Enhancing the Techno-Functionality of Pea Flour by Air Injection-Assisted Extrusion at Different Temperatures and Flour Particle Sizes
Bibliographic record
Abstract
Industrial applications of pulses in various food products depend on pulse flour techno-functionality. To manipulate the techno-functional properties of yellow pea flour, the effects of flour particle size (small vs. large), extrusion temperature profile (120, 140 and 160 °C at the die) and air injection pressure (0, 150 and 300 kPa) during extrusion cooking were investigated. Extrusion cooking caused the denaturation of proteins and gelatinization of starch in the flour, which induced changes in the techno-functionality of the extruded flour (i.e., increased water solubility, water binding capacity and cold viscosity and decreased emulsion capacity, emulsion stability, and trough and final viscosities). In general, the large particle size flour required less energy input to be extruded and had higher emulsion stability and trough and final viscosities compared to the small particle size flour. Overall, among all of the treatments studied, extrudates produced with air injection at 140 and 160 °C had higher emulsion capacity and emulsion stability, making them relatively better suited food ingredients for emulsified foods (e.g., sausages). The results indicated air injection's potential as a novel extrusion technique combined with modification of flour particle size distribution and extrusion processing conditions to effectively manipulate product techno-functionality and broaden the applications of pulse flours in the food industry.
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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".