基于可见/近红外光谱的水稻品种快速鉴别研究

提出了一种应用可见/近红外光谱技术与化学计量学相结合的快速、无损鉴别稻谷品种的新方法。收集了5个品种水稻共150个样本作为实验样本，通过可见/近红外光谱仪扫描这些样本获得了从350 nm到1 075 nm波长范围的光谱信息。将样本的光谱信息进行小波分解以消除高频噪声。将去噪声后的光谱数据经主成分分析压缩成数目较少的新变量(主成分)，压缩得到的前4个主成分能够解释99.891%的原始光谱信息。将前4个主成分作为BP神经网络的输入，不同水稻品种值的二进制代码值作为BP神经网络的输出，建立稻谷品种的模式识别模型。模型的预测误差阈值是0.2,模型是3层网络结构，各层的节点分别是4,9和3。150个样本被随机的分成包含100个样本的建模集和50个样本的预测集。结果表明，预测未知的50个样本的正确率达到96%。说明该方法具有较高的鉴别准确度，为稻谷品种的快速无损鉴别提供了一种新的方法。

Discrimination of Varieties of Paddy Based on Vis/NIR Spectroscopy Combined with Chemometrics

A simple,fast and non-destructive method based on visible/near infrared reflectance(Vis/NIR) spectroscopy and chemometrics was put forward for discriminating varieties of paddy.Firstly,A field spectroradiometer was used for collecting spectra in the wavelength range from 325 to 1 025 nm.The Vis/NIR spectra were acquired from 150 samples of five varieties of paddy.Secondly,original spectral data were decomposed as low-frequency wavelet coefficients and high-frequency wavelet coefficients by wavelet transform(WT) at first level.High-frequency wavelet coefficients were deleted as they contained too many noise,so the reconstructed signals from low-frequency wavelet coefficients were used as replacer of original spectral data.Thirdly,principal component analysis(PCA) compressed the above data into several new variables that were the linear combination of original spectral variables.The analysis suggested that the first four PCs(principle components) could account for 99.89% of the original spectral information,it means that the four PCs could explain most variation of original variables.In order to set up the model for discriminating varieties of paddy,the four diagnostic PCs were applied as inputs of back propagation artificial neural network(BP-ANN),and the values of varieties of different paddy were applied as the outputs of BP-ANN.The threshold of error was set as 0.2,the optimal structure of BP-ANN was three layers with nodes as 4-9-3.The whole 150 samples were randomly divided into two parts,one of which that consisted of 100 samples was used to model,and the other one containing 50 samples was used to predict.This model has been used to predict the varieties of 50 unknown samples,and the discrimination rate 96% has been obtained.It proved that the model was very reliable and practicable.In short,it is feasible to discriminate varieties of paddy based on visible/near infrared reflectance(Vis/NIR) spectroscopy and chemometrics.