The Effect of Processing on Bioactive Compounds and Nutritional Qualities of Pulses in Meeting the Sustainable Development Goal 2
Bibliographic record
Abstract
Diversification of plant-based food sources is necessary to improve global food and nutritional security. Pulses have enormous nutritional and health benefits in preventing malnutrition and chronic diseases while contributing positively to reducing environmental footprint. Pulses are rich in diverse nutritional and non-nutritional constituents which can be classified as bioactive compounds due to their biological effect. These bioactive compounds include but are not limited to proteins, dietary fibres, resistant starch, polyphenols, saponins, lectins, phytic acids, and enzyme inhibitors. While these compounds are of importance in ensuring food and nutritional security, some of the bioactive constituents have ambivalent properties. These properties include having antioxidant, anti-hypertensive and prebiotic effects. Others have a deleterious effect of decreasing the digestibility and/or bioavailability of essential nutrients and are therefore termed antinutritional factors/compounds. Various processing techniques exist to reduce the content of antinutritional factors found in pulses. Traditional processing of pulses comprises soaking, dehulling, milling, germination, fermentation, and boiling, while examples of emerging processing techniques include microwaving, extrusion, and micronization. These processing techniques can be tailored to purpose and pulse type to achieve desired results. Herein, the nutritional qualities and properties of bioactive compounds found in pulses in meeting the sustainable development goals are presented. It also discusses the effect of processing techniques on the nutritional and non-nutritional constituents in pulses as well as the health and environmental benefits of pulse-diet consumption. Major challenges linked to pulses that could limit their potential of being ideal crops in meeting the sustainable development goal 2 agenda are highlighted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".