Using a Risk-based Approach to Evaluate Mitigation Options for Fresh Produce and Propose Microbiological Sampling Strategies in the Growing Field

Introduction: Fresh produce can become contaminated along the farm-to-fork (F2F) continuum due to contact with different hazards. Examples of contamination sources are irrigation water, soil amendment, wild and domestic animals, and worker’s health and hygiene among others. Given the wide range of contamination sources, an F2F modeling approach is advisable to provide a systematic way to (1) characterize the potential for contamination along F2F stages, and (2) compare the efficacy of different mitigation options to reduce contamination and, therefore, reduce foodborne illness.

Purpose: The purpose of this project was to develop and demonstrate a methodology that could (1) support the investigation of “contamination scenarios” using probabilistic simulation techniques, (2) allow for the comparison of different intervention strategies, and (3) offer a risk-based sampling approach for microbiological contamination in the growing field.

Methods: A pilot study evaluated Escherichia Coli O157:H7 contamination of romaine lettuce during the production and harvest stages. An Agent-Based Modeling framework was used to predict the contamination prevalence and levels in the growing field. Input values were derived using a combination of data from literature review, field trials, and expert judgment for stage-specific contamination sources.

Results: The notional results from the hypothetical case studies suggested that contamination levels could be significantly reduced by (1) limiting wild animal access to the growing field, and (2) assigning sufficient buffer zones between the field and the neighboring cattle farm. Results also suggested that, improving the quality of irrigation water could reduce contamination prevalence. Furthermore, the application of a risk-based sampling approach indicated that contaminated units (i.e., lettuce heads) could be identified with a higher probability than standard “Z” sampling patterns.

Significance: This methodology offers a transparent, practical, and robust modeling approach with which to evaluate the efficacy of different mitigation options and identify areas in the growing field for targeted sampling activities.