Potential of Cold Plasma Technology in Ensuring the Safety of Foods and Agricultural Produce: A Review
Bibliographic record
Abstract
Cold plasma (CP) is generated when an electrical energy source is applied to a gas, resulting in the production of several reactive species such as ultraviolet photons, charged particles, radicals and other reactive nitrogen, oxygen, and hydrogen species. CP is a novel, non-thermal technology that has shown great potential for food decontamination and has also generated a lot of interest recently for a wide variety of food processing applications. This review discusses the potential use of CP in mainstream food applications to ensure food safety. The review focuses on the design elements of cold plasma technology, mode of action of CP, and types of CP technologies applicable to food applications. The applications of CP by the food industry have been demonstrated for food decontamination, pesticide residue removal, enzyme inactivation, toxin removal, and food packaging modifications. Particularly for food processing, CP is effective against major foodborne pathogenic micro-organisms such as Listeria monocytogenes and Salmonella Typhimurium, Tulane virus in romaine lettuce, Escherichia coli O157:H7, Campylobacter jejuni, and Salmonella spp. in meat and meat products, and fruits and vegetables. However, some limitations such as lipid oxidation in fish, degradation of the oligosaccharides in the juice have been reported with the use of CP, and for these reasons, further research is needed to mitigate these negative effects. Furthermore, more research is needed to maximize its potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".