Food packaging and its oxygen transfer models in active multilayer structures: A theoretical review
Bibliographic record
Abstract
Many food products are sensitive to oxygen which can promote rancidity, aerobic microorganism growth, browning, vitamin degradation, flavor loss, and other undesirable reactions. Active packaging concepts have been successfully applied to delay these deteriorative phenomena. In particular, oxygen scavengers help limit O 2 ingress into packaging thus extending product shelf-life and thereby reducing food waste. Besides oxygen barrier polymers, other composite materials have been developed to reduce the overall O 2 permeability into the package by adding oxygen scavengers or barrier fillers into the packaging structures. Typical thermoplastic food packaging barrier properties are summarized, including commercially available ones, and reported in the patent literature. Furthermore, various mathematical models have been developed to predict the oxygen transport behavior in active packaging to simulate oxygen transport in polymeric and composite materials. Such models are helpful to provide a better understanding of the mass transport fundamentals involved and shorten product development cycle time and cost. This review provides an overview of various oxygen absorber systems used in active food packaging. It also summarizes the mathematical models that simulate oxygen transport and absorption in different polymer films. Finally, these models can be adapted for other polymers and O 2 scavengers to simulate O 2 transfer on active polymeric materials. Simulation tools help the fundamental understanding of the O 2 transport behavior and facilitate to developing novel active packaging systems that extend food shelf-life and enhance their safety.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".