A Novel Accurate Peak Extraction Algorithm of Mass Spectrometry Based on Iterative Adaptive Curve Fitting
In the analysis of mass spectrometry, the peak identification from the overlapped region is necessary yet difficult. Although various methods have been developed to identify these peaks, especially the continuous wavelet transformation, their applications are still limited and it is hard to deal with the complex overlapped peaks. In this study, a novel peak extraction algorithm of mass spectrometry based on iterative adaptive curve fitting is proposed to address these challenges. It fully utilizes the global optimization characteristics of adaptive curve fitting. Initial peak parameters are obtained using a window searching method, and the residuals between the adaptive fitting peak and the original data indicate the fit’s effectiveness and provide information about the peaks in overlap. Using this information, we performed iterative adaptive fitting, continuously updating the overlapped peaks until the residuals met the completion criteria. All of the peaks within the overlapped region can be successfully extracted by the final fitting. The proposed method is evaluated by the simulated data, the real signal from a public data set, and the spectra of two different mass spectrometry instruments. The results demonstrate that this method can more effectively extract peaks with severe overlap and multiple overlapped peaks, resist noise interference, and offer the potential to process peaks with a high dynamic range. More importantly, the proposed method accurately identifies overlapped peaks in the actual spectra from various mass spectrometry instruments, which helps the qualitative and quantitative analyses to a great extent.
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