A small-window moving average-based fully automated baseline estimation method for Raman spectra.
A fully automated and model-free baseline-correction method for vibrational spectra is presented. It iteratively applies a small, but increasing, moving average window in conjunction with peak stripping to estimate spectral baselines. Peak stripping causes the area stripped from the  spectrum to initially increase and then diminish as peak stripping proceeds to completion; a subsequent increase is generally indicative of the commencement of baseline stripping. Consequently, this local minimum is used as a stopping criterion. A backup is provided by a second stopping criterion  based on the area under a third-order polynomial fitted to the first derivative of the current estimate of the baseline-free spectrum and also indicates whether baseline is being stripped. When the second stopping criterion is triggered instead of the first one, a proportionally scaled simulated  Gaussian baseline is added to the current estimate of the baseline-free spectrum to act as an internal standard to facilitate subsequent processing and termination via the first stopping criterion. The method is conceptually simple, easy to implement, and fully automated. Good and consistent  results were obtained on simulated and real Raman spectra, making it suitable for the fully automated baseline correction of large numbers of spectra.
Michael Smith Laboratories, The University of British Columbia, Vancouver, Canada.
H Georg
HG
Schulze
Rod B
RB
Foist
Kadek
K
Okuda
André
A
Ivanov
Robin F B
RF
Turner