Fresh produce is a highly perishable commodity. While understanding the drivers of spoilage is important for the quality management of all food, it is especially important for fresh produce given the limited options for reducing or delaying the inevitable postharvest decay and spoilage. Thus, in this dissertation, we present three studies that aim to understand, and develop tools that predict, the postharvest bacterial quality (i.e., bacterial concentration) of spinach. Broadly, the work presented here includes: (i) a year-long survey of the bacterial quality of spinach cultivated in the U.S. and (ii) development of an in silico tool to predict the bacterial concentration on spinach post-packaging. A key finding, detailed in this dissertation, is that growing region has a substantial effect on the bacterial quality of spinach; specifically, packaged baby spinach cultivated in California had significantly higher bacterial levels than that cultivated in Arizona, partially due to the difference in preharvest temperature between these growing regions. Considering this evidence on the variable postharvest quality of spinach, there is a need for digital tools that can be used by the industry to: (i) predict the postharvest quality of spinach (e.g., for accurate shelf life dating) and (ii) obtain decision support (e.g., for evaluating interventions to improve product quality). We thus developed a Monte Carlo simulation to predict bacterial growth on spinach, which illustrated challenges with obtaining salient data for the development of such models. The Monte Carlo simulation was subsequently validated using data from packaged spinach sourced from two separate supply chains, which demonstrated that it had limited prediction accuracy; more specifically, the Monte Carlo simulation was found to underpredict bacterial concentration on spinach from both supply chains, although the level of underestimation varied by the supply chain. Subsequent efforts to improve the performance of the Monte Carlo simulation, by substituting mechanistic microbiological models with a Gaussian Process Model, yielded limited improvements in prediction accuracy. While the work presented in this dissertation has focused on spinach, it can also inform subsequent studies on produce quality. As other produce commodities are cultivated in geographically distinct growing regions, it is important to assess the need for region-specific quality management strategies across the produce industry. Furthermore, the Monte Carlo simulation detailed in this dissertation represents one iteration in a field-wide effort to develop tools to predict food quality and/or spoilage; like in any iterative process, the limitations of this Monte Carlo simulation can be used to inform, and thus improve, future models that predict the bacterial quality of produce.