Exploration of compressive sensing in the classification of frozen fish based on two-dimensional correlation spectrum

In order to classify imported frozen fish, effectively a spectral data compression method was presented based on two-dimensional correlation spectroscopy. In the experiment, the near-infrared spectral data of Oncorhynchus keta, Oncorhynchus nerka and Oncorhynchus gorbuscha of Salmonidae were collected. And two-dimensional correlation spectroscopy among the three fish samples was constructed. The study found that the auto-correlation peaks intensities at 650 nm, 1724 nm and 1908 nm were almost zero, which were taken as the separation point of the spectra. Therefore, each spectral data is divided into 4 segments and the integral of each segment is obtained. The original spectra of 201 points in each group were compressed into 4 points. Then, the compressed spectral data were input into the support vector machine to establish the discriminant model of three kinds of frozen fish. At the same time, the Competitive Adaptive Reweighted Sampling and the Successive Projections Algorithm were used to screen the original spectra. The classification results were compared with the result of the spectral data compression method of two-dimensional correlation spectroscopy. The result shows: the compression rate of the proposed method is 98.01%; the accuracy rate of support vector machine training set is 100%; the accuracy rate of validation set is up to 100%. The results shows that the proposed spectral data compression method based on two-dimensional correlation spectral technology has high compression rate and accurate classification.