## 3 Results and Discussion
In addition to comparing the two groups of methods with each other, we also checked for the presence of differentiation within each of the groups. To do this, a pairwise assessment of all the data grouped in Table 2 was carried out using the Tukey test (Table 3). Similar to the data presented above in Table 3, one can also notice two groups, which combine all the listed methods of processing food raw materials. Conversely, differences within each of these groups are not reliable (\(p\) values from 0.24 to 0.99). Thus, according to the value of the predicted residual ARP in food products, two clearly differentiated clusters can be identified. They differ from each other at statistically significant levels. However, we do not exclude that the obtained values may partly be of an artifactual nature, primarily due to the existing practices of using certain technological methods for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Type of Processing**} & \multicolumn{4}{c}{**Statistical Parameters 1**} \\ \cline{3-6}  & & **Mean** & **St. Error** & **St. Dev (\(\sigma\))** & **95\% CI** \\ \hline
1 & Baking & 17.16 & 0.69 & 14.98 & 15.82–18.50 \\
2 & Conservation & 35.89 & 2.06 & 20.85 & 31.85–39.94 \\
3 & Boiling & 32.39 & 1.69 & 31.34 & 29.07–35.71 \\
4 & Drying & 39.22 & 3.52 & 27.23 & 32.32–46.11 \\
5 & Frying & 16.17 & 0.79 & 14.34 & 14.62–17.73 \\
6 & Smoking & 18.99 & 3.02 & 19.57 & 13.07–24.90 \\
7 & Double boiler & **20.78** & 1.95 & 17.47 & 16.95–24.61 \\ \hline \hline \end{tabular}  
* Note: Mean, average; St. dev, standard deviation; St. error, standard error; 95% CI, 95% confidence interval. Explanation of background color in table in the text below.  
\end{table} 
Table 2: ARP values predicted for products processed by different methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Method/Number 1** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
1 & Baking & & **<0.001** & **<0.001** & **<0.001** & 0.99 & 0.99 & 0.79 \\
2 & Conservation & 11.49 & & 0.76 & 0.96 & **<0.001** & **<0.001** & **<0.001** \\
3 & Boiling & 14.38 & 2.07 & & 0.24 & **<0.001** & **0.0021** & **<0.001** \\
4 & Drying & 10.77 & 1.36 & 3.26 & & **<0.001** & **<0.001** & **<0.001** \\
5 & Frying & 0.91 & 11.62 & 14.01 & 10.97 & & 0.98 & 0.58 \\
6 & Smoking & 0.75 & 6.16 & 5.48 & 6.72 & 1.15 & & 0.99 \\
7 & Double boiler & 2.0 & 6.76 & 6.25 & 7.21 & 2.47 & 0.63 & \\ \hline \hline \end{tabular}  
* Note: Under the diagonal are the values of the Tukey test, and above the diagonal are the levels of statistical significance for the obtained values. The numbers of processing methods at the top of the table correspond to the numbering on the left; \(p\) values less than \(<\)0.05 in bold.  
\end{table} 
Table 3: Pairwise comparison for no difference between the ANN predicted data groups (grouped by type of processing of raw materials). processing various types of food raw materials. In this case, it would be logical to move on to a discussion of the results predicted for different product groups. For division into groups and classification, we used filters already available in the FNDDS database itself [4]. The values are presented in Table 4.  
Analyzing the data presented in Table 4, one can notice that the first group \"Apples, processed products\" demonstrates the highest ARP value (118.16). The result obtained is not surprising, since this group also includes fresh fruits, with an increased content of both dietary fiber and water-soluble vitamins (ascorbic acid). It leads to an increase in the predicted ARP values for this group. The use of the Tukey test (table not shown) revealed indisputable differences at a statistically significant level only for three product groups: Apples, Legumes and Cereals, and Vegetables (\(p<0.001\)).