Smoothness Prior Approach for Spectral Smoothing and Baseline Correction

Smoothness prior approach for spectral smoothing is investigated using Fourier frequency filter analysis. We show that the regularization parameter in penalized least squares could continuously control the bandwidth of low-pass filter. Besides, due to its property of interpolating the missing values automatically and smoothly, a spectral baseline correction algorithm based on the approach is proposed. This algorithm generally comprises spectral peak detection and baseline estimation. First, the spectral peak regions are detected and identified according to the second derivatives. Then, generalized smoothness prior approach combining identification information could estimate the baseline in peak regions. Results with both the simulated and real spectra show accurate baseline-corrected signals with this method.