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

Abstract

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 °C) and humidity (seed water activity, aw = 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 (R2 = 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.

A Predictive Model for the Growth Diameter of Mold under Different Temperatures and Relative Humidities in Indoor Environments

“…Given that numerous primary models have not been validated for accuracy [53,54], some models could not accurately predict mold growth in indoor environments [52,55]. The goodness of fit of modeling was determined using the coefficient of determination (Adj.R 2 ) [49,56], root mean square error (RMSE) [31,57], accuracy factor (A f ) and deviation factor (B f ) [31,38,52]. The RMSE indicates the average error, while Adj.R 2 represents the proportion of the variance.…”

“…For accuracy of the two primary models, the A f and B f of both models were within the acceptable range [68] but the Gompertz model values were closer, which means that the Gompertz model has a higher accuracy and smaller deviation. Jolanta Wawrzyniak et al [31] evaluated the risk of mold and its toxins in stored seeds based on a mathematical model of predictive microbiology, and found that the prediction model established based on the modified Gompertz model had a good predictive ability (Adj.R 2 = 0.90, RMSE = 0.547). In this study, the Adj.R 2 under different conditions was above 0.90 (Tables 1 and 2), but the RMSE was high, which means these two primary models might cause large errors.…”

“…Most existing mold growth prediction models are primarily used in specific food environments to protect food safety by inoculating target molds into standard media or more homogeneous food products. Wawrzyniak et al [31] used a modified Gompertz model to predict mold growth at temperatures ranging from 12 to 30 • C and water activity (a w ) ranging from 0.8 to 0.9, demonstrating that the modified Gompertz model could predict the fungal population and grain mold risk in barley grain well at different temperatures and a w values [32]. Other substrate characteristics (pH, salinity, etc.)…”