Ultrasound Viscosity Measurements Allow Determination of Gas Volume Fraction in Foamed Gels
Bibliographic record
Abstract
Abstract Aerated whey protein gels were obtained by gelation of preheated solutions at room temperature by calcium, magnesium and ferrum ( II ) ions at different pH and protein concentration. The density of aerated and non‐aerated gels was determined by the flotation method. Dynamic viscosity of aerated and non‐aerated gels was measured using an ultrasonic viscometer. Air fraction of obtained aerated gels ranged from 24.5 to 76.8%. There was a linear increase in the product of viscosity and density values with increasing concentration of non‐aerated gels. For aerated gels, the highest values of the product of viscosity and density were noted at 7.5 and 8.0% (w/w). A formula for calculation of the air fraction in aerated gels by ultrasound viscosity measurements was derived. Air fraction values obtained by density measurements correlated with the air fraction values obtained by ultrasound viscosity measurements ( R 2 = 0.99). Measurements of the product of viscosity and density values by an ultrasound viscometer allow the determination of the air fraction of aerated gels. Practical Applications Different methods can be applied to obtain aerated whey protein gels. One common method capitalizes on the gel reversibility. Another common method is based on the gelation of preheated solutions induced at room temperature by salt addition. Foamed gels can be applied in food technology and as matrices for active ingredient release. Ultrasonic viscometers are used for continuous measurement of viscosity under conditions where measurement is difficult and does not influence the sample shape. Viscoelastic properties of the gels can be measured using oscillatory rheometry, but ultrasound viscosity measurements can also characterize the viscosity of the gel samples. Ultrasound viscometers are cheaper than oscillatory rheometers and the measurements are simpler.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".