Model of Fungal Development in Stored Barley Ecosystems as a Prognostic Auxiliary Tool for Postharvest Preservation Systems

Abstract

Postharvest preservation and storage have a crucial impact on the technological quality and safety of grain. The important threat to stored grain quality and nutritional safety of cereal products is mould development and their toxic metabolites, mycotoxins. Models based on predictive microbiology, which are able to estimate the kinetics of fungal growth, and thus, the risks of mycotoxin accumulation in a mass of grain are promising prognostic tools that can be applied in postharvest management systems. The study developed a modelling approach to describe total fungal growth in barley ecosystems stored at different temperatures (T = 12–30 °C) and water activity in grain (aw = 0.78–0.96). As the pattern of fungal growth curves was sigmoidal, the experimental data were modelled using the modified Gompertz equation, in which constant coefficients reflecting biological parameters of mould development (i.e. lag phase duration (τlag), maximum growth rate (μmax) and the maximum increase in fungal population level (Δmaxlog(CFU)) were expressed as functions of storage conditions, i.e. aw and T. The criteria used to evaluate the overall model performance indicated its good precision (R2 = 0.95; RMSE = 0.23) and high prediction accuracy (bias factor and accuracy factor Bf = 1.004, Af = 1.035). The formulated model is able to estimate the extension of fungal contamination in a bulk of grain versus time by monitoring temperature and intergranular relative humidity that are readily measurable in practice parameters; therefore, it may be used as a prognostic support tool in modern postharvest management systems.

Screening of Mycotoxigenic Fungi in Barley and Barley Malt (Hordeum vulgare L.) Using Real-Time PCR—A Comparison between Molecular Diagnostic and Culture Technique

“…In this context, Wawrzyniak made an interesting approach by predicting the level of fungal contamination of stored barley grain with the use of mathematical models based on the criterion of the early stage of mold development [27,28]. However, to date, for mycological routine analyses of barley kernels, the agar plating technique in combination with microscopic visualization is being used as the standard method in recent studies [29][30][31][32][33].…”

Mitigating aflatoxin B1 in high-moisture sorghum silage: Aspergillus flavus growth and aflatoxin B1 prediction

“…It is widely used to model bacterial and fungal growth ( Arroyo et al, 2005 ; Ochoa-Velasco et al, 2018 ). For instance, it has been used to model fungal growth in stored barley grains at different temperatures and water activities ( Wawrzyniak, 2021 ) and to study the growth of Fusarium verticilloides and Rhizopus stolonifer in the presence of antifungals such as thymol and carvacrol ( Ochoa-Velasco et al, 2018 ). Moreover, Marín et al (2006) modeled the growth of Aspergillus carbonarius and the production of ochratoxin A as a function of temperature in a synthetic grape medium using the Gompertz equation.…”

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

“…Besides detecting of fungal infection, predicting the level of fungal growth in agricultural and food products is important in postharvest management systems to find a solution and so decrease the product losses 24 , 25 . In case of potato, the disease spreads quickly in the stores and in future developing stages it destroys the internal tissue of the potato tuber without any disease symptoms in the surface of the tubers so that it cannot be detected by visible inspections.…”