  LIBS (Laser-Induced Breakdown Spectroscopy) is a versatile technique for
multi-element analysis, offering rapid analysis with minimal sample
preparation. However, challenges persist in baseline correction due to
background radiation, causing non-linear interference with emission lines. This
necessitates proper baseline correction for accurate quantification of elements
in LIBS spectra. Our study focuses on the quantification of total carbon in
soil samples, conducting a comparative analysis of filters and methods for
noise removal and baseline correction in LIBS spectra. We exhaustively tested
all combinations of filters and methods, optimizing their parameters to
establish the strongest correlation between corrected spectra and carbon
content in a training sample set. We then evaluated these combinations using
optimized parameters on a separate test sample set. Through rigorous
evaluation, we found that the combination of the Savitzky-Golay filter and the
4S Peak Filling method achieved the most effective baseline correction. This
combination demonstrated a Pearson's correlation coefficient of 0.93 and a root
mean square error of 0.21. Notably, it outperformed a linear regression model
based solely on the carbon emission line at 193.04 nm, which yielded a
correlation of 0.91 with a root mean square error of 0.26. Our proposed
procedure presents a novel approach for baseline correction in LIBS spectra,
opening new possibilities for the development of multivariate methods utilizing
specific spectral ranges.
