玉米亚正常籽粒生活力近红外光谱判别方法研究

应用近红外光谱分析技术，针对玉米诱导过程中产生的亚正常种子，发展亚正常种子的单籽粒生活力判别方法。该研究应用了一种基于Kolmogorov-Smirnov检验的方法(KS法)对近红外光谱进行特征提取，并比较了十种采用不同预处理数据和特征提取方法建立的模型的判别性能，每种模型试验了1134种参数组合，并对采用多种方法和参数组合建立的模型进行交叉验证。结果表明，采用矢量归一化预处理，KS法提取特征波长，并去除低信噪比区域的数据建立的模型判别效果最好。发芽籽粒和不发芽籽粒的平均正确识别率分别达到92.20%和84.86%。该方法将发芽籽粒的筛选准确率由随机筛选的不足40%提高至85%以上，可显著提高筛选效率。

Study on Discrimination Method of Maize Seed Viability Based on Near-Infrared Spectroscopy

Maize subnormal kernels are generated from haploid induction and have lower weight and viability than normal kernels. The germination percentage of subnormal kernels is below 40%. In the present work, a new approach to discriminating the viability of the subnormal maize kernels based on near infrared spectroscopy was developed and the feather selection method based on Kolmogorov-Smirnov (KS) test was applied into near infrared spectra analysis for the first time. The partial least squares model was established and validated with 600 spectral samples from 200 maize subnormal kernels, half of which have the ability to geminate within 3 days through the standard germination test and the other half cannot geminate within 7 days. The spectra were collected by a FT-NIR spectrometer in the diffuse reflectance mode. Ten models established via different preprocessing and feather selection methods were compared. Each model with 1 134 different parameter sets were evaluated through Monte Carlo cross validation. The optimum model was obtained by using 482 wavelengths in the range of 4 027 to 5 500 cm-1 and 6 858 to 9 088 cm-1 via the combination of the smoothing, vector normalization, KS feature selection, and low-signal-to-noise-ratio wavelength elimination. The highest correct discrimination rates for the seeds with germination ability and without germination ability arrive at 92.20% and 84.86%, respectively.