## 3 Results and Discussion
### Multi-Layer Perceptron vs. Convective Drying
The process of convective drying was carried out at the temperature of T = 50 \({}^{\circ}\)C and T = 70 \({}^{\circ}\)C. After drying, a number of physiochemical analyses were carried out in order to design MLP-type neural networks. [PERSON] et al. (2020) [73] devised two solutions with ANN. The first MLP model was aimed at predicting color parameters (Lab), the concentration of vitamin C and \(\beta\)-carotene, and the second MLPN model was to forecast the physical properties (completely dissolved solid substance and extraction effectiveness) and the chemical properties (i.e., the total content of polyphenols and antioxidant activity) of extracts of the aforementioned root vegetables [73]. The first model comprised data on the type of root vegetables and drying method in the input data, and the output data were responsible for parameters concerning color, vitamin C concentrations and \(\beta\)-carotene concentrations. The second MLP model aimed at forecasting physiochemical characteristics (information included in the output layer) consisted of the input data such as the type of root vegetables, drying method and the type of prepared extract. As a result of the learning process, the two optimal MLP networks were obtained with a high determination rate of R\({}^{2}\) = 0.887 for the first solution and R\({}^{2}\) = 0.878 for the second solution, respectively. MLPN effectively forecasted the way of drying and the type of prepared extract on the basis of the type of root vegetables [73]. In other research aimed at modeling neural networks based on MLP, the authors used a combination of various layers and neurons with different activation functions. Neural networks with one or two hidden layers were randomly selected with a pre-determined number of neurons (from 3 to 15 neurons) for each network. The next step entailed determining a network aimed at forecasting the moisture ratio for the research material, namely dried kiwi fruits [74]. In other research, experiments on mulberry fruit drying were carried out at the temperature level of 50, 60 and 70 degrees Celsius and with air velocity at the level of 1.5, 2 and 2.5 m-s-1. MLPN with different thresholds and neurons, as well as the Levenberg-Marquardt algorithm and Tanh-Sigmoid threshold function, was used to model the drying quality. The results showed that the best type of neural network with a 3-12-3 structure and threshold function (Logsig i Purelin) obtained the best result in comparison with other topologies with the highest coefficient at the level of 0.9998 [75]. Between 2007 and 2010, several other MLP-assisted convection drying studies were conducted with sweet potatoes [76], betroot and potatoes [77], and tomato, among others [78; 79] (Table 1). In these studies, the results for MLPN were also promising and even provided an opportunity to be tried in further research experiments, as mentioned above. In summary, in the works, the effect of changes in drying air temperature and speed shortened the drying time, while higher relative humidity prolonged the process itself. The speed and relative humidity of the drying air improved the energy indicators. The use of MLPN practically gave high efficiency in predicting and classifying the dried material and parameters in the drying process. The high determination rate can be translated into the high popularity of using just this topology in this research problem.