## 3 Results
### PCA and HCA Analysis  
The original data set of samples was a 5 \(\times\) 12 matrix. PCA analysis reduced the original 12 variables to 2 principal components. 81.59% of variance was described by the first principal component PC1, while 11.33% by the second component PC2. The two-dimensional plot with PC1 and PC2 as the axes is shown in Figure 5. Namely, a high magnitude (near to +1 or \(-\)1) for factor loading means that the variable is highly correlated with that factor, but >0.5 can be enough for importance [51]. The positive part of the first principal component reflected TA, TSS, TS, NSS, Glu, Fru and color parameters. The negative part of the first principal component reflected mostly TPC and DPPH. The second principal component described mostly pH. TA, TSS, TS, NSS, Glu, Fru and color parameters were closely correlated to each other (they were located in the same point). TPC was strongly correlated with DPPH, which was caused by the antioxidant ability of polyphenols.  
A score plot of the first two principal components (Figure 6a) is most commonly used to display the cluster outcomes of given data sets where samples having similar scores are positioned closely together. This technique facilitates classification and individualization of samples in an objective and reproducible manner, the more closely together particular samples cluster in the scores plot, the more similar they are [52]. Sample ACJ formed an individual cluster I, while PUF and RUF were in cluster II, located more closely to ACJ than samples after DF. PDF and RDF (cluster III) were'similar', located in the in the diagonally opposite quadrant from PUF. The only sample located in the same quadrant as  
\begin{table}
\begin{tabular}{c c c c} \hline  & **ACJ** & **RUF** & **RDF** \\ \hline TS (g\(\cdot\)250 mL\({}^{-1}\)) & 24.5 & 18.8 & 14.3 \\ NSS (g\(\cdot\)250 mL\({}^{-1}\)) & 6.5 & 7.3 & 6.0 \\ \%RI & 27.0 & 20.7 & 15.7 \\ TS/NSS & 3.8 & 2.6 & 2.4 \\ \hline \end{tabular}
\end{table}
Table 4: Total sugars (TS) and non-sugar soluble solids (NSS) content in a glass (250 mL) of cloudy apple-cranberry juice (ACJ), retentate after ultrafiltration (RUF), and retentate after diafiltration (RDF), together with the percentage of sugars reference intake (%RI) and the ratio of TS to NSS.  
Figure 5: Principle component analysis of cloudy apple-cranberry juice processed by ultrafiltration and diafiltration, the response of 12 determined parameters.  
ACJ was RUF, so it can be concluded that it was the most similar sample to untreated juice. The RUF sample was also the most similar to ACJ in the hierarchical cluster analysis of FTIR spectra.  
Clusters formation can be also presented as a dendrogram chart (Figure 6b). The horizontal axis of the dendrogram represents the distance or dissimilarity between clusters, while the vertical axis represents the objects and clusters. Each joining (fusion) of two clusters is represented on the graph by the splitting of a horizontal line into two horizontal lines. The horizontal position of the split, shown by the short vertical bar, gives the distance (dissimilarity) between the two clusters. Based on the positions of lines, if the final number of clusters was to be 2, the sample ACJ, PUF and RUF would form the first cluster (I), and PDF with RDF samples the second cluster (II). If there were to be 3 clusters, the ACJ sample would split from the first cluster and form an individual cluster, PUF and RUF would form the second, while PDF with RDF forming the third cluster. The horizontal distance for each cluster is different, showing different similarity of samples inside the clusters: samples after DF were more similar to each other (distance 2.15) than samples after UF (distance 3), while the similarity of ACJ sample to UF samples was the weakest (distance 4.6)  
Cluster analysis confirmed the information about the significant influence of applied membrane technologies on juice properties, and also about the differences between UF and DF.