A Predictive Model for Assessment of the Risk of Mold Growth in Rapeseeds Stored in a bulk as a Decision Support Tool for Postharvest Management Systems

摘要

Reliable prediction of the risk of mold development in a stored bulk of rapeseeds may help to maintain seed quality and ensure the highest quality and safety of cooking oil. Mathematical models based on predictive microbiology that are able to assess the risk of fungal growth and the mycotoxins formation in a stored seed ecosystems are promising prognostic tools, which may improve postharvest management systems. The aim of the study was to develop a predictive model of fungal growth in bulks of rapeseeds stored under conditions, in which seeds are at risk of quality deterioration. It was formulated on the basis of data reflecting actual seed ecosystems with a hazardous initial level of mold spores (characteristic of seeds that vegetate and are harvested under adverse weather conditions) stored at a wide range of temperature (12-30 degrees C) and humidity (seed water activity, a(w) = 0.80-0.90). The predictive model was based on the modified Gompertz equation, whose coefficients are related with biological parameters of mold growth (i.e., lag phase duration, maximum growth rate and fungal population level at the stationary phase). The biological parameters of the model were described using the second-degree polynomial functions of temperature and water activity. The criteria used to assess the model efficiency pointed to its good predictive quality (R-2 = 0.90; RMSE =0.547). Moreover, the model was characterized by high accuracy (bias factor B- f = 1.045 and accuracy factor A( f) = 1.050). The formulated model of fungal growth can be used as a decision support tool to improve systems managing postharvest seed preservation processes.