Effects of extrusion screw speed, feed moisture content, and barrel temperature on the physical, techno‐functional, and microstructural quality of texturized lentil protein
Bibliographic record
Abstract
Utilizing lentil protein as a novel ingredient for producing texturized vegetable proteins (TVPs) can provide new opportunities for the production of next-generation hybrid meat products. TVPs from lentil protein isolate were manufactured using low-moisture extrusion cooking at different combinations of screw speed (SS), feed moisture content (MC), and barrel temperature (BT) profile. In total, seven different combinations of processing treatments were tested, and the resulting TVPs were characterized for their physical (rehydration ratio, texture profile analysis, color, and bulk density), techno-functional (oil and water holding capacities), and microstructural properties. The processing conditions of higher SS and lower MC resulted in increased values of several textural profile attributes (springiness, cohesiveness, and resilience), increased water holding capacity (WHC), and decreased bulk density. Compared to raw lentil protein, TVPs showed enhanced oil holding capacity, though WHC either decreased or remained constant. The extrusion response parameters (die pressure, torque, and specific mechanical energy) showed positive correlations with several physical properties (texture, WHC, and total color change), revealing their potential for serving as important TVP quality indicators. TVPs produced at SS, MC, and BT of 450 rpm, 30%, and 140°C, respectively, showed relatively better overall physical and techno-functional quality and can be used as meat extenders in hybrid meat patties. Overall, this research evidenced the viability of lentil protein as a potential ingredient for producing low-moisture TVPs.
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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 | 0.001 |
| 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".