Recursive Wavelet Peak Detection of Analytical Signals

• DOI: 10.1007/s10337-016-3155-4
• Publication Date: 2016-08-13T14:42:29Z
• Authors: Xia Tong; Zhimin Zhang; Fanjuan Zeng; Chunyan Fu; Pan Ma; Ying Peng; Hongmei Lu; Yizeng Liang
• Keywords: 0301 basic medicine; 03 medical and health sciences

A novel algorithm, entitled recursive wavelet peak detection (RWPD), is proposed to detect both normal and overlapped peaks in analytical signals. Recursive peak detection is based on continuous wavelet transforms (CWTs), which can be used to obtain initial peak positions even for overlapped peaks. Genetic algorithm (GA) and Gaussian fitting are used to refine peak parameters (peak positions, widths, and heights). Finally, area of peaks can be calculated by numeric integration. Simulated and ultrahigh performance liquid chromatographic ion trap time-of-flight mass spectrometry (UPLC-IT-TOF-MS) data sets have been analyzed by RWPD, MassSpecWavelet, and peakfit package by Tom O’Haver. Results show that RWPD can obtain more accurate positions and smaller relative fitting errors than MassSpecWavelet and peakfit, especially in overlapped peaks. RWPD is a convenient tool for peak detection and deconvolution of overlapped peaks, and it has been developed in R programming language and is available at https://github.com/zmzhang/RWPD .