Challenges and opportunities related to the use of chitosan as a food preservative
Bibliographic record
Abstract
Chitosan has attracted a growing attention as a food preservative due to its versatility, nontoxicity, biodegradability and biocompatibility. This review aims to provide a critical appraisal of the limitations and opportunities of the use of chitosan as a food preservative. The application of chitosan as a food preservative necessitates insights into mechanisms of chitosan-mediated cell death and injury, factors affecting chitosan activity and effects of chitosan on food safety and quality. Chitosan exerts antimicrobial activity by perturbing the negatively charged cell envelope of micro-organisms with its polycationic structure. Intrinsic characteristics, including molecular weight and degree of deacetylation (DD), and other ambient conditions, including pH, temperature and neighbouring components, affect chitosan activity. Because the antimicrobial activity of chitosan is mainly based on ionic interactions with negatively charged components of the bacterial cell envelope, the food matrix can strongly interfere with the antimicrobial activity of chitosan. Despite its limited antimicrobial efficacy, chitosan demonstrates both bactericidal and bacteriostatic effects in specific food products. Moreover, chitosan can also enhance the efficacy of commercial intervention technologies, such as heat and pressure treatment, and aid the preservation of food quality, including retardation of lipid oxidation, weight loss and deterioration in sensory attributes.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".