Effect of pH shifting and temperature on the functional properties of a commercial pea protein isolate
Bibliographic record
Abstract
Abstract Background and Objectives In the present study, the effect of a pH‐shifting method in combination with heat was investigated for its effects on the resulting surface and functional properties of a commercial pea protein isolate. The pH shifting process was performed at both acidic (pH 2) and alkaline (pH 10) pH and then adjusted back to neutrality. The heat‐treated samples were further subjected to heating and later neutralized at pH 7. Findings The results of these treatments indicated that an alkaline pH shifting, as well as its combination with heat, resulted in a significant increase in the solubility of the proteins, whereas an acidic pH shifting, and in combination with heat, reduced the stability of the proteins in solution. Additionally, some functional properties were enhanced by pH shifting, such as foaming capacity or emulsion stability, while other properties showed no alteration or were negatively impacted, such as foaming stability. Furthermore, analysis of the bubble structure of foams using a dynamic foam analyzer revealed that bubble sizes for samples shifted at pH 2 in combination with heat presented the biggest increase in bubble growth over time, creating a less stable foam. Conclusions The application of pH shifting and the use of heat can aid in the improvement of pea protein functionality and allow tailoring of these proteins for specific applications. Significance and Novelty This study utilized a simple method to achieve modifications in the protein structures, providing insights into the application of pea proteins into food products as emulsifying and foaming agents.
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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 | 0.001 |
| 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".