Cold plasma-integrated bio-based packaging for meat quality and safety monitoring: a mini review
Bibliographic record
Abstract
Cold plasma technology and bio-based materials are emerging as sustainable solutions for enhancing meat safety and quality. However, conventional packaging lacks real-time monitoring capabilities, leading to food waste and health risks. This review critically examines the integration of cold plasma with bio-based materials to develop intelligent systems for monitoring meat quality and safety. Cold plasma enhances bio-based materials by improving antimicrobial properties and pH-responsiveness, enabling real-time detection of spoilage markers. Plasma-treated chitosan films embedded with silver nanoparticles detect pathogens, while anthocyanin-doped cellulose hydrogels exhibit colorimetric responses to spoilage-induced pH changes. Despite these advances, challenges such as sensor accuracy under industrial conditions, lipid oxidation during plasma treatment, and regulatory uncertainties hinder scalability. Additionally, consumer acceptance of plasma-treated materials requires further study. Future research should prioritize optimizing plasma parameters for sensor compatibility, validating industrial scalability, and establishing safety protocols for plasma-bio-based composites. By addressing these gaps, this integration promises eco-friendly, multifunctional packaging that ensures meat safety, extends shelf life, and aligns with global sustainability goals.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".