Characterization of Apple Juice Foams for Foam‐mat Drying Prepared with Egg White Protein and Methylcellulose
Bibliographic record
Abstract
ABSTRACT: Intrinsic stability and rheological properties of apple juice foams for foam mat drying were studied. Foams were prepared from clarified apple juice by adding various concentrations of 2 foaming agents of different nature: a protein (egg white at 0.5%, 1%, 2%, and 3% w/w) and a polysaccharide (methylcellulose at 0.1%, 0.2%, 0.5%, 1%, and 2% w/w), and whipping at different times (3, 5, and 7 min). In general, egg white foams were less stable but showed a higher degree of solidity (stronger structures), higher foaming capacity, and smaller bubble average diameter than methylcellulose foams. Foam stability increased with increasing concentrations of either methylcellulose or egg white. Increasing whipping times increased the stability of egg white foams only. Stability parameters (maximum drainage and drainage half‐time) were correlated in terms of rheological parameters of the continuous phase (consistency index and apparent viscosity at 30/s, respectively). The correlations ( R 2 = 0.766 and 0.951, respectively) were considered acceptable because they were independent of whipping time and foaming agent nature and concentration. Results on foam rheology obtained by dynamic and vane tests were in agreement, but the latter method was more sensitive. Optimal concentrations to obtain the most solid foams (0.2% methylcellulose and 2% to 3% egg white, respectively) were the same concentrations required for maximum foaming capacity. Based on this observation and previous models, an empirical expression was proposed to predict the degree of solidity (in terms of inverse phase angle and yield stress) only as a function of foam structural properties (air volume fraction and average bubble size). The model proved to be satisfactory to fit experimental results ( R 2 = 0.848 and 0.975, respectively), independently of whipping time, foaming agent nature and concentration.
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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".