Effect of Gradual Heating and Fat/Oil Type on Fat Stability, Texture, Color, and Microstructure of Meat Batters
Bibliographic record
Abstract
The effects of endpoint cooking temperature (40, 50, 60, 70, 80, and 90 °C) on emulsion stability, texture, color, and microstructure of meat batters prepared with different fats/oils were studied. Canola oil treatments showed the highest cooking loss whereas hydrogenated palm oil provided the most stable meat batters. Rendered beef fat was less stable than regular beef fat. Increasing endpoint cooking temperatures resulted in a progressive reduction of water holding capacity in all treatments. As temperature was raised, meat batters showed higher hardness and cohesiveness values, but no appreciable changes in cohesiveness above 60 °C. Canola and hydrogenated palm oil treatments showed the highest hardness and chewiness values. Lightness (L(*) ) values of all meat batters increased significantly with increasing temperature from 40 to 60 or 70 °C; no major changes observed above 70 °C. Light microscopy revealed no substantial changes in the microstructure of all the stable meat batters cooked to between 50 and 70 °C. Heating to 90 °C changed the microstructure in all meat batters except the hydrogenated palm oil treatments, which still showed nonround fat particles and a less aggregated protein matrix.
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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 | 0.001 |
| 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 | 0.000 |
| 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".