Structural and Instrumental Textural Properties of Meat Patties Containing Soy Protein
Bibliographic record
Abstract
The effect of two different types of soy protein namely soy protein flour (SPF) and texturized soy protein (TSP); soy protein extender concentration; cooking times; and cooking temperatures on structural and textural properties of pan‐fried patties were studied. Beef patties were formulated using extra lean (10 kg fat/100 kg) ground beef samples, with different concentrations of soy protein (0, 2, 3.5, and 5% kg/kg total mass). They were formed into patties, and cooked on a griddle at different temperatures (177 and 187°C) and cooking times (10, 15, and 20 min). Water holding capacity (WHC) and total cooking loss (TCL) were determined. Instrumental textural profiles of the cooked samples were obtained using a Universal Testing Machine Instron. Porosity and pore size distributions were determined by a mercury intrusion porosimeter. The results indicated that increasing soy protein concentration increased WHC and reduced TCL. Beef patties extended with TSP were harder and more cohesive than those extended with SPF. Total mean porosities at the 5% soy protein extender concentration were 0.42 and 0.40 for the SPF and TSP extended samples, respectively. Samples extended with SPF had up to 84% capillary pores.
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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".