Foam-mat freeze drying of egg white and mathematical modeling
Bibliographic record
Abstract
Eggs are a rich source of high-quality proteins as they contain all amino acids necessary for the human body. They also contain all vitamins (except for vitamin C) and many essential minerals. Eggs mainly consist of egg white (albumen) and egg yolk. Egg white is mainly made up of proteins and has excellent foaming properties; it is widely used in the baking and confectionary industries (e.g., cake mixtures, meringue). Dehydration is widely used for the preservation of egg. Dehydrated egg products usually have a shelf life of one year under refrigeration. Spray drying and pan drying are widely used for producing egg powder. The higher drying temperature associated with these drying methods could adversely affect the nutritional value of egg. Freeze drying is well known for its excellent dehydrated product quality. The high cost of operation associated with freeze drying restricts its usage to high value products like coffee. Foam-mat drying can be used for the products that can be foamed to increase the surface area to improve the mass transfer rate. But the higher drying temperature involved in this method is not suitable for producing a high quality dehydrated product. Foam-mat freeze drying is one of the promising methods of drying, which tries to utilize the advantages of both freeze drying and foam-mat drying to produce better quality egg white powder. Preliminary experiments showed that the stability of foams made with egg white alone is not adequate for foam-mat freeze drying. Experiments were thus conducted using different stabilizers (Methyl cellulose, Propylene glycol alginate and Xanthan gum) to optimize foam stability. Bubble size distribution was determined using microscopy to understand foam structure. The results showed that Xanthan gum at 0.125% provide sufficient stability for freeze drying. Experiments were conducted to study foam-mat freeze drying of egg white, in an effort to determine the suitability of this method. The results showed that th
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".