Antioxidant activities of bambara groundnut (Vigna subterranea) protein hydrolysates and their membrane ultrafiltration fractions
Bibliographic record
Abstract
In this study, the bambara protein isolate (BPI) was digested with three proteases (alcalase, trypsin and pepsin), to produce bambara protein hydrolysates (BPHs). These hydrolysates were passed through ultrafiltration membranes to obtain peptide fractions of different sizes (<1, 1-3, 3-5 and 5-10 kDa). The hydrolysates and their peptide fractions were investigated for antioxidant activities. The membrane fractions showed that peptides with sizes <3 kDa had significantly (p < 0.05) reduced surface hydrophobicity when compared with peptides >3 kDa. This is in agreement with the result obtained for the ferric reducing power, metal chelating and hydroxyl radical scavenging activities where higher molecular weight peptides exhibited better activity (p < 0.05) when compared to low molecular weight peptide fractions. However, for all the hydrolysates, the low molecular weight peptides were more effective diphenyl-1-picrylhydrazyl (DPPH) radical scavengers but not superoxide radicals when compared to the bigger peptides. In comparison with glutathione (GSH), BPHs and their membrane fractions had better (p < 0.05) reducing power and ability to chelate metal ions except for the pepsin hydrolysate and its membrane fractions that did not show any metal chelating activity. However, the 5-10 kDa pepsin hydrolysate peptide fractions had greater (88%) hydroxyl scavenging activity than GSH, alcalase and trypsin hydrolysates (82%). These findings show the potential use of BPHs and their peptide fraction as antioxidants in reducing food spoilage or management of oxidative stress-related metabolic disorders.
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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 | 0.001 |
| 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".