Nutrient content and viscosity of Saskatchewan-grown pulses in relation to their cooking quality
Bibliographic record
Abstract
Pulses are staple foods that are gaining recognition as sources of non-gluten proteins, slow digestible starch, and dietary fiber. Several factors contribute to the cooking quality of pulses including genetics, environment, and their interactions. In this study, four cultivars each of faba bean, lentil, and pea were evaluated for nutrient content, flour viscosity measured by a rapid visco analyzer, and acid and alkaline extract viscosity determined by a cone-plate viscometer. These properties were analyzed in relation to seed hydration and firmness of cooked pulses measured by a texture analyzer to better understand their relationships with and contribution to pulse cooking quality. Pea had the lowest protein (18.7%–22.3%) and highest starch (43.0%–46.3%) followed by lentil (protein 25.1%–26.7%, starch 38.4%–45.5%) and finally faba bean (protein 26.5%–29.2%, starch 38.4%–41.8%). Significant differences (P < 0.05) were observed among cultivars within each crop in hydration capacity and firmness of cooked seeds. Rapid visco analyzer viscosity of pulse flours showed significant differences (P < 0.05) among crops and cultivars, and was significantly correlated with firmness. Firmness was significantly correlated with protein and ash content. The results suggest that firmness of cooked pulses is significantly influenced by seed components and starch behavior during heating, indicating the importance of viscosity in determining the cooking quality of pulses.
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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 | 0.001 | 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 | 0.000 |
| 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".