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Interactive effects of temperature, pH, and water activity on the growth kinetics of Shiga-toxin producing Escherichia coli O104:H4
- Vijay K. Juneja, Sudarsan Mukhopadhay, Dike Ukuku, Cheng-An Hwang, Vivian C.H. Wu, Harshavardhan Thippareddi
- Journal of food protection 2014 v.77 no.5 pp. 706-712
- Escherichia coli O157, Shiga toxin, alfalfa, broccoli, culture media, food microbiology, foods, microbial growth, models, pH, pathogens, population density, regression analysis, risk, temperature, water activity
- The risk of non-O157 Escherichia coli strains has become a growing public health concern. Several studies characterized the behavior of E. coli O157:H7; however, no reports are available on the influence of multiple factors on E. coli O104:H4. This study examined the effects and interactions of temperature (7-46C), pH (4.5-8.5) and water activity (aw 0.95-0.99) on the growth kinetics of E. coli O104:H4 and developed predictive models to estimate its growth potential in foods. Growth kinetics studies for each of the 23 variable combinations from a central composite design were performed. Growth data were used to obtain the lag-phase duration (LPD), exponential growth rate (EGR), generation time (GT) and maximum population density (MPD). These growth parameters as a function of temperature, pH and aw as controlling factors were analyzed to generate second-order-response surface models. The results indicate that the observed MPD was dependent on the pH, aw and temperature of the growth medium. Increasing temperature resulted in a concomitant decrease in LPD. The regression analysis suggests that temperature, pH and aw significantly affect the LPD, EGR, GT and MPD of E. coli O104:H4. A comparison between the observed values and those of E. coli O157:H7 predictions obtained using the USDA-Pathogen Modeling Program indicated that E. coli O104:H4 grows faster than E. coli O157:H7. The developed models were validated with alfalfa and broccoli sprouts. These models will provide risk assessors and food safety managers a rapid means of estimating the likelihood that the pathogen, if present, would grow in response to the interaction of the three variables assessed.