Mapping pea seed composition through strategic selection of accessions from the Nordic gene bank.

Abstract

This study aims to utilise natural variation in pea seed composition from NordGen collections to identify key traits for optimized plant-based ingredients functionality while minimizing refined extraction processes. Given the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions. Protein content, thousand kernel weight, perimeter, and G-value were determined as primary criteria via PCA, capturing variations in protein composition and other key components. Protein and starch content ranged from 21.2 to 36.9 % and 21.0-48.1 %, respectively. Image analysis linked geometry to composition, aiding pea selection and application. X-ray scattering differentiates peas based on starch structure. Proteomic profiling revealed that legumin and vicilin varied most, with legumin dominant in smooth peas and vicilin in wrinkled ones, enabling control of their ratio through selection. This study highlights the potential of using natural variation of seed composition for less-refined plant-based ingredients for various applications.

Plant phenotyping relevance

画像由来特徴量とアルゴリズムを用いて多数のエンドウ遺伝資源から種子形質・組成を推定し、化学分析対象を選抜するワークフローが研究の中心であるため、植物フェノタイピング手法の実質的応用と判断します。

Code and data availability

The paper's pea seed images, NordGen descriptor dataset, and analysis code are not publicly deposited; the authors state data will be made available on request. No public paper-specific asset is actionable.

No evidence-backed public reproduction asset is currently recorded.