MICROBIAL MODELING AND BIOINFORMATICS FOR FOOD SAFETY AND SECURITY
Location: Residue Chemistry and Predictive Microbiology
Title: Dynamic predictive model for the growth of salmonella spp. in liquid whole egg
| Singh, Aikansh - |
| Korasapati, Nageswara - |
| Subbiah, Jeyamkondan - |
| Fronging, Glenn - |
| Thippareddi, Harshavardhan - |
Submitted to: Journal of Food Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 6, 2011
Publication Date: March 1, 2011
Citation: Singh, A.A., Korasapati, N.R., Juneja, V.K., Subbiah, J., Fronging, G., Thippareddi, H. 2011. Dynamic predictive model for the growth of salmonella spp. in liquid whole egg. Journal of Food Science. 76(3)225-232.
Interpretive Summary: Liquid egg and egg products can be contaminated with Salmonella spp. during breaking and other unit operations during processing. The processors are required to evaluate the potential growth of Salmonella spp. in such products before the product can be used or further processed.
This study investigated the growth kinetics of this microorganism in liquid whole egg to develop a dynamic model that can be used to estimate the growth in liquid egg under different temperature conditions. The model will serve as an excellent tool for regulators as well as the processing plant personnel to evaluate the microbiological safety of the product during storage and distribution.
A dynamic model for the growth of Salmonella spp. in liquid whole egg (ca. pH 7.8) under continuously varying temperature was developed. The model was validated using two (5 to 15C; 600 h and 10 to 40C; 52 h) sinusoidal continuously varying temperature profiles. Liquid whole egg (LWG) adjusted to pH 7.8 was inoculated with ca. 2.5-3.0 log CFU/mL of Salmonella spp. and the growth data at several isothermal conditions (5, 7, 10, 15, 20, 25, 30, 35, 37, 39, 41, 43, 45 and 47C) was collected. A primary model (Baranyi model) was fitted for each temperature growth data and corresponding maximum growth rates were estimated. Pseudo-R-square values were greater than 0.97 for primary models. Modified Ratkowsky model was used to fit the secondary model. The pseudo-R-square and root mean square error were 0.99 and 0.06 log CFU/mL, respectively for the secondary model. A dynamic model for the prediction of Salmonella spp. growth under varying temperature conditions was developed using fourth-order Runge-Kutta method. The developed dynamic model was validated for two sinusoidal temperature profiles, 5-15C (for 600 h) and 10-40C (for 52 h) with corresponding RMSE values of 0.28 and 0.23 log CFU/mL, respectively between predicted and observed Salmonella spp. populations. The developed dynamic model can be used to predict the growth of Salmonella spp. in LWG under varying temperature conditions.