EFFECTS OF ADDED WEIGHTING AGENT AND XANTHAN GUM ON STABILITY AND RHEOLOGICAL PROPERTIES OF BEVERAGE CLOUD EMULSIONS FORMULATED USING MODIFIED STARCH
Bibliographic record
Abstract
ABSTRACT Stability of beverage emulsion is measured by the rate at which the emulsion creams, flocculates or coalesces, and is generally dependent on rheology of water phase, difference in specific gravities of the two phases and droplet size/distribution of the emulsion. The effects of weighting agents (sucrose acetate isobutyrate and brominated vegetable oil) and xanthan gum on modified starch‐based emulsions were evaluated in this study. Emulsion was formed by addition of 9% coconut oil, in the presence or absence of weighting agents, into the water phase containing modified starch at 10, 12 or 14% without or with the addition of 0.3% xanthan gum. Stabilities of emulsions were evaluated both in the concentrated form used for storage and dilute form used in beverages. The addition of xanthan gum into the water phase decreased the flow behavior index (n) from 0.88 down to 0.31 and increased elastic modulus (G′) over 20 times at elevated frequency (ω = 50 rad/s) and elevated the stability of the emulsion. The xanthan gum‐added emulsion had smaller particle size and demonstrated 14 and 5 times slower phase separation compared to the emulsions without or with the addition of weighting agents, respectively. When the elastic modulus was larger than the viscous modulus (G′ > G″), the emulsions demonstrated greater stability. In dilute beverage solutions, creaming was observed in the absence of xanthan gum.
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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 | 0.001 |
| 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".