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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".