Summary of Awards to Date

Modeling tools for design of science-based Listeria environmental monitoring programs and corrective action strategies


Jan. 1, 2019 - Dec. 31, 2019

Amount Awarded



Renata Ivanek, Ph.D.
Cornell University


Martin Wiedmann, Ph.D.


Well-designed environmental monitoring programs for Listeria as a strategy to identify and eliminate Listeria monocytogenes risks are essential for the produce industry and are increasingly mandated by both regulatory agencies and buyers. Along with this, industry needs science-based tools to implement responses to Listeria detection that are both appropriate for a specific facility and its unique processes and effective in reducing risk of contaminated products. As it is not practical to test out different corrective actions and sampling strategies in a given facility, our objective is to use computer modeling to identify the optimal approaches for a particular setting. Specifically, a model we have previously developed will be adapted to fresh produce processing facilities and validated with sampling data collected through an on-going project. Simulations with validated models will characterize various corrective action and monitoring schemes for their ability to detect and control Listeria in the unique settings of different facilities. As a result, the project will provide industry with science-based resources for selecting appropriate corrective action approaches and demonstrating the equivalency of different sampling strategies in their unique facilities. 

Technical Abstract

While contamination of fresh produce with Listeria monocytogenes (Lm) may occur throughout the field-to-consumer chain, contamination events are often traced back to sources in the environment and equipment of processing facilities. Hence environmental monitoring (EM) programs with appropriate corrective actions have become a key tool to control Lm and reduce the risk of finished product contamination. In addition to more specific government guidance documents, more prescriptive supply chain and buyer EM requirements are also becoming more common. Along with implementing science-based Listeria EM programs, development of improved approaches to appropriate follow-up actions in response to Listeria detection is essential for the produce industry. In particular, industry needs appropriate tools to optimize the response to Listeria presence/absence testing across various product handling areas, as these responses are specifically taken to control Lm contamination of processing plant environments and prevent finished product contamination. However, practically speaking, science-based Listeria control programs cannot be designed solely based on testing data. For example, it is not feasible to test different corrective action approaches in each facility. We propose a modeling and computational approach where we will leverage our existing agentbased model (ABM) to evaluate different scenarios for (i) corrective actions in response to Listeria detection (e.g., uniform responses versus differential responses based on specific location and/or frequency of Listeria detection, quantification of Listeria levels), as well as (ii) routine EM programs (e.g., different frequencies of Zone 1 sampling, including no Zone 1 sampling) in produce processing facilities. This project is specifically designed to evaluate whether site-specific attributes may justify differential EM strategies for Listeria spp. and responses taken within produce processing facilities. First, the ABM will be tailored to several individual fresh produce processing plant environments and conditions; each individual ABM will be validated for the specific facility using longitudinal sampling data, collected through our ongoing complementary project funded by the USDA Specialty Crop Research Initiative. Second, computer simulation using the adapted and validated models will provide data on Listeria spp. contamination scenarios that will be used to evaluate combinations of monitoring and response schemes that would provide equivalent effectiveness in controlling Lm contamination of processing plant environments and finished products. As a result, we expect to provide data to support site-specific and risk-differentiated practices for the produce industry. These models will have a direct impact on participating facilities, by providing customized tools for evaluating risk and making decisions, while the simulation results are expected to provide the broader produce industry with data and solutions to justify their Listeria control programs. The results of this study will attempt to fundamentally reverse the “one-size-fits-all” mentality towards Listeria control and contamination prevention for a fresh produce industry that is characterized by varied and changing practices, capabilities and requirements.