Hybrid meat batter system: effects of plant proteins (pea, brown rice, faba bean) and concentrations (3–12%) on texture, microstructure, rheology, water binding, and color
Bibliographic record
Abstract
A lean meat batter system was mixed with four plant proteins at 3, 6, 9 and 12% (w/w): pea protein A (PA), pea protein B (PB), brown rice protein (BR) and faba bean protein (FB). Texture profile analysis (TPA) revealed that increasing plant protein levels hardened the hybrid meat batters, with PA and PB leading to the hardest gels. TPA results were supported by the micrographs, demonstrating that the two pea proteins formed large aggregates, contributing to a firmer hybrid meat gel. Dynamic rheology showed that the incorporation of plant proteins lowered the modulus of rigidity (G') during the heating stage (20 -72°C), yet the 6% PA treatment produced a final G' (after cooling) closest to the control (CL). NMR T2 relaxometry also demonstrated that plant proteins reduced the water mobility in hybrid meat batters. Results were in line with the cooking loss, except for a higher cooking loss in the BR formulation compared to the CL. Color measurement showed that increasing plant protein levels led to darker and yellower meat batters; however, the effect on redness varied among treatments. Overall, the findings suggest that pea proteins have superior functionality and compatibility within a lean poultry meat protein system, compared to BR and FB tested here.
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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".