## 3 Results and Discussion
### Multi-Layer Perceptron vs. Convective Drying
After the appropriate preparation of research material for drying, changes in the moisture ratio of quince slices were evaluated during convective drying with a thermo-convection dryer as part of the research. The obtained experimental data concerning moisture content as a function of drying time for nine measurements, i.e., from the group of drying air temperatures of 40, 50 and 60 \({}^{\circ}\)C and with the proper preset and supplied velocity of air flow at the level of 1, 2 and 3 m\(\cdot\)s\({}^{-1}\), respectively, made it possible to obtain the most optimal MLPN [68]. As a result of numerous configurations with MLPN, the best-performed ANN based on cross-validation contained two hidden layers and consisted of 90 artificial hidden neurons in each hidden layer. The predictions were found to be consistent with the experimental data and were noted, achieving a coefficient of determination (R\({}^{2}\)) higher than 0.99. In the same year, the team of [PERSON] et al. (2020) [71] used convective drying combined with innovation in the form of an acoustic wave to classify the obtained dried strawberry fruits. The growing interest in sound emission and the way of obtaining acoustic wave frequency spectrum was dependent on the availability of advanced electronic devices, including computer systems. The data extracted from the sound required the construction of measurement and a research station beforehand. The utilitarian goal was to produce an artificial neural network MLP capable of classifying the qualitative state of the obtained dried fruit [71]. The research material consisted of two varieties of wild strawberry: the Chilean variety (_Fragaria chiloensis_) and the Virginian variety (_Fragaria virginiana_). In the study, strawberry fruits belonging to the ripe and overripe groups were dried: ripe and overripe. Fresh strawberry fruits were dried using a convective method at the temperature of the drying agent at 60 \({}^{\circ}\)C, with determined drying air velocity at v = 1.0 m\(\cdot\)s\({}^{-1}\) in co-current in a thin stationary layer. In accordance with the author's original research methodology, in the next stage of the study, using a measuring and testing station that was designed to mimic the free fall of fruit during transport to the production line [71], sound measurements were carried out for the resulting dried strawberries. In the last step, the ANN consisted of an input layer, for which input variables such as frequency (Hz) were specified, and an output layer corresponding to the degree of quality of the strawberry (ripe or overripe). The research showed that the MLP 2:14:1 with 14 neurons in the hidden layer was characterized by the highest classification capabilities, and its classification rate was 98%. For comparison, one analysis of the wave spectrum shift of the acoustic signal was used as a basic method for the non-invasive evaluation of quality conditions in watermelon [72]. It proved to be an effective method, and its low evaluation cost made it possible to determine the quality class of watermelons efficiently using acoustic resonance. The efficiency of identifying watermelon ripeness was achieved at 95% [72]. Another interesting research was conducted by [PERSON] et al. (2020) [73] and was based on the physicochemical properties of root vegetables after drying. It is worth noting that this method entailed applications of ANN using the MLP topology in convective drying in order to identify the above root vegetables. The research material comprised the following: celeriac root (_Apium graveolens_), femnel (_Foenculum vulgare_), carrot (_Daucus carota_), yellow carrot (_Daucus carota_) (Petroselinum Hortense) and purple carrot (_Daucus carota_). Prior to conducting the experiment, a pre-treatment was performed; namely, the vegetables were washed, peeled and cut into slices with a diameter of d = 1.5-2 cm and thickness of h = 0.3-0.5 mm [73].