Effects of protein formula and extrusion cooking conditions on the techno‐functional properties of texturised pea proteins
Bibliographic record
Abstract
Summary Pea protein concentrate and isolate were blended to obtain protein blend formulas (PBFs) with three levels of protein content and investigated for their potential for developing texturised vegetable proteins (TVPs). PBFs were extruded at three screw speeds and two feed moisture contents to obtain TVPs. Nitrogen solubility index (NSI) and techno‐functional properties, i . e . water holding capacity, oil absorbance capacity, emulsion capacity and emulsion stability, of the raw materials and the TVPs were examined. Raising the protein content through PBF decreased NSI for both the raw formulas and the TVPs. Regardless of the PBF, extrusion processing substantially decreased NSI, but increased oil absorption capacity. Extrusion's impact on water holding capacity, emulsion capacity and stability was complex and might have been impacted by the protein content and source. Overall, PBF's impact on the techno‐functionality of TVPs was more pronounced than that of extrusion conditions. At higher PBF, the NSI, emulsion capacity and stability values of TVPs were negatively impacted, implying that high protein content does not equate to better TVP quality. Future, TVP techno‐functionality research should focus on a wider range of extrusion screw speed and feed moisture content or the effects of protein content and source.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".