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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".