Scenario-based risk estimation using a newly developed predictive model for Vibrio parahaemolyticus in fresh flounder

In this study, we developed a predictive growth model for Vibrio parahaemolyticus in fresh flounder using strains with higher growth potential than those in existing V. parahaemolyticus flounder models. Growth data were fitted to Baranyi and second-order polynomial models, both of which exhibited high predictive performance. The secondary models yielded coefficients of determination (R 2 ) ranging from 0.977 to 0.992 for individual strains in tryptic soy broth (TSB) and 0.997 for the cocktail in fresh flounder. Validation metrics indicated an acceptable fit, with bias factors of 0.974–1.132 in TSB and 0.969 in flounder, and accuracy factors of 1.038–1.169 and 1.047, respectively. The models were validated using simulated fluctuating temperature profiles, which confirmed their high predictive performance. Monte Carlo simulations were used to compare the risk of vibriosis associated with consuming ready-to-eat flounder across three seasonal scenarios (baseline, summer, and winter). The probabilities of infection estimated by the model developed in this study were consistently higher than those estimated by previous models across all scenarios. These findings highlight the importance of stringent temperature management during seafood distribution. Furthermore, the developed model provides a realistic and effective tool for microbial risk assessment and seafood safety management. • Vibrio parahaemolyticus (VP) is a major concern for seafood safety • A predictive model was developed using VP cocktails including fast-growing strains • The developed model fitted well under fluctuating temperatures in fresh flounder • Scenario analysis showed higher probability of infection using the developed model • The developed model provides more realistic data for seafood safety management