Emulsifying and Foaming Properties of Commercial Yellow Pea (<i>Pisum sativum</i>L.) Seed Flours
Bibliographic record
Abstract
Commercial yellow pea seed flours prepared by a patented wet-milling process and pea protein isolate (PPI) were analyzed for emulsifying and foaming properties at pH 3.0, 5.0, and 7.0 and compared to soybean protein isolate (SPI). PPI and SPI formed emulsions with significantly smaller (p < 0.05) oil droplet sizes, 16-30 and 23-54 microm, respectively, than flours that primarily contained fiber such as Centara III and IV, or those that consisted mainly of starch: Centu-tex, Uptake 80 and Accu-gel. PPI was a better emulsifier than SPI at pH 7.0, and a better foaming agent at pH 3.0 and pH 7.0, although foaming capacity varied with sample concentration. Centu-tex and Uptake 80 have exactly the same chemical composition, but the latter has a much smaller flour particle size range, and had significantly smaller (p < 0.05) emulsion oil droplets. Incorporation of pea starch into SPI emulsions produced a synergistic effect that led to significant increases (p < 0.05) in emulsification capacity (reduced emulsion oil droplet size) when compared to SPI or starch alone. These results showed that PPI had generally significantly higher (p < 0.05) emulsion and foam forming properties than SPI, and that pea starch could be used to improve the quality of SPI-stabilized food emulsions.
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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) | 0.000 | 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 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".