Mixing Properties and Gluten Yield of Dough Enriched with Pea Protein Isolates
Bibliographic record
Abstract
Over the last few years, many studies were carried out on the use of legume-based ingredients to supplement cereal-based matrices and produce nutritionally enhanced products. However, little is known about the influence of supplementation on the mixing properties of the enriched cereal-based matrices. The objective of this work was to study the impact of supplementing cereal-based matrices with commercial pea protein isolate or pea protein isolate produced by ultrafiltration/diafiltration using a 50 kDa membrane on the dough mixing properties. Studies were performed using a PertenÒ Glutomatic to estimate gluten yield, namely in terms of gluten index, wet gluten, dry gluten and water binding capacity, and using a BrabenderÒ Farinograph to estimate water absorption, dough development time, stability, mixing tolerance index and minimum and maximum water content for dough formation. Four levels of pea protein isolate enrichment were considered: 0, 5, 10 and 15%. Results indicated that level of enrichment has little effect on measured mixing properties compared to the pea protein isolates considered. Isolate processed by membrane technologies takes part to the dough formation which does not seem to be the case with commercial isolate. Higher amount of water is required for dough formation with matrices enriched with commercial pea isolate compared to membrane processed isolate, while stronger dough properties are observed for matrices enriched with membrane processed isolate. This is attributable to the properties of the isolate, namely solubility and state of the proteins (native or denatured), which could impact how they interact with wheat proteins.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".