Edible Films and Coatings from Soybean and Other Protein Sources
Bibliographic record
Abstract
Abstract Proteins are abundant in nature and are highly functional. They can be converted into edible films and coating for food applications. The formation of protein films and coatings require a denaturation step to unfold the protein molecules and to promote intermolecular interactions via the formation of disulfide and hydrogen bonds, as well as hydrophobic interactions. Plasticizers, such as low‐molecular‐weight polyols and organic acids, are often added to the film‐forming formulations to impart flexibility essential for end‐use handling. By and large, edible films are produced by solvent casting. Solution properties (viscosity, surface tension, etc.) must be optimized in order to produce coherent film of consistent material properties. Alternatively, dry processing involves extrusion of proteins at elevated temperature (above glass transition) in the presence of small quantity of water and plasticizer. Material properties of protein films can be modified by incorporating additives, heat curing, and/or irradiation/chemical treatments to achieve optimal mechanical strength and extensibility essential for end‐use handling. Protein films and coatings are strong gas barriers when they are dry but exhibit poor moisture barrier properties due to their inherent hydrophilic nature. Edible films and coatings are versatile carriers for bioactives (e.g. antimicrobials, antioxidants, nutraceuticals, and micronutrients), flavors, colors, and other additives, making them a useful tool in product innovation.
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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) | 0.000 | 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) | 0.006 | 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".