Nondestructive Testing of Egg Freshness Based on Laser Doppler Vibrometry

This study proposes a nondestructive method for evaluating egg freshness using laser Doppler vibrometry. A vibration measurement platform was constructed to collect vibration signals from intact eggs representing four freshness grades. The vibration signals were processed using multiplicative scatter correction, standard normal variate transformation, moving average filtering, Savitzky-Golay smoothing, and first-order Savitzky-Golay differentiation. Principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were then used for feature extraction and dimensionality reduction. Six classification models were developed and compared, including support vector machine, k-nearest neighbor, random forest, naive Bayes, discriminant analysis, and linear discriminant analysis. The model based on moving average filtering, successive projections algorithm, and discriminant analysis using measurements from three egg locations achieved a classification accuracy of 96.7%. The model based on moving average filtering, competitive adaptive reweighted sampling, and random forest using blunt-end measurements also achieved an accuracy of 96.7%. These results demonstrate that laser Doppler vibrometry combined with machine learning can distinguish eggs with different freshness levels, providing a promising nondestructive approach for egg-quality evaluation.