Unlocking the Nutraceutical Potential of Legumes and Their By-Products: Paving the Way for the Circular Economy in the Agri-Food Industry
Bibliographic record
Abstract
Legumes, including beans, peas, chickpeas, and lentils, are cultivated worldwide and serve as important components of a balanced and nutritious diet. Each legume variety contains unique levels of protein, starch, fiber, lipids, minerals, and vitamins, with potential applications in various industries. By-products such as hulls, rich in bioactive compounds, offer promise for value-added utilization and health-focused product development. Various extraction methods are employed to enhance protein extraction rates from legume by-products, finding applications in various foods such as meat analogs, breads, and desserts. Moreover, essential fatty acids, carotenoids, tocols, and polyphenols are abundant in several residual fractions from legumes. These bioactive classes are linked to reduced incidence of cardiovascular diseases, chronic inflammation, some cancers, obesity, and type 2 diabetes, among other relevant health conditions. The present contribution provides a comprehensive review of the nutritional and bioactive composition of major legumes and their by-products. Additionally, the bioaccessibility and bioavailability aspects of legume consumption, as well as in vitro and in vivo evidence of their health effects are addressed.
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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 | 0.001 |
| Open science | 0.000 | 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".