Purpose: The manipulation of pH and incubation temperature offers one possible means for Salmonella control in cut tomato products. With the regression model of growth rate with pH and temperature variables built first, the purpose of this research was to expand our existing research efforts on modeling Salmonella in fresh cut tomatoes, resulting in regression models able to predict both growth rate and lag time of Salmonella as a function of both pH and temperature.
Methods: Whole red round tomatoes were dip-inoculated in a cocktail of Salmonella strains obtained from the CDC. These strains were human isolates from cases associated with prior tomato salmonellosis outbreaks. Inoculated tomatoes were dried, cut into slices and incubated at temperatures from 10 to 30°C in 5-degree intervals. The pH of the cut tomatoes was adjusted from 3.8 to 4.2 by adding 5% citric acid. Samples were enumerated by plate counts on XLT4 agar until Salmonella populations reached stationary phase. Growth rates were calculated by DMfit software; lag time is calculated in Excel.
Results: A plot of square root (SQRT) of the growth rate (GR) of Salmonella under various temperature and pH=4.0 was linear with time, such that SQRT(GR)= 0.022T+1.148 (R2=0.79), which is slower than the growth rate under tomato’s natural pH (~pH=4.4) found in previous studies. Moreover, Salmonellagrowth was suppressed at pH=3.8 at all temperatures from 10 to 30°C.
Significance: The models of Salmonella in cut tomatoes built in this project provide useful tools of estimating the risk by growth rate and lag time posed by different temperature abuse and pH manipulation.