Nondestructive egg freshness assessment using hyperspectral imaging and deep learning with distance correlation wavelength selection

Conventional egg freshness assessment methods based on the Haugh unit are destructive and time-consuming. Accordingly, this study investigated the use of hyperspectral imaging (450–1100 nm) for nondestructive egg freshness evaluation. Spectral data were preprocessed using standard normal variates to minimize spectral variability, followed by wavelength selection - a crucial step for improving model predictability. Particularly, distance correlation, a statistically robust yet rarely explored method in hyperspectral wavelength selection, was employed to identify informative wavelengths. The selected wavelengths were incorporated into various regression models, namely convolutional neural network, gradient boosting trees, multiple linear regression, partial least squares regression, and support vector regression models. We observed that the convolutional neural network model incorporating the distance correlation method demonstrated the best performance (correlation coefficient of 0.9056 and root mean square error of 4.4152), outperforming the other models using commonly applied wavelength selection methods. Pseudocolor maps of egg freshness were generated based on the best obtained model.