Evaluation of Pulsed Xenon UV Lamp on Inactivation of Listeria monocytogenes on Stainless- Steel Surfaces
Bibliographic record
Abstract
Foodborne diseases remain a major global public health concern, with Listeria monocytogenes posing a significant risk, particularly for immunocompromised individuals. Ensuring food safety and minimizing contamination has become a top priority for food production facilities. \nUV-C disinfection has emerged as a safe and effective method for inactivating various microorganisms. With the phase-out of traditional mercury UV-C lamps due to the Minamata Convention on Mercury, pulsed xenon UV lamps (PX-UV) have gained popularity as a mercury-free alternative. PX-UV lamps offer high-intensity UV-C irradiation in a short time and a wider range of wavelengths, making them environmentally friendly and versatile. \nTo better understand the factors affecting the disinfection efficacy of PX-UV lamps, an investigation of operational parameters was conducted. The study focused on the impact of exposure time, frequency, UV fluence (dose), angle of irradiance, and shielding effects due to the presence of soiling agents on the reduction of Listeria monocytogenes on stainless steel surfaces. \nRadiometry analysis revealed that pulse frequency, in the range of 1 Hz to 25 Hz, influenced the fluence delivered per pulse, with higher frequencies resulting in lower UV fluence per pulse. However, regardless of pulse frequency, angle, or exposure time, the total UV fluence received was the main determinant of the log reduction. Notably, a 5.59-log reduction was achieved with 30 seconds of exposure to UV light at 25 Hz, corresponding to a fluence of 151.2 mJ/cm2. \nExperimental studies conducted in the presence of soiling agents have demonstrated the significance of the type of soiling material. It not only could affect the growth of bacteria but also could play a role in shielding the bacteria from UV radiation. The composition of the soiling material has implications for both the susceptibility of bacteria to UV radiation and their ability to proliferate. The thesis focuses on three categories of soiling agents: proteins, carbohydrates, and lipids. Among these groups, it was observed that protein soils had the most significant impact on promoting bacterial growth, while the log reduction value remained unchanged. Carbohydrates also had an effect on bacterial growth, although to a lesser extent compared to proteins. On the other hand, lipids exhibited a shielding effect, leading to a reduction in the log reduction value of L. monocytogenes. These findings highlight the varying effects of different soiling agents on bacterial growth and the subsequent log reduction achieved during disinfection. \nThis research highlights the significance of UV-C fluence as the primary consideration when using PX-UV lamps for no-touch disinfection, emphasizing the importance of selecting appropriate operational parameters to achieve the desired fluence. However, further research in this field can enhance our understanding of this technology. Some potential areas of future study include investigating the efficacy of UV disinfection on different types of surfaces, exploring the disinfection of fresh food products, and examining a wider range of soiling agents to better comprehend the impact of material structure on UV efficacy. These studies would contribute to expanding our knowledge and improving the effectiveness of UV-based disinfection methods in various applications and particularly food industries.
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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".