Egg quality detection is important to food processing and people consumption. The aim of this study is to detect egg freshness, scattered yolk and eggshell cracks by applying hyperspectral imaging (HSI), multivariate analysis and image process. The transmission visible-near infrared hyperspectral images of egg samples were acquired in the wavelength range of 401-1002 nm. Standard normal variate (SNV) was applied to normalize the spectral data, and iteratively retains informative variable (IRIV) was used to optimize wavelength selection. Based on the feature wavelengths, egg freshness quantitative model was established by using Extreme Gradient Boosting (XGBoost), with coefficient of determination for prediction (R2p) of 0.91 and root mean square error for prediction (RMSEP) of 4.64. An algorithm including image contrast enhancement, denoising and threshold segmentation was proposed to extract the morphological features of yolk. Based on the morphological feature ratio, the recognition accuracy of scattered yolk eggs reached...
Non-destructive detection of egg qualities based on hyperspectral imaging.
Jun Sun, School of Electrical and Information Engineering of Jiangsu University, Zhenjiang, 212013, China. E-mail email@example.com
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Kunshan Yao, Jun Sun, Chen Chen, Min Xu, Xin Zhou, Yan Cao, Yan Tian; Non-destructive detection of egg qualities based on hyperspectral imaging.. IFIS Food and Health Sciences Database 2022; doi:
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