Impact of pre‐treatment (soaking or germination) on nutrient and anti‐nutrient contents, cooking time and acceptability of cooked red dry bean (<i>Phaseolus vulgaris</i> L.) and chickpea (<i>Cicer arietinum</i> L.) grown in Ethiopia
Bibliographic record
Abstract
Summary Pulses are processed in diverse ways prior to consumption. Soaking and germination are among the most common traditional, household‐level food processing strategies. This study was carried out to determine the effects of soaking, germination, cooking and their combinations on the contents of selected nutrients and anti‐nutrients of red dry bean and chickpea. In addition, the effects of pre‐treatment on cooking time and the acceptability of dishes prepared from red dry bean and chickpea were determined. The nutrient compositions (zinc, iron and calcium) of most soaked‐cooked and germinated‐cooked red dry bean and chickpea samples were not significantly different than those of respective controls. However, soaking and germination pre‐treatments significantly lowered the phytate and tannin contents of the red dry bean and chickpea samples, with a few exceptions, and overall, polyphenol contents were lower after soaking‐cooking than after germination‐cooking. Most scores for sensory attributes of bean‐based and chickpea‐based dishes prepared from soaked or germinated samples were not significantly different than those of the controls. For most red dry bean and chickpea samples, longer germination times yielded superior results in terms of reductions in cooking time, tannin content, and phytate:zinc and phytate:iron molar ratio.
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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 | 0.001 | 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".