Dry Fractionation of Canadian Faba Bean Genotypes Differing in Seed Size and Levels of Vicine, Convicine and Seed Coat Tannins
Bibliographic record
Abstract
ABSTRACT Three faba bean genotypes, Fabelle, Malik, and Snowbird, possessing variations in seed quality traits and produced in different Canadian prairie locations in two growing years, were dry fractionated by air classification. Selected quality parameters of coarse and fine fractions useful for faba bean ingredient processing were assessed. Seed coat loss is higher for large‐seeded regular tannin genotype Malik than for the others. Genotype differences affected the protein content of dehulled flour and the fine fraction, with the low vicine/convicine (v and cv) containing genotype Fabelle providing higher levels (32.0%–35.5%) than the other two genotypes. Protein fractionation efficiency values showed that 50%–55% of seed protein can be shifted to the fine fraction, with high starting protein content resulting in higher protein concentration in the fine fraction. The coarse fraction (yield 70.3%–72.8%) provided 18.1%–20.2% protein‐containing starchy (64.4%–66.8%) FB ingredients for further use. Although phytates and v and cv tended to fractionate and shift to the fine fraction, concentrations of the most abundant minerals of dehulled FB seeds were similar to the levels in whole seed. FB starches and proteins of the three genotypes showed their typical characteristic properties with lesser effect from the growing environment. This study provides information on the diversity range of the products resulting from the dry fractionation of faba bean genotypes of varying quality traits and grown in different locations.
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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 | 0.001 | 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 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".