A Novel Overlapping ME Peaks Decomposition Algorithm Based on Iterative Derivative Sharpening

In microchip electrophoresis (ME) signals, multiple subpeaks low-resolution overlapping peaks seriously interfere with the measurement of peak parameters. However, the existing peak analysis methods either address the problem of two subpeaks overlapping peaks or set peak parameters empirically. In this study, an automatic overlapping peaks decomposition algorithm based on signal smoothing, iterative sharpening, and peak fitting is proposed. First, the Savitzky–Golay (SG) filter is used for smoothing ME signals because it outperforms other smoothing methods in peak retention ability, smoothness, and preservation of the high-frequency information content of the signal. Second, an iterative peak sharpening strategy is proposed based on the principle of the even-order derivative method. Finally, overlapping peak analysis methods based on swarm intelligence algorithms, gradient algorithms, and simplex algorithms, respectively, are proposed. For the swarm intelligence-based fitting algorithms, the fitting errors (fes) of ME signals using the proposed peak sharpening strategy are reduced by 13.5%, 23.7%, and 40.0% on average, respectively.