Random forest regression to predict Farinograph traits from GlutoPeak output in wheat wild relative backcross lines

Abstract

Background and Objectives

Flour quality is a key target of hard winter wheat breeding. The Farinograph is important for assessing quality before cultivar release in the United States, but large sample size requirements and long test times render it impractical for early-stage selection relative to the GlutoPeak. To improve GlutoPeak utility for breeding, we calculated new parameters from device raw output and used random forest regression to predict key Farinograph parameters in a winter wheat population containing wild relative introgressions.

Findings

The key quality parameters of absorption, bake absorption, tolerance stability, and mixing tolerance index were moderately well predicted (R² ranging from 0.488 to 0.745). Classification of samples as acceptable or unacceptable for mixing tolerance index and tolerance stability was more accurate than prediction of numeric values.

Conclusions

New features calculated from the GlutoPeak raw data were useful predictors of quality. Prediction accuracies are sufficient to improve breeding populations.

Significance and Novelty

This study is the first to use wheat wild relative introgressions in GlutoPeak Farinograph prediction, the first to generate features from raw data, and is one of the few random forest models for quality prediction. The tools that we provide will improve ability to cull poor-quality lines early in the breeding pipeline can support efficient wheat cultivar development.