A dynamic predictive model for the growth of Salmonella spp. and Staphylococcus aureus in fresh egg yolk and scenario-based risk estimation. (December 2020)

• Record Type: Journal Article
• Authors: Park, Jin Hwa; Kang, Mi Seon; Park, Kyung Min; Lee, Hee Young; Ok, Gyeong Sik; Koo, Min Seon; Hong, Seok In; Kim, Hyun Jung
• Is Part Of: Food control. Volume 118(2020)
• Publication Date: 2020-12
• Subjects: Salmonella spp. -- Staphylococcus aureus -- Egg yolk -- Dynamic predictive model -- Validation -- Probability of infection; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107421
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: The present study aimed to develop and validate a predictive model of Salmonella spp. and Staphylococcus aureus growth in fresh egg under isothermal and non-isothermal (fluctuating temperature) conditions and also estimate infection probability based on Monte Carlo simulation and scenario analysis. Egg yolk and egg white were inoculated with six serovars of Salmonella and three strains of S. aureus and stored at 5, 10, 20, 30, and 37 °C. No growth of Salmonella spp. and S. aureus was observed at all temperatures in egg white. However, growth of both strains was observed at above 10 °C in egg yolk. Primary growth models for both strains in egg yolk were developed based on the Baranyi model. Secondary models were developed as a function of temperature for lag phase duration (LPD) and maximum specific growth rate ( μ max ) using Davey and second-order polynomial, and square root models, respectively. The primary and secondary models for both strains were fitted well with a high degree of goodness-of-fit (R 2 ≥ 0.99). Bias factor (B f ) was 0.96–1.08, and root mean square error (RMSE) was 0.04–0.15 log CFU/mL. The model was validated and found to be applicable to a scenario involving the consumption of an egg omelet, and other strains of S. aureus, including food isolates. As the results of validation, the developed model was conservatively applicable to other strains (B f : 0.88–1.07, RMSE: 0.05–0.23 log CFU/mL) and egg products (B f : 0.94–1.09, RMSE: 0.04–0.06 log CFU/mL of μ max ). The dynamic model was validated for 3 temperature profiles: 1) rapidly changing and 2) slowly changing at 10–25 °C, and 3) 2-h cycles change at 15–30 °C with corresponding RMSE values less than 0.5 log CFU/mL. Using a baseline simulation model, the probability of infection for Salmonella spp. and S. aureus from the consumption of ready-to-eat egg products (e.g., rolled omelet) was estimated as zero. However, scenario analysis that factored for possible initial contamination by pathogens in egg products suggested that the probability of infection has a positive relationship with initial contamination level. The developed growth model can provide useful data for quantitative microbial risk assessment and risk management options of Salmonella spp. and S. aureus during the consumption of egg-related foods. Highlights: The growth of Salmonella spp and S. aureus in fresh egg yolk were modeled. The model could be applicable for rolled omelet and S. aureus food isolates. Developed models performed well under various dynamic temperature profiles. Scenario analysis suggests the risk of foodborne pathogens from RTE egg products.