Abstract
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. Keywords: hyperspectral transmittance imaging, non-destructive detection, blood-spot, egg DOI: 10.25165/j.ijabe.20191206.5376 Citation: Feng Z, Ding C Q, Li W H, Cui D. Detection of blood spots in eggs by hyperspectral transmittance imaging. Int J Agric & Biol Eng, 2019; 12(6): 209–214.
Highlights
Blood spots appearing on the yolk or in the albumen will affect the quality and grade of eggs
The determination will be made by well-trained workers based on their observation results. Since this method is labor intensive and heavily depending on the experience of the workers, the traditional method is hard to meet the demand of high accuracy and throughput
Patel et al.[4] used a machine vision system to acquire the gray images of blood spot eggs and trained the neural network model for blood spot detection by the histograms generated from the images
Summary
Blood spots appearing on the yolk or in the albumen will affect the quality and grade of eggs. Patel et al.[4] used a machine vision system to acquire the gray images of blood spot eggs and trained the neural network model for blood spot detection by the histograms generated from the images. Usui et al.[7] applied the near infrared spectroscopy to detect blood spots in white-shelled eggs, established detection model by the partial least square (PLS) method and achieved 96.8% accuracy for blood-spot eggs. Xu et al.[9] analyzed visible spectroscopy of brown-shelled eggs by the least squares support vector machines (LS-SVM) method and acquired 91.7% accuracy for blood-spot detection. Gielen et al.[8] found that the absorption peak of the pigment protoporphyrin in brown-shelled eggs was at 589 nm, while that of hemoglobin in blood spots was at 577 nm Their absorption peak locations were very close, which resulted in the relative lower detection accuracy for brown-shelled eggs with blood spots. In order to achieve higher accuracy, the influence of the protoporphyrin needs to be compensated
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