Challenges in the microbiological food safety of fresh produce: Limitations of post-harvest washing and the need for alternative interventions
Bibliographic record
Abstract
Fresh produce (processed fruit and vegetables) continues to be the main source of foodborne illness outbreaks implicating pathogens such as Escherichia coli O157:H7, Salmonella, Listeria monocytogenes and human parasites (e.g. hepatitis A, Cyclospora). Previously, outbreaks were primarily limited to leafy greens, tomatoes, and cantaloupes, but more recently there has been a trend of more diverse produce types (e.g. cucumbers and papayas) being implicated. Although on-farm good agriculture practices (GAP) contribute to preventing pathogens entering the fresh produce chain, it cannot be relied upon completely due to the open nature of farming. As a consequence, there is an identified need for interventions that can remove field-acquired contamination, especially given fresh produce is eaten raw. In the following review, an overview of foodborne illness outbreaks linked to contaminated fresh produce will be described along with potential sources of contamination. Post-harvest washing that was once considered decontamination is now viewed as a high-risk cross-contamination point. The challenges in monitoring the post-harvest wash process will be discussed along with processing factors that need to be considered. A range of alternative, or supplemental, non-aqueous interventions will be described including irradiation, ultraviolet light, high hydrostatic pressure, gas phase (ozone and chlorine dioxide), and hydroxyl radicals generated through advanced oxidative process or gas plasma. All have been proved to be effective at pathogen control on the laboratory scale and are poised to enter commercial application. The current status of these alternative interventions along with challenges of integrating into commercial practice will be described.
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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 | 0.003 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".