## 3 Results and Discussion
### Multi-Layer Perceptron vs. Spray Drying
[PERSON] and others [76] presented a rather flexible approach to the meta-analysis of the prediction model with particle sizes being controlled by PLS-ANFIS and ANN in the process of spray drying. The PLS-ANFIS model resulted in being more effective and successful compared with a classical solution using ANN [76]. [PERSON] et al. (2008) [102] researched the impact of the strength of the raw material flow, the temperature of the inlet air and the velocity of the sprayer in a semi-industrial dryer on the properties of orange juice. Seven performance indicators were studied, i.e., residual moisture content of orange juice powder, particle size, bulk density, average moisturizing time, insoluble solids, outlet air temperature and dryer performance. Supervised artificial neural network MLP trained by back propagation algorithms were developed to predict seven performance indicators based on three input variables (feed flow rate, atomizer speed and inlet air temperature). [PERSON] et al. (2009 [101] devised artificial neural networks in order to predict certain quality parameters such as the effectiveness of drying, solubility, color, total anthocyanin content and anti-oxidation activity of the spray-dried pomeranate juice. In developing predictive models, the type of carrier (gum arabic, maltodextrin, wax starch) was determined. Spray drying was conducted at an inlet air temperature of 130 \({}^{\circ}\)C for all experiments [101]. The optimal MLP model (3-10-8-5) was compared with the RMS model because of its ability to model and predict. It turned out that the MLPN was more precise and accurate in predicting than the RMS, even with the limited number of experiments [101]. [PERSON] et al., 2018 [85], in this work, designed an innovative solution with electron microscopy, image analysis and artificial intelligence in order to differentiate the classes of fruit powders obtained in the process of spray-drying. The author made an endeavor to compare microscope images with digital images, including selected quality parameters such as color, shape coefficients and the coefficients of shape and image texture. The research material consisted of strawberry powders. The process of spray-drying was carried out at the same inlet air temperature of 165-170 \({}^{\circ}\)C for each class. It is worth noting that the same carrier, namely maltodextrin, was used in all the research to obtain strawberry powders. Learning sets were designed on the basis of the sets of representative characteristics, such as color and texture for images with a digital camera and color and texture for images with SEM. As a result, it was possible to create MLP neural networks characterized by a high determination ratio (Table 2). The first model consisted of 30 descriptors (12 parameters of  
Figure 3: Spray drying supported by artificial neural networks. RGB and 18 parameters of GLCM) responsible for the color and texture of images; there were four neurons responsible for the classes of strawberry powders in the hidden layer. The MLP 30-19-4 network with 19 neurons in the hidden layer reached a quality rate at the level of R\({}^{2}\) = 0.998. For comparison, another MLPN, in which SEM image analysis was applied, determined the coefficients of shape such as (Circ), aspect ratio (AR), roundness (Round), solidity (Solidity), skewness (Skew), kurtosis (Kurt), Feret factor (Wsp.Feret), circularity factor W1 (W1) and area (Area) in the output layer. It should be noted that the MLP 9-9-4 model with nine neurons in the hidden layer reached the coefficient of quality at the level of 0.944. The last model with the MLP structure consisting of 12 neurons (selected texture parameters on the basis of GLCM matrix) in the input layer and 1 neuron in the hidden layer responsible for the same number of classes (the output layer), like the two previous models, reached the effectiveness of recognizing at the level of 0.944.