Jia Shi-Qiang, Guo Ting-Ting, Liu Zhe, Yan Yan-Lu, An Dong, Gu Jian-Cheng, Li Shao-ming, Zhang Shao-Ming, Zhu De-Hai
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Nov;34(11):2984-8.
It is generally accepted that near infrared reflectance spectroscopy (NIRS) can be used to identify variety authenticity of bare maize seeds. In practical, maize seeds are covered with seed coating agents. Therefore it's of huge significance to investigate the feasibility of identifying coated maize seeds by NIRS. This study employed NIRS to quickly determine the variety of coated maize seeds. Influence of seed coating agent on NIR spectra was discussed. The NIR spectra of coated maize seeds were obtained using an innovative method to avoid the impact of the seed coating agent. Coated seeds were cut open, and the sections were scanned by the spectrometer, so as to acquire the information of the seed itself. Then, support vector machine (SVM), soft independent modeling of class analogy (SIMCA), and biomimetic pattern recognition (BPR) was employed to establish the identification model for four maize varieties, and yield 93%, 95.8%, 98% average correct rate respectively. BPR model showed better performance than SVM and SIMCA models. The robustness of identification model was tested by seeds harvested from four regions and model showed good performance.
人们普遍认为,近红外反射光谱法(NIRS)可用于鉴别裸玉米种子的品种真实性。实际上,玉米种子都包有包衣剂。因此,研究用NIRS鉴别包衣玉米种子的可行性具有重大意义。本研究采用NIRS快速测定包衣玉米种子的品种。讨论了包衣剂对近红外光谱的影响。采用一种创新方法获取包衣玉米种子的近红外光谱,以避免包衣剂的影响。将包衣种子切开,用光谱仪扫描切面,从而获取种子本身的信息。然后,利用支持向量机(SVM)、类类比软独立建模法(SIMCA)和仿生模式识别(BPR)为四个玉米品种建立鉴别模型,平均正确率分别为93%、95.8%、98%。BPR模型表现优于SVM和SIMCA模型。用来自四个地区收获的种子对鉴别模型的稳健性进行测试,模型表现良好。