Application of novel engineered water nanostructures techniques for eggshell surface decontamination
Bibliographic record
Abstract
Hen eggs play a vital role in the human diet and their safety is critical to avoid the risk of foodborne illness for consumers. Engineered Water Nanostructures (EWNS) generated via an electrospray system is a novel and nanotechnology-based technique, which can be utilized for inactivating bacteria. The primary objective of this work was to assess this technique's effectiveness as a chemical-free and non-thermal approach for decontaminating egg surfaces and investigate its potential as an alternative to the conventional method of washing eggs. The results showed that under EWNS operating conditions of 5 min of exposure time, 1 μL/min/needle of water flow rate, and 9.0 kV/cm of electric field strength, the highest inactivation efficiency of 97.6 and 80.4% were obtained for decontaminating egg surfaces contaminated by E. coli and Salmonella spp., respectively. Results also proved no significant difference in physical properties, chemical components, and eggshell cuticle coverage of EWNS-treated eggs compared to unwashed eggs.
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) | 0.000 | 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 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".