Abstract:
The potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial
to quality control officers and consumers alike. In this study, handheld NIR spectrometer in the range of 740 nm to 1070 nm and
chemometrics were used to simultaneously determine egg freshness based on marked date of lay for eggs stored under cold and
ambient conditions. 'e spectra acquired from the eggs were preprocessed using multiplicative scatter correction and principal
component analysis (MSC-PCA). Linear discriminant analysis (LDA) was used to build identification model to predict the
category of freshness, while partial least square regression (PLS-R) was used to determine the marked date of lay. 'e performance
of LDA model was above 95% identification rate in both calibration and prediction set for the eggs stored under ambient and cold
storage. For eggs stored in ambient storage, LDA had 95.54% identification rate at 5 principal components, while at cold storage
LDA has 100% identification rate at 5 principal components for determining the marked date of lay, and partial least square
regression (PLS-R) gave R � 0.87 and RMSEI � 2.57 for ambient storage and R � 0.88 and RMSEI � 2.66 for cold storage in
independent set, respectively. 'e results show that handheld spectrometer and multivariate analysis could be used for rapid and
nondestructive measurement of egg freshness. 'is provides a novel solution for egg integrity prediction along the value chain
