Structural and functional properties of fava bean legumin and vicilin protein fractions
Bibliographic record
Abstract
Abstract In this study, we examined the physicochemical and functional properties of fava bean globulin fractions that are rich in legumin or vicilin proteins. The sulphur containing amino acids, branched chain amino acids, and arginine/lysine ratio obtained for legumin (1.89%, 18.32%, and 1.43%) are significantly (P < 0.05) higher than the 1.24%, 17.94%, and 1.05%, respectively, for the vicilin fraction. SDS-PAGE results show that the legumin fraction had a wider range of polypeptide sizes (approximately 14–140 kDa) when compared to the approximately 12–68 kDa for vicilin. The surface hydrophobicity (So) of legumin (86.07) was significantly (P < 0.05) lower compared with vicilin (118.19). The legumin had higher protein solubility (approximately 40–50%) than the vicilin (0%) at pH 3 and 4, but vicilin solubility was higher at pH 6–8. The vicilin had higher (83.58%) in vitro protein digestibility than the legumin (78.24%). However, the legumin had higher oil-holding capacity, lower least gelation concentration, and formed emulsions at pH 3, 7, and 9 with smaller mean oil droplet sizes than the vicilin. Foam formation was better with increased levels of α-helix secondary structure. We conclude that pH of the environment was a stronger determinant of protein functionality than the sample protein concentration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".