Maintaining the predictive abilities of egg freshness models on new variety based on VIS-NIR spectroscopy technique

Abstract

This research was performed to study calibration model transfer between White Leghorns eggs and Bantam eggs for prediction of egg freshness by visible near infrared (VIS-NIR) spectroscopy. Transmission spectra of the two varieties were acquired in the equatorial region of the eggs. And albumen pH as the freshness evaluating parameter was measured using traditional destructive methods. After outliers were eliminated by Mahalanobis distance combined with principal component analysis (PCA), partial least squares regression (PLSR) with different preprocessing methods was used to develop prediction models. Global updating, direct standardization (DS) and slope/bias correction (SBC) were evaluated to transfer calibration models from one variety to another. The Kennard-Stone (KS) algorithm was used to select standardization samples. White Leghorns eggs and Bantam eggs as the master variety in turn were compared to find superior master variety. Application of the slope/bias correction (SBC) algorithm obtained the best prediction results of albumen pH. And the better slope/bias correction (SBC) transfer performance with a rp of 0.908 and a RMSEP of 0.133 was found when Bantam eggs were as the superior master variety.