Eco-friendly rapeseed protein-chitosan hybrid nanocomposite films for active food packaging and preservation
Bibliographic record
Abstract
Rapeseed proteins are an interesting bioresource for the growing bio-economy due to their numerous physiochemical properties, making them a renewable resource for food packaging applications. In this study, rapeseed protein-chitosan blends were compatibilized by montmorillonite (MMT) and citric acid to develop active food packaging films. Three blends were assessed for mechanical, thermal, water vapor permeability, opacity, water uptake, antioxidant activities, recyclability, and biodegradability. Response surface methodology (RSM) was used to optimize the effects of reinforcement, cross-linking, and pH on the film's tensile strength. The predicted optimal ratios were 3.40%, 5% (w/w), at a pH of 5, resulting in a tensile strength of 26.86 MPa and 40% elongation at break. The study revealed that dual compatibilization with MMT and citric acid enhanced compatibility, barrier properties, water stability, light resistance, and thermal stability. Furthermore, active packaging films were tested by preserving the quality of berries for 9 days at room temperature, showing it maintained fruit quality and extended shelf life. This study demonstrates a sustainable, efficient method for creating bioplastic films with high strength, durability, and excellent properties suitable for food preservation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".