Predictive modeling for monitoring egg freshness during variable temperature storage conditions.

The overall aim of this research was to develop egg freshness prediction models in terms of selected quality indices. Six experiments (4 constant temperatures and 2 variable temperatures) were carried out on hen eggs for a total period of 10, 21, 26, 13, and 105 d at storage temperatures of 30, 20, 20 to 10, 30 to 10, and 5 and 10°C, to observe trends in the relative weight loss (RWL), Haugh unit (HU), yolk index (YI), albumin index (AI), yolk pH, and albumin pH. The results showed that there was an increasing trend in the RWL and a decreasing trend in the YI, AI, and HU for all temperature conditions. The changes in the yolk and albumin pH were not uniform. The data from the constant temperature conditions were used to determine the coefficients of the egg quality prediction models, which consisted of the primary model controlling the change rate of the quality indicator at a temperature condition in differential equation form, and the secondary model controlling the change rate with temperature, which was in quadratic polynomial form. The models were applied to the data from the fluctuating temperature conditions, and the zeroth, third, and eighth order kinetic models described the stepwise change in the RWL, HU, and YI, respectively. The accuracy and bias factor values for the RWL, HU, and YI were 1.116 and 0.940, 1.028 and 1.001, and 1.038 and 0.966, respectively. It can be concluded that the models can be used to predict egg freshness in terms of the RWL, HU, and YI at any temperature condition with in the range of 5 to 30°C during storage.