*3.7. Multivariate Analysis*

Multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. To understand the relationship among 130 eggplant genotypes with various morpho-physiological traits, principal component, biplot, and heatmap analysis were done which revealed different clusters of genotypes that performed better in differ-

ent aspects. The genotypes by traits biplot were constructed from a two-way matrix of 10 morpho-physiological traits and 130 eggplant genotypes using the relative value of the trait (Figure 4). Again, biplot analysis showed the trait profiles of the genotypes, especially, those genotypes positioned far away from the origin and the results indicated a correlation between traits with genotypes. Again, traits on opposite sides of the origin are negatively correlated and traits near each other are positively correlated. Moreover, traits at 90◦ to each other are not correlated, concerning the origin. The principal component (PC) analysis identified a total of 10 principal components (PCs) for the morpho-physiological traits. Among them, the first two PC explained 82.26% of the entire morpho-physiological variations (Figure 4). This biplot revealed superior genotypes with higher levels of expression of favorable trait combinations. The total outcome proposed that TF, FD, SLA, FW, PH, and YPP could help to detect superior genotypes in elite germplasm.

**Figure 4.** Genotypes by traits (G × T) biplot based on 130 germplasm and 10 quantitative traits of eggplant. DFF =Days to first flowering (day), FD = Fruit Diameter (cm), FL = Fruit Length (cm), FW = Fruit Weight (g), YPP =Yield Per Plant (kg), NDVI = Normalized Difference Vegetation Index, PH = Plant height (cm), SLA = Single Leaf Area (cm2), SPAD = Soil Plant Analyses Development, TF = Total Number of Fruits.
