The traditional moldy wheat identification and detection method require complex processing steps, which take a long time and have less feature extraction ability, resulting in poor moldy wheat identification and detection. In this paper, a F-C-BLS terahertz spectral image recognition method for moldy wheat is proposed based on broad learning system. The F-C-BLS moldy wheat classification and recognition model is constructed to enhance the image quality and improve the network feature extraction. Experimental results show that the classification accuracy of our F-C-BLS network is 5.11%, 5.27%, 3.89 and 4.06% higher than that of BLS, RF, CNN and RNN, respectively. Therefore, our algorithm can effectively provide a new and effective method for the early identification of wheat mold.
The spectral characteristics of potassium sorbate in milk powder in the range of 0.2~2.0 THz have been measured with THz time-domain spectroscopy(THz-TDS). Its absorption and refraction spectra are obtained at room temperature in the nitrogen atmosphere. The results showed that potassium sorbate at 0.98 THz obvious characteristic absorption peak. The simple linear regression(SLR) model was taken to analyze the content of potassium sorbate in milk powder. The results showed that the absorption coefficient increases as the mixture potassium sorbate increases. The research is important to food quality and safety testing.
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