Antioxidant activities of enzymatic rapeseed protein hydrolysates and the membrane ultrafiltration fractions
Bibliographic record
Abstract
In this study, rapeseed protein isolate was hydrolyzed with various proteases to obtain hydrolysates that were separated by membrane ultrafiltration into four molecular size fractions (<1, 1–3, 3–5, and 5–10 kDa). Alcalase hydrolysis significantly (p < 0.05) produced the highest yield of protein hydrolysate while Flavourzyme produced the least. The <1 kDa fraction was the most abundant after the membrane ultrafiltration of the protein hydrolysates, which indicates that the proteases were efficient at reducing the native rapeseed proteins into low molecular weight peptides. Antioxidant properties of the resulting hydrolysates and membrane fractions were characterized and results showed the Pepsin + Pancreatin (P + P) protein hydrolysate had significantly highest (p < 0.05) scavenging activity against DPPH radical among the unfractionated enzymatic hydrolysates. But the P + P hydrolysate was not as effective as other hydrolysates during long-term inhibition of linoleic acid oxidation. For most of the samples, fractionation into the <1 kDa peptides significantly (p < 0.05) improved DPPH and superoxide scavenging properties when compared to the unfractionated protein hydrolysates. Only the <1 kDa fraction showed ferric reducing antioxidant power and the effect was dose-dependent. Overall, Alcalase and Proteinase K seem to be more efficient proteases to release antioxidant peptides from rapeseed proteins when compared to P + P, Flavourzyme and Thermolysin.
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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) | 0.001 | 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 | 0.001 |
| 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".