Identification of egg's freshness using NIR and support vector data description

Near infrared (NIR) spectroscopy combined with pattern recognition was attempted to discriminate egg's freshness. A new algorithm support vector data description (SVDD) was employed to solve the classification problem due to imbalance number of training samples. Original spectra of eggs in wavelength range of 10,000-4000 cm(-1) were acquired. SVDD was performed to calibrate discrimination model, and some parameters of SVDD model were optimized. Meanwhile, several conversional two-class classification methods (i.e. partial least square discrimination analysis, PLS-DA; K-nearest neighbors, KNN; artificial neural network. ANN; support vector machine, SVM) were also used comparatively for classification. Experimental results showed that SVDD got better performance than the conversional classification models in same condition. The identification rates of fresh eggs and unfresh eggs were both 93.3%. This work indicates that it is feasible to identify egg's freshness using NIR spectroscopy, and SVDD is an excellent choice in solving the problem of imbalance number of training samples. Published by Elsevier Ltd.