Prediction of Poultry Egg Freshness Using Vis-Nir Spectroscopy with Maximum Likelihood Method

Crucial physio-chemical changes occuring in eggs during storage after laying lead to loss of egg freshness. In this research, a new method for prediction of egg freshness using transmission visible near infrared spectroscopy was investigated. For this purpose 300 eggs were stored at two control conditions: refrigerator (4–5°C, 75%RH) and room (24–25°C, 40%RH) then by special egg holder, transmission spectroscopy was measured. For two eggs groups, 25 eggs in each group, in six days were tested by spectroscopy, after that Haugh unit and air cell height was measured directly. The non-destructive visible near infrared spectroscopy spectral measurements from 300 to 1100 nm (832 length of wave) were done as well as Haugh unit, air cell height for each egg and created the database for both environments. Finally a maximum likelihood latent root regression algorithm was developed to predict Haugh unit and air cell height by spectrum observation. The database was randomly divided into two parts. Training data, was used for maximum likelihood latent root regression parameter tuning and training of the model and testing data, was used just for model evaluation. Results indicated that maximum likelihood latent root regression method showed good prediction ability with coefficient of determination (R²) value up to 0.82 and 0.86 for Haugh unit and air cell height, respectively for testing data set. The results showed this method was better in comparison with partial least square regression (R² up to 0.79 and 0.72 for air cell height and Haugh unit) which was already used for this prediction.