Characterization of Hydrophobic Flavor Release Profile in Oil‐in‐Water Emulsions
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Bibliographic record
Abstract
An instrumental approach to better understand the release and persistence of flavor in oil-in-water emulsions has been developed. Emulsions were prepared with various whey protein (0.1% to 3.16%), sunflower oil (1% to 8%), and ethyl hexanoate (0% to 0.04%) concentrations. Flavor release profile in real time was measured at 37 degrees C using a specially designed glass cell connected directly to a gas chromatograph equipped with a flame ionization detector. The intensity of flavor released from the emulsion stirred at a shear rate of 100 s(-1) was monitored as a function of time and data were fitted to a 1st-order kinetic equation. Maximum intensity and decay rate constant were both determined from the model and the persistence index (inversely associated to decay rate constant) was calculated. For constant aroma concentration in the emulsion, maximum intensity significantly decreased as whey protein and oil concentrations increased. For increasing aroma concentration, maximum intensity was directly proportional to the ethyl hexanoate concentration when the oil content was kept constant but leveled off when oil content was increased. Persistence of flavor significantly increased with increasing protein and oil concentrations while aroma concentrations had no effect when oil content was constant. The results showed that oil concentration had a greater influence on flavor release characteristics than protein concentration. Aroma concentration in the oil phase, rather than in the emulsion, determines the kinetics of hydrophobic flavor release. The method provides a useful tool for the rapid and reproducible measurement of flavor release profile.
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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 | 0.001 |
| 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 it