Legume Seed Protein Digestibility as Influenced by Traditional and Emerging Physical Processing Technologies
Bibliographic record
Abstract
The increased consumption of legume seeds as a strategy for enhancing food security, reducing malnutrition, and improving health outcomes on a global scale remains an ongoing subject of profound research interest. Legume seed proteins are rich in their dietary protein contents. However, coexisting with these proteins in the seed matrix are other components that inhibit protein digestibility. Thus, improving access to legume proteins often depends on the neutralisation of these inhibitors, which are collectively described as antinutrients or antinutritional factors. The determination of protein quality, which typically involves evaluating protein digestibility and essential amino acid content, is assessed using various methods, such as in vitro simulated gastrointestinal digestibility, protein digestibility-corrected amino acid score (IV-PDCAAS), and digestible indispensable amino acid score (DIAAS). Since most edible legumes are mainly available in their processed forms, an interrogation of these processing methods, which could be traditional (e.g., cooking, milling, extrusion, germination, and fermentation) or based on emerging technologies (e.g., high-pressure processing (HPP), ultrasound, irradiation, pulsed electric field (PEF), and microwave), is not only critical but also necessary given the capacity of processing methods to influence protein digestibility. Therefore, this timely and important review discusses how each of these processing methods affects legume seed digestibility, examines the potential for improvements, highlights the challenges posed by antinutritional factors, and suggests areas of focus for future research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".