Real-Time Polymerase Chain Reaction-Based Quantification for Modeling the Growth of Bacillus cereus Emetic Strain on...

Temperature abuse of chilled foods can promote Bacillus cereus multiplication, increasing the risk of foodborne illness. Therefore, growth modeling is a practical tool for designing effective temperature control strategies. In this study, we monitored B. cereus growth in shumai (traditional Chinese dumplings) at isothermal conditions (10-35°C). Quantification of cereulide synthetase (ces) gene by real-time polymerase chain reaction (qPCR) enabled the measurement of logarithmic bacterial increases during storage. Growth responses to temperatures were described using the Baranyi model, whereas Ratkowsky and hyperbolic models characterized temperature effects on growth rates and lag phase. The models fit the growth data well, with high R² and low root mean squared errors (RMSE) values. The estimated theoretical minimum growth temperature (T_min) for B. cereus in shumai was 6.3°C. Model validation under isothermal (18°C and 32°C) and dynamic (6- and 4-step fluctuating) temperature profiles showed good agreement with observed data. Acceptable prediction zone analysis (-1.0 to 0.5 log CFU/g) yielded a proportion of prediction error (pPE) of 0.92-1.00, confirming model reliability. This qPCR-based predictive modeling approach provides a robust tool for estimating B. cereus growth in complex food matrices, supporting risk assessment of temperature abuse in chilled food products. PRACTICAL APPLICATIONS: Growth models help determine shelf life, optimize processing and storage conditions, and support food safety systems such as hazard analysis and critical control points (HACCP). However, because bacterial growth behavior varies with specific food matrices, tailored predictive growth models are often required. Real-time PCR, with its high accuracy and resistance to interference from background microflora, offers a reliable method for bacterial growth monitoring during challenge tests, facilitating the development of robust predictive models across diverse food products and strengthening both food safety and quality control.