Salt and pH-Induced Attractive Interactions on the Rheology of Food Protein-Stabilized Nanoemulsions
Bibliographic record
Abstract
This research aimed to investigate the possibility of forming gelled nanoemulsions (NEs) by inducing attractive interactions among the nanodroplets. The effect of salt concentration and changes in pH on the stability and gelation behavior of 2, 4, and 5% sodium caseinate (SC) and whey protein isolate (WPI)-stabilized 40% canola oil-in-water NEs were investigated. For the effect of salt, sodium chloride was added in a concentration of 0.1, 0.5, and 1 M in the continuous phase of the NEs at neutral pH, whereas to study the effect of acidification, the pH of the NEs was adjusted to the isoelectric point (pI) of the proteins. The addition of salt led to attractive gelation in WPI NEs because of a screening of charge. In contrast, the gel strength of SC-stabilized NEs was reduced with salt, which was attributed to the loss of close packing of droplets and their surrounding repulsive barriers because of charge screening and to the steric barrier of interfacial SC preventing droplet aggregation. All the NEs with pH at the pI of proteins transformed into strong attractive gels made of droplet aggregates irrespective of the type or concentration of protein because of the complete charge neutralization. The strength of the acidified NE gels increased with a decrease in droplet size and the type of protein used. Overall, research on the effect of different environmental factors on the stability and gelation behavior of protein-stabilized NEs could be useful for possible applications of these nanoscale materials in various food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".