对玉米种子高光谱图像的光谱维信息进行分析, 探索利用高光谱图像技术鉴定玉米杂交种纯度的可行性。 实验中利用高光谱成像系统采集玉米品种农华101的母本和杂交种的高光谱图像, 波长范围871～1699 nm; 在每个玉米样本上提取感兴趣区域的平均光谱信息, 利用处理后的数据建立农华101母本和杂交种的鉴定模型。 讨论了样品的摆放方式(种子胚正对光源和背对光源, 种子在样品台上的位置)和实验环境对鉴定模型性能的影响。 鉴定模型对不同摆放方式和实验环境下获得的同种样品的光谱的正确识别率和正确拒识率均达到90%以上, 模型稳健性良好。 利用Qs方法选择特征波段<参考文献原文>, 发现在1 230 nm附近（1 195～1 246 nm）农华101的母本和杂交种差异最大。 实验中利用特征波段内的数据进行建模和测试, 正确识别率和正确拒识率达到90%以上, 与利用全波段(925～1597 nm)获得的识别效果相当。 分析结果表明, 利用高光谱图像技术鉴定玉米杂交种纯度是可行的。

The feasibility of employing hyperspectral image technology to identify maize hybrid purity was studied by analyzing the spectral information of maize hyperspectral image. The hyperspectral images of hybrid and female parent of maize variety NH101 in the range of 871～1 699 nm including 308 wavelengths were collected by hyperspectral imaging system. We extracted average spectral information of interested region on maize seed and built identification models of hybrid and female parent of maize variety NH101 based on processed spectral data. The influences of different sample laying modes (seed embryo facing the light source, seed embryo backward light source, and seed put in different locations on sample stage) and experimental environments on the performance of identification models were discussed. Spectral collected under different sample laying modes and experimental environments were used to test the robustness of identification models. The average correct acceptance rates and average correct rejection rates are more than 90%. The feature spectral bands (1 195～1 246 nm) with which the differences between hybrid and female parent are the largest were extracted by a wavelength selection method based on standard deviations, called Qs. The performance of identification models built based on spectral data in feature spectral bands reached the same level of models built based on spectral data in the full range of 925～1 597 nm. The results demonstrated the feasibility of using hyperspectral image technology as an objective and rapid method for the identification of maize hybrid purity.