Dynamic predictive model for the growth of Salmonella spp. in liquid whole egg

Abstract: A dynamic model for the growth of Salmonella spp. in liquid whole egg (LWE) (approximately pH 7.8) under continuously varying temperature was developed. The model was validated using 2 (5 to 15 °C; 600 h and 10 to 40 °C; 52 h) sinusoidal, continuously varying temperature profiles. LWE adjusted to pH 7.8 was inoculated with approximately 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 47 °C) was collected. A primary model (Baranyi model) was fitted for each temperature growth data and corresponding maximum growth rates were estimated. Pseudo‐R² values were greater than 0.97 for primary models. Modified Ratkowsky model was used to fit the secondary model. The pseudo‐R² 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 4th‐order Runge–Kutta method. The developed dynamic model was validated for 2 sinusoidal temperature profiles, 5 to 15 °C (for 600 h) and 10 to 40 °C (for 52 h) with corresponding root mean squared error 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 LWE under varying temperature conditions. Practical Application: Liquid egg and egg products are widely used in food processing and in restaurant operations. These products can be contaminated with Salmonella spp. during breaking and other unit operations during processing. The raw, liquid egg products are stored under refrigeration prior to pasteurization. However, process deviations can occur such as refrigeration failure, leading to temperature fluctuations above the required temperatures as specified in the critical limits within hazard analysis and critical control point plans for the operations. The processors are required to evaluate the potential growth of Salmonella spp. in such products before the product can be used, or further processed. Dynamic predictive models are excellent tools for regulators as well as the processing plant personnel to evaluate the microbiological safety of the product under such conditions.     

Modeling cooling of ready-to-eat meats by 3D finite element analysis: Validation in meat processing facilities

A three-dimensional (3D) finite element model for simulating heat transfer during cooling of irregular-shaped ready-to-eat meat products was developed and validated. The heat transfer model considered conduction as the governing equation, subject to convection, radiation and moisture evaporation boundary conditions. A 3D finite element algorithm developed in Java™ was used to solve the model. The algorithm generated solutions for meshes containing 4-node tetrahedral volume elements and 3-node triangular boundary elements. Product geometries were generated from CT-scan images of the meat products. The model was adapted to receive input parameters that can be easily provided by a meat processors including air relative humidity, air temperature, air velocity, type of casing, duration of water shower, product weight, and estimated core temperature of product prior to entering the cooling chamber. Model validation was conducted in four commercial facilities, under normal processing conditions. Temperatures predicted by the model were in agreement with observed values. Average root-mean-square error (RMSE) was 1.19 ± 0.54 °C for core temperatures, 1.73 ± 0.48 °C for temperatures 0.05 m from core to surface, and 2.01 ± 1.01 °C for surface temperatures. The developed heat transfer model was integrated with predictive microbiology models through a food safety website: numodels4safety.unl.edu. The integration can be useful for estimating the severity of cooling deviations and resulting microbiological safety caused by unexpected cooling process disruptions.