Effect of high‐pressure treatment on trypsin hydrolysis and antioxidant activity of egg white proteins
Bibliographic record
Abstract
Summary High‐pressure processing (HPP) is known to modify the functional properties of the proteins by changing its structure that can lead to protein denaturation, aggregation or gelation, depending on the protein system and the applied pressure. The potential to modify functional properties of protein and their products using HPP has been explored widely in last decade. In this study, the effect of HPP on the degree of trypsin hydrolysis (DTH) and antioxidant activity (AA) of egg white protein (EWP) and freeze‐dried egg white powder (DEW) was evaluated. EWP and DEW were subjected to trypsin hydrolysis for up to 120 min, with and without HP treatments [pressure level (350–550 MPa) and treatment time (5–15 min)]. HP treatment caused substantial increase in DTH of EWP and DEW, increasing it from 2.78% (EWP control) and 2.20% (DEW control) to 11.3% (HP‐treated EWP) and 8.41% (HP‐treated DEW), respectively. HP treatment also had an emphatic effect on AA of EWP (concentration 10% w/v) with AA increasing from 9.34% for control EWP to 19.0% after 5‐min HP treatment at 350 MPa and 25.00% after the same treatment at 550 MPa. Overall, HPP caused an increase in DTH and AA of EWP and DEW over the control. SDS‐PAGE and differential scanning calorimetric studies confirmed the effect of HP on the hydrolysis of egg proteins.
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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".