Principal component analysis
Principal component analysis (PCA) for all studied traits was conducted
(Fig. 5). PCA was based on a correlation matrix and presented as bi-plot
ordinations of RILs (PC scores). Two components were extracted using
eigenvalues > 1 to ensure meaningful implementation of the
data by each factor. The PCA of the 11 lines extracted two major
principal components (eigenvalues > 1) that accounted
collectively for 56% of the variance between the lines. Principal
component 1 (PC1, X ‐axis) explained 37% of the data set
variation, and PC2 (Y ‐axis) explained 19% of the data set
variation.
Both the correlations and the PCA showed a negative association between
the two components representing reproductive variables (GY and HI) and:
MPH, DSP, DSM, DT<75%, LSD (r=-0.8**, -0.69*, -0.62*, -0.64*
with GY respectively). Along that axis of association the white
commercial cultivar as well as the brown segregants are the highest
yielding and lowest MPH, DSP, DSM, DT<75%, LSD.
Injera color and appearance were grouped and were negatively associated
(r=-0.61*) with EGC. Another group which was obtained was negatively
associated with TDM and odor intensity, which included the traits: Chl
Tot, Chlb, flavor and AGW (r=-0.71*,-0.8**,-0.6*, -0.64*, with TDM
respectively).