Postharvest supply chain with microbial travelers: A farm-to-retail microbial simulation and visualization framework

Abstract

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 the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes. The postharvest 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 the United States, 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 the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed 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 that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.