A Spectral Wavelength Selection Algorithm Based on RMSECV Curve
ZHOU Yan1, CAO Hui2*, JU Lin-cang1
1. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China 2. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
摘要： 提出了一种利用偏最小二乘回归系数矩阵筛选光谱波段的算法。该算法利用偏最小二乘回归系数作为筛选光谱波长的依据, 参考(root-mean-squares error of cross-validation, RMSECV)曲线, 使初选波长数大大降低。在此基础上通过循环选择将无效信息光谱波长剔除, 同时增强了所建模型的预测精确性。通过生产过程的Raman光谱数据验证，该算法比传统的利用回归系数筛选波长的算法更好地提高了模型的精确性，同时降低了模型的复杂程度，是一种高效实用的算法。
关键词：波长选择;偏最小二乘;拉曼光谱
Abstract：The present paper presents a partial least squares (PLS) regression coefficient matrix based wavelength selection algorithm. The regression coefficient was used as the criterion for the wavelength selection, and the Root-Mean-Squares Error of Cross-Validation (RMSECV) curve was referred to, to decrease the primary number of wavelength selected. Based on it, the uninformative wavelength can be eeleted through iteration steps, and the prediction accuracy of the model can also be improved. The algorithm was compared with the existing methods via a hydrogenation process Raman spectra data set, and the results indicated that the new algorithm can produce a more accurate and concise model than the existing ones.
Key words：Wavelength selection;Partial least squares;Raman spectra
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