Breeding for plant‐based proteins in pulse and legume crops: Perspectives, challenges and opportunities
Bibliographic record
Abstract
Abstract The consumption of plant proteins is increasing worldwide as a viable alternative to animal‐derived proteins in the marketplace. The projected increase in global population to at least 10 billion by 2050 is placing greater pressure on the food supply, particularly due to the rising demand for large‐scale protein production. This protein transition is caused by macro‐drivers such as changing consumer demographics, environmental sustainability, animal ethics, regulatory influences, and changing dietary patterns. Research efforts worldwide have explored various food applications for plant protein ingredients, including meat analogs, dairy alternatives, beverages, bakery products, and hybrid products. We provide here a review of the potential for legume breeding programs to incorporate traits that target the emerging plant‐based protein market and aim to promote discussion among (but not limited to) plant breeders and geneticists, plant physiologists, agricultural economists, food scientists and chemists, and agricultural engineers. The prospects, progress, and tools developed when breeding for protein content, quality, structure, and functionality of several food legumes (common bean— Phaseolus vulgaris , pea— Pisum sativum , lentil— Lens culinaris , chickpea— Cicer arietinum , faba bean— Vicia faba , and mung bean— Vigna radiata ) are presented. We also present some of the physiological processes that affect the accumulation of nitrogen and protein metabolism in tropical legume crop species, providing some insight into potential breeding targets for improving protein concentration, quality, and structural and functional properties. Finally, a perspective of industrial processing technologies for extracting and transforming plant proteins is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".