Study of the interactions between pectin in a blueberry puree and whey proteins: Functionality and application
Bibliographic record
Abstract
Protein and fiber, especially pectin, can form complexes at acidic pH. Studies on these complexes under actual food conditions are scarce. The aim of this work was to study interactions between whey proteins and blueberry puree, in particular its pectin, and to evaluate the impact on the functionality of the puree alone or incorporated into a model beverage. After the addition of a whey protein isolate (WPI) into purees at pH 3.5 or 6.5, the soluble pectin and protein contents and the viscosity of the resulting mixtures were determined. The decrease in the solubility of pectin (80%) and proteins (94%) indicated the formation of protein-pectin complexes by electrostatic interactions at pH 3.5, contributing to increase the mixture viscosity. The amount of soluble pectin in blueberry limited the formation of complexes when more WPI was added (5%). Heating the puree prior to the WPI addition solubilized pectin, which limited the formation of insoluble complexes and reduced the viscosity increase. The solubility of the blueberry polyphenols did not decrease after WPI addition. Finally, the non-heated puree enriched in WPI was used to prepare smoothies. This time, the protein-pectin complexation, probably reinforced by the final pasteurization of the smoothies, contributed to reduce the smoothie viscosity and can be explained in particular by particles of smaller sizes. Although the smoothie stability can be improved, the interactions between blueberry pectin in a puree and whey proteins allowed to design a novel functional ingredient that may be helpful in formulating beverages rich in fiber and protein.
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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 | 0.000 | 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 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".