SIMULATING THE EFFECTS OF CROSS-CONTAMINATION OF ESCHERICHIA COLI O157:H7 ON FRESH-CUT LETTUCE DURING POST-HARVEST PROCESSING FROM AN AGENT BASED PERSPECTIVE

Abstract

Fresh-cut leafy greens are potential vehicles for foodborne pathogens such as Escherichia coli O157:H7 and are at high risk of causing foodborne illnesses. Cross-contamination during post-harvesting processing of leafy greens are of great concern as it has been linked to many outbreaks in the US. An agent-based simulation was developed to represent the spatial and temporal E. coli O157:H7 cross-contamination dynamics in a processing facility for fresh-cut romaine and iceberg lettuces using NetLogo. The model was designed to (1) track E. coli O157:H7 and lettuce movements in time, (2) evaluate microbial contamination in different equipment/surface and calculate the probability events of cross-contamination between lettuces and equipment, and (3) determine the number of fresh-cut contaminate processed bags and their level of contamination at the end of the processing line. An extension was also added to the main model to model E. coli O157:H7 growth due to temperature abuses in a cold storage facility. A user-friendly interface was created to follow spatial and temporal variations in model outputs. The number of contaminated bags, the lettuce contamination levels, were computed, and visualized on plots and diagrams. Diagrams representing equipment variables were also produced to track changes in these variables. Experimental data of cross contamination from literature was used to describe the facility and validated the model. Sensitivity analysis of different factors influencing cross-contamination was tested. The key factor affecting cross-contamination is the chlorination concentration dose rate. The number of contaminated bags is affected significantly by the initial level of contamination of the incoming lettuce heads and the probability of contamination in the incoming produce. The level of contamination as well as probability of contamination in the facility environment (equipment) affect the number of bags contaminated. Batch size affects the number of contaminated bags when the first income lettuce batch is contaminated. Storage room temperature fluctuations showed the importance of real-time monitoring to avoid microorganism growth and thus prevent an increase in the number of contaminated bags. This work provides insights on applications of real-time cross-contamination data in fresh-cut leafy green processing operations. It analyzes the knowledge of cross-contamination information and its impact on processing performance by studying the effect of mitigation strategies.