LLE-PLS Nonlinear Modeling Method for Near Infrared Spectroscopy and Its Application
YANG Hui-hua1,2, QIN Feng2, WANG Yong1,3, WU Yun-ming4, SHI Xiao-hao4, LIANG Qiong-lin1, WANG Yi-ming1, LUO Guo-an1,3*
1. Analysis Center, Tsinghua University, Beijing 100084, China 2. College of Computer and Control, Guilin University of Electronic Technology, Guilin 541004, China 3. The Modern Research Center for TCM, East China University of Science and Technology, Shanghai 200237, China 4. Green Valley Holding Co., Ltd., Shanghai 201203, China
Abstract 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 algorithm. 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·mL-1 and r2 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 Salvianolate by coupling NIR spectra with LLE-PLS modeling method.
Key words：Locally linear embedding;Partial least squares;Near infrared spectroscopy;Salvianolate
Corresponding Authors:
LUO Guo-an
E-mail: luoga@tsinghua.edu.cn
Cite this article:
YANG Hui-hua,QIN Feng,WANG Yong, et al. LLE-PLS Nonlinear Modeling Method for Near Infrared Spectroscopy and Its Application[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(10): 1955-1958.
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