The application of linear regression on wavelet coefficients for robust calibration of spectral data with highly variable background was successfully demonstrated with synthetic and real data. A Monte Carlo study was made to investigate the performance of the methods in both the cases where the background variation in the prediction set was the same as in the calibration set and where the variation was different. Multivariate linear regression on wavelet coefficients proved to be competitive in the first case and superior in the second case with respect to partial least squares (PLS) calibration. Results on real near-infrared (NIR) data confirmed the simulation study. As a study of regression on wavelet coefficients, this is the first application study of regression on wavelet coefficients that shows how the wavelet’s property of vanishing moments can be used for reducing the effects of varying background. As a background correction method, the proposed approach avoided errors introduced in the estimation process. In addition, the strategy proposed here can be applied to data collected by various other analytical techniques as well.

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