Edible Films and Coatings from Soybean and Other Protein Sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 it