Safety of edible coatings on fruits and vegetables
Bibliographic record
Abstract
Edible coatings are a combination of substances that are applied onto foods to enhance their shelf life and that can be consumed by humans. Coatings are often composed of a combination of proteins, lipids, and/or polysaccharides and can contain plasticizers to increase flexibility and elongation. Surfactants and emulsifiers are sometimes added to decrease surface water activity and prevent moisture loss. The ideal edible coating slows the loss of desirable flavor volatiles and water vapor as well as restricts the exchange of gases, creating a modified atmosphere but not creating anaerobic conditions, all while not adding off-flavors to the food. In this review, the different components used in edible films and coatings are examined, along with their benefits and weaknesses. Additionally, this study reviews possible safety issues associated with consuming ingredients used in edible films and coatings. Edible films and coatings are more successful when multiple ingredients are used together to create a good moisture and gas barrier, thus creating the possibility for interactions. Most, but not all, ingredients used in edible films and coatings do not pose a risk to people when consumed at the levels present in coatings. Thus, it is imperative to review and consider new data on the safety of ingredients used in coatings.
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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 | 0.001 |
| 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 | 0.000 |
| 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".