Physical and technofunctional properties of yellow pea flour and bread crumb mixtures processed with low moisture extrusion cooking
Bibliographic record
Abstract
The potential utilization of yellow pea flour and bread crumb blends was investigated to generate nutritionally-dense extruded products with superior physical and/or technofunctional properties. Yellow pea flour mixed with bread crumb at different ratios were processed using low-moisture twin-screw extrusion cooking conditions to examine the effect of blending ratios and feed moisture contents on physical (that is, radial expansion index, extrudate density, microstructure, texture, and color) and technofunctional (that is, emulsifying capacity, emulsifying stability, water solubility [WS], water binding capacity [WBC], oil binding capacity [OBC], and pasting) properties of the final products. Compared to the two feed materials alone, samples produced with yellow pea flour and bread crumb blends showed lower hardness and higher crispiness. Moisture content (12% to 18%) was found to significantly affect physical and technofunctional properties. With an increase in feed moisture content from 12% to 18%, the WBC of the extrudates increased while the WS decreased. Extrudates produced with higher feed moisture content, and higher yellow pea flour content had higher setback viscosity. Among all formulas and feed moisture contents studied, extrudates produced with 50% yellow pea flour and 50% bread crumb at 12% feed moisture content had the highest radial expansion and bigger cells with thinner cell walls. This study has shown that incorporation of yellow pea flour and bread crumb in extrusion cooking process could be used to develop nutritionally-dense foods with improved physical and technofunctional properties.
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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".