Effects of enzymatic hydrolysis and ultrafiltration on physicochemical and functional properties of faba bean protein
Bibliographic record
Abstract
Abstract Background and objectives Faba bean proteins isolates did not show sufficient functional properties for food applications due to poor solubility. Enzymatic treatments and ultrafiltration may improve solubility and functional properties of faba bean protein. The aim of the present work was to investigate the effect of different proteases (pepsin, trypsin, flavourzyme ® 500 L, neutrase ® 0.8 L), hydrolysis time and ultrafiltration technique on the physicochemical and functional characteristics of faba bean protein. Findings The protein solubility increased from 24.4% to 88.8% at pH 7 and 81.0% at pH 5 by pepsin hydrolysis (15 min). Their foaming capacity (FC) increased from 31.2% to 122.2% at pH 5 and 66.7% to 131.2% at pH 7 and the oil holding capacity (OHC) increased from 6.12 to 8.21 g/g by pepsin hydrolysis. Fraction I (Mw > 10 kDa) and II (Mw: 5–10 kDa) obtained after pepsin hydrolysis and ultrafiltration demonstrated further improved foaming and oil holding capacity and much improved emulsifying capacity. Conclusions Enzymatic hydrolysis and ultrafiltration provided a strategy to significantly improve faba bean protein solubility and functional properties. Significance and novelty Faba bean protein hydrolysates and ultrafiltration fractions are good sources of protein with excellent solubility and functionality.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".