Smoothness Prior Approach for Spectral Smoothing and Baseline Correction

• 作 者: Lu Li He Chen Siying Chen Yinchao Zhang Long Gao
• 机 构: School of Optoelectronics,Beijing Institute of Technology,Beijing Institute of Space Mechanics & Ele
• 出 处: Journal of Beijing Institute of Technology
• 发表日期: 2017年01期
• 关键词: smoothness priors low-pass filter second derivative spectral smoothing baseline

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.

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 includes spectral peak detection and baseline estimation. First, the spectral peak regions are detected and identified according to the second derivatives.Then, generalized smoothness prior to 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.