Impact of protein blend formulation and extrusion conditions on the physical properties of texturised pea protein‐extended beef burgers
Bibliographic record
Abstract
Summary This study aimed to assess the physical properties of pea‐based texturised vegetable proteins (TVPs) and TVP function as a meat extender (20%–40% w/w) in beef burgers. TVPs were produced with varying protein blend formulas (PBFs) (70%, 76%, 82% protein on a dry basis), extrusion screw speeds (350–450 r.p.m.) and feed moisture contents (FMCs) (38% and 42%, db). Increasing PBF raised TVP hydration time and integrity index, while FMC and screw speed had no discernable trend on these properties. A select group of TVPs were applied as extenders in beef burgers which were tested for their cooking properties and textural quality. Higher FMC lowered total cooking loss (TCL) for some burgers but overall did not impact burger cooking properties or texture. Increasing the extension level reduced TCL and change in burger diameter (CBD). TVPs produced at the lowest PBF and higher FMC studied had the greatest potential in meat extender applications, as they decreased TCL and CBD while maintaining comparable texture to burgers without TVP addition. These lower protein TVPs with enhanced cooking quality can be economically advantageous for the vegetarian and flexitarian applications of plant‐based extenders.
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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 | 0.001 | 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 | 0.001 |
| 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".