Dielectric barrier discharge cold plasma alleviated the immunoreactivity of egg white proteins with improved digestibility and functional properties
Bibliographic record
Abstract
With the increasing number of children allergic to egg worldwide, there is an urgent need to develop strategies for effectively utilizing egg protein. As a novel food processing that can effectively reduce allergenicity and enhance protein quality and functional properties, cold plasma (CP) is gaining attention. The study investigated the impacts of the dielectric barrier discharge CP on the immunoreactivity, in vitro digestibility, functional properties, and structural changes of egg white protein (EWP). The results showed that the EWP treated with CP for 30 min had a significant decrease in immunoglobulin (IgG)-binding capacity by approximately 20% and improved in vitro digestibility. Further studies demonstrated that the decrease in the IgG-binding capacity of EWP correlated well with the alterations in the secondary and tertiary structures. The content of α-helix was decreased by 6.5% and the surface hydrophobicity increased by 2.5 folds after the 30-min treatment, compared with the untreated samples. The oxidation of peptide amino groups induces structural changes, accompanied by the oxidation of amino acid residues. This phenomenon has been validated through surface hydrophobicity and multispectral analysis. Additionally, CP also improved the foaming and emulsifying capacity of EWP by 140% and 8.03%, respectively. These findings suggest that CP treatment could be served as a potential technology to reduce the immunoreactivity, and improve the in vitro digestibility and functionality of EWP.
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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) | 0.001 | 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".