[Study on discrimination of varieties of corn using near-infrared spectroscopy based on GA and LDA].

A new method for the fast discrimination of varieties of corn based on near-infrared spectroscopy using genetic algorithm and linear discriminant analysis (LDA) was proposed. First, data of NIS of 37 varieties of corn was collected, second, genetic algorithm used for choosing the feature band of spectrum, then PCA and LDA were used to extract features, and finally corn seeds were classified. The result showed that GA could remove noise band effectively and improve the generalization ability of LDA.

[DFT feature analysis of corn varieties based on near infrared spectra].

The present paper develops a new approach to the analyse of corn based on discrete Fourier transform (DFT). The experiment data is of 37 varieties of corn seed with the Fourier transform near infrared spectrometer in the wave number range from 4 000 to 12 000 cm(-1). Analyse of the origin data found that as the wave number increases, the data noise also increases.

[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.

[A frequency selection method of NIR spectroscopy for discrimination of maize seed varieties].

A frequency selection method of NIR spectroscopy was proposed in the present paper for discrimination of maize seed varieties. A criterion function was defined to evaluate the discriminative ability of NIR spectroscopy at different frequencies, and then features of maize seed varieties were extracted accordingly for further processing. By eliminating correlation between features at different frequencies, the selected features are guaranteed to contain as much information of inter-variety difference as possible.