Predictive models for the survival/death and cross-contamination of Campylobacter jejuni and Salmonella Typhimurium during poultry scalding and chilling

In laboratory scale scalding and chilling tests, processing water and chicken skins were inoculated with Campylobacter jejuni and Salmonella Typhimurium to examine the effects of scalding temperature (50∼60°C) and chlorine level (0∼50 ppm), associated with water age (0 and 10 h for scalding water, 0 and 8 h for chilled water) on bacterial survival/death. D-values for the main subpopulation ( D_main) and more resistant subpopulation (D _res) of Campylobacter jejuni and Salmonella Typhimurium were calculated.; Survival/death models were then developed using the Weibull distribution model, log(N_t/N₀) = −btn (where b and n are the constants describing the distribution of the microbial resistance to detrimental factors), as primary model, and the modified Davey and polynomial/response models as secondary models. The Weibull based models had fewer parameters and better fit (MSEs < 0.4) than the exponential, logistic and Gompertz based models, and were validated using the data collected in pilot plant scale scalding (at 50 and 55°C) and chilling (with 0 and 50 ppm chlorine) tests. The errors of predictions was <1.1 log cycle except for S. Typhimurium survival/death during scalding at 55°C. The models were tuned by adding a correction factor (CF).; To describe the probability (P) of individual chicken drumstick contaminated by C. jejuni or S. Typhimurium during chilling, a model, P = 1/[ 1 + exp(−y)], was developed based on the laboratory data collected using chicken drumsticks in a chiller simulator, where y is a polynomial function of initial contamination ratio (3.3∼43.3%), chlorine level (0∼50 ppm) and water age (0 and 8 h). Contamination probability greatly depended on the pre-chill contamination ratio and the chlorination of chilled water. Water age was also an important factor for the cross-contamination of S. Typhimurium. The Hosmer and Lemeshow statistic indicated a good fit between the data and the model. The model was able to predict the contamination probability based on processing conditions, or, conversely, to define processing conditions required for controlling contamination probability under a desired level.; The survival/death and cross-contamination models developed in this study could be applied in poultry scalding and chilling after further validation through commercial poultry processing. The predictive models could also been modified for other poultry processing points.