Physicochemical and compositional properties of blended beef patties formulated with pea and faba bean protein isolates and texturized pea protein
Bibliographic record
Abstract
Abstract This study investigated the physicochemical characteristics of blended beef patties formulated with pea and faba bean protein isolates (PPI and FPI, respectively) and hydrated texturized pea protein (HTPP, 1 part TPP: 2 parts water). Minced beef was combined with nothing (control) or 4.25% PPI/FPI and 0%, 8.5%, 21.3%, or 42.5% HTPP. The pH, Warner‐Bratzler shear force (WBSF), texture profile analysis (TPA), compression juiciness, cooking loss, color, and chemical composition were determined. In general, plant proteins increased pH values and ash content, and decreased cooking loss and fat content of blended meat patties. The addition of PPI/FPI did not lead to substantial changes in texture or color but resulted in lower cooking loss. HTPP resulted in decreased WBSF, hardness, and other TPA attributes. The combination of PPI/FPI as binders/gelling agents and HTPP as a meat extender resulted in a softer texture than conventional beef patties. This study provides an indication of PPI, FPI, and HTPP functionality in blended meat product formulation.
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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".