Physicochemical Effects of the Lipid Phase and Protein Level on Meat Emulsion Stability, Texture, and Microstructure
Bibliographic record
Abstract
The effects of beef fat (25%) substitution with rendered beef fat, canola oil, palm oil, or hydrogenated palm oil at varying meat protein levels (8%, 11%, and 14%) were studied in emulsified beef meat batters. There was no significant difference in fat loss among meat batters made with beef fat, rendered beef fat, or palm oil. Hydrogenated palm oil provided the most stable batters at all protein levels. Increasing meat protein to 14% resulted in high fat loss in batters prepared with canola oil, which did not occur in the other formulations. This indicates that the physicochemical characteristics of fat/oil affect emulsion stability. Cooked batter hardness was higher (P < 0.05) when protein level was raised; highest in hydrogenated palm oil batters when compared at similar protein levels. As protein level was raised springiness values were increased in all the meat treatments. Springiness was higher in the canola oil treatments. Light microscopy revealed fat globule coalescence in canola oil meat batters prepared with 14% protein, as well as the development of fat channels and more protein aggregation; both seem to result in lower emulsion stability. Hydrogenated palm oil batters showed fat particles with sharp edges as opposed to the round ones seen in all other treatments.
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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".