Formulating high-protein pea flour pasta with optimized cooking tolerance using manitoba yellow field pea fractions
Bibliographic record
Abstract
About 3.4 million tonnes of yellow field peas leave Canadian farms each year, yet only a small share ends up in value-added food products sold domestically. This research tested whether replacing 20–60% of durum semolina with air-classified Manitoba yellow pea flour could produce dried pasta with at least 20 g protein per 100 g while keeping cooking loss below 10% the informal industry ceiling for acceptable quality. Six formulations (five pea-semolina blends plus a semolina control) were extruded on a pilot-scale La Monferrina P6 press at the Winnipeg College of Pulse Crop Utilization Science between January and May 2023. Proximate composition, cooking quality (optimum cooking time, cooking loss, swelling index, water absorption), and instrumental texture (firmness, adhesiveness) were measured. The 40% pea flour blend reached 22.7 g protein per 100 g dry basis and held cooking loss at 8.4%, making it the highest substitution level that remained inside the 10% threshold. Firmness dropped by 26.5% compared with the control, but a trained sensory panel rated the texture as acceptable. Dietary fiber also jumped to 9.1 g per 100 g in the 40% blend versus 3.2 g in the control. These results suggest that Manitoba yellow pea flour at 40% inclusion offers a practical route to high-protein, high-fiber pasta without crossing the cooking-tolerance line that would discourage consumers.
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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".