Effect of Extrusion Conditions on the Characteristics of Texturized Vegetable Protein from a Faba Bean Protein Mix and Its Application in Vegan and Hybrid Burgers
Bibliographic record
Abstract
The aim of this study was to produce texturized vegetable proteins (TVPs) from faba bean protein via low-moisture extrusion. The effect of extrusion variables including temperature (110, 125, and 140 °C at the die), feed moisture content (30, 35, and 40%), and screw speed (200, 300, and 400 rpm) on the TVP properties were investigated. An increase in feed moisture content or extruder temperature reduced the specific mechanical energy and torque by 40-45% during extrusion. An increase in feed moisture created TVPs with lower bulk densities and rehydration ratios while an increase in extruder temperature or screw speed increased the bulk density of the TVPs. An increase in screw speed also caused a decrease in the water holding capacity of the milled TVP flours. The TVP flours had a 33-70% higher oil holding capacity than the raw material. The texture profile showed that an increase in feed moisture influenced TVP hardness, gumminess, and chewiness with higher values compared to the treatments with lower moisture contents. Springiness, cohesiveness, and resilience were more affected by a change in screw speed with higher values at 200 rpm. The best parameters were selected (125 °C, 40% MC, 300 rpm) to produce TVP to use as a partial (hybrid burger) and complete (vegan burger) replacement of beef in a burger patty. The replacement of 25% beef with TVPs in a hybrid burger increased the cooking yield and moisture retention and decreased the thickness and diameter change compared to the beef burger without TVPs. In a vegan formulation, the faba bean TVP burger had lower cooking yield and moisture retention than commercial products.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".