## 3 Results and Discussion
### HCA, PCA, and Correlation Analysis
Specifically, the average TPC in cluster VI (2.58 mg GAE/g) was significantly different from all clusters except clusters IV (2.90 mg GAE/g), V (2.70 mg GAE/g), and VII (2.73 mg GAE/g). Likewise, the average ABTS\({}^{\bullet+}\) scavenging activity (4.33 mg AAE/100 g) was significantly different from all clusters except cluster V (4.61 mg AAE/100 g). On the other hand, the average DPPH\({}^{\bullet}\) scavenging activity and RP in cluster VI (2.12 and 2.05 mg AAE/g, respectively) were significantly different from the other clusters. These observations indicate that the accessions in cluster VI exhibited low effectiveness against reactive radicals [12]. Figure 3: Hierarchical cluster analysis (**A**), scree plot of principal components (**B**), score plot of barley accessions (**C**), and loading plots of variables (**D**) in the PCA. ABTS: ABTS\({}^{\bullet+}\) scavenging activity, AL: Awn length, CL: Culm length, DH: Days to heading, DM: Days to maturity, DHM: Days from heading to maturity, DPPH: DPPH\({}^{\bullet}\) scavenging activity, GPP: Number of grains per panicle, RP: Reducing power, SL: Spike length, TGW: One-thousand grains weight, TPC: Total phenolic content, \(\beta\)-GL: \(\beta\)-glucan content. Figure 4: Boxplots showing the variations of quantitative traits across barley accession clusters and compared to the control cultivars. Different letters on boxplots indicate significantly different means based on [PERSON]’s multiple range test (\(p<0.05\)). ABTS: ABTS**\({}^{\star\star}\)** scavenging activity, AL: Awn length, CL: Culm length, DH: Days to heading, DM: Days to maturity, DHM: Days from heading to maturity, DPPH: DPPH\({}^{\star}\) scavenging activity, GPP: Number of grains per panicle, RP: Reducing power, SL: Spike length, TGW: One-thousand-grain weight, TPC: Total phenolic content, \(\beta\)-GL: \(\beta\)-glucan content. PCA was also computed to further view the distribution of the barley accessions and their associations with all the quantitative parameters. The PCA yielded four components with eigenvalues greater than one (Figure 3B). The score plot of the barley accessions and loading plot of variables were computed along PC1 and PC2, which together explained about 51% of the total variance observed. As shown in Figure 3C, the PCA separated the barley accessions according to their cluster. In particular, the four accessions in cluster II were distributed on the positive end of both the PC1 and PC2 axes. As shown in Figure 3D and Table 4, the biochemical traits including TPC, DPPH\({}^{\bullet}\) scavenging activity, ABTS\({}^{\bullet\bullet}\) scavenging activity, RP, and \(\beta\)-glucan content were the major contributors along PC1 each, except \(\beta\)-glucan content displaying a positive loading factor (FL) greater than 0.5. \(\beta\)-glucan content was the only biochemical trait with a negative FL greater than 0.5 along PC1. Among the agronomical traits, only TGW had a negative FL higher than \(-\)0.5 along PC1. In contrast, the other agronomical traits were the major contributors to PC2. DH, DM, and SL, AL had positive FL above 0.5, while DHM GPP displayed negative FL above 0.5. In general, the PCA supported the HCA observation and signified that quantitative agronomical traits along with biochemical traits could be used to distinguish between large populations of barley genotypes. To observe the relationship between quantitative agronomical and biochemical traits, a correlation analysis was also conducted (Figure 5). As depicted in Figure 3D, the results aligned with the findings from the PCA. The growth-related traits, namely DH and DM, displayed a significant and positive correlation with each other (\(r=0.88\), \(p<0.001\)). This observation was consistent with the results of numerous previous studies [3; 18]. Additionally, SL also exhibited positive correlations with CL (\(r=0.34\)) and AL (\(r=0.45\)), both being significant at \(p<0.01\).