ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra

A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

Automated summary

In Raman spectroscopy, baseline correction is a critical step that has been recently improved with procedures relying on asymmetric loss functions. A novel baseline correction procedure called ISREA has been developed, utilizing smoothing splines to estimate the baseline, mimicking asymmetric square root loss, and avoiding direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through extensive numerical experiments, ISREA has been shown to be simple, fast, and capable of yielding consistent and accurate baselines that preserve meaningful Raman peaks.