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Automated Method for Subtraction of Fluorescence from Biological Raman Spectra

Lieber, Chad A., Mahadevan-Jansen, Anita·2003·Applied Spectroscopy

Abstract

One of the challenges of using Raman spectroscopy for biological applications is the inherent fluorescence generated by many biological molecules that underlies the measured spectra. This fluorescence can sometimes be several orders of magnitude more intense than the weak Raman scatter, and its presence must be minimized in order to resolve and analyze the Raman spectrum. Several techniques involving hardware and software have been devised for this purpose; these include the use of wavelength shifting, time gating, frequency-domain filtering, first- and second-order derivatives, and simple curve fitting of the broadband variation with a high-order polynomial. Of these, polynomial fitting has been found to be a simple but effective method. However, this technique typically requires user intervention and thus is time consuming and prone to variability. An automated method for fluorescence subtraction, based on a modification to least-squares polynomial curve fitting, is described. Results indicate that the presented automated method is proficient in fluorescence subtraction, repeatability, and in retention of Raman spectral lineshapes.

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Metadata

Authors
Lieber, C. A., & Mahadevan-Jansen, A.
Venue
Applied Spectroscopy
Published
2003-11
Volume
57
Issue
11
Pages
1363-1367
ISSN
  • 0003-7028 (ISSN-L)
  • 1943-3530
OpenAlex
W2127673401
Cited by
1,049
References
0
FWCI
5.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Automated Method for Subtraction of Fluorescence from Biological Raman Spectra. https://app.cochat.ai/papers/10.1366/000370203322554518
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Lieber, C. A., & Mahadevan-Jansen, A. (2003). Automated Method for Subtraction of Fluorescence from Biological Raman Spectra. Applied Spectroscopy. https://doi.org/10.1366/000370203322554518

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.