Hydration properties and texture fingerprints of easy‐ and hard‐to‐cook bean varieties
Bibliographic record
Abstract
The objective of this study was to understand the factors that affect the hydration and cooking profiles of different bean varieties. During this study, nine bean varieties were classified as either easy-to-cook (ETC) or hard-to-cook (HTC) based on a subjective finger pressing test and an objective cutting test. Rose coco, Red haricot, and Zebra beans were classified as ETC, while Canadian wonder, Soya fupi, Pinto, non-nodulating, Mwezi moja, Gwaku, and New mwezi moja were HTC. The effect of different soaking (pre)-treatments on the cooking behavior and/or water absorption of whole or dehulled beans was investigated. Dehulling, soaking in high pH and monovalent salt solutions reduced the cooking time of beans, while soaking in low pH and CaCl2 solutions increased the cooking time. Moisture uptake was faster in ETC and dehulled beans. Soaking at high temperatures also increased the hydration rate. The results point to pectin-related aspects and the rate of water uptake as possible factors that influence the cooking rate of beans.
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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 | 0.000 |
| 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".