Impact of Pulsed Electric Fields and pH on Enzyme Inactivation and Bioactivities of Peptic Hydrolysates Produced from Bovine and Porcine Hemoglobin
Bibliographic record
Abstract
The production of bioactive peptides from hemoglobin via peptic hydrolysis is a promising alternative to valorizing slaughterhouse blood proteins. Nevertheless, it has some limitations such as low yield, high cost of enzymes, and the use of chemical reagents. The latter is aggravated by the pH increase to inactivate the enzyme, which can affect the bioactivity of the peptides. Thus, this study aimed to evaluate the effect of pulsed electric fields (PEF) on the pepsin inactivation and biological activities (antimicrobial and antioxidant) of hemoglobin hydrolysates. Bovine (Hb-B) and porcine (Hb-P) hemoglobin were hydrolyzed with pepsin for 3 h and treated with PEFs to inactivate the enzyme. The degree of hydrolysis (DH) did not show significant changes after PEF inactivation, whereas peptide population analysis showed some changes in PEF-treated hydrolysates over time, suggesting residual pepsin activity. PEF treatments showed no significant positive or negative impact on antimicrobial and antioxidant activities. Additionally, the impact of pH (3, 7, and 10) on bioactivity was studied. Higher pH fostered stronger anti-yeast activity and DPPH-scavenging capacity, whereas pH 7 fostered antifungal activity. Thus, the use of hemoglobin from the meat industry combined with PEF treatments could fit the circular economy concept since bioactive peptides can be produced more eco-efficiently and recycled to reduce the spoilage of meat products. Nevertheless, further studies on PEF conditions must be carried out to achieve complete inactivation of pepsin and the potential enhancement of peptides' bioactivity.
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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) | 0.001 | 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".