Foodborne disease outbreaks associated with produces have been increasing in occurrence worldwide. This study investigated microbial contamination levels on thirteen kinds of agricultural products from farms stage to evaluate potential hazards associated with foodborne illness. A total of 1,820 samples were collected in major cultivating area from 2013 through 2015, and analyzed to enumerate aerobic bacterial counts, coliforms/E. coli, Bacillus cereus and Staphylococcus aureus. In addition, the prevalence study for four kinds of microorganisms (Escherichia coli, E. coli O157:H7, Salmonella spp. and Listeria monocytogenes) was performed on each sample. Aerobic bacterial counts ranged from 0.01 to 7.18 log CFU/g, with the highest bacterial cell counts recorded for watermelon. Coliforms were detected in 651 samples (35.8%) with a minimum of 0.01 log CFU/g and a maximum of more than 5 log CFU/g. B. cereus was detected in 169 samples (9.3%) ranging from < 0.01 to 2.48 log CFU/g among total samples analyzed. S. aureus was detected in 14 samples (0.7%) with a minimum of 0.01 log CFU/g and a maximum of 1.69 log CFU/g. E. coli was detected in 101 samples (5.5%) among 1,820 samples. E. coli O157:H7, Salmonella spp. and L. monocytogenes were not detected in any of the samples. The microbial contamination levels of several agricultural products determined in this study may be used as the fundamental data for microbiological risk assessment (MRA).

This study was conducted to develop a predictive model for the growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds. We collected E. coli kinetic growth data during red kohlrabi seed sprouting under isothermal conditions (10, 15, 20, 25, and 30°C). Baranyi model was used as a primary order model for growth data. The maximum growth rate (μmax) and lag-phase duration (LPD) for each temperature (except for 10°C LPD) were determined. Three kinds of secondary models (suboptimal Ratkowsky square-root, Huang model, and Arrhenius-type model) were compared to elucidate the influence of temperature on E. coli growth rate. The model performance measures for three secondary models showed that the suboptimal Huang square-root model was more suitable in the accuracy (1.223) and the suboptimal Ratkowsky square-root model was less in the bias (0.999), respectively. Among three secondary order model used in this study, the suboptimal Ratkowsky square-root model showed best fit for the secondary model for describing the effect of temperature. This model can be utilized to predict E. coli behavior in red kohlrabi sprout production and to conduct microbial risk assessments.