## 3 Results and Discussion  
The contents of chlorogenic acid in different variants of the experiment are presented in Table 1. These results were treated as input data and were used to develop RSM models (response surfaces). The variables were the ethanol volume concentration (40, 68 or 95%) and extraction time (7, 20 or 30 days). The research variants were differentiated by the type of extraction, i.e., depending on the initial temperature of ethanol during the pouring of elderberry flowers (heat extraction at 50 \({}^{\circ}\)C and cold extraction at 25 \({}^{\circ}\)C) as well as the drying of the raw material from a fresh or frozen state. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ethanol** & **Extraction** & **Initial Extraction** & **Raw** & **Chlorogenic Acid** \\
**Concentration** & **Time (d)** & **Temperature (\({}^{\circ}\)C)** & **Material** & **Content (\(\mu\)g/cm\({}^{3}\))** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 1: Chlorogenic acid contents (\(\mu\)g/cm\({}^{3}\)) in ethanol extracts, depending on the research variant: input data for RSM model optimization. On the basis of the performed RSM analysis, the response curve presented in Figure 3 (as a 3D model) and in Figure 4 (in 2D) was obtained in the case of the first variant. Figure 4: Model of the response surface (2D) regarding the contents of chlorogenic acid in ethanol extracts of elderberry flowers obtained from fresh raw material that was initially treated with warm ethanol at 50 \({}^{\circ}\)C.  
Figure 3: Model of the response surface (3D) regarding the contents of chlorogenic acid in ethanol extracts of elderberry flowers obtained from fresh raw material that was initially treated with warm ethanol at 50 \({}^{\circ}\)C. The 3D model is shown only once for a better understanding of the RSM concept. The analysis of the response surface makes sense when the variables of the model, i.e., in our case, the concentration of ethanol and the extraction time, significantly affect the obtained shape of the curves. It does not have to prove the influence of both factors; at least one is enough. In addition, in RSM analysis, the most common model is not linear but quadratic. Therefore, thanks to this method, it is possible to unambiguously determine whether the linear component (L) or the square component (Q) have statistically significant effects on the model. For the first variable, its linear and square components were analyzed. The same was true for the second variable. Therefore, we obtained information about four components. The fifth and final component that may affect the RSM model was the interaction of the linear components of the principal factors (LxL). The significance of the impacts of these five components were analyzed using a Pareto chart, as presented in Figure 5a. Due to the fact that at least one of the five analyzed factors of variability had an absolute value higher than the minimum (at \(p\) = 0.05), it can be assumed that the determined response curves in Figure 4 are useful for further analysis. In the analyzed case, as many as four variables had significant impacts on the shapes of the curves in the RSM model. The concentration of ethanol (linear component L) had the greatest influence on the model. To a lesser but still statistically significant extent, the model was also influenced by the  
Figure 5: (**a–d**) Pareto charts showing the statistical significance of the factors of variability affecting the shapes of the response surfaces (2D) shown in Figures 4, 6, 8 and 9, respectively. extraction time (linear component), the ethanol concentration (as a squared component) and the interaction of extraction time and ethanol concentration (linear components L \(\times\) L). The extraction time as a square component had no statistical effect.