Comparative Proteomic Analysis of Egg White Proteins under Various Storage Temperatures
Bibliographic record
Abstract
Although the effect of storage temperature was suggested to be a more important factor than that of storage time on changes in unfertilized egg white proteins, no comprehensive analysis of the thermally induced egg white protein changes was carried out. This study presents a proteomic analysis of the changes in unfertilized egg white proteins after 15 days of storage at 4, 20, and 37 °C. Using two-dimensional electrophoresis followed by MALDI-TOF MS/MS, 32 protein spots representing 8 proteins were identified with significant differences in abundance when stored at different temperatures. An accelerated degradation of ovalbumin, possibly resulting from the reduction of antiprotease, was observed after the storage at higher temperature. In addition, an increase in the formation of ovalbumin complexes and a decrease in lipocalin family proteins were detected with increasing storage temperature, which may indicate a thermally promoted change in chicken eggs. The decrease of clusterin during the high-temperature storage was suggested to be an effective biomarker for egg quality evaluation. These findings will give insight into the effects of storage temperature on changes in unfertilized egg white proteins during storage and provide a better understanding of the thermally induced biochemical changes that may affect the egg deteriorative process.
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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".