Laplacian Regularized Least Squares Regression and its Dynamic Parameter Optimization for Near Infrared Spectroscopy Modeling

Abstract

Partial least square (PLS) is the most commonly used algorithm for near infrared (NIR) modeling. NIR modeling features that it's cheap, easy and fast to measure the NIR spectroscopy while expensive, difficult and time-consuming to measure the reference value for this spectroscopy. PLS often faces the challenge of that limited samples are available in training set to build a predicative model. To tackle this problem, a novel NIR modeling method - Laplacian regularized least squares regression (LapRLSR) and its dynamically adaptive parameters optimization method was presented. Based on the semi-supervised learning framework, LapRLSR can take the advantage of many unlabeled spectra to promote the prediction performance of the model though there are only few labeled samples. The proposed LapRLSR modeling algorithm was applied to the online monitoring of the concentration of salvia acid B in the column separation procedure of TCM manufacturing, and the results demonstrated that its prediction capability outperformed PLS and regularized least square regression method.