玉米品种近红外光谱的DFT特征分析方法

摘要

提出了一种基于离散傅里叶变换(discrete Fourier transform,DFT)的玉米品种特征分析新方法.实验数据为37个玉米晶种种子的近红外漫反射光谱数据,波段范围为4 000～12 000 cm-1.文中通过对原始数据进行分析,发现扫描频率较高的部分噪声也比较大.文中首先定义了一种类间、类内差异度Qm的计算方法,以度量特征选择的有效性;然后利用Qm对原始数据和DFT变换后的数据进行分段分析.实验结果表明,4 000～7 085 cm-1波段的DFT数据相对于全波段原始数据,Qm曲线均值、峰值明显提高.均值从原始的4.804 9提高到8.513 8,峰值最大值从原始的35.924 0提高到60.821 6,峰值最小值从原始的2.891 8提高到3.741 5.且变换后数据特征点(即Qm值大的点)较原始数据集中,最有利于提取玉米品种特征.%The present paper develops a new approach to the analyse of comn based on discrete Fourier transform (DFT). The experiment data is of 37 varieties of comn seed with the Fourier transform near infrared spectrometer in the wave number range from 4 000 to 12 000 cm-1. Analyse of the origin data found that as the wave number increases, the data noise also increases.Firstly, the paper defines a calculation method of interspecifie and intraspecific differences Qm to measure the effectiveness of feature selection. Secondly, Qm was used to analyse the original data and DFT-section data. Experimental results show that by choosing data of DFT with wave number range from 4 000 to 7 085 em-1 , the mean value and the peak value of the the Qrn curve markedly improved relative to the full band original data. The mean value was enhanced from the original 4. 804 9 to 8. 513 8,and the max of the peak value was enhanced from the original 35. 924 0 to 60. 821 6, while the min of the peak value was enhanced from the original 2. 891 8 to 3. 741 5. Data feature points (Qm value of large point) are more concentrated than the original data after DFT. Such a result is most conducive to extracting the characteristics of com seed.