A robust baseline elimination method based on community information

• Excellent community provides threshold, global and local slope information.
• Global and local slope information further confirm peak distribution information.
• An adaptive iteratively reweighted genetic programming model the baseline.

Baseline correction is an important pre-processing technique used to separate true spectra from interference effects or remove baseline effects. In this paper, an adaptive iteratively reweighted genetic programming based on excellent community information (GPEXI) is proposed to model baselines from spectra. Excellent community information which is abstracted from the present excellent community includes an automatic common threshold, normal global and local slope information. Significant peaks can be firstly detected by an automatic common threshold. Then based on the characteristic that a baseline varies slowly with respect to wavelength, normal global and local slope information are used to further confirm whether a point is in peak regions. Moreover the slope information is also used to determine the range of baseline curve fluctuation in peak regions. The proposed algorithm is more robust for different kinds of baselines and its curvature and slope can be automatically adjusted without prior knowledge. Experimental results in both simulated data and real data demonstrate the effectiveness of the algorithm.