Detection of blood spots in eggs by hyperspectral transmittance imaging

Blood spots are one of undesired inclusions in eggs, whose detection success is highly dependent on shell color. This research reports a method for detecting blood spots in light brown-shelled eggs on the basis of hyperspectral transmittance images. The normalized spectra of intact eggs and their shells were acquired. Five feature wavelengths of intact eggs selected by the successive projections algorithm and 3 absorption peak locations of eggshells were regarded as characteristic bands. The k-nearest neighbor (kNN) and support vector machine (SVM) algorithms were adopted to develop detection models. The latter achieved better performance. The overall classification accuracy increased to 100% by merging normalized spectra of intact eggs at 5 feature wavelengths with 3 absorption peaks of eggshells as input variables of SVM-based model. Moreover, a practical SVM-based model with 96.43% overall classification accuracy was established by replacing inputs with normalized spectra of intact eggs at characteristic bands.