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	LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application
	Yang Hui-hua ; Qin Feng ; Wang Yong ; Wu Yun-ming ; Shi Xiao-hao ; Liang Qiong-lin ; Wang Yi-ming ; Luo Guo-an
	2010-05-06 ; 2010-05-06
关键词	Theoretical or Mathematical/ infrared spectroscopy least squares approximations spectrochemical analysis/ near infrared spectra algorithm-partial least squares infrared spectra locally linear embedding nonlinear dimension reduction algorithm information processing column chromatography salvia acid Salvianolate multiplicative scattering correction vector normalization physicochemical properties/ A0765G IR spectroscopy and spectrometers A8280D Electromagnetic radiation spectrometry (chemical analysis)
中文摘要	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 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 ldr mL/sup -1/ and r/sup 2/ of 0. 998 8, suggesting that LLE-PLS is better than PLS in modeling and predictioa 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.
语种	中文 ; 中文
出版者	Editorial Board of Spectroscopy and Spectral Analysis ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/13387]
专题	清华大学
推荐引用方式 GB/T 7714	Yang Hui-hua,Qin Feng,Wang Yong,et al. LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application[J],2010, 2010.
APA	Yang Hui-hua.,Qin Feng.,Wang Yong.,Wu Yun-ming.,Shi Xiao-hao.,...&Luo Guo-an.(2010).LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application..
MLA	Yang Hui-hua,et al."LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application".(2010).

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