Examining the Effect of Organic Acids on Inactivation of Hepatitis E Virus
Bibliographic record
Abstract
Infection with hepatitis E virus genotype 3 (HEV-3) is an emerging cause of illness in developed countries. In North America and Europe, HEV-3 has been increasingly detected in swine, and exposure to pigs and pork products is considered the primary source of infection. We have previously demonstrated the prevalence of the HEV-3 genome in commercial pork products in Canada. In this study, we investigated the application of citric acid and acetic acid to inactivate HEV-3 on food and on food contact surfaces. For this purpose, plastic, stainless steel, and pork pâté surfaces were inoculated with HEV-3 and were treated with acetic acid or citric acid at 1, 3, or 5%. The infectivity of posttreatment viral particles was determined by cell culture. A greater than 2-log reduction in viral infectivity was observed on plastic and stainless steel treated with the organic acids, but the treatment was less effective on HEV infectivity on pork pâté (average reductions of 0.47 log citric acid and 0.63 log acetic acid). Therefore, we conclude that citric acid and acetic acid have potential application to control HEV-3 on food contact surfaces but are not suitable for food. Citric acid reduces HEV-3c infectivity by a maximum of 2.5 log on stainless steel and plastic. Acetic acid reduces HEV-3c infectivity by a maximum of 2.4 log on stainless steel and plastic. Citric acid and acetic acid reduce HEV-3c infectivity by a maximum of 0.7 log on pork pâté.
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) | 0.001 | 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".