Application of Wavelet Component Selection and Orthogonal Signal Correction in the Multivariate Calibration by Near-Infrared Spectroscopy

Abstract

To improve the quality of input data of multivariate calibration model, a new hybrid preprocessing algorithm (WPT-GA-OSC) was proposed, which is the combination of wavelet packet transform (WPT), genetic algorithm (GA) and orthogonal signal correction (OSC) algorithm. At first, WPT algorithm is applied to split the raw spectra into different frequency components. Then, based on the root mean square error of prediction models, the genetic algorithm is employed to select the WPT components related to analyte as the input data of regression model. At last, to further improve the quality of input data, OSC algorithm is applied to each GA-filtered component to eliminate the information irrelevant to analyte information. To validate the WPT-GA-OSC algorithm, it was applied to develop the calibration model for oil concentration measurement of corn. Compared with the conventional preprocessing algorithm, the WPT-GA-OSC algorithm can take full advantages of multiscale property of near infrared (NIR) spectra, and also can significantly decrease the prediction error by up to 48.3%, indicating that it is a promising way for filtering the spectral data to develop the NIR calibration model.