Inactivation of foodborne viruses by novel organic peroxyacid-based disinfectants
Bibliographic record
Abstract
Viruses are responsible for most enteric foodborne illnesses worldwide. The foods most frequently involved are fresh fruits and vegetables since they undergo little or no processing. Washing with a chemical disinfectant is a convenient way of inactivating viruses on foods. Peracetic acid, widely used as a disinfectant in the food industry, has the drawback of leaving a strong odor and is ineffective alone against some foodborne viruses. In this study, four disinfectants, namely per levulinic acid with or without sodium dodecyl sulfate, peracetic acid and a commercial peracetic acid-based disinfectant were tested on murine norovirus 1 (MNV-1), hepatitis A virus (HAV), and hepatitis E virus (HEV). Disinfectant concentrations were 50, 80, 250, 500, and 1000 mg l–1 and contact times were 0.5, 1, 5, and 10 min. Under these conditions, per levulinic acid supplemented with 1% SDS reduced MNV-1 infectious titer by 3 log cycles vs. 2.24 log cycles by peracetic acid within 0.5 min. On stainless steel at 80 ppm, only peracetic acid produced 3-log reductions within 0.5 min. None of these peroxyacids was able to reduce infectious titers of HAV or HEV by even 2 log cycles at any concentration or time-tested. This study will guide the development of new chemical formulas that will be more effective against major foodborne viruses and will have less impact on food quality and the environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".