Current Trends in the Utilization of Essential Oils for Polysaccharide- and Protein-Derived Food Packaging Materials
Bibliographic record
Abstract
Essential oils (EOs) have received attention in the food industry for developing biopolymer-derived food packaging materials. EOs are an excellent choice to replace petroleum-derived additives in food packaging materials due to their abundance in nature, eco-friendliness, and superior antimicrobial and antioxidant attributes. Thus far, EOs have been used in cellulose-, starch-, chitosan-, and protein-based food packaging materials. Biopolymer-based materials have lower antioxidant and antibacterial properties in comparison with their counterparts, and are not suitable for food packaging applications. Various synthetic-based compounds are being used to improve the antimicrobial and antioxidant properties of biopolymers. However, natural essential oils are sustainable and non-harmful alternatives to synthetic antimicrobial and antioxidant agents for use in biopolymer-derived food packaging materials. The incorporation of EOs into the polymeric matrix affects their physicochemical properties, particularly improving their antimicrobial and antioxidant properties. EOs in the food packaging materials increase the shelf life of the packaged food, inhibit the growth of microorganisms, and provide protection against oxidation. Essential oils also influence other properties, such as tensile, barrier, and optical properties of the biopolymers. This review article gives a detailed overview of the use of EOs in biopolymer-derived food packaging materials. The innovative ways of incorporating of EOs into food packaging materials are also highlighted, and future perspectives are discussed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.002 |
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".