Nonthermal Plasma–Liquid Interactions in Food Processing: A Review
Bibliographic record
Abstract
Nonthermal processing methods are often preferred over conventional food processing methods to ensure nutritional quality. Nonthermal plasma (NTP) is a new field of nonthermal processing technology and seeing increased interest for application in food preservation. In food applications of NTP, liquid interactions are the most prevalent. The NTP reactivity and product storability are altered during this interaction. The water activated by NTP (plasma-activated water [PAW]) has gained considerable attention during recent years as a potential disinfectant in fruits and vegetable washing. However, detailed understanding of the interactions of NTP reactive species with food nutritional components in the presence of water and their stability in food is required to be explored to establish the potential of this emerging technology. Hence, the main objective of this review is to give a complete overview of existing NTP-liquid interactions. Further, their microbial inactivation mechanisms and the effects on food quality are discussed in detail. Most of the research findings have suggested the successful application of NTP and PAW for microbial inactivation and food preservation. Still, there are some research gaps identified and a complete analysis of the stability of plasma reactive species in food is still missing. By addressing these issues, along with the available research output in this field, it is possible that NTP can be successfully used as a food decontamination method in the near future.
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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 | 0.003 | 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) | 0.005 | 0.002 |
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".