New food safety challenges of viral contamination from a global perspective: Conventional, emerging, and novel methods of viral control
Bibliographic record
Abstract
Food- and waterborne viruses, such as human norovirus, hepatitis A virus, hepatitis E virus, rotaviruses, astroviruses, adenoviruses, and enteroviruses, are major contributors to all foodborne illnesses. Their small size, structure, and ability to clump and attach to inanimate surfaces make viruses challenging to reduce or eliminate, especially in the presence of inorganic or organic soils. Besides traditional wet and dry methods of disinfection using chemicals and heat, emerging physical nonthermal decontamination techniques (irradiation, ultraviolet, pulsed light, high hydrostatic pressure, cold atmospheric plasma, and pulsed electric field), novel virucidal surfaces, and bioactive compounds are examined for their potential to inactivate viruses on the surfaces of foods or food contact surfaces (tools, equipment, hands, etc.). Every disinfection technique is discussed based on its efficiency against viruses, specific advantages and disadvantages, and limitations. Structure, genomic organization, and molecular biology of different virus strains are reviewed, as they are key in determining these techniques effectiveness in controlling all or specific foodborne viruses. Selecting suitable viral decontamination techniques requires that their antiviral mechanism of action and ability to reduce virus infectivity must be taken into consideration. Furthermore, details about critical treatments parameters essential to control foodborne viruses in a food production environment are discussed, as they are also determinative in defining best disinfection and hygiene practices preventing viral infection after consuming a food product.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".