There are several smoothing procedures for spectral data which are affected by occasionally occurring outliers. Most of the known methods are based on local averages (or fits) of the spectral data. We introduce here an outlier-insensitive, robust smoothing method which rejects the influence of huge noise spikes. The proposed smoothing algorithm can be tuned by two parameters. The first corresponds to the signal-to-noise ratio, the second to the halfwidths of the spectral bands. We apply this new technique to several spectra and prove the advantages of our method of identifying peaks and baselines in Raman spectroscopy.

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