Kinetic modelling of polyphenol degradation during common beans soaking and cooking
Bibliographic record
Abstract
Phenolic compounds are phytonutrients with anti-inflammatory attributes that are significant for brain, heart and gut health. Losses of natural phenolic compounds in foods occur due to degradation during processing. The extent of degradation depends on the processing conditions applied. In this study, the degradation of total phenolic compounds during the processing of common beans ( Phaseolus vulgaris L. ) cultivars was investigated. The effects of soaking time, soaking water temperature and cooking conditions on polyphenol degradation were examined. The total phenolic compounds were determined as gallic acid equivalents. The result shows that increase of hydration time and process water temperature significantly (p < 0.05) increased polyphenol degradation. There was a strong positive Pearson correlation (r > 0. 85) between the rate of water uptake and polyphenol degradation regardless of the water temperature and cultivar. The rate of degradation varied from 0.041 – 0.098 and 0.014–0.069 mg/g per hour for Kabulangeti and Maine cultivar, respectively. The addition sodium chloride (NaCl) and potassium carbonate (K 2 CO 3 ) during cooking to soften the beans significantly increased the degree of degradation. The activation energy for degradation was estimated as 45.4 and 26.3 kJ/mol for Kabulangeti and Maine cultivar, respectively.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".