文献类型： 外文期刊

作者： Zhang Yuan ¹ ; Zhang Lu-da ¹ ; Bai Qi-lin ² ; Chen Shao-jiang ³ ;

作者机构： 1.China Agr Univ, Coll Sci, Beijing 100193, Peoples R China

2.Shanxi Acad Agr Sci, Crop Genet Res Inst, Taiyuan 030031, Peoples R China

3.China Agr Univ, Natl Maize Improvement Ctr China, Beijing 100193, Peoples R China

关键词： NIR;BPANN;High-oil maize

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2009 年 29 卷 3 期

页码：

收录情况： SCI

摘要： Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content, which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.