Potential for in-field pre-harvest control of foodborne human pathogens in leafy vegetables: Identification of research gaps and opportunities
Bibliographic record
Abstract
Leafy vegetables (LVs) used as raw ingredients in salads have become a crucial part of our healthy diets. However, they are considered to be high-risk foods due to the lack of reliable measures to fully mitigate food safety risks in the absence of cooking prior to consumption. Indeed, outbreaks of foodborne illnesses and recalls associated with LVs continue to occur. This highlights the potential for additional strategies, such as pre-harvest sanitization, to better address the risks. This review undertook a comprehensive analysis of the current state of pre-harvest technologies that apply chemical sanitisers via treated irrigation water or via sanitization sprays of the field crop. Several potential chemical sanitisers were shown to be effective against various food-borne pathogens when applied pre-harvest to crops. The review identified significant knowledge gaps concerning the efficacy of chemical sanitisers including their effect on the ecosystem health such as plant health, soil health, impacts on the natural leaf and soil microbiome. Addressing these gaps will provide a better understanding of the feasibility of these sanitization methods, including cost-benefit analyses. It is proposed that a risk framework, tailored to specific crops, soil types and weather conditions, should be developed to provide a science-based justification for the implementation of pre-harvest sanitization to improve the safety of LVs. • Pre-harvest sanitization of leafy vegetables can reduce food safety risks. • Spraying chemical sanitizers on the crop shows potential to reduce microbial loads. • Preliminary cost-benefit analysis suggests that field sanitization can be feasible.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".