UV-C inactivation of microorganisms in droplets on food contact surfaces using UV-C light-emitting diode devices
Bibliographic record
Abstract
The main objective of this study was to investigate the effectiveness of ultraviolet light (UV-C) emitting diodes for the decontamination of stainless steel food contact surfaces. Listeria monocytogenes (ATCC 19115), Escherichia coli (ATCC 25922), and Salmonella enterica serovar Typhimurium (ATCC 700720) were chosen as challenge microorganisms. Target microorganisms were subjected to UV-C dosages of 0, 2, 4, 6, and 8 mJ cm −2 at an average fluence of 0.163 mW/cm 2 using a near-collimated beam operating at 279 nm wavelength. Escherichia coli showed lower sensitivity to UV-C light compared to Salmonella Typhimurium and followed first-order kinetics. Escherichia coli and Salmonella Typhimurium were reduced by more than 3-log 10 cycles at the maximum UV dosage of 12 mJ cm −2 . In contrast, Listeria monocytogenes followed the Weibull model with an apparent shoulder in the initial doses. A maximum reduction of 4.4-log 10 was achieved at the highest exposure level. This study showed that UV-C LED devices represent an excellent alternative for the inactivation of foodborne microorganisms in droplets. Results clearly demonstrate that UV-C LED devices can serve as an additional sanitation method to routine cleaning practices, which are commonly utilized in the food industry.
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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) | 0.000 | 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".