Relationship between physicochemical and cooking properties, and effects of cooking on antinutrients, of yellow field peas (<i>Pisum sativum</i>)
Bibliographic record
Abstract
Abstract The relationship between the physicochemical and cooking properties of yellow peas was examined in this study. A positive correlation was found between seed weight and water hydration capacity. The Peleg model, which was modified, could be used to describe the water absorption characteristics of peas and could be used to predict the rate of water absorption in the initial water absorption period. Cooking time could be measured objectively using the Mattson cooker. Cooking time was found to decrease with an increase in water hydration capacity. Hardness of cooked peas, measured using a texture analyser, was negatively correlated with both seed weight and water hydration capacity. Seed coats had a significant effect on water hydration and cooking quality of peas. Peas with relatively thin seed coats exhibited higher water hydration capacity, shorter cooking times and softer texture after cooking. The effects of soaking and cooking on trypsin inhibitor activity (TIA) and oligosaccharide levels in peas were also studied. Cooking was more effective than soaking in reducing TIA levels and oligosaccharides (raffinose, stachyose and verbascose) in peas. Copyright © 2003 Society of Chemical Industry
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".