An Alternative Approach for Predicting Probability of Pathogen Growth on Iceberg Lettuce Using Logistic Regression

Introduction:   Bacterial pathogens such as Escherichia coliO157:H7 can infect with low dose ingestion such as < 100 CFU/g. Conventional predictive models that intend to describe entire growth kinetics do not directly evaluate the risk of infection. In order to evaluate the risk of infection of bacterial pathogen, probabilistic model that can directly predict small amount of growth will be useful. 

Purpose:   The objective of this study was to develop a probabilistic model to predict the probability of the time to 1-log increase of E. coli O157:H7, Salomonella spp., and Listeria monocytogeneson iceberg lettuce during chilled to room temperature storage. 

Methods:   Changes in the cell number of E. coli O157:H7, Salomonella spp., and L. monocytogenes on fresh-cut iceberg lettuce was evaluated between 5 to 25°C. The time for 1-log increase from the initial cell number was calculated from the obtained growth kinetics. The whole kinetic data was evaluated whether 1-log increase (1) or not (0) on each sampling interval.  The evaluated data was modeled using logistic regression procedure as a function of temperature, time, and kind of bacteria. 

Results:   The probability of time to 1-log increase was successfully modeled for each bacterium using logistic regression with high accuracy (percent concordant; 85.9%, AIC; 65.7). Furthermore, we obtained the probability density distribution by differentiation of the obtained probability model. This function enabled to calculate the probability of 1-log increase within arbitrary periods of storage time. 

Significance:   The developed model allowed us to estimate not only the probability of time to 1-log increase of each pathogen on iceberg lettuce but also its probability density distribution. The model developed in this study can be used to evaluate the infection risk of each pathogen in conjunction with those dose-response models.