## 3 Results and Discussion
### Multi-Layer Perceptron vs. Convective Drying
The results showed that the proposed MLP network using Gray Level Co-occurrence Matrix (GLCM)-based texture descriptors [63] did not meet the expected of classifying research samples. Therefore, [PERSON] team et al. (2022) [60] used the method of deep learning for comparison, which substantially improved the effectiveness of the classification of dried fruits and vegetables [60]. In the same year, [PERSON] [64] also conducted research aimed at improving the characteristic of convective drying and optimizing drying conditions for selected varieties of oranges, i.e., Valencia, Washington and Navel. As a result of the research [64], [PERSON] designed two structures for the MLP model, which became one of the methods of using machine learning in his research. The results showed the effective prediction of moisture ratio with MLPN in the case of Valencia, Washington and Navel varieties. Nevertheless, the Valencia variety obtained a higher determination ratio. The model structure characteristics for MLP learning consisted of five neurons in the input layer, three neurons in the hidden layer and one output layer responsible for moisture ratio. The coefficient of determination for the 5-3-1 MLP network was at R\({}^{2}\) = 0.981. For comparison, the author evaluated the drying rate (the dependent variable in the output player) in MLPN by modifying the independent variables in the input layer and in the hidden layer. As a result of the prediction for the second structure of the MLP 3-2-1 in orange fruit for the same Valencia variety, a lower rate was obtained than in the case of the Washington and Navel variety, and its value was R\({}^{2}\) = 0.862 (Table 1). In his research, [PERSON] (2022) [65] also evaluated and compared the effectiveness of two different methods of forecasting models for moisture ratio when drying apple slices. Golden Delicious variety was used as research material. The first stage required preparing raw materials for drying. Apples were washed and peeled, the cores were hollowed out, and the raw material was cut into 20 \(\times\) 20 \(\times\) 6 mm cubes. Another step involved convective drying. Experiments were carried out at four different temperatures of drying using a drying agent (air) similar to the team of [PERSON] team et al. (2022) [60]. In the above research, there was no fiche airflow. The drying process was performed at different air speed levelsin a convection dryer. As a result of the drying and forecasting process using the MLP in the work of [PERSON] (2022) [65], the best results were obtained. The topology of the MLP 3-10-10-1 included two hidden layers, and the coefficient of determination was R\({}^{2}\) = 0.999 [65]. For comparison, the teams of [PERSON] et al. (2018) [66] and [PERSON] et al. (2015) [67], concerning apple fruit drying, developed dedicated solutions using an MLP-type network as well. [PERSON] et al. (2018) [66] experimentally researched the impact of drying temperate and air velocity on the quality parameters of apples based on the input data consisting of color difference, volume ratio and water absorption capacity in convective drying. On the other hand, [PERSON] et al. (2015) [67] devised MLPN as a non-invasive tool supporting fast evaluation and control of changes in the color and moisture of apples during drying. MLPN achieved a satisfying quality factor value higher than 0.92 [67] based on the changes in color and moisture of apple slices. Another team consisting of [PERSON] et al. (2020) [68], in similar drying temperature conditions, carried out forecasting of moisture content for quince fruits. Quince (named in Latin (Cydonia oblong a Mill) is an edible fruit similar in shape to apples; it is also a yellow fruit with a slightly hairy peel. Quince, similar to apples, is used in the food industry to produce jams, jellies and preserves and can also serve as an addition to baked and dried food articles [69]. Quince are characterized by medicinal and antioxidant properties and are also rich in various polyphenic compounds [70].