Effects of pre‐emulsifying fat/oil on meat batter stability, texture and microstructure
Bibliographic record
Abstract
Summary The effects of pre‐emulsified beef fat and canola oil (CO) (25%) with Tween 80 (T‐80) or sodium caseinate (SC) were studied in beef meat batters prepared at three protein levels (9%, 12% and 15%). Raising meat protein level to 15% resulted in low emulsion stability of products prepared with CO. Using pre‐emulsified beef fat with Tween 80 (BF‐T80) showed significantly higher fat and water losses at all protein levels. There were no differences in fat and water losses between pre‐emulsified beef fat and CO when SC was used at the 9% and 12% protein levels compared to the controls (non pre‐emulsification). Light microscopy revealed fat globule coalescence in the CO meat batters prepared with 15% protein and BF‐T8 treatments, as well as formation of fat channels and more protein aggregation; both resulted in lower emulsion stability. Using SC to emulsify fat/oil produced a finer dispersion of fat globules compared to all the 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 | 0.001 |
| 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".