Detection of the Freshness of Yanbian Yellow Beef by Hyperspectral Imaging
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Abstract:
In order to improve the quality and safety of Yanbian yellow beef, hyperspectral imaging technique was used to detect its freshness. Hyperspectral images of Yanbian yellow beef sample were collected by the imaging system, and then subjected to black-and-white correction. The processed hyperspectral images were denoised via the combined use of S-G filter and mean filter before being processed by principal component analysis to reduce dimension. The total volatile basic nitrogen (TVB-N) content of Yanbian yellow beef was determined by the semi-micro nitrogen determination method. The strong classifiers of Yanbian yellow beef were established through multiple BP-ANN weak classifiers. The TVB-N content of Yanbian yellow beef was placed as an input into the strong classifier and taken as the index, and then the freshness of Yanbian yellow beef was detected according to the classification results. The results showed that at the wavelength of 700~750 nm, the TVB-N content was the highest, suggesting a higher degree of internal damage in meat and poorer freshness of meat. On the 10th to 11th day, the TVB-N content of the Yanbian yellow beef changed significantly. After the hyperspectral image was denoised, the signal frequency fluctuated from-15 dB~15 dB to-5 dB~5 dB, and the fluctuation of signal frequency gradually decreased and tended to be stable. These results indicate that the proposed method was highly accurate with high denoising ability: The detection method can effectively remove the noise existing in the image, and the accuracy of this method was as high as 99%.
