Abstract：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.
[1] Xu X, Li L, Dong X, et al. Journal of Experimental Botany, 2013 (online). [2] PU Xin-chun, HAN Jian-guo(浦心春，韩建国). Handbook of Methods of Seedling Accessment and Seed Vigor Test(种苗评定与种子活力测定方法手册). Beijing: Beijing Agricultural University Press(北京：北京农业大学出版社), 1993. [3] HAN Liang-liang, MAO Pei-sheng, WANG Xin-guo, et al(韩亮亮，毛培胜，王新国，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 27: 86. [4] YIN Jia-hong, MAO Pei-sheng, HUANG Ying, et al(阴佳鸿，毛培胜，黄 莺，等). Infrared(红外), 2010, 31: 39. [5] Tigabu M, Odén P C. New Forest, 2003, 25: 163. [6] Tigabu M, Odén P C. Seed Science and Technology, 2003, 31: 317. [7] Tigabu M, Odén P C. Seed Science and Technology, 2004, 32: 593. [8] GUO Ting-ting, WU Wen-jin, SU Qian, et al(郭婷婷, 邬文锦, 苏 谦, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2009, 40: 87. [9] Yu J, Ongarello S, Fiedler R, et al. Bioinformatics, 2005, 21: 2200. [10] Wu W, Guo Q, Jouan-Rimbaud D, et al. Chemometrics and Intelligent Laboratory Systems, 1999, 45: 39. [11] Centner V, Massart D, Noord O E, et al. Analytical Chemistry, 1991, 68:3851. [12] Cai W, Li Y, Shao X. Chemometrics and Intelligent Laboratory Systems, 2008, 90: 188. [13] De Jone S. Chemometrics and Intelligent Laboratory Systems, 1993, 18: 251.
