Emulsifiers for the Food Industry
Bibliographic record
Abstract
Abstract Emulsifiers are widely used in the food industry. Emulsions are thermodynamically unstable systems consisting of two immiscible phases, such as oil and water, which could be in the form of either oil‐in‐water (o/w) emulsion or water‐in‐oil (w/o) emulsion. Putting mechanical energy into the system (e.g. by mixing) in a way that subdivided one phase will increase the total amount of interfacial area and energy. Thus, the lower the amount of interfacial free energy per unit area, the larger the amount of new interfacial area that can be created for a given amount of energy input. The subdivided phase is called the dispersed phase, and the other phase is the continuous phase. Emulsifiers are more concentrated in the interfacial region than in the bulk solution phase that is amphiphilic in nature, with the lipophilic (or hydrophobic) part of the molecule preferring to be in a lipid (nonpolar) environment and the hydrophilic part preferring to be in an aqueous (polar) environment. Examples of emulsifiers are phospholipids, proteins, polysaccharides, and other surface‐active polymers. This article emphasizes the thermodynamic approach of emulsion and emulsifiers as well as their industrial food applications in detail.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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".