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LIBS (Laser-Induced Breakdown Spectroscopy) is a versatile technique formulti-element analysis, offering rapid analysis with minimal samplepreparation. However, challenges persist in baseline correction due tobackground radiation, causing non-linear interference with emission lines. Thisnecessitates proper baseline correction for accurate quantification of elementsin LIBS spectra. Our study focuses on the quantification of total carbon insoil samples, conducting a comparative analysis of filters and methods fornoise removal and baseline correction in LIBS spectra. We exhaustively testedall combinations of filters and methods, optimizing their parameters toestablish the strongest correlation between corrected spectra and carboncontent in a training sample set. We then evaluated these combinations usingoptimized parameters on a separate test sample set. Through rigorousevaluation, we found that the combination of the Savitzky-Golay filter and the4S Peak Filling method achieved the most effective baseline correction. Thiscombination demonstrated a Pearson's correlation coefficient of 0.93 and a rootmean square error of 0.21. Notably, it outperformed a linear regression modelbased solely on the carbon emission line at 193.04 nm, which yielded acorrelation of 0.91 with a root mean square error of 0.26. Our proposedprocedure presents a novel approach for baseline correction in LIBS spectra,opening new possibilities for the development of multivariate methods utilizingspecific spectral ranges.

Comparison of Algorithms for Baseline Correction of LIBS Spectra for Quantifying Total Carbon in Brazilian Soils