Growth models for Salmonella, E. coli O157:H7 and L. monocytogenes give different predictions for pathogen growth in cut leafy greens transportation, but are consistent in identifying higher risk conditions

Leafy greens are frequently implicated in foodborne disease outbreaks and cut-leafy greens are a food that re-quires time and temperature control for safety. Predictive microbiology uses mathematical models to predict the growth of bacteria based on environmental conditions. The objective of our study was to compare published square root growth models for Salmonella (n = 6), pathogenic E. coli (n = 6) and Listeria monocytogenes (n = 4) using real world transport temperature data. Data from trucks transporting fresh-cut leafy greens during cross-country shipments were used as temperature inputs to the models. Bacterial growth was computed using the temperatures from each probe in every truck over the duration of transit, which resulted in 12-18 growth predictions per truck for each model. Each model generally gave significantly different predictions than other models for the same organism. The exception was for the two Salmonella models predicting the least growth and the two Salmonella models predicting the most growth which gave predictions that were not significantly different. Although different models tended to give different predictions, their ability to rank risk by truck was generally consistent across models. While absolute risk might be dependent upon choice of model, relative risk is independent of model choice.

Automated summary

This study compared six growth models for three types of bacteria (Salmonella, pathogenic E. coli, and Listeria monocytogenes), and found that different models gave significantly different predictions for the same bacteria. However, the ability of the models to rank risk by truck was generally consistent.