## 3 Results and Discussion  
### Multi-Layer Perceptron vs. Convective Drying  
Recently changing consumer habits, food supply and economic aspirations require the optimization of processes and applications of modern technology aimed at preserving food and obtaining a high-quality index of finished food articles. High level of food waste causes food producers to lean towards ways of extending the shelf life of food articles. Efforts are being made to reduce industrial energy consumption in the process of food production via automatization, optimization and applications of artificial intelligence in individual processes. New ways of production management, production organization, processing, marketing and waste management are also applied. Therefore, it seems justified to apply artificial intelligence in the process of quality evaluation of the final product obtained in the process of drying (Figure 2) and during the process of kinetic control of drying parameters also online. Within the space of the last ten and twenty years, much research has been carried out related to the field of drying. This paper highlights the use of vegetables and fruits in convection drying. Convective drying is the most widespread method in the food industry. This technique is based on using drying agents such as hot air or gas, which have direct contact with the product during the drying process. The idea of drying kinetics is based on the simultaneous movement of mass and heat, i.e., the drying agent gives off heat to the moist raw material and simultaneously absorbs moisture from it [58]. Figure 2: Convective drying supported by artificial neural networks. Unfortunately, convective drying can have a negative impact on the quality of the obtained product because it leads to far-reaching physiochemical changes. At present, actions are taken to preserve food in a slow way in order to retain the content of bioactive compounds in dried fruits and vegetables and to keep a high-quality index [59]. That is why removing an appropriate amount of water from fruits and vegetables requires preparing an intelligent model of drying, allowing for determining optimal parameters of the drying agent, humidity, temperature and stream, among others, in order to obtain the highest possible quality index of the final product. Convective drying can be carried out with the implementation of recommended movement of the dried material. In such a situation, the raw material that is to be dried is in the direction with the co-current or counter-current of the drying agent [59]. Countercurrent-flow drying is used when raw material is characterized by properties that are highly resistant to high temperatures. Such material has direct contact with the drying agent throughout the whole process of drying. It translates into obtaining more dried fruits or vegetables than in the case of co-current drying. In co-current drying, the flow of raw material and the drying agent moves in the same direction. When the drying agent combines with raw material, it can have an impact on destroying the tissue structure of the latter. This method, as it was mentioned before, allows for obtaining products with higher humidity than the counter-current method because the drying agent emits thermal energy and increases its humidity while flowing. As part of the research made by [PERSON] team et al., (2022) [60], an endeavor was made to create effective neural models with machine learning, i.e., multi-layer perceptron network (MLPN) (Table 1) and DL in the process of quality evaluation of slices of dried sweet potatoes. Sweet potatoes (named in Latin _Ipomoea botatas_) are one of the healthiest foods in the world [61], and it is recommended by NASA as a superfood used in space expeditions [62]. Experimental data were obtained by drying the slices of sweet potatoes (2 \(\pm\) 0.1 mm) at four different air temperatures (60, 70, 80 and 90 \({}^{\circ}\)C) and a fixed air velocity value of 1.0 m\(\cdot\)s\({}^{-1}\).