Protein Denaturation, Rheology, and Gelation Characteristics of Radio-Frequency Heated Egg White Dispersions
Bibliographic record
Abstract
Gel properties of radio frequency (RF) heated egg white dispersions at 27.12 MHz were studied as function of concentration (2.5–2.5 kg/100 kg sample), pH (3–11) and heating time (60–180 s). Egg white dispersions demonstrated a gradual liquid-solid transformation as they denatured and gelled during RF treatment. The critical concentration and heating period for egg white protein denaturation and gelation were found to be 7.5% (w/w) and 150 seconds. The elastic modulus (G′) of RF-heated samples increased with concentration and heating period (temperature), whereas complex viscosity (η*) increased exponentially with concentration. In an alkaline condition, the egg white dispersion did not produce a gel; however, in acidic condition it resulted in a strong gel with significantly (P < 0.05) higher G′. This could be attributed to the high dielectric constant (ε′) and loss factor (ε″) values of acidified samples as compared to the alkaline and control egg white dispersion. Effect of heating rate (1, 5, 10, and 20°C/min) in situ on rheometer plate significantly affected gel rigidity; the RF treated sample rigidity was comparable to samples heated at the rate of 5 and 10°C/min. Differential scanning calorimetry, dielectric measurement, and sodium dodecyl sulfate (SDS) PAGE electrophoresis results were used to confirm gelation behavior during both conventional and RF heating conditions.
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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".