Gelling properties of pea protein isolate in combination with protein from wild harvested Ulva sp.
Bibliographic record
Abstract
Plant-based proteins as food functional ingredients are increasing in demand due to lower climate impact and consumer interests in vegetarian and vegan diets. Furthermore, marine macro- and microalgae are also considered as food protein sources for the same reasons. The macroalgae Ulva sp. (common name sea lettuce) is a highly nutritious and fast-growing seaweed species that is of interest to extract protein from as it grows in Danish fjords. Pea protein isolates are available commercially, but with varying functional properties as gelling agents in foods. The study aim was to assess the functional properties of Ulva protein compared to a commercial pea protein isolate (PPI). The gelling properties of Ulva protein and PPI were analyzed at food relevant pH conditions. Gelling properties of Ulva protein were assessed, but showed poor gelling ability alone, while PPI forms heat induced soft gel depending on protein concentration. Combinations with 10% and 30% relative substitutions with Ulva protein in PPI gels were analysed. Rheological small amplitude oscillation measurements revealed synergistic effects of the two protein sources combined with significantly increased gel strength and stability at low Ulva protein inclusion, and significantly increased gel strength but decreased stability at increased Ulva protein inclusion. Based on the results, Ulva protein may have potential as functionality ingredient in complex plant-based foods. Further research on sensory properties regarding flavour and colour is ongoing.
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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".