Challenges in the microbiological food safety of fresh produce: Limitations of post-harvest washing and the need for alternative interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.001 |
| 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 it