Accelerated Screening of Wheat Gluten Strength Using Dual Physicochemical Tests in Diverse Breeding Lines.

Abstract

Introducing fast, reliable, and low-input technologies that utilize wholemeal wheat is essential for efficiently screening gluten quality in wheat breeding lines. Although the GlutoPeak Tester (GPT) has been widely studied for gluten assessment, its application in breeding programs remains underexplored. This study presents a comprehensive approach to optimizing a GPT protocol using a diverse set of genotypes collected over seven harvest years and multiple environments. To improve screening capabilities, a quick and simple protein fractionation (PF) technique was integrated into the workflow. Key GPT parameters-such as peak maximum time, maximum torque, and aggregation energy-along with the newly proposed PM-AM parameter, showed strong correlations with established quality traits. PF data, especially insoluble glutenin percentage and the ratio of insoluble to soluble glutenin, provided additional insights into gluten composition. This extensive dataset supports the use of GPT and PF as a dual, high-throughput screening tool. When applied within specific wheat classes and benchmarked against established checks, this method offers a robust strategy for ranking breeding lines based on gluten performance. The use of wholemeal samples further streamlines the process by eliminating the need for milling, making this protocol particularly suitable for early-stage selection in wheat breeding programs.

Plant phenotyping relevance

小麦育種系統のグルテン品質を高速評価するGPT/PFワークフローの最適化・検証が研究の中心であり、種子品質形質のハイスループット表現型スクリーニング法を提示している。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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