Modeling the effect of temperature and pH on the growth of salmonella in cut tomatoes

Outbreaks of salmonellosis associated with fresh cut produce have increased in recent years, including outbreaks linked to tomatoes. When tomatoes are cut, Salmonella can be transferred from the tomato surface to the flesh, where conditions are favorable for multiplication of the organism. The manipulation of pH and control of storage temperature may be feasible methods for Salmonella control in cut tomato products. The purpose of this research was to expand our existing understanding of Salmonella growth in fresh cut tomatoes, and to develop regression models able to predict both growth rate and lag time of Salmonella in tomatoes as a function of pH and temperature. Whole red round tomatoes were dip-inoculated in a cocktail of four Salmonella strains isolated by the Centers for Disease Control and Prevention (CDC) from human cases associated with prior tomato salmonellosis outbreaks. Inoculated tomatoes were dried, sliced and incubated at temperatures from 10 to 30 °C. The pH of the cut tomatoes was adjusted from 3.7 to 4.4 by the addition of 5% citric acid. Salmonella were enumerated by plate counts on XLT4 agar until Salmonella growth reached stationary phase. Growth rates and lag time were calculated by an Excel add-on DMFit. Regression models were built using SAS. The growth rate of Salmonella was described by multivariate regression functions of temperature and pH with good fit (both with p0.40). After square-root transformation of the growth rate and incorporation of a temperature*pH interaction term, the model’s fit gets further improved (with p0.70). The model was compared to the existing Combase model by using its data of relevant temperature and pH combination to build a regression model of same kind. However due to the limitation of the Combase model’s data collection that Combase does not have growth rate data if pH is lower than 3.9 , model validation was not possible. Hence the model is validated by cross-validation method. The experimental growth data is split in to a training set and a testing set. Model is built upon the training set and validated by the testing set The model developed here will be used in the quantitative microbial risk assessment for Salmonella in cut tomatoes.M.S.Includes bibliographical referencesby Wenchao L