Predictive model for growth of bacillus cereus at temperatures applicable to cooling of cooked pasta

• Autores: Vijay Juneja, Chase E. Golden, Abhinav Mishra, Mark A. Harrison, Tim B. Mohr
• Localización: Journal of food science, ISSN 0022-1147, Vol. 84, Nº 3, 2019, págs. 590-598
• Idioma: inglés
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• Resumen

  • A model was developed to predict the growth of Bacillus cereus from spores during cooling of cooked pasta. Cooked pasta was inoculated with a cocktail of four strains of heat-shocked (80 °C/10 min) B. cereus spores to obtain a final spore concentration of approximately 2 log CFU/g. Thereafter, growth was determined at isothermal temperatures starting at 10 °C and every three degrees up to 49 °C. Samples were removed periodically and plated on mannitol egg yolk polymyxin agar. The plates were incubated for 24 hr at 30 °C. Baranyi, Huang, and modified Gompertz primary growth models were used to fit growth data. The modified Ratkowsky secondary model was used to fit growth rates determined by the primary growth models with respect to temperature. All three primary models fitted the growth data well. The modified Ratkowsky secondary model adequately fit growth rates generated by the three primary models (R2 values ranging from 0.96 to 0.98). After acceptable prediction zone (APZ) validation and goodness of fit statistical analyses, it was determined that the Baranyi primary growth model was best suited for these data. For both single-rate exponential cooling and biphasic linear cooling model validation, all Baranyi model predictions (n = 24 and 28, respectively) fell within the APZ (?1.0 to 0.5 log CFU/g). The model will assist institutional food service settings to determine the safety of cooked pasta subjected to longer cooling times or stored at improper temperatures. Practical Application Predictive model can be used to estimate extent of microbial growth during cooling of cooked pasta and in designing HACCP program and setting of critical control levels. Retail food industry would need fewer challenge studies to validate the safety of their products. The model will provide regulatory agencies and food industry with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning.