Recent Progress on the Application of Chitosan, Starch and Chitosan–Starch Composites for Meat Preservation—A Mini Review
Bibliographic record
Abstract
The preservation of meat via sustainable methods and packaging is an area of continued interest driven by the need to address food security. The use of biomaterial films and coatings has gained significant attention due to their non-toxicity and biodegradability compared with conventional synthetic films. Starch and chitosan are sustainable sources for the preparation of films/coatings owing to their relatively low cost, natural abundance derived from numerous sources, biocompatibility, biodegradability, and antimicrobial, antioxidant, and film-forming attributes. These remarkable features have notably increased the shelf life of meat by inhibiting lipid oxidation and microbial activity in food products. Furthermore, recent studies have successfully incorporated binary biopolymer (starch and chitosan) systems to combine their beneficial properties upon composite formation. This literature review from 2020 to the present reveals that chitosan- and starch-based films and coatings have potential to contribute to enhanced food security and safety measures whilst reducing environmental issues and improving sustainability, compared with conventional synthetic materials.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".