Stability of Mexican Oregano Essential Oil Double Emulsions Obtained by Ultrasound Formulated With Whey Protein Concentrate and Tween 80
Bibliographic record
Abstract
Water-in-oil-in-water (W/O/W) emulsions have a great potential use for food applications because they can protect sensitive compounds, such as essential oils. The aim of this study was to determine the effect of ultrasonic homogenization parameters: intensity (42 or 54 µm) and time (5 or 7.5 min); and formulation: oil phase proportion (20 or 30%) and emulsifier concentration (0 or 0.3%); on the physical and stability properties of Mexican oregano essential oil (OEO) in double emulsions. The emulsions were made in a two-step process, primary emulsions (W/O) containing OEO in the oil phase and ascorbic acid solution in the water phase, were stabilized with lecithin; while secondary emulsions were stabilized with 6% (w/w) of whey protein concentrate (WPC) and with or without Tween 80 (T80). Creaming, viscosity and droplet size distribution were measured to determine the stability of the W/O/W emulsions; as well as other physical properties like density and pH. The prepared W/O/W emulsions had droplet sizes between 2.89(±0.589) µm and 4.123(±0.964) µm. The most stable emulsions, with no creaming developed after 25 days of storage, were the ones formulated with WPC with T80, and additionally, 30% of the primary emulsion. Besides, higher intensity and longer time of ultrasonic homogenization conditions applied enhances W/O/W stability. Empirical models were developed for viscosity and creaming properties of W/O/W emulsions, with 99.7% of correlation coefficients, finding optimum values for specific homogenization conditions and formulation. Further studies are suggested to evaluate OEO in W/O/W emulsions as controlled release systems in food.
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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".