Enhanced gelation of field pea proteins through formation of multicomponent systems using various polysaccharides
Bibliographic record
Abstract
The potential for enhanced gelation of globular plant proteins through the inclusion of food-grade polysaccharides was established experimentally for pea protein isolate in combination with ither locust bean gum, guar gum or [kappa]-carrageenan. Both factorial and response surface statistical designs were constructed to screen, optimize and verify physicochemical factors significantly contributing to the gelation of these mixed systems. Design factors included protein concentration, protein to polysaccharide ratio, protein to salt ratio and pH. Evaluation of the elastic (G') and storage (tan [delta]) modulus, acquired from small amplitude oscillatory rheological testing, was used to characterized the resulting networks. Behavior of the bipolymer systems were additionally considered through differential scanning calorimetry and solubility assessment. The addition of guar gum and carrageenan resulted in comparable improvements in pea protein gelation. Improved gelation was not evidenced by the interaction ofthese polysaccharides with pea protein but rather by their incompatibility within solution. Results based on graphical and numerical optimization showed that protein-guar gum systems displayed well-defined gel networks at pHs closer to pea protein's IEP. At a pH of 5.32, protein concentrations could vary anywhere between 11.59 and 28.41% while maintaining protein-polysaccharide ratios below 60.63. Carrageenan improved pea protein gelation at higher alkaline pHs (i.e. pH > 7.70). In such systems however, protein levels above 13.9% and protein-polysaccharide ratios less than 41.30 were necessary. As such when developing a favorable gel from a composite system, guar gum systems demonstrated more flexibility and less restriction in terms of physiochemical parameters (i.e. protein and polysaccharide levels).
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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".