Robust baseline correction for Raman spectra by constrained Gaussian radial basis function fitting

• Published in: Chemometrics and intelligent laboratory systems Vol. 253; p. 105205
• Main Authors: Park, Sungwon, Kim, Hongjoong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2024
• Subjects: Baseline correction; Linear programming; Quantitative analysis; Raman spectroscopy; Linear programming; Baseline correction; Raman spectroscopy; Quantitative analysis
• ISSN: 0169-7439
• DOI: 10.1016/j.chemolab.2024.105205

Accurate baseline correction is a fundamental requirement for extracting meaningful spectral information and enabling precise quantitative analysis using Raman spectroscopy. Although numerous baseline correction techniques have been developed, they often require meticulous parameter adjustments and yield inconsistent results. To address these challenges, we have introduced a novel approach, namely constrained Gaussian radial basis function fitting (CGF). Our method involves solving a curve-fitting problem using Gaussian radial basis functions under specific constraints. To ensure stability and efficiency, we developed a linear programming algorithm for the proposed approach. We evaluated the performance of CGF using simulated Raman spectra and demonstrated its robustness across various scenarios, including changes in data length and noise levels. In contrast to standard methods, which frequently require complicated parameter adjustments and may exhibit varying errors, our approach provides a simple parameter search and consistently achieves low errors. We further assessed CGF using real Raman spectra, leading to enhanced accuracy in the quantitative analysis of the Raman spectra of chemical warfare agents. Our results emphasize the potential of CGF as a valuable tool for Raman spectroscopy data analysis, significantly advancing sophisticated analytical techniques. •We introduce innovative methods for baseline correction.•We also consider the development of a curve fitting problem and an efficient linear programming algorithm.•Empirical results demonstrate our methods’ superior accuracy in both simulated and real data.•Our methods demonstrate robustness across various measurement scenarios.