玉米近红外光谱在仿生模式识别中的特征提取方法研究

近红外光谱特征提取是近红外光谱定性分析的关键步骤，其质量直接影响定性分析结果。采用漫透射方法测量8个玉米品种的近红外光谱，经预处理后，分别采用PCA， ICA， PLS-DA和小波分解四种方法对光谱进行特征提取，并使用仿生模式识别方法建立了8个玉米品种识别模型，最后使用测试集数据进行模型测试。结论如下：使用PLS-DA方法进行特征提取后建立的模型正确识别率优于使用PCA， ICA和小波分解特征提取后建立的模型。

Abstract

Near infrared spectrum is an important step in near infrared spectrum qualitative analysis, which influences the qualitative analysis results directly. Diffuse transmittance measurements mode was used to collect spectral data of eight maize varieties. PCA, ICA, PLS-DA and wavelet transformation were used to extract features of pretreated data. Finally, we used the test set data to test the recognition models of eight maize varieties which were built based on biomimetic pattern recognition (BPR). We draw a conclusion that PLS-DA can make models get higher average correct recognition rate than PCA, ICA and Wavelet transformation.

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沈立峰, 贾仕强, 郭婷婷, 邬文锦, 严衍禄, 安冬. 玉米近红外光谱在仿生模式识别中的特征提取方法研究[J]. 光谱学与光谱分析, 2012, 32(4): 939. SHEN Li-feng, JIA Shi-qiang, GUO Ting-ting, WU Wen-jin, YAN Yan-lu, AN Dong. Study of Feature Extraction Methods for Maize’s Near Infrared Spectra in Biomimetic Pattern Recognition[J]. Spectroscopy and Spectral Analysis, 2012, 32(4): 939.

玉米近红外光谱在仿生模式识别中的特征提取方法研究

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