Interactions of High Methoxyl Pectin with Whey Proteins at Oil/Water Interfaces at Acid pH
Bibliographic record
Abstract
The interactions between whey protein isolate (WPI) and high methoxyl pectin (HMP) at pH 3.5 were investigated in situ using ultrasound (US) and diffusing wave spectroscopy (DWS). HMP was added to 10% oil-in-water emulsions containing 1% WPI. At neutral pH, no protein-pectin interactions were observed as both molecules are negatively charged, while at pH 3.5 bridging flocculation occurred via electrostatic interactions. Four different stages were distinguished during the addition of HMP in WPI-stabilized emulsions at pH 3.5. At a concentration below a critical value, no interactions were observed. At concentrations >0.02% HMP, a change in the l factor indicated a change in the ordering of the emulsion droplets, influenced by long-range interactions. At higher concentrations (in the range between 0.04 and 0.06% HMP), attenuation showed significant changes in the surface of the oil droplets, changes which affected the droplet-droplet interactions. At pectin concentrations >0.05%, attenuation of sound and 1/l* decreased, while velocity of sound and particle size increased, as a result of bridging flocculation. These results demonstrated for the first time that methods such as US and DWS combined permit the observation of the early stages of the interactions between two biopolymers at the interface. This is significant in light of increasing efforts in engineering complex interfacial layers.
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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".