Investigating cold plasma jet effectiveness for eggshell surface decontamination
Bibliographic record
Abstract
Ensuring the safety of eggs, a valuable source of high-quality protein and essential vitamins, is critical to prevent foodborne illness. Cold plasma, an emerging green technology, generates various reactive species that effectively inactivate microorganisms, attracting global attention for its potential in food safety. This study aims to explore the application of cold plasma as a chemical-free, non-thermal approach for decontaminating egg surfaces and to assess its potential as an alternative to conventional egg-washing methods. Various operation conditions of a cold plasma jet device, including power levels (300–400 W), exposure times (20–60 s), distances between the nozzle and eggshell (1–3 cm), airflow rates (30–35 L/min), and feed gases (nitrogen, air (20 %–65 % relative humidity), helium and air mixtures), were examined to decontaminate hen eggshells inoculated with Escherichia coli and Salmonella enterica bacteria. The results showed a maximum log reduction and deactivation efficiency of 1.94 and 98.74 % for E. coli , and 1.11 and 92.20 % for Salmonella , after a 60 s treatment of egg surface using a cold plasma device set at 1 cm distance, 400 W power, and 35 L/min airflow with 65 % relative humidity. Moreover, our findings indicated no significant differences in egg quality, eggshell cuticle chemical composition, and cuticle coverage between untreated eggs and those treated with cold plasma. This suggests the potential of this non-chemical, non-thermal emerging technique to be commercialized as a substitute for conventional washing methods. • Cold plasma effectively decontaminates eggshell inoculated with E. coli/S. enterica . • Cold plasma deactivates 98.74 % of E. coli and 92.20 % of S. enterica on egg surfaces. • Cold plasma has no adverse impact on egg quality and eggshell cuticle properties.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".