Effect of Water Content and Pectin on the Viscoelastic Improvement of Water-in-Canola Oil Emulsions
Bibliographic record
Abstract
This study aimed to investigate gelation in glycerol monooleate (GMO)-stabilized water-in-canola oil (W/CO) emulsions by increasing water content (20–50 wt.%) and the addition of low methoxyl pectin (LMP) in the aqueous phase. A constant ratio of GMO to water was used to keep a similar droplet size in all emulsions. Hydrogenated soybean oil (7 wt.%) was used to provide network stabilization in the continuous phase. All fresh emulsions with LMP in the aqueous phase formed a stable and self-supported matrix with higher viscosity and gel strength than emulsions without LMP. Emulsion viscosity and gel strength increased with an increase in water content. All emulsions showed gel-like properties (storage moduli (G’) > loss moduli (G’’)) related to the presence of LMP in the aqueous phase and increased water content. Freeze/thaw analysis using a differential scanning calorimeter showed improved stability of the water droplets in the presence of LMP in the aqueous phase. This study demonstrated the presence of LMP in the aqueous phase, its interaction with GMO at the interface, and fat crystals in the continuous phase that could support the water droplets’ aggregation to obtain stable elastic W/CO emulsions that could be used as low-fat table spreads.
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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".