Inactivation of Deposited Bioaerosols on Food Contact Surfaces with UV-C Light Emitting Diode Devices
Bibliographic record
Abstract
Abstract The airborne transmission of infectious diseases and bioaerosol-induced cross-contamination pose significant challenges in the food, dairy, and pharma industries. This study evaluated the effectiveness of 279 nm UV-C LED irradiation for decontaminating bioaerosols, specifically containing microorganisms like E. coli (C3040- Kanamycin resistant), Salmonella Enteritis (ATCC 4931), and Pseudomonas fragi (ATCC 4973), on food contact surfaces. Borosilicate glass, silicon rubber, and stainless steel (316L) surfaces were selected for experimentation for their usage in the food industry. A 50µL cell suspension was aerosolized at 25 psi pressure using a 4-jet BLAM Nebulizer within a customized glass chamber and then deposited onto the surface of the coupons. The serial dilution approach was used for the microbial enumeration, followed by double plating. With a low RMSE and high R2 values, the biphasic kinetic model demonstrated the excellent goodness of fit parameters. At a UV-C dose of 6 mJ cm− 2, glass surfaces showed the maximum microbial inactivation ( i.e. 2.80, 3.81, and 3.56 log CFU/mL for E. coli, Salmonella, and P. fragi, respectively). Stainless steel and silicon rubber showed significant microbial inactivation but were consistently lower than glass. Our research indicates that UV-C LEDs can disinfect bioaerosols on food contact surfaces effectively.
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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 | 0.000 |
| 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".