Range‐independent background subtraction algorithm for recovery of Raman spectra of biological tissue

Abstract

An important requirement for the use of Raman spectroscopy for tissue diagnostic applications is an appropriate algorithm that can faithfully retrieve weak tissue Raman signals from the measured raw Raman spectra. Although iterative modified polynomial‐fitting‐based automated algorithms are widely used, these are sensitive to the choice of the fitting range, thereby leading to significantly different Raman spectra for different start and stop wavenumber selection. We report here an algorithm for automated recovery of the weak Raman signal, which is range independent. Given a raw Raman spectrum and the choice of the start and the stop wavenumbers, the algorithm first truncates the spectrum to include the raw data within this wavenumber range, linearly extrapolates the truncated raw spectrum beyond the points of truncation on the two sides by using coefficients of linear least‐square fit, adds two Gaussian peaks of appropriate height and width on the extrapolated linear wings on either side and then iteratively smoothens the data with all these add‐ons such that the smaller of the ordinate values of the smoothed and the starting raw data serve as the input to each successive round of iterative smoothing until the added Gaussian peaks are fully recovered. The algorithm was compared with the modified polynomial‐based algorithms using mathematically simulated Raman spectrum as well as experimentally measured Raman spectra from various biological samples and was found to yield consistently range‐independent and artifact‐free Raman signal with zero baseline. Copyright © 2012 John Wiley & Sons, Ltd.

Characterization of different substrates for Raman spectroscopic imaging of eukaryotic cells

“…Many others have proposed various theories to pre-process the Raman spectra to get rid of the substrate background contribution. [16,[19][20][21][22][23][24] New trend in recent decade is towards lab-on-a-chip applications for live cell Raman imaging. [25,26] Lab-on-a-chip requires fabrication of substrates to achieve enclosed fluidic compartment.…”

Cone–shell Raman spectroscopy (CSRS) for depth‐sensitive measurements in layered tissue

“…The tissue sample was a chicken tibia with muscle tissue (thickness ˜2.3 mm) on its top. The Raman spectra so measured were processed using a routine methodology and normalized with respect to its mean intensity over a spectral range of 750–1750 cm –1 . The optical power onto the sample surface was measured to be 60 mW.…”

Improved Savitzky–Golay-method-based fluorescence subtraction algorithm for rapid recovery of Raman spectra

“…Recently, Krishna et al [30] proposed a rangeindependent background-subtraction algorithm (RIA) based on the Savitzky-Golay smoothing method. The RIA algorithm is iterative smoothing of the measured raw Raman spectrum in such a manner that the high-frequency Raman peaks are gradually eliminated, finally leading to the underlying broad baseline that can be subtracted from the raw spectrum to yield the true Raman signal.…”