Development of antioxidant peptides from brewers’ spent grain proteins
Bibliographic record
Abstract
Brewers’ spent grain (BSG), the most abundant brewing by-product contains up to 24% (w/w) of protein on a dry basis but is used as low-value animal feed. This study was conducted to develop antioxidant peptides from BSG proteins. Protease hydrolysis significantly increased BSG protein solubility to 94.4% at neutral pH. Peptides prepared by Alcalase, and its combination with Neutrase, Flavourzyme, or Everlase showed the highest DPPH radical scavenging activities ranging between 72.6 and 74.9%. The highest superoxide radical scavenging activity of 19.3% was observed in the hydrolysate resulted from Alcalase and Flavourzyme combination. Everlase and FoodPro PHT combined treatment was the most effective in producing ferrous ion chelating peptides. Molecular structures analysis suggests that histidine significantly contributed to DPPH radical scavenging activity of BSG peptides due to the high proton donation ability of its imidazole ring. Highly hydrolyzed BSG protein could have more positive charges to stabilize negatively charged superoxide radicals. Ferrous ion chelating ability was negatively correlated to degree of hydrolysis, suggesting that longer peptides are more likely to form compact structures to trap ferrous ions. This research has demonstrated the potential to use BSG as a cost-effective raw material to generate natural antioxidants for food applications.
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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".