## 3 Results and Discussion
### Multi-Layer Perceptron vs. Spray Drying
Due to the fact that the above research showed a high ratio of recognizing fruit powders through coloring strength [86], it induced the researchers to conduct further research works on other solutions using artificial neural networks. The above research focused on analyzing the possibilities of applying computer image analysis and neural modeling in order to evaluate selected quality discriminants of the spray-dried chokeberry powders. Two imaging techniques were used using a digital camera and a scanning microscope. It was found that effective recognition of quality classes of juice from dried chokeberries occurs using color. The best color space model affecting the recognition ability of digital images turned out to be the YCbCr and RGB color space models (Table 2). However, another research concentrated on the analysis of rhubarb powders [87]. The aim of the above research was to classify spray-dried powders based on graphic data obtained from bitmap obtained in the process of spray-drying. The neural model was developed with a multi-layer perceptron topology. There were variables expressed in the form of 46 image descriptors in the input layer, i.e., RGB, YCbCr, HSV (B) and HSL color models [103; 104; 105]. As a result of the learning process, it was possible to create MLPN 46-11-10, which was characterized by a high coefficient \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Application** & **Descriptors** & \begin{tabular}{c} **Inlet Air** \\ **Temp. [°C]** \\ \end{tabular} & **Carrier** & \begin{tabular}{c} **Fruit and** \\ **Vegetable** \\ \end{tabular} & *** Structure** & **R\({}^{2}\)** & **Year** & **Ref.** \\ \hline classification & \begin{tabular}{c} Circularity (Circ.), \\ Solidity, Round, Feret \\ factor, AW (water \\ activity), MC \\ (moisture content), \\ \end{tabular} & 80 & \begin{tabular}{c} maltodextrin, gum \\ arabic, inulin \\ \end{tabular} & raspberry & 8-44-9 & 0.999 & 2021 & [88] \\ \hline classification & \begin{tabular}{c} RGB, YCbCr, HSV \\ features \\ \end{tabular} & - & \begin{tabular}{c} Maltodextrin used \\ during the research has \\ dextrose equivalent \\ (DE), respectively: \\ \end{tabular} & rhubarb & 46-11-10 & 0.91 & 2020 & [87] \\ \begin{tabular}{c} **H\({}^{\prime}\)**–DE 26 and \\ \end{tabular} & \begin{tabular}{c} **H\({}^{\prime}\)**–DE 12 \\ \end{tabular} & \begin{tabular}{c} **H\({}^{\prime}\)**–DE 12 \\ \end{tabular} & \begin{tabular}{c} **H\({}^{\prime}\)**–DE 12 \\ \end{tabular} & \\ \hline classification & YCbCr & 150, 160, 170 & maltodextrin & chokeberry & 15-25-3 & 0.994 & 2019 & [86] \\ \hline classification & RGB & 150, 160, 170 & maltodextrin & chokeberry & 15-10-2 & 0.999 & 2019 & [86] \\ \hline classification & GLCM feature & 165-170 & maltodextrin & strawberry & 12-1-4 & 0.944 & 2018 & [85] \\ \hline classification & shape factors & 165-170 & maltodextrin & strawberry & 9-9-4 & 0.944 & 2018 & [85] \\ \hline classification & color coefficients & 165-170 & maltodextrin & strawberry & 30-19-4 & 0.998 & 2018 & [85] \\ \hline prediction & \begin{tabular}{c} Yield (\%) Solubility \\ (\%), Antioxidant \\ activity (\%), Total \\ anthocyanin content \\ (mg = L), Color (DE) \\ \end{tabular} & 130 & \begin{tabular}{c} maltodextrin, arabic \\ gum, waxy starch \\ \end{tabular} & pomeranate & 3-10-8-5 & 0.87 & 2009 & [101] \\ \hline predict & \begin{tabular}{c} feed flow rate, \\ atomizer speed, inlet \\ air-temperature \\ \end{tabular} & \
\begin{tabular}{c} 110, 130, 150, \\ 170, 190 \\ \end{tabular} &
\begin{tabular}{c} maltodextrin, liquid \\ glucose, and \\ methylcellulose \\ \end{tabular} & orange & 3-14-10-7 & 0.966 & 2008 & [102] \\ \hline \hline \end{tabular}  
* Structure of Artificial Neural Network: input layer-hidden layer (one and two)—output layer.  
\end{table} 
Table 2: Applications of multi-layer perceptron by using spray-drying from 2007 to present. of quality at the level of 0.91. The aforementioned ANN consisted of 11 neurons in the hidden layer and 10 neurons in the output layer.