The correction of baseline drift is an important step in data preprocessing. An interval linear fitting method based on automatic
critical-point-seeking was improved, which made it possible for the baseline to drift automatically. Experimental data were
acquired from the sulfamic acid catalytic reaction of the aspirin system, which consisted of different proportions of aspirin.
A simulated baseline with different interval values of moving average smoothing determined setting parameters in this method.
After baseline drifts caused by fluorescence were removed, the differences of characteristic aspirin peaks proved the efficiency
of this method.
Raman spectroscopy is used worldwide in materials characterization for its ability to obtain information on vibrations from
samples. It can also be used for on-line monitoring using a fiber-optic Raman probe (1,2). The Raman spectra show the characteristics
for species in sharp and dense peaks. However, during the application of Raman spectroscopy, fluorescence of organic compounds
in the samples, which are sometimes several orders of magnitude more intense than the weak Raman scatter, can interfere with
the Raman signals (3). A phenomenon of baseline drift shows up, making the resolution and analysis of Raman spectra impractical.
Both instrumental (4) and mathematical methods have been developed to reduce the drifted baseline caused by fluorescence.
The use of laser excitation wavelengths such as 785–1064 nm, which does not eliminate fluorescence (5), is the most traditional
instrumental method. Raman scattering is directly proportional to the fourth power of frequency; as the excitation wavelength
increases, the sensitivity of the Raman becomes severely reduced. The use of anti-Stokes Raman spectroscopy is another method,
based on theory (6). Mathematical methods (7–10) include the first- and second-order derivatives, wavelet transform, median
filter, and manual polynomial fitting. These methods are useful in certain situations, but still have some limitations. For
example, derivatives are effective, but as a result the shape of the Raman spectrum is changed; wavelet transform can be differentiable
in the high- and low-frequency components of the signals; however, it is difficult to choose a decomposition method. Manual
polynomial fittings require the user to identify the "non-Raman" locations manually (11), and afterwards the baseline curve
is formed by fitting these locations. Consequently, the result involves the inevitable subjective factors and, in addition,
the workload is always heavy. Therefore, it is important to choose an optimal decomposition method.
Piecewise linear fitting based on critical-point-seeking was proposed in this study. The method determines an optimum corrected
spectrum by correlation analysis, which can conquer these limitations. A Raman spectrum from the sulfamic acid catalytic reaction
of an aspirin system was used as a study subject. By using this method, the Raman spectrum drifted baseline was automatically
eliminated, leaving only the corrected spectrum.