## 3 Results and Discussion  
The first point to pay attention to is that when using data with low values (for example, vitamin concentrations) for training, with a simultaneous low level of system robustness, the probability of overfitting of the ANN increases. This manifests itself, for example, in obtaining negative ARP values, which contradicts the chemical meaning and logic of the experiment. Similar values appeared in two groups of data: on products that differed significantly from those that were analyzed by us (for example, soft drinks, exotic fruits and products of their processing, etc. ), as well as on products with vitamin fortification or modified recipes due to the introduction of various kinds of food additives into them. We were forced to leave for analysis only those products and related product groups that we used in training the ANN (a total of 1315 items). At the same time, we were forced to increase the robustness, as mentioned in Section 2, by simulating data noise within 1% of chemical composition values. This approach significantly accelerated learning and obtained adequate results [18; 1; 38]. Turning to the analysis of the data obtained, we note that the grouping was carried out by us both taking into account the already adopted system in the FNDDS library itself (by product groups), and according to the features, we identified ourselves (by the product processing method). As a result of training, ARP values were predicted. Next, we carried out pairwise comparisons of data groups with the results predicted by the ANN. We identified in total almost 1.5 dozen components with the concentrations and the ARP values of which there is a correlation. For the analysis, we used only those components that had a Pearson correlation value (r) > 0.25 (at \(p\leq 0.000833\) according to the Bonferroni statistical correction). The values are presented in Table 1.  
Analyzing the presented table, we can come to one of the first non-trivial conclusions about the relationship between the antiradical capacity of food systems and the amount of dietary fiber in it. At the same time, the correlation of the data has a moderate strength (the only one of identified cases). An explanation for this can be found in the chemical structure of dietary fiber. Most often, these are polymer components containing various functional groups in their molecules, which, apparently, can act as stabilizers of physicochemical parameters of products, as well as structure them, forming a \"molecular framework\" [39; 40]. It can be expected that a similar mechanism underlies the relationship between the concentration of carbohydrates in foods and their ARP. One of discussed topics related to dietary fiber is the ability to regulate the concentration of microcomponents (dosage) in food with their help, as well as the ability to influence the rate of their absorption in the human intestine from chyme. Conversely, an inverse relationship is known between their concentration in food and the likelihood of oncological diseases of the intestine and cardiovascular pathologies [40; 41; 42]. According to our data, it seems reasonable to add a number of components during the development of new food products that can both repair the lost ARP (or change the current ARP) and influence the physical/epidemiological properties of the food system. Among such components, we primarily outline dietary fiber and a number of other substances (Table 1). Research work on the enrichment of products from various commodity groups with dietary fiber, in our opinion, is moving in a promising direction. Especially, it is an actuality for those products that are subject to rapid \"aging\" and loss of their nutritional value [43]. In addition, the analysis of results obtained (Table 1) allows us to note that the revealed weak correlations between the ARP parameters and the concentrations of some vitamins are quite expected. Thus, antiradical properties are peculiar to a number of vitamins: C, K, carotenoids, folic acid, etc.