A method for detection of spectral peak position in mass spectrometry data processing

Abstract

The invention relates to a spectrum peak position detection method in mass spectrum data processing. The method comprises the following steps of performing CWT (Continuous Wavelet Transform) on an original spectrum diagram, and forming a two-dimensional wavelet coefficient matrix; setting a plurality of particles in the matrix so that the particles move according to a fixed rule and are gradually gathered to a position of a local extreme point; measuring the matrix position, and forming a measuring value matrix by using a noise threshold value and an original spectrum peak intensity modification measuring value; searching ridge points in the measuring value matrix; connecting a ridge line; and determining the spectrum peak position according to the ridge line. The method has the advantages that the original spectrum peak intensity and wavelet coefficient matrix information are integrally used for performing spectrum peak position detection; the problem of false detecting rate raising during the weak peak and overlapping peak detection by a conventional CWT method is solved; and the sensitivity of an algorithm and the resolving capability on the overlapping peaks are improved.

Description

technical field [0001] The invention relates to mass spectrum data preprocessing and information extraction methods, in particular to a mass spectrum peak position detection method based on continuous wavelet transform. Background technique [0002] Mass spectrometry has become a widely used analytical technique due to its advantages of high sensitivity and high resolution. After the mass spectrometer generates the original spectral data, it is necessary to use an algorithm to detect the precise peak position and intensity to obtain the composition and structure information of the sample. The detection result of the peak position directly affects the sensitivity and precision of the mass spectrometer. The commonly used peak finding methods include direct comparison method, derivative method, curve fitting and continuous wavelet transform (hereinafter referred to as: CWT) and so on. The direct comparison method and the derivative method are sensitive to spectral noise and b...

AI Technical Summary

Problems solved by technology

This method simply determines the position of the spectral peak based on the wavelet coefficient. When detecting weak spectral peaks, it is susceptible to noise interference and has the problem of increased false detection rate.

When spectral peaks overlap, strong peaks have a serious impact on the wavelet coefficients of weak peaks, causing local extremums at weak peaks to weaken and even become negative values. At this time, it is impossible to detect weak peaks, which seriously affects the algorithm's ability to detect overlapping peaks. resolution

Images