The role of the 7S/11S globulin ratio in the gelling properties of mixed β-lactoglobulin/pea proteins systems
Bibliographic record
Abstract
Using a combination of pea protein isolates (PPI) and β-lactoglobulin (βlg) presents an interesting approach to improve the gelling properties of PPI compared to the use of PPI alone. While controlling the 7S/11S ratio has proven effective in improving the gelling properties of PPI, more complex models need to be studied. Specifically, the effects of diverse 7S/11S ratios and the inclusion of βlg and 2S albumin on gel formation remain unexplored. Therefore, this study aimed to 1) determine the optimal 7S/11S ratios and 2) identify the interactions involved in the formation of model βlg—pea protein gels generated from globulin and albumin-enriched fractions. Results revealed that pea protein gels with a 7S/11S ratio of ∼ 1.89 achieved the highest firmness (131 Pa). Gelation was primarily driven by hydrophobic interactions (∼ 41%) involving 7S vicilin and 2S albumin, while most of the 11S legumin did not contribute to the protein gel. Conversely, a 7S/11S ratio of ≤ 1 facilitated the formation of the firmest gels (300–657 Pa) in the presence of βlg. These gels were primarily formed via disulfide bonds (36–65%) with βlg, 11S legumin, and 2S albumin as the main proteins involved, while 7S vicilin gelled independently through hydrophobic interactions. Based on these results, we propose a mechanism illustrating the interactions in mixed βlg—pea protein gels. This study provides new insights into how 7S/11S ratios, gel firmness, and protein interactions interplay during the gelation of mixed βlg–pea protein systems, paving the way for innovation in developing a new food category.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".