###### Abstract
Research on such prognostic tools combining knowledge in microbiology, mathematics, and IT for modern postharvest systems is still ongoing. Numerous studies have been conducted on the use of sigmoidal models in the form of the modified Gompertz equation [18] and the Baranyi-Roberts equation [19], reflecting the typical microbial growth curve to describe the influence of abiotic factors on the growth of individual fungi in agar media or cereal matrices under laboratory conditions [20; 21; 22; 23]. In recent research, the modified Gompertz equation has also been successfully applied to describe the behavior of fungi in the mass of stored grain and rapeseed, depending on temperature and humidity conditions [24; 25]. Nevertheless, it is worth emphasizing that the development of kinetic models based on the above equations requires the use of data that meet assumptions related to the structure of the used equation. Thus, their use is limited when microorganism development has not covered all stages of a typical growth curve. This drawback is not present in the regression based on the B-spline, which, using the so-called knots and control points, provides a better fit of the curve to empirical data compared to the classic regression. The principle of approximation using the B-spline technique consists in dividing the domain of the variable into subsets and fitting them with piecewise curves [26]. The optimal number and location of control points and knots, ensuring efficient approximation of described relationships, is determined during the B-spline optimization process. As a result, information contained in modelled data is \"compressed\" in the system of knots and control points shaping the B-spline curve. Thanks to its advantages, the B-spline regression technique has found applications not only in computer graphics [27], but also in other fields, including biology and life sciences [28; 29; 30; 31; 32]. Recently, methods of artificial intelligence imitating phenomena occurring in nature and operating well in modeling non-linear systems are gaining in importance in the description of microbial growth. One of them is the artificial neural network (ANN) technique being an analogy to the human nervous system, whose architectures based on structural units, i.e., artificial neurons arranged in layers, are internally linked by a dense network of connections. The signal flow and transformation in such models is carried out in accordance with a specific mathematical algorithm. ANNs are very useful tools in modeling because they are able to perform complex and accurate approximations without special assumptions [33]. For many years, investigations on the application of neural networks in predictive modeling have focused mainly on the description of growth in single bacteria species or modeling of kinetic growth parameters (e.g., maximum growth rate) of fungal strains [34; 35] and the accumulation of mycotoxins associated with their development [36; 37]. In recent years, researches using ANNs have also been devoted to the modeling of mold development in cereal and oilseed ecosystems treated holistically [38; 39]. These studies showed a slight advantage of ANN over the use of kinetic models for the prediction of fungal growth in the stored mass of grain. Another artificial intelligence technique that has been found useful in the optimization of various non-linear problems uses genetic algorithms (GAs) [40; 41]. This technique consists of searching for the optimal value/solution of the objective function by imitating biological evolutionary processessuch as crossover and mutation [41; 42]. In most cases, there are two representations of the analyzed problem that, by analogy with the living world, can be called genotype and phenotype [43]. A genotype is an abstract, primary-level representation (in the living world it is a sequence of purine and pyrimidine alkalis, while in the GAs, it is a set of parameters that allows for the creation of a full-fledged solution) on which crossover and mutation processes operate [43; 44].