Survival and Inactivation by Advanced Oxidative Process of Foodborne Viruses in Model Low-Moisture Foods
Bibliographic record
Abstract
Enteric viruses, such as human norovirus (NoV) and hepatitis A virus (HAV), are the major causes of foodborne illnesses worldwide. These viruses have low infectious dose, and may remain infectious for weeks in the environment and food. Limited information is available regarding viral survival and transmission in low-moisture foods (LMF). LMFs are generally considered as ready-to-eat products, which undergo no or minimal pathogen reduction steps. However, numerous foodborne viral outbreaks associated with LMFs have been reported in recent years. The objective of this study was to examine the survival of foodborne viruses in LMFs during 4-week storage at ambient temperature and to evaluate the efficacy of advanced oxidative process (AOP) treatment in the inactivation of these viruses. For this purpose, select LMFs such as pistachios, chocolate, and cereal were inoculated with HAV and the norovirus surrogates, murine norovirus (MNV) and feline calicivirus (FCV), then viral survival on these food matrices was measured over a four-week incubation at ambient temperature, by both plaque assay and droplet-digital RT-PCR (ddRT-PCR) using the modified ISO-15216 method as well as the magnetic bead assay for viral recovery. We observed an approximately 0.5 log reduction in viral genome copies, and 1 log reduction in viral infectivity for all three tested viruses following storage of select inoculated LMFs for 4 weeks. Therefore, the present study shows that the examined foodborne viruses can persist for a long time in LMFs. Next, we examined the inactivation efficacy of AOP treatment, which combines UV-C, ozone, and hydrogen peroxide vapor, and observed that while approximately 100% (4 log) inactivation can be achieved for FCV, and MNV in chocolate, the inactivation efficiency diminishes to approximately 90% (1 log) in pistachios and 70% (< 1 log) in cereal. AOP treatment could therefore be a good candidate for risk reduction of foodborne viruses from certain LMFs depending on the food matrix and surface of treatment.
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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".