Persistence of foodborne viruses on various frozen berries initially frozen at different temperatures and stored for up to two years
Bibliographic record
Abstract
Transmission of foodborne viruses such as human norovirus (HuNoV) and hepatitis A virus (HAV) via frozen berries has become a significant public health concern. In this study, we investigated the long-term persistence of murine norovirus-1 (MNV-1, a HuNoV surrogate) and HAV on five types of berries initially frozen at -20, -80 or -196 °C and stored at -20 °C for up to 24 months. MNV-1 titers decreased by up to 3.2 log after 24 months, particularly on strawberries initially frozen at -196 °C, whereas HAV was more stable, with the highest reduction of 1.7 log observed under the same conditions. Viral persistence was influenced by berry type, initial freezing temperature, and storage duration. The greatest reductions were consistently observed on strawberries, possibly due to increased dehydration and recrystallization over time. Freezing at -196 °C often caused freeze-cracking of the berries, which became more frequent as frozen storage progressed, accelerating long-term dehydration and structural damage. Additionally, subsequent ice crystal growth and recrystallization likely enhanced dehydration, further reducing viral infectivity. Despite gradual reductions in titer, sufficient viral inactivation was not achieved. This reaffirms that freezing alone is not an adequate viral inactivation strategy, serving primarily as a preservation method that stabilizes food while temporarily suppressing microbial activity, rather than an effective means of eliminating pathogenic viruses. Additional interventions such as physicochemical or non-thermal disinfection are essential to ensure adequate reduction of viral infectious titer in frozen berries.
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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 | 0.001 | 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".