Predictive Models For The Growth And Sodium Hypochlorite Inactivation Of Salmonella In Chicken Fillets With Different Temperatures

Salmonella has been considered as one of the major pathogens associated with poultry products around the world.Predictive microbiology has been used as an important tool to quantitatively predict the status of microorganisms experiencing environment changes.The objective of this study was to develop microbial predictive models for the growth,survival and inactivation of Salmonella in chicken products at different temperatures and antimicrobial treatment in poultry processing.First,the growth kinetics of different serotypes of Salmonella in chicken fillets at different temperatures were investigated.In the temperature range of 4-33?,the order of the growth rate obtained from secondary models was Derby>Agona>enteritis>Indiana>Typhimurium.In the temperature range of 4?33 ?,the growth rate of S.Typhimurium was significantly lower than the other four strains(p<0.05).However,below 20 ?,there was no significant difference in the growth rates for Salmonella strains of Der y,Agona,enterits,and Indiana,and the difference became clear gradually with increasing temperature.At 33 ?,the growth rate of S.Derby was 1.74 times of S.Typhimurium.Different strains of Salmonella have different growth rates at the same temperature.Secondly,chicken fillets were inoculated with three-strains of cocktail(S.Typhimurium,S.enteritidis and S.Agona)and then stored at temperatures of 13,16,25 and 33?.Using USDA Integrated Pathogen Modeling Program(IPMP)2013,growth data were collected for fitting into four primary models.Three secondary models were selected for describing the maximum growth rates derived from primary models.IPMP-Global Fit was used to directly construct both primary model and secondary models and determine kinetic parameters.Statistical indices of R2 and RMSE were used for model evaluation.Independent trials were conducted,and Bias factor(Bf)and Accuracy factor(Af)were calculated for model validation.The modified Gompertz model described growth data the best,followed by Huang model.The maximum growth rates of Salmonella in chicken fillets were 0.072,0.094,0.433,0.527/h at 13,16,25 and 33?,respectively.R2 for Arrhenius model describing the maximum growth rate obtained from the modified Gompertz model was 0.99,which was the best among the selected secondary models(RMSE=0.046,R2=0.99).IPMP-Global Fit was easy to use and the overall error was smallest when the secondary model was Ratkowsky model(RMSE=0.418,R2=0.93).Bf and Af of internal and external verification are both less than 1.15,which further illustrated the applicability and reliability of the model established in this study.To study the inactivation of Salmonella by sodium hypochlorite widely used in poultry processing,a 5-factor response surface model based on the JMP experimental design was developed to evaluate the effects of initial contamination levels,sodium hypochlorite concentration,washing temperatures,organic interference concentrations and washing time on reduction of Salmonella inoculated in chicken.These treatments achieved 30.2%-98.4%reduction of Salmonella in chicken fillets.The expression for the linear equation was obtained using least squares(RMSE = 3.520,R2 = 0.978).Among the five factors,sodium hypochlorite concentration and organic interference concentration had the most significant effect on bacterial decontamination(p<0.0001).Stepwise stratification led to a simplified model that has a satisfactory performance as evidenced by statistical indices(R2 = 0.991;p<0.0001;RMSE = 2.329)and external validation parameters(Bf= 1.03,Af=1.04).The selected model is able to predict decontamination efFects of sodium hypochlorite on Salmonella in chicken products.Predictive models for the growth and sodium hypochlorite inactivation of Salmonella in chicken fillets with different temperatures provide a scientific tool for the risk assessment and control of Salmonella contamination in chicken products,and can also be used to evaluate the intervention techniques in food supply chain.