## 3 Results and Discussion
### Machine Learning  
As a result of the machine learning process, 30 MLPNs were obtained, which had a high determination index for the test set of 0.99. The MLPNs generated for the learning set showed an equally high level of efficiency in recognizing blackcurrant powder samples. According to the established principle in machine learning, the obtained models meet the criterion of good fit to the network (rule of overfitting by under fitting) [43]. In the MLPN structure, it is worth noting that the range of the number of neurons in the hidden layer had an impact on finding the optimal MLP network. In the case of activation functions for neurons in the hidden layer, the tanh and exponential functions turned out to be the most efficient giving the best results from learning these networks. Tanh and exponential appear to be more flexible activation functions than the others, resulting in a better model determination rate on the test set [29,44]. In practice, based on machine learning principles, the choice of activation functions depends on the decision problem at hand as well as the network topology. The nonlinearity of these activation functions can also affect the more effective recognition performance of these data.  
The machine learning experiments also observed a slightly higher success rate for identifying blackcurrant powders when the amount of carrier in solution was 50% (Table 3) than for samples with 30% of the carrier (Table 4).  
Figure 4: The infrared spectra wavenumber for 12 blackcurrant powder samples with different carrier contents.  
In the case of the type of carrier, the process of identifying individual samples was most successful with the selection of milk whey protein (W70), gum arabic (GA70), maltodextrin (MD70), inulin (IN70) and microcrystalline cellulose (C70), taking into account the value of the smallest error made on the test collection with its 30% (Table 4) and 50% (Table 3) share in the solution. Worse results for the recognition of blackcurrant powder samples were obtained with fiber. The smallest error made on the test set among the acquired MLPNs in the machine learning process for 30% carrier content was achieved by MLP network 3-6-1 (model C70 zW70), for which the error reached a value of 0.0025. In comparison, when recognizing the carrier with its 50% content in solution, the smallest error on the test set was achieved by models such as MLP 3-8-1 (MD50 zGA50), MLP 3-9-1 (IN50 zGA50) and MLP 3-9-1 (W50 zGA50), for which the error reached 0.00005 \(\pm\) 0.000001. It was observed that MLPNs with 50% content of gum arabic (GA50) reproduced the results for three models well. This means that these MLPNs in the group with 50% carrier content effectively predict the results for gum arabic. As a comparison with the data obtained by the FTIR spectroscopy method, it can be concluded that as the carrier content increases, the absorption intensity and the number of correct MLPN models increases, which generalizes well the relationship between the input data and the result.  
MLP-type networks were developed with high coefficient of determination and low error committed on the test set for currant powder samples involving milk whey protein (W70), gum arabic (GA70), maltodextrin (MD70), inulin (IN70) and microcrystalline cellulose (C70). Slightly worse learning results were achieved with fiber (F) relative to the other blackcurrant powder samples. The smallest error made on the test set among the acquired MLPNs was shown at 30% carrier content for the 3-6-1 MLP network (model C70 zW70), for which the error reached a value of 0.0025. It was also shown that at 50% carrier content in solution, the smallest error on the test set was achieved by models such as MLP 3-8-1 (MD50 zGA50), MLP 3-9-1 (IN50 zGA50) and MLP 3-9-1 (W50 zGA50), for which the error reached 0.000005 \(\pm\) 0.000001.