Industrial-scale fractionation of fava bean, chickpea, and red lentil: A comparative analysis of composition, antinutrients, nutrition, structure, and functionality
Bibliographic record
Abstract
Legumes are emerging as sustainable protein sources capable of replacing animal proteins and meeting global dietary needs. This study systemically compared the compositional profiles, antinutritional factors, amino acid profiles, protein quality, structural characteristics, and techno-functional properties of fava bean, chickpea, and red lentil flours, along with their dry- and wet- fractionated protein-enriched fractions (PF). Wet-fractionated PFs exhibited higher protein content (58.36 - 83.79 g/100 g), while dry-fractionated PFs retained more total dietary fibre (7.62 - 14.64 g/100 g). Wet fractionated fava bean (84.12%) and red lentil (84.06%) showed the highest in vitro protein digestibility (IVPD), while dry-fractionated chickpea showed the highest IVPDCAAS at 62.43%. The protein composition was generally preserved across treatments, though changes in secondary structure varied depending on the legume source. Surface hydrophobicity (H 0 62,739 - 99,381) increased following wet fractionation. In terms of functionality, wet-fractionated PFs showed the highest water-holding capacity (2.83 g/g, red lentil), foaming capacity (139.1%, fava bean) and emulsifying capacity (108.1 m 2 /g, red lentil), but with relatively poor foaming and emulsifying stability. Conversely, dry-fractionated PFs exhibited higher protein solubility, lower least gelation concentration (8 - 10%), and superior oil-holding capacity (3.98 g/g, Chickpea), likely due to reduced structural disruption, which limits protein aggregation and denaturation. Despite higher levels of antinutritional factors, dry fractionation emerges as a promising, cost-effective, and sustainable technology to produce legume protein concentrates with improved functionality and nutritional quality comparable to those obtained by wet-fractionated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".