Computational Fluorescence Suppression in Shifted Excitation Raman Spectroscopy

Nia C. Jenkins, Katjana Ehrlich, Andras Kufcsak, Stephanos Yerolatsitis, Susan Fernandes, Irene Young, Katie Hamilton, Harry A. C. Wood, Tom Quinn, Vikki Young, Ahsan R Akram, James Stone, Robert R. Thomson , Keith Finlayson, Kevin Dhaliwal, Sohan Seth

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.
Original languageEnglish
Pages (from-to)2374-2383
Number of pages18
JournalIEEE Transactions on Biomedical Engineering
Issue number8
Publication statusPublished - 19 Jul 2023

Keywords / Materials (for Non-textual outputs)

  • Biomedical
  • Fluorescence
  • Lung Tissue
  • Machine Learning
  • Optical Fiber
  • Raman Spectroscopy
  • Regularization
  • Shifted Excitation
  • Sparsity
  • Smoothness


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