Baseline correction using morphological and iterative local extremum (MILE)

Baseline drift problem usually exists in the spectral analysis due to the detector nonlinearities and temperature variations. Therefore, the baseline must be corrected before accurate data analysis. So, we proposed a novel and high-precise algorithm, baseline estimation using morphological and iterative local extremum (MILE). We first obtained all local extrema of the measured spectrum by derivation. A coarse baseline is then estimated by the PCHIP interpolation method. Subsequently, find the local extrema again of coarse baseline and update by their adjacent data. The update processing iterates multiple times. Finally, the spectrum is corrected by subtracting the estimated baseline. In order to evaluate the performance of our algorithm, we corrected the baseline drift of various spectra, including chromatograms and Fourier Transform Spectrometer spectra. All results achieve a high precision, proving that our algorithm has better accuracy and robustness.

Automated summary

Baseline drift problem commonly exists in spectral analysis due to detector nonlinearities and temperature variations. To address this, we propose a novel and high-precise algorithm called baseline estimation using morphological and iterative local extremum (MILE). Our algorithm first obtains all local extrema of the measured spectrum and estimates a coarse baseline using the PCHIP interpolation method. It then iteratively updates the baseline by finding local extrema of the coarse baseline and their adjacent data. By subtracting the estimated baseline, the spectrum is corrected with high precision.