Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method

Baseline correction is necessary for the qualitative and quantitative analysis of samples because of the existence of background fluorescence interference in Raman spectra. The asymmetric least squares (ALS) method is an adaptive and automated algorithm that avoids peak detection operations along with other user interactions. However, current ALS-based improved algorithms only consider the smoothness configuration of regions where the signals are greater than the fitted baseline, which results in smoothing distortion. In this paper, an asymmetrically reweighted penalized least squares method based on spectral estimation (SEALS) is proposed. SEALS considers not only the uniform distribution of additive noise along the baseline but also the energy distribution of the signal above and below the fitted baseline. The energy distribution is estimated using inverse Fourier and autoregressive models to create a spectral estimation kernel. This kernel effectively optimizes and balances the asymmetric weight assigned to each data point. By doing so, it resolves the issue of local oversmoothing that is typically encountered in the asymmetrically reweighted penalized least squares method. This oversmoothing problem can negatively impact the iteration depth and accuracy of baseline fitting. In comparative experiments on simulated spectra, SEALS demonstrated a better baseline fitting performance compared to several other advanced baseline correction methods, both under moderate and strong fluorescence backgrounds. It has also been proven to be highly resistant to noise interference. When applied to real Raman spectra, the algorithm correctly restored the weak peaks and removed the fluorescence peaks, demonstrating the effectiveness of this method. The computation time of the proposed method was approximately 0.05 s, which satisfies the real-time baseline correction requirements of practical spectroscopy acquisition.