## 3 Results and Discussion
### Profiling of Various Ginsenosides from the Roots, Stems, Leaves, and Berries of Panax ginseng  
In this study, the constructed UPLC-QTOF/MS with an in-house library was applied to profile various ginsenosides from the roots, stems, leaves, and berries of _P. ginseng_. The numbers of individual samples were as follows: ginseng root (GR) (\(n=10\)), stem and leaf (GS) (\(n=10\)), and berry (GB) (\(n=10\)). To extract various ginsenosides from individual samples, 70% (\(v/v\)) methanol was used. The extracts were analyzed using UPLC-QTOF/MS; each piece of data was then processed using the UNIFI software. Figure 2 shows the representative BPI chromatograms of ginseng extracts from GR, GS, and GB. UPLC was able to separate the ginsenosides and other metabolites in 30 min. The intensities of several peaks were different dependent on the sample. For example, overall, the GR sample had lower intensity peaks compared to the other samples. The GS sample had higher intensity peaks than the GB sample, eluted for 5-10 min and 18-26 min. However, the GB sample had higher intensity peaks than the GS sample, eluted for 5-10 min. These demonstrated that GR, GS, and GB have distinct metabolite compositions, including ginsenosides.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **RT** & \multirow{2}{*}{**Ginsenosides**} & **Molecular** & **Expected** & **Observed** & **QTOF/MS** & **Mass** & \\  & **(min)** & & **Formula** & **Neutral** & **Neutral** & **(ESI-1)** & **Accuracy** & **Adducts** \\ \hline  
nsenoside Rd (Rd) & C\({}_{\text{AB}}\)H\({}_{\text{O}}\)\({}_{\text{18}}\) & 946.55 Figure 3: (**A**) Principal component analysis (PCA) score plot and (**B**) loading plot of ginseng extracts from ginseng roots (GR) (\(n=10\)), stems and leaves (GS) (\(n=10\)), and berries (GB) (\(n=10\)). In the PCA loading plot, each point was identified as follows: ginseng (a) Ro (b) Rb1, (c) M-Rb1, (d) M-Rc, (e) M-Rb2, (f) M-Rd, (g) Re, (h) F3, (i) unknown, and (j) Rd.  
Figure 2: BPI chromatogram of ginseng extracts from three samples of (**A**) ginseng roots (GR), (B) stems and leaves (GS), and (**C**) berries (GB).  
First, all the processed data were exported to the EZinfo software for principal component analysis (PCA). PCA helps visualize the general clustering trends among individual samples, and the scatter of the points indicates the similarities or differences between the metabolite data of the samples. In the score plot, the GR, GS, and GB were well-differentiated (Figure 3A). Moreover, in the loading plot, we identified the main variables that differentiate the three groups in the score plot. Each point represented the \(m/z\)-RT pairs of molecules. Based on the in-house library, the nine variables of the loading plot were identified as several ginseng (Figure 3B). Next, in the data processing, a total of 39 ginsenges were identified from the three samples. Using the in-house library, the numbers of identified ginsenges from three samples were as follows: 27 in GR, 32 in GS, and 29 in GB. The ginsenges datasets were then subjected to hierarchical clustering analysis (HCA). As a result, the three groups were clearly differentiated. The different levels of the 39 ginsenges in the ginseng roots, stems and leave, and berries are described in the heatmap in Figure 4. This demonstrated the major ginsenges that were found in each ginseng part.  
Figure 4: Hierarchical clustering analysis of the 39 ginseng datasets from the ginseng root (GR) (\(n=10\)), stems and leaves (GS) (\(n=10\)), and berries (GB) (\(n=10\)).