玉米品种近红外光谱的特征分析与鉴别方法

以玉米种子的4 000~12 000 cm-1波段的漫反射近红外光谱为研究对象,提出了一种鉴别玉米品种的新方法。采用主成分分析法(PCA)来研究数据特征,发现近红外光谱在特征空间中具有显著的长条状分布特征,为此我们研究了改变样本点在PCA空间中的分布对品种鉴别的影响,并提出了归一化主成分分析(NPCA)的特征提取算法,同时还根据近红外光谱的数据分布特点提出了一种主方向仿生模式识别的分类算法,进一步提高了鉴别正确率。鉴别模型对第一测试集的平均正确识别率达到了97.67%,平均正确拒识率达到了98.40%,30个品种中的13个达到了100%的正确识别率;对第二测试集的平均正确拒识率达到了98.90%,有11个品种达到了100%的正确拒识率,具有较高的鉴别准确度。

Feature analysis and discrimination of varieties of corn based on near infrared spectra

A new method for the discrimination of varieties of corn was proposed based on the data set of near-infrared spectroscopy range from 4 000 to 12 000 cm(-1) of corn seed varieties. Principal component analysis (PCA) method was used to study the feature of the data, and the authors found that the near-infrared spectroscopy of corn seed varieties has a clear feature of zonal distribution, so the correlativity between the change in the distribution of the principal component and the discrimination result was studied, according to which the normalized principal component analysis (NPCA) method was proposed. Besides, principal direction biomimetic pattern recognition (PBPR) was proposed according to the feature, which got a better discrimination result. The average correct recognition rate attained 97.67% for test set I, and the average correct rejection rate attained 98.40%, with 13 of the 30 varieties reaching the correct recognition rate of 100%; The average correct rejection rate attained 98.90% for the test set II , and 11 of the 30 varieties reached the correct rejection rate of 100%. It was proved that the method had a high correct discrimination rate.