Figure 3. Metabolite profiling distinguishes between SBW subclasses with 100% accuracy.

Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 12 wheat lines, was used to distinguish between subclasses of soft white winter (SWW) comprising soft white winter (SWW), soft red winter (SRW), and soft white spring (SWS). (Panel 3A) To visualize inherent clustering patterns, the scatter plot represents unsupervised analysis through the PCA model. Model fit: R2X(cum) = 48.9%, with 2 components, and Q2(cum) = 4.9%. (Panel 3B) To determine contributing sources of variation, the scatter plot represents supervised analysis of the OPLS-DA model. Subclasses demonstrate complete separation, and the propensity of wheat lines to localize near lines of similar growth habit, as observed with hard bread wheat lines, was observed in soft bread wheat lines: the divergence of SRW and SWW from a common parent cluster indicates chemical similarity. Model fit: R2Y(cum) = 99.1%, Q2Y(cum) = 64.9%. (Panel 3C-Inset) The misclassification table for the OPLS-DA model indicates that 100% of wheat lines (12 out of 12 lines) were correctly classified, with low probability (p = 7.20E−05) of random table generation as assessed by Fisher’s Exact Probability. (Panel 3C) To visualize the misclassification rate, the dendrogram was constructed using single linkage hierarchical clustering and sorted by size. Two main clusters comprise 1) SWS and 2) the 2 winter habit subclasses, with cluster 2 branching into 2A, comprising SRW lines, and 2B, comprising SWW lines, suggesting that SBW subclasses have unique chemical profiles.