Differentiation of storage time of wheat seed based on near infrared hyperspectral imaging

Seed aging during storage is one of the main factors that influence the quality of wheat seed. Current detection methods based on NIR spectra were mostly for group seeds, they had poor stability for single seed detection because of sample uniformity. In this study, the characteristic changes of single wheat seed during storage procedure were measured through hyperspectral imaging technology. Firstly, hyperspectral imaging data of wheat grain including six years from 2007 to 2012 had been collected. The original spectra showed clear difference in the band of 1400-1600 nm, which may be caused by the decreasing of moisture and protein content during storage; principal component analysis (PCA) was applied to analyze the spectral data of wheat grain including six years, the clustering chart of the principal components indicated that the grain between same or similar year have an clustering characteristic, and the characteristic difference would become obviously with the increasing of storage time; soft independent modeling of class analogy (SIMCA) was applied to classify the grain of different years, results showed that the classification accuracy of the dichotomy between adjacent years could reach 97.05%, and the accuracy of the mixed classification of six years could also reach 82.5%. These results indicated that hyperspectral imaging technology could be used to differentiate the quality change of wheat seed during different storage time, which could provide support for the intelligent monitoring of stored wheat seeds.