Structural, physicochemical, and functional properties of white and blue lupin vicilin and legumin fractions
Bibliographic record
Abstract
The aim of this study was to isolate the vicilin and legumin protein fractions of lupin seeds and determine their physicochemical and functional properties. White lupin vicilin (WLVL) exhibited a significantly higher methionine + cysteine content (2.92 g/100 g protein) than the legumin fractions, with a corresponding amino acid score of 127 %. Blue lupin vicilin (BLVL) exhibited high surface hydrophobicity (459.12), whereas the blue lupin legumin (BLLEG) fraction had a high least gelation concentration (18 %). All fractions exhibited U-shaped solubility curves, with minimum values at pH 5, while BLLEG showed superior in vitro protein digestibility (87.47 %). BLVL demonstrated high emulsifying properties across all pH values, maintaining small droplet sizes (7–9 μm) and high stability (95–100 %). BLVL also exhibited superior foaming capacity (75–85 %), whereas WLVL showed excellent foam stability (65–80 %). These findings revealed significant species-specific and fraction-specific differences in lupin globulins, with BLVL emerging as a promising ingredient for food applications. • Blue lupin vicilin had a more flexible structure than the white lupin vicilin. • Emulsifying and foaming properties of blue lupin vicilin better than the white lupin vicilin. • White lupin legumin exhibited a stronger gelling capacity than the blue lupin legumin. • White lupin vicilin had higher methionine + cysteine content than all other fractions. • pH had a greater effect on the mean oil droplet size of emulsions than protein concentration.
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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 | 0.000 | 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".