineyard. The model uses weather data (air temperature, relative humidity and rain) as input; the A. carbonarius sub-model was run from setting to ripening, growth stages predicted by the grapes sub-model, that uses as well meteorological data as input. A prototype model is now available, the next mandatory step regards the collection of a suitable set of vineyard data, including geo-referenced OTA contamination and related meteorological data to manage the validation, crucial step before OTA-grapes model delivery as support for stakeholders in the grape chain.