Effect of different cooking methods and heating solutions on nutritionally‐important starch fractions and flatus oligosaccharides in selected pulses
Bibliographic record
Abstract
Abstract Background and objectives Consumption of pulses is recommended by health organizations. Pulses are usually soaked and cooked in water before consumption which alters their nutrient composition and ultimately health benefits. This study investigated the effect of four cooking methods (boiling, pressure, microwave, and slow) and four heating solutions (water, salt, sugar, and citric acid) on composition of nutritionally‐important starch fractions and flatus oligosaccharides in faba bean, lentil, and pea. Findings The three pulses had slowly digestible starch (SDS) as the highest fraction with faba bean and lentil exhibited higher SDS than pea, but pea contained the highest level of resistant starch (RS). Rapidly digestible starch (RDS) was comparable among the three pulses. Stachyose and verbascose were the dominant oligosaccharides in faba bean, lentil, and pea. Regardless of the heating solution, slow cooking was more effective in improving starch nutritional fractions, i.e., producing lowest RDS and highest SDS in the three pulses with some exceptions. The RS fraction increased in the three pulses subject to cooking method and pulse type. Pressure and slow cooking methods were effective in reducing oligosaccharides in the three pulses. Conclusions The results suggest that slow cooking in water or salt solution has great potential to improve starch nutritional fractions and diminish flatus oligo‐sugars. Significance and novelty This is the first study to demonstrate the effectiveness of slow cooking in enhancing starch nutritional fractions and reducing flatus oligo‐sugars in pulses. The method could hold a promise for implementation especially small‐ and large‐volume slow cookers are commercially available.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".