UV-C-Activated Riboflavin Crosslinked Gelatin Film with Bioactive Nanoemulsion for Enhanced Preservation of Fresh Beef in Modified Atmosphere Packaging
Bibliographic record
Abstract
This study explores a new eco-friendly approach for developing bioactive gelatin films using UV-C irradiation-induced photo-crosslinking. Riboflavin, a food-grade photoinitiator, was selected at an optimal concentration of 1.25% (w/w) for crosslinking gelatin under UV-C exposure for 4 to 22 min. Physicochemical analyses revealed enhanced tensile strength, reduced water vapor permeability, and lower water solubility in films crosslinked for up to 13 min. FTIR analysis demonstrated significant molecular changes, confirming the formation of crosslinking connections in gelatin–riboflavin films. Antimicrobial nanoemulsion (NE) (0.5, 0.75, 1% v/v) was incorporated into crosslinked films and applied to fresh beef. The 1% NE film exhibited the strongest antimicrobial effect, extending shelf-life by 20 days. In vitro release study confirmed Fickian diffusion behavior in the 1% NE film. This study also investigated the synergy between 1% NE film and three different types of modified atmosphere packaging (MAP) on the microbiological and physicochemical properties of beef for 26 days. The best results were achieved with 1% NE film under MAP1 and MAP2, which preserved meat redness and prevented lipid oxidation, extending the shelf-life up to 26 days. Therefore, UV-C irradiation-induced crosslinked bioactive film combined with high-oxygen MAP offers a promising solution for prolonging the shelf-life of beef.
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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) | 0.000 | 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".