The effects of extrusion on nutrient content of Canadian pulses with a focus on protein and amino acids
Bibliographic record
Abstract
Alternative sources of protein will be required in both human and animal nutrition to support ingredient sustainability and nutrient demands of a growing world population. Extrusion is one technique utilized to process pulses and is reported to increase starch and protein digestibility but also has the potential to transform nutrients into non-nutritious compounds. This study sought to compare the effects of extrusion on nutrient composition in Amarillo peas, Dun peas, lentils, chickpeas, and faba beans, with soybean meal (control). Each pulse was extruded at 18% or 22% moisture and 110, 130, or 150 °C. Compared to whole samples, extrusion increased crude protein content of Amarillo and Dun peas, and lentils, and it decreased in soybean meal ( P < 0.05). Compared with whole samples, extrusion increased methionine content in chickpeas and lentils ( P < 0.05), with no effect in Amarillo or Dun peas, faba beans, and soybean meal. Cysteine content increased in extruded Amarillo peas compared with whole pulses, and decreased in soybean meal ( P < 0.05). Results suggest that extrusion can positively affect protein and amino acid content of pulses, however, specific changes differ by pulse/legume type.
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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".