title: [The NIR spectra based variety discrimination for single soybean seed].
abstract: With the development of soybean producing and processing, the quality breeding becomes more and more important for soybean breeders. Traditional sampling detection methods for soybean quality need to destroy the seed, and does not satisfy the requirement of earlier generation materials sieving for breeding. Near infrared (NIR) spectroscopy has been widely used for soybean quality detection. However, all these applications were referred to mass samples, and they were not suitable for little or single seed detection in breeding procedure. In the present study, the acousto--optic tunable filter (AOTF) NIR spectroscopy was used to measure the single soybean seed. Two varieties of soybean were measured, which contained 60 KENJIANDOU43 seeds and 60 ZHONGHUANG13 seeds. The results showed that NIR spectra combined with soft independent modeling of class analogy (SIMCA) could accurately discriminate the soybean varieties. The classification accuracy for KENJIANDOU43 seeds and ZHONGHUANG13 was 100%. The spectra of single soybean seed were measured at different positions, and it showed that the seed shape has significant influence on the measurement of spectra, therefore, the key point for single seed measurement was how to accurately acquire the spectra and keep their representativeness. The spectra for soybeans with glossy surface had high repeatability, while the spectra of seeds with external defects had significant difference for several measurements. For the fast sieving of earlier generation materials in breeding, one could firstly eliminate the seeds with external defects, then apply NIR spectra for internal quality detection, and in this way the influence of seed shape and external defects could be reduced.
authors.affiliation: National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
authors.forename: Da-Zhou
authors.initials: DZ
authors.lastname: Zhu
authors.forename: Kun
authors.initials: K
authors.lastname: Wang
authors.forename: Guang-Hua
authors.initials: GH
authors.lastname: Zhou
authors.forename: Rui-Feng
authors.initials: RF
authors.lastname: Hou
authors.forename: Cheng
authors.initials: C
authors.lastname: Wang
mesh_terms: D001947:Breeding; D012639:Seeds; D013025:Glycine max; D019265:Spectroscopy, Near-Infrared