Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint

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. The 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. The 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. The results show that handheld spectrometer and multivariate analysis could be used for rapid and nondestructive measurement of egg freshness. This provides a novel solution for egg integrity prediction along the value chain.