Baseline correction method based on improved asymmetrically reweighted penalized least squares for the Raman spectrum

• Published in: Applied optics (2004) 2020-12, Vol.59 (34), p.10933
• Main Authors: Ye, Jianfeng, Tian, Ziyang, Wei, Haoyun, Li, Yan
• Format: Article
• Language: English
• Subjects: Algorithms; Asymmetry; Background noise; Fluorescence; Least squares; Raman spectra
• ISSN: 1559-128X; 2155-3165
• DOI: 10.1364/AO.404863

We present a baseline correction method based on improved asymmetrically reweighted penalized least squares (IarPLS) for the Raman spectrum. This method utilizes a new S-type function to reduce the risk of baseline overestimation and speed up the reweighting process. Simulated spectra with different levels of noise and measured spectra with strong fluorescence background from different samples are used to validate the performance of the proposed algorithm. Considering the drawbacks of the weighting rules for the asymmetrically reweighted penalized least squares (arPLS) method, we adapt an inverse square root unit (ISRU) function, which performs well in baseline correction. Compared with previous penalized least squares methods, such as asymmetric least squares, adaptive iteratively reweighted penalized least squares, and arPLS, experiments with the simulated Raman spectra have confirmed that the proposed method yields better outcomes. Experiments with the measured Raman spectra show that the IarPLS method can improve real Raman spectra within 20 ms. The results show that the proposed method can be successfully applied to the practical Raman spectrum as a strong basis for quantitative analysis.