Postharvest Supply Chain with Microbial Travelers: a Farm-to-Retail Microbial Simulation and Visualization Framework

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through post-harvest supply chain processes. The Post-harvest Supply Chain with Microbial Travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to USA, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate calibration of model parameter ranges for microbial indicator populations, mesophilic aerobic microorganisms (APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined APC supermarket levels were most influenced by removal due to spray water washing and microbial growth on the tomato surface at post-harvest locations, while TC levels were most influenced by growth on the tomato surface at post-harvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how post-harvest supply chain practices influence the fate of microbial contamination on fresh produce. IMPORTANCE: Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and post-harvest handling is an important food safety goal. As studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The Post-harvest Supply Chain with Microbial Travelers (PSCMT), presented here, provides a unique tool for post-harvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing post-harvest research and interventions to improve understanding and control of fresh produce contamination.