Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

By G. Schulze, A. Jirasek, Marcia M. L. Yu, A. Lim, R. Turner, M. Blades. Applied Spectroscopy, 2005

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Data Pre-processing (2009, unknown): “However, more sophisticated methods to perform this operation exist, such as wavelets ( Schulze et al. , 2005 ; Davis et al. , 2007 ) and iterative polynomial baseline fi tting (IPBF) ( Lieber and MahadevanJansen, 2003 ) to mention just two. ch002.indd 36 11/18/2008 2:15:54 PM IPBF uses the same…”
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution (2017, methodology): “A detailed review and comparison of baseline correction methods can be found in Schulze et al.[7].”
Review of Fluorescence Suppression Techniques in Raman Spectroscopy (2015, background): “The derivatives of the fluorescence background are always smaller in magnitude than those of Raman peaks; thus, taking the derivative of a measured Raman spectrum will eliminate the background components irrespective of their magnitudes and thus enhance the sharp Raman signal (90).”