Inactivation of murine norovirus and hepatitis A virus on various frozen fruits using pulsed light
Bibliographic record
Abstract
The frozen fruit sector has experienced significant growth due to improved product quality as well as the advantage of long-term preservation. However, freezing alone does not eliminate foodborne viruses, a major public health concern and considerable economic burden. One promising disinfecting treatment is pulsed light, shown previously to inactivate hepatitis A virus (HAV) and murine norovirus-1 (MNV-1) on the surface of fresh berries. Viral loads were reduced by 1–2 log, with minor visual quality deterioration observed. In this study, an FDA-compliant pulsed light treatment (11.52 J/cm2) was applied to frozen fruits and berries. Infectious MNV-1 and HAV titers were reduced by 1–2 log on most frozen fruits. A noteworthy finding was that reductions of both viruses on cranberries exceeded 3.5 log cycles. Although pulsed light caused a measurable rise in temperature on the product surface, no visible physical changes (e.g., color) were observed, and the fruit pieces were still frozen after treatment. Although the reduction of infectious titer by pulsed light alone was not large (1–2 log), considering the low amount of virus typically found on fruit, it may be beneficial in the frozen fruit sector. It would be easy to combine with other treatments, and synergic interactions might increase virus inactivation.
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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".