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Automated Method for Subtraction of Fluorescence from Biological Raman Spectra
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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Cited by
Using Raman spectroscopy to characterize biological materials
Butler, H. J., Ashton, L., Bird, B., Cinque, G., Curtis, K., Dorney, J., Esmonde-White, K., Fullwood, N. J., Gardner, B., Martin-Hirsch, P. L., et al. (2016) · Nature Protocols · doi:10.1038/nprot.2016.036 · Cited by 1,309
Review of multidimensional data processing approaches for Raman and infrared spectroscopy
Gautam, R., Vanga, S., Ariese, F., & Umapathy, S. (2015) · EPJ Techniques and Instrumentation · doi:10.1140/epjti/s40485-015-0018-6 · Cited by 696
High-fat diet alters gut microbiota physiology in mice
Daniel, H., Gholami, A. M., Berry, D., Desmarchelier, C., Hahne, H., Loh, G., Mondot, S., Lepage, P., Rothballer, M., Walker, A., et al. (2014) · The ISME Journal · doi:10.1038/ismej.2013.155 · Cited by 678
Deep convolutional neural networks for Raman spectrum recognition: a unified solution
Liu, J., Osadchy, M., Ashton, L., Foster, M., Solomon, C. J., & Gibson, S. J. (2017) · The Analyst · doi:10.1039/c7an01371j · Cited by 507
Guo, S., Popp, J., & Bocklitz, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00620-3 · Cited by 361
Metadata
- Authors
- Lieber, C. A., & Mahadevan-Jansen, A.
- Venue
- Applied Spectroscopy
- Publisher
- SAGE Publications
- 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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