Chicken egg quality assessment from visible/near infrared observations

Egg is a fragile component within the human diet. Important changes occur in egg during storage. Prediction of these changes is critical in order to grade the eggs upon their quality and freshness. The objectives of this study were to evaluate the application of visible and near infrared spectroscopy as a non-destructive method for the assessment of egg quality and freshness. Therefore, visible and near infrared transmittance spectral data ranging from 350 to 2500 nm was collected with the help of a radiospectrometer on 360 freshly laid eggs. A partial least squares model was built in order to link the spectral data with the most widely used destructive methods, namely Haugh Units and albumen pH in tenus of egg quality and the number of storage days in teens of egg freshness.;The second part of the study was based on building calibration models for predicting egg freshness in terms of number of storage day and egg quality in terms of Haugh Units and albumen pH. The results showed that the models had good predictive ability and R2 for number of storage days, Haugh Units and albumen pH were 0.89, 0.79 and. 0.90, respectively. RMSECV for these three parameters were 1.65, 5.05 and 0.06, respectively.;The ability of maximum R2 method to select the relevant wavelengths in order to build a partial least squares (PLS) predictive model was investigated in the first part of the study. The results showed that this method improved the predictive ability of the model. Coefficient of determination (R2) and root mean square error of cross validation (RMSECV) were calculated in order to select sets of wavelengths to build the model with the best predictive ability.