Back Extrusion Rheology for Evaluating the Transitional Effects of High Pressure Processing of Egg Components
Bibliographic record
Abstract
Abstract In this study, a back extrusion concept was used to evaluate the rheological properties of egg components treated by high pressure processing. Rheological properties, such as viscosity index (ɳ) and apparent elasticity (Ea) of egg components, were evaluated using a central composite design with three independent variables included at five levels. The variables used were high pressure (350−550 MPa), treatment time (10–20 min) and temperature (15−35C). Apparent viscosity was also evaluated using a conventional rheology, using a parallel plate rotational viscometer. Viscosity index and apparent elasticity of various egg components were found to increase significantly (P < 0.05) with an increase in treatment intensity. A relationship between apparent viscosity (rheological) and viscosity index (textural properties) was established. It revealed positive correlations for all egg components (egg white, egg yolk and whole liquid egg) with R2 values higher than 0.95. Practical Applications Textural/rheological characteristics play a significant role in determining the functional properties of whole liquid egg, egg white or egg yolk. Liquid foods are usually characterized by conventional viscometry and solid foods by their texture. The transition between liquid and solid covering the semi‐solid consistency poses measurement problems for both techniques. Back extrusion rheology can be used to bridge the gap between the two. High pressure processing allows the liquid egg components to be transformed from liquid to a gel‐like to a solid consistency, depending on the applied pressure level, temperature and time. Hence, back extrusion rheology is ideally suitable for studying texture formation in egg products during high pressure processing.
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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 | 0.001 | 0.001 |
| 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".