## 3 Results
### Relationship Analysis Between Phenotypic and Biochemical Traits under Optimum and Stress Conditions  
Different methods were used to classify the genotypes; the most suitable was multivariate analysis. In addition, data mining is a useful method for large dataset collection, discovery, and modelling to identify novel patterns to explain more challenging and decisive results. Multivariate methods of analysis, such as principal component analysis (PCA), have been used for classification. The data obtained from the normal and stress treatments were used to create the PCA plot. In the normal condition, the two components PC1 and PC2 reported 73.75% of the initial variation. The first and second contributions were 49.25 and 24.49%, respectively (Figure 5A). Concerning the distribution of genotypes on the PCA plot, genotypes located far from the center of the plot in the positive patterns of distinct traits showed the best performance, while the genotypes at the center of the plot showed the poor results in the negative orientation of traits. As seen in Figure 5A, the first primary component (PC1) was positively influenced by traits CWT and CAT, while CI, CFW, and CDW were negatively linked to the first component. The second main component (PC2) was negatively associated with the APX trait. As seen in Figure 5B, the first primary component described roughly 42.13% of the variation and was positively influenced by CI, CFW, CDW, and CAT. The second main component clarified 39.63% of the variance and was positively linked with CW and negatively associated with APX and CWT. Under the T1.00 treatment, the two components PC1 and PC2 described 78.13% of the total trait difference (Figure 5C). APX, CAT, CI, CFW, and CDW were correlated positively with the first component. Under the high-stress condition, a total variation of 86.40% was explained by PC1 and PC2. CI, CFW, CDW, CWT, PC, CAT, and APX were associated positively with the primary component (Figure 5D). As shown in Figure 5D, Choman and Shawre correlated positively with the CI, CFW, CDW, CWT, PC, CAT, and APX traits. For determining the significant correlation among different characters, a simple Pearson correlation (r) analysis of nine characteristics under unstressed and stressed conditions was conducted (Figure 6). Under the optimum condition, six significant r values were found between the nine traits. CI displayed a strong significant and positive relationship with CFW (r = 0.82*, \(p\)-value = 0.04) and CDW (r = 0.89*, \(p\)-value = 0.02), and showed a strong negative association with CWT (r = \(-\)0.84*, \(p\)-value = 0.04). CFW highly and significantly correlated with CDW (r = 0.97**, \(p\)-value = 0.001). Significant and positive r values were found between CL and CW (r = 0.87*, \(p\)-value = 0.02), and between CWT and CAT (r = 0.88*, \(p\)-value = 0.02). Under T0.50 stress, CFW was highly and significantly correlated with CDW (r = 0.94**, \(p\)-value = 0.005). CI correlated positively and significantly with CDW (r = 0.84*, \(p\)-value = 0.04) and CAT (r = 0.83*, \(p\)-value = 0.04). Further, CAT had strong positive and significant correlations with CI (r = 0.89*, \(p\)-value = 0.02) and APX (r = 0.85*, \(p\)-value = 0.03). Notably, CFW showed strong positive correlations with CDW (r = 0.86*, \(p\)-value = 0.03) under the T1.0 stress condition. In the T1.50 stress treatment, seven positive and significant relationships were registered. The correlation values demonstrated highly positive and significant correlations between CI and CAT (r = 0.96**, \(p\)-value = 0.003), between CFW and CDW (r = 0.92**, \(p\)-value = 0.001), and between CDW and PC (r = 0.92**, \(p\)-value = 0.001). APX indicated a positive and significant correlation with CI (r = 0.89*, \(p\)-value = 0.018) and CL (r = 0.83*, \(p\)-value = 0.04). These findings show that a positive and significant association between CFW and CDW was observed under stressful and optimized conditions. CI was also positively and significantly associated with CAT in all stressed treatments.