NIR光谱的LLE-PLS非线性建模方法及应用

传统的偏最小二乘(PLS)建模方法不能有效反映近红外(NIR)光谱与分析样本的物理化学性质之间存在的非线性关系。局部线性嵌入(LLE)是一种新的非线性降维方法，属于流形学习方法，它能有效地发现高维数据中的本真低维结构。结合LLE和PLS，提出一种近红外光谱非线性建模的新方法，并用于建立丹参多酚酸盐柱层析过程中丹酚酸B含量的回归校正模型。该方法首先用LLE对NIR光谱数据降维，再用PLS建立校正模型。结果表明，与多元散射校正、一阶导等预处理方法结合PLS建模比较，参数优化后的LLE-PLS方法能更准确地预测丹酚酸B的含量，其交叉验证均方根误差为0.128 mg·mL-1、决定系数为0.998 8。基于NIR光谱及LLE-PLS建模，可实现丹参多酚酸盐柱层析过程的在线检测。

LLE-PLS Nonlinear Modeling Method for Near Infrared Spectroscopy and Its Application

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 x mL(-1) and r2 of 0.9988, 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.