Advanced Spectroscopy Technique for Biomedicine - Biomedical Optical Imaging Technologies: Design and Applications - page 26

Fig. 1.13 ( a ) Mercury-argon lamp image of a 100 m slit on the CCD through the HoloSpec
spectrograph, demonstrating the image aberration. ( b ) CCD image of 58 fibers aligned along a
parabolic line at the entrance of the spectrograph, demonstrating that the image aberration has
been corrected (Adapted from Huang et al. [ 11 ], with permission)
After initialization, the system is ready for real-time measurements. Measurements
are started via a control signal that can be triggered from the keyboard, hand switch,
foot switch, or a signal generated by the program itself. There are two shutters in the
system, which essentially have identical response times. One internal shutter lies in
the front of the CCD camera to prevent overexposure or exposure during the readout
process. The other external shutter is used to control the laser exposure to prevent
excessive illumination of the sample ( in vivo skin). Two additional procedures are
implemented in data acquisition, including saturation detection and cosmic ray
rejection. Once the spectrum is obtained, it can be automatically processed by
preloaded algorithms, such as for biochemical composition analysis.
The most important step in real-time Raman spectroscopy is the rejection of NIR
autofluorescence background that is superimposed on the Raman signal. The most
commonly used method in biomedical Raman measurement is single polynomial
curve fitting. The major weakness of polynomial fitting is its dependence on the
spectral range and the choice of polynomial order. Lieber et al. proposed an iterative
modified polynomial method to improve the fluorescence background removal [ 33 ].
Recently, we proposed a new algorithm for fluorescence background removal (the
Vancouver Raman Algorithm), which combines peak removal with a modified
polynomial fitting method [ 34 ]. The algorithm is less dependent on the choice
of the polynomial order and substantially improves the fluorescence background
removal, particularly for spectra with high noise or intense Raman peaks. It starts
from a single polynomial fitting P() using the raw Raman signal O(), followed
by calculation of its residual R() and its standard deviation DEV, where is the
Raman shift in cm 1 . The quantity of DEV is considered an approximation of the
noise level. In order to construct data for the next round of fitting, we compared
the original data with the sum of the fitted function and the value of its DEV,
defined as SUM. The data set is reconstructed following the rule that if a data
point is smaller than its corresponding SUM, it is kept; otherwise, it is replaced by