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• W3097627840 abstract "Slightly sprouted wheat kernels are difficult to distinguish with the naked eye, and the mixing of sprouted kernels into sound wheat kernels will seriously reduce the quality of wheat products. This study explored the use of near-infrared hyperspectral imaging technology to identify sound wheat kernels and slightly sprouted wheat kernels, and obtained hyperspectral images on both sides of each wheat kernel. A variety of common spectral preprocessing methods and two characteristic extraction algorithms (competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA)) were used to combine two traditional machine learning models (linear discriminant analysis (LDA), and support vector machine (SVM)) and a special deep learning model (deep forest (DF)) to establish classification models. After analysis, it was found that the modelling effect of using the reverse side spectral data was slightly better than the ventral side spectral data, and the Savitzky–Golay smoothing (SG)-CARS-DF model was the optimal model combination. Finally, combined with actual needs, the modelling effect of the characteristic wavelengths extracted from the reverse side spectral data in the mixed spectral data sets containing different ratios of reverse side spectral data was analysed, and the results were also satisfactory. The results showed that it was better to calibrate the model with the hyperspectral data on the reverse side of wheat kernels, as this would be more helpful for identifying sound and slightly sprouted wheat kernels." @default.
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• W3097627840 date "2020-12-01" @default.
• W3097627840 modified "2023-09-28" @default.
• W3097627840 title "Non-destructive identification of slightly sprouted wheat kernels using hyperspectral data on both sides of wheat kernels" @default.
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• W3097627840 doi "https://doi.org/10.1016/j.biosystemseng.2020.10.004" @default.
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