# Physicochemical and Antioxidant Properties of Selected Polish and Slovak Honeys
## 3 Results and Discussion
### Statistical Discrimination of Honeys  
#### 3.2.1 Cluster Analysis  
Based on the Elbow Method, K-Means clustering was performed with K = 4 to segment the honey samples into distinct groups. To visualize the clustering results, Principal Component Analysis (PCA) was used to reduce dimensionality, allowing the clusters to be plotted in a 2D space (Figure 1b). In turn, the projection of variables onto the plane of main factors is presented in Figure 1a. The scatter plot displays the data points, colored according to their assigned cluster. Each point is annotated with its corresponding honey type (A--acacia, L--time, R--rapeseed, H--homeydew (forest honey)) to enhance interpretability. The results suggest that the clusters successfully separate different honey types based on their chemical composition. The distribution of honey types within each cluster was analyzed to assess the clustering effectiveness. The contingency table (Table 1) shows the number of samples for each honey type within the three clusters. The results indicate that certain honey types are grouped together, suggesting that the clustering aligns with their chemical composition. The influence of individual physicochemical parameters on the main components (PC1 and PC2) is presented in Figure 1a. It can be stated that the values of the first main factor are mainly influenced by parameters describing antioxidant activity, the color parameter (\(a^{\star}\)) and electrical conductivity, indicating the content of mineral compounds in honey. In turn, the second main factor was most strongly influenced by the content of glucose and fructose, as well as parameters that are not directly related to the honey variety, such as water content and diastase number. Among the determined polyphenols, the content of p-coumaric acid also had a large influence on PC2, which probably influenced the assignment of some of the lime honey samples to cluster number 3 (Figure 1b). The observed relationships confirm the fact that darker honeys (lower \(L^{\star}\) value) have a higher antioxidant potential [32]. To further evaluate cluster quality, the Silhouette Score was computed and resulted in a value of 0.2857. In this case, the score is not particularly high, which can be attributed to overlapping chemical components between some honey types. This suggests that while the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Honey** & **Acacia** & **Lime** & **Rapeseed** & **Honeydew** \\ \hline
1 & 0 & 0 & 0 & 8 \\
2 & 0 & 4 & 10 & 0 \\
3 & 0 & 4 & 0 & 0 \\
4 & 10 & 2 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of honey types across clusters. Figure 1: PCA of honey samples. Projection of variables onto the plane of main factors (**a**); projection of cases onto the plane of main factors with cluster annotations (**b**). Honey type: A—acacia, L—lime, R—rapeseed, H—homeydew. clusters capture certain patterns, there is still some degree of similarity between different honey types, leading to less distinct separation. The chemical composition of nectar honeys can be influenced by environmental factors, including shifts in the production season, which causes the simultaneous occurrence of different nectar sources. Even honeys considered typical may be contaminated to some extent with nectar or pollen from other plants, which causes their similarity to other types of honey. Similarity between nectar honeys might be due to the fact that same plants are blooming in the same period or shortly one after another. For example, together with large-leaved lime (_Tilia platphylphyllos_) such melliferous plants like snowberry (_Symphoricapos albus_) and white clover (_Trifolium repens_) are blooming [12]. It is also necessary to consider the influence of abiotic factors which, by affecting plants and insects, may indirectly affect the composition of nectar [33].