Effect of variety, growing location and year on composition, certain antinutritional factors, and functionality of faba beans (<i>Vicia faba</i>) grown in Canada
Bibliographic record
Abstract
Abstract Background and Objectives Faba bean is a rich source of proteins, carbohydrates, vitamins, and minerals. It serves as a staple food in Asia, Africa, and the Mediterranean region. Faba bean production in Canada has recently increased due to the interest in sustainable plant‐based proteins. Identifying faba bean varieties that can grow across different environments and provide good quality, nutrition, and functionality is important. This study aimed to determine the effect of variety, growing location, and year on the composition, certain anti‐nutrients, and functionality of the faba bean varieties grown in Canada. Findings Variety, growing location, and year had significant effects on the protein content, crude fat, ash, phytic acid, stachyose, verbascose, trypsin inhibitor activity, and functional properties of faba beans, including oil emulsion and water holding capacity. Starch content, total dietary fiber, and least gelling concentration were significantly affected by variety and growing location, whereas raffinose, oil absorption capacity, foaming capacity, and stability were significantly affected by variety and growing year. Significant interactions of variety, growing location, and year were observed for most characteristics. Environment played a greater role in affecting faba bean characteristics than variety except for starch content, total dietary fiber, oligosaccharides, and foaming stability. Conclusions It was found that varietal and environmental factors affected each trait differently. Selecting suitable varieties and growing conditions would improve the quality of faba bean. Significance and Novelty Information from this study will be useful to breeders, growers, and food manufacturers to improve the production and utilization of faba beans.
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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 | 0.000 | 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) | 0.000 | 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".