Study on principal component analysis and multi-class discrimination against the combination to identify easy-confused fur

In this paper, the principal component analysis was performed on infrared spectral data, which included 60 groups of cattle and horse hair surfaces and 60 groups of cattle and horse flesh surfaces, respectively, by using SPSS 22. Using 100 sets of data as modelling samples (50 sets of cattle fur and horse fur), the multi-class discriminant analysis was carried out by using SPSS, the typical discriminant function and class function of cattle fur and horse fur were established and the back substitution verification was carried out. The typical discriminant function and the class function of cattle fur and horse fur were verified by 20 sets of data validation samples (10 groups of cattle fur and horse fur). The results show that the principal component analysis can reduce the dimension effectively, reduce the hair surfaces spectra from 2696 wavelength variables to nine wavelength variables and decrease the flesh surfaces spectra from 2696 wavelength variables to 13 wavelength variables. The cumulative contribution rate of the new wavelength variables is up to 99.89 and 99.88%, respectively. The back substitution accuracy rate of the typical discriminant function is 100%, and the verification accuracy rate is 100%. From the clustering graph, the established class function of cattle fur and horse fur back substitution clustering is well, and the correct clustering rate of the verification clustering graph is 100%.