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Salmonella genus has foodborne pathogen species commonly involved in many outbreaks related to the consumption of chicken meat. Many studies have aimed to model bacterial inactivation as a function of the temperature. Due to the large heterogeneity of the results, a unified description of Salmonella spp. inactivation behavior is hard to establish. In the current study, by evaluating the root mean square errors, mean absolute deviation, and Akaike and Bayesian information criteria, the double Weibull model was considered the most accurate primary model to fit 61 datasets of Salmonella inactivation in chicken meat. Results can be interpreted as if the bacterial population is divided into two subpopulations consisting of one more resistant (2.3% of the total population) and one more sensitive to thermal stress (97.7% of the total population). The thermal sensitivity of the bacteria depends on the fat content of the chicken meat. From an adapted version of the Bigelow secondary model including both temperature and fat content, 90% of the Salmonella population can be inactivated after heating at 60 °C of chicken breast, thigh muscles, wings, and skin during approximately 2.5, 5.0, 9.5, and 57.4 min, respectively. The resulting model was applied to four different non-isothermal temperature profiles regarding Salmonella growth in chicken meat. Model performance for the non-isothermal profiles was evaluated by the acceptable prediction zone concept. Results showed that >80% of the predictions fell in the acceptable prediction zone when the temperature changes smoothly at temperature rates lower than 20 °C/min. Results obtained can be used in risk assessment models regarding contamination with Salmonella spp. in chicken parts with different fat contents., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The objective of this study was to develop and validate a model to predict the growth of Salmonella in chicken meat under different isothermal and dynamic temperature conditions. Three different primary and secondary models and combinations were selected from the literature and were tested against 250 isothermal and 4 dynamic growth curves. Judging with different statistical indices, the primary model of Huang was considered to provide the best fit, as evaluated by the Akaike and Bayesian Information Criteria, Mean Absolute Error, and Root Mean Square Error. Moreover, the Ratkowsky and Huang square-root models were considered to be the best secondary models to describe the experimental data, as evaluated by the Proportion of Relative Errors, Percent Discrepancy, and Percent Bias. The estimated minimum growth temperature for Salmonella was approximately 6 °C. After the validation, a few simulations were conducted to evaluate the bacterial growth in contaminated chicken meat stored in a domestic refrigerator. The results and models attained from this study may be used to perform the risk assessment studies concerning Salmonella growth in chicken meat. • The model is based on 250 datasets regarding isothermal growth of Salmonella in chicken meat selected from Combase. • The model accurately predicts Salmonella growth in different non-isothermal temperature profiles. • Simulations that predict bacterial growth in a domestic refrigerator were conducted. • Results can be used to conduct risk assessment. [ABSTRACT FROM AUTHOR]