Nonthermal Plasma–Liquid Interactions in Food Processing: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it