Oxygen scavenging systems for food packaging applications: A review
Bibliographic record
Abstract
Abstract During the last decades, the food industry has seen several changes in packaging technology and applications because of new consumer demands and market trends. These drivers can be summarized as requirements for high quality, freshness, extended shelf‐life, and security of food products. Oxygen scavengers, as a type of active packaging, absorb the dissolved oxygen or the oxygen in the headspace to protect the oxygen‐sensitive food products against oxidative degradation. In this review, organic and inorganic oxygen scavengers such as iron, ascorbic acid and its derivatives, illumination‐activated scavengers, reducible organic compounds, and unsaturated hydrocarbon‐based scavengers, including polyunsaturated fatty acids and polybutadiene, have been thoroughly discussed, and the mechanism of action of each system is briefly explained. In addition, some of the issues associated with the applied traditional transition metal catalysts to accelerate the oxidation rate in oxygen scavenging systems are listed, and the results from a recent study directed towards addressing those concerns by applying TiO 2 photocatalyst are presented.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".