The Characteristic Spectral Selection Method Based on Forward and Backward Interval Partial Least Squares

文献类型： 外文期刊

第一作者： Qu Fang-fang

作者： Qu Fang-fang;Ren Dong;Hou Jin-jian;Zhang Zhong;Wang Ji-hua;Hou Jin-jian;Lu An-xiang;Wang Ji-hua;Xu Hong-lei

作者机构：

关键词： Near-Infrared Spectroscopy;FiPLS;BiPLS;FB-iPLS;Greedy search;Characteristic intervals

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2016 年 36 卷 2 期

页码：

收录情况： SCI

摘要： In the near-infrared spectroscopy, the Forward Interval Partial Least Squares (FiPLS) and Backward Interval Partial Least Squares (BiPLS) are commonly used modeling methods, which are based on the wavelength variable selection. These methods are usually of high prediction accuracy, but are strongly characteristic of greedy search, which causes that the intervals selected are not good enough to indicate the analyte information. To solve the problem, a spectral characteristic intervals selection strategy (FB-iPLS) based on the combination of FiPLS and BiPLS is proposed. On the basis of spectral segmentation, both FiPLSs are used to select useful intervals, and BiPLS is used to delete useless intervals, so as to perform the selection and deletion of the characteristic variables alternatively, which conducts a two-way choice of the target characteristic variables, and is used to improve the robustness of the model. The experiments on determining the ethanol concentration in pure water are conducted by modeling with FiPLS, BiPT S and the proposed method. Since different size of intervals will affect the result of the model, the experiments here will also examine the model results with different intervals of these three models. When the spectrum is divided into 60 segments, the FB-iPT S method obtains the best prediction performance. The correlation coefficients (r) of the calibration set and validation set are 0. 967 7 and 0. 967 0 respectively, and the cross-validation root mean square errors (RMSECV) are 0. 088 8 and 0. 057 1, respectively. Compared with FiPLS and BiPLS, the overall prediction performance of the proposed model is better. The experiments show that the proposed method can further improve the predictive performance of the model by resolving the greedy search feature against BiPLS and FiPLS, which is more efficient for and representative of the selection of characteristic intervals.

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