文献类型：期刊文献

英文题名：LLE-PLS nonlinear modeling method for near infrared Spectroscopy and us application

作者：Yang Hui-hua; Qin Feng; Wang Yong; Wu Yun-ming; Shi Xiao-hao; Liang Qiong-lin; Wang Yi-ming; Luo Guo-an

机构：[1]Tsinghua Univ, Anal Ctr, Beijing 100084, Peoples R China;[2]Guilin Univ Elect Technol, Coll Comp & Control, Guilin 541004, Peoples R China;[3]E China Univ Sci & Technol, Modern Res Ctr TCM, Shanghai 200237, Peoples R China;[4]Green Valley Holding Co Ltd, Shanghai 201203, Peoples R China

年份：2007

卷号：27

期号：10

起止页码：1955

外文期刊名：SPECTROSCOPY AND SPECTRAL ANALYSIS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000250794700013)】；

语种：英文

外文关键词：locally linear embedding; partial least squares; near infrared spectroscopy; Salvianolate

摘要：The traditional near infrared (NIR) spectra modeling algorithm-partial least squares (PLS) can't effectively reflect the nonlinear correlations existing between the near infrared spectra and the chemical or physical properties of samples. Locally linear embedding (LLE) is a newly proposed nonlinear dimension reduction algorithm, which is a kind of manifold learning algorithrn. It can find out the intrinsic dimension from high dimensional data effectively and map the high dimensional input data points to a global low dimensional coordinates while keeping the spatial relations of the adjacent points, i. e. the geometry structure of the high dimensional space. No application of LLE in the information processing of NIR spectra has been reported. By combining LLE and PLS, a novel nonlinear modeling method LLE-PLS for NIR spectra was proposed. In the proposed method, LLE and PLS were adopted to deduct the dimensions of NIR spectra and build regressor, respectively. The LLE-PLS method was applied to correlate the NIR spectra with the concentrations of salvia acid B in the elution of column chromatography of Salvianolate. The results showed that LLE-PLS outperformed other preprocessing methods such as multiplicative scattering correction, the 1st derivative, vector normalization, minimum-maximum normalization, detrend, debias, and the 2nd derivative. After parameter optimization, LLE-PLS can accurately predict the concentration of salvia acid B, with a minimum RMSECV of 0.128 mg center dot mL(-1) and r(2) of 0.998 8, suggesting that LLE-PLS is better than PLS in modeling and prediction. The parameter of the number of nearest neighbor k of LLE-PLS and output dimension d can affect the performance of the method. The research showed that k is robust to RMSECV, and an excessively low or high output dimension d will result in a greater error because of insufficient or excessive information extraction. It can be concluded that LLE-PLS can effectively model the nonlinear correlations between spectra and physicochemical properties of the samples. And it is feasible to actualize online monitoring of the process of column chromatography of Salzianolate by coupling NIR spectra with LLE-PLS modeling method.