Feature Analysis and Discrimination of Varieties of Corn Based on Near Infrared Spectra

A new method for the discrimination of varieties of corn was proposed based on the data set of near-infrared spectroscopy range from 4 000 to 12 000 cm(-1) of corn seed varieties. Principal component analysis (PCA) method was used to study the feature of the data, and the authors found that the near-infrared spectroscopy of corn seed varieties has a clear feature of zonal distribution, so the correlativity between the change in the distribution of the principal component and the discrimination result was studied, according to which the normalized principal component analysis (NPCA) method was proposed. Besides, principal direction biomimetic pattern recognition (PBPR) was proposed according to the feature, which got a better discrimination result. The average correct recognition rate attained 97.67% for test set I, and the average correct rejection rate attained 98.40%, with 13 of the 30 varieties reaching the correct recognition rate of 100%; The average correct rejection rate attained 98.90% for the test set II, and 11 of the 30 varieties reached the correct rejection rate of 100%. It was proved that the method had a high correct discrimination rate.