Development and validation of a stochastic model for predicting the growth of Salmonella typhimurium DT104 from a low initial density on chicken frankfurters with native microflora.

The presence of native microflora is associated with increased variation of Salmonella growth among batches and portions of chicken meat and as a function of temperature. However, variation of Salmonella growth can be modeled using a 95% prediction interval (PI). Because there are no reports of predictive models for growth of Salmonella on ready-to-eat poultry meat products with native microflora and because Salmonella is usually present at low levels on poultry meat, the current study was conducted to develop and validate a stochastic model for predicting the growth of Salmonella from a low initial density on chicken frankfurters with native microflora. One-gram portions of chicken frankfurters were inoculated with 0.5 log CFU of a single strain (ATCC 700408) of Salmonella Typhimurium DT104. Changes in pathogen numbers over time, N(t), were fit to a two-phase linear primary model to determine lag time (lambda), growth rate (mu), and the 95% PI, which characterized the variation of pathogen growth. Secondary quadratic polynomial models for natural log transformations of lambda, mu, and PI as a function of temperature (10 to 40 degrees C) were obtained by nonlinear regression. The primary and secondary models were combined in a computer spreadsheet to create a tertiary model that predicted the growth curve and PI. The pathogen did not grow on chicken frankfurters incubated at 10 to 12 degrees C, but mu ranged from 0.003 log CFU/g/h at 14 degrees C to 0.176 log CFU/ g/h at 30 degrees C to 0.1 log CFU/g/h at 40 degrees C. Variation of N(t) increased as a function of time (i.e., PI was lower during lag phase than during growth phase) and temperature (i.e., PI was higher at 18 to 40 degrees C than at 10 to 14 degrees C). For dependent data (n = 338), 90.5% of observed N(t) values were in the PI predicted by the tertiary model, whereas for independent data (n = 86), 89.5% of observed N(t) values were in the PI predicted by the tertiary model. Based on this performance evaluation, the tertiary model was considered acceptable and valid for stochastic predictions of Salmonella Typhimurium DT104 growth from a low initial density on chicken frankfurters with native microflora.