Stability of hydrocolloid enriched oil-in-water emulsions in beverages subjected to thermal and nonthermal processing
Bibliographic record
Abstract
Storage stability of concentrated oil-in-water beverage emulsions subjected to thermal and non-thermal processing was evaluated over 14 days at 22 °C. Emulsions were made with canola oil and aqueous dispersions of Type “A” and Type “B” gelatin and xanthan. They were also conjugated with propylene glycol alginate (PGA), modified starch and modified gum Arabic was studied at pH 3.4 and 7.0. Increase in apparent viscosity was observed with storage for gelatin Type “A” emulsions (pH 7.0) and gelatin Type “B” emulsions (pH 3.4). All emulsions showed shear-thinning behavior associated with droplet flocculation. Increase in the slope of particle size distribution was more obvious for protein (gelatin) stabilized emulsions. Concentrated gelatin Type “A”-modified starch had smaller particle size and greater stability at pH 3.4, followed by gelatin Type “B”-modified starch and gelatin Type “B”-xanthan-PGA both at pH 7.0. Simulated orange beverage (pH 3.0) and dairy beverage (pH 6.8) using stabilized emulsions were pasteurized by heat and high pressure. Emulsions formulated by modified starch produced better stability in both beverage types. Gelatin Type “A” and modified starch conjugate resulted in greater stability compared to other conjugated emulsions. However, gelatin alone failed to stabilize the emulsion systems. The ringing was characteristically associated with emulsions formed with gelatin alone. Neither thermal processing nor high-pressure treatment resulted in destabilization of emulsions.
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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".