Egg freshness prediction using a comprehensive analysis based on visible near infrared spectroscopy

Abstract

The aim of this study was to calculate six single freshness indices into a synthesized indicator named “comprehensive freshness indicator” which could predict egg freshness based on visible near infrared spectroscopy. The transmission spectra were acquired in the equatorial region of 91 White Leghorn eggs. After each spectral measurement, six single freshness indices including egg shape index, yolk index, Haugh unit, albumen pH, air cell diameter and eggshell thickness were destructively measured. Pearson correlation analysis was used to analyze correlations between single indices. The six single freshness indices were calculated into the comprehensive freshness indicator based on contribution rates and load coefficients obtained from principal components analysis. A partial least squares regression with different preprocessing methods was developed to predict the six single freshness indices and the comprehensive freshness indicator based on wavelength from 480 to 960 nm. The comprehensive freshness indicator compared with single freshness indices achieved better predictive ability with predictive correlation coefficient of 0.891 and root mean square error of 1.000. The results illustrated that the comprehensive freshness indicator could predict egg freshness, while evaluation standards need further researched. Highlights Calculate a synthesized indicator named comprehensive freshness indicator (CFI). Correlations between six single freshness indices were analyzed. Build models for comprehensive and nondestructive assessment of egg freshness. Comprehensive freshness indicator (CFI) successfully predict egg freshness.