Understanding the Salmonella Inactivation Mechanisms of 365, 395 and 455 nm Light Pulses Emitted from Light-Emitting Diodes
Bibliographic record
Abstract
Salmonella is a foodborne pathogen responsible for several outbreaks in low-water activity (aw) foods. Treatment using light pulses emitted from light-emitting diodes (LED) is an emerging decontamination method to inactivate foodborne pathogens. The objective of this study was to understand the antibacterial mechanisms of light pulses with 365, 395 and 455 nm wavelengths against Salmonella Typhimurium in low-aw conditions. The 365 nm light pulses showed better inactivation efficacy against low-aw S. Typhimurium than the 395 nm light pulses. For instance, the 365 nm LED treatment with an ~217 J/cm2 dose produced a reduction of 2.94 log (CFU/g) in S. Typhimurium cell counts, as compared with a reduction of 1.08 log (CFU/g) produced by the 395 nm LED treatment with the same dose. We observed a significant generation of intracellular reactive oxygen species (ROS) in S. Typhimurium cells after treatments with the 365, 395 and 455 nm light pulses at low-aw conditions. The LED treatments also showed a significant membrane lipid oxidation of S. Typhimurium cells after treatments with 365, 395 and 455 nm light pulses. Overall, a major role of ROS generation was observed in the inactivation efficacy of the 365, 395 and 455 nm light pulses against S. typhimurium at low-aw conditions.
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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".