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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #345106

Research Project: Data Acquisition, Development of Predictive Models for Food Safety and their Associated Use in International Pathogen Modeling and Microbial Databases

Location: Residue Chemistry and Predictive Microbiology Research

Title: Dynamic predictive model for growth of Bacillus cereus from spores in cooked beans

Author
item Juneja, Vijay
item Mishra, Abhinav - University Of Georgia
item Pradhan, Abani - University Of Maryland

Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/18/2017
Publication Date: 1/25/2018
Citation: Juneja, V.K., Mishra, A., Pradhan, A. 2018. Dynamic predictive model for growth of Bacillus cereus from spores in cooked beans. Journal of Food Protection. 81(2):308-315.

Interpretive Summary: An opportunistic pathogen, Bacillus cereus, can produce two types of toxins, emetic and diarrheal, causing two types of foodborne illness. This study investigated the growth kinetics of this microorganism in cooked beans and compared four different mathematical growth models that can be used to estimate the growth in cooked beans. A dynamic model that can be used to estimate the growth under different temperature conditions was developed. The model will serve as an excellent tool for regulatory agencies as well as the food industry to evaluate risk of B. cereus growth in beans during processing, distribution and storage.

Technical Abstract: Kinetic growth data of Bacillus cereus from spores in cooked beans at several isothermal conditions (between 10 to 49C) were collected. Samples were inoculated with approximately 2 log CFU/g of heat-shocked (80C/10 min) spores and stored at isothermal temperatures. B. cereus populations were determined at appropriate intervals by plating on mannitol egg yolk polymyxin agar and incubating at 30C for 24 h. Data were fitted into primary growth models, namely Baranyi, Huang, modified Gompertz, and three-phase linear models. All four models were fitted to the experimental growth data collected in the range of 13-46C. Performances of these models were evaluated using accuracy and bias factors, coefficient of determination (R2), and root mean square error. Based on these criteria, Baranyi model described the growth data the best, followed by Huang, modified Gompertz, and three-phase linear model. The maximum growth rates of each primary model were fitted as a function of temperature using the modified Ratkowsky model. The high R2 values (0.95–0.98) indicate that the modified Ratkowsky model can be used to describe the effect of temperature on the growth rates of all four primary models. Acceptable prediction zone (AZP) approach was also used for the validation of the model using observed data collected during single and two-step dynamic cooling temperature profiles. When the predictions using the Baranyi model were compared with the observed data using the APZ analysis, all 24 observations in the exponential single rate cooling were in the APZ, which was set between -0.5 and 1. Similarly, 26 out of 28 predictions for the two-step cooling profiles were within the APZ limits. The developed dynamic model can be used to predict potential B. cereus growth from spores in beans under varying temperature conditions or during extended chilling of cooked beans.