A MALDI mass spectrometry peak detection method based on partial differential equation

Abstract

The invention discloses a MALDI mass spectrum peak detection method based on partial differential equation, which firstly performs baseline correction on the MALDI mass spectrum signal, then uses derivative spectrum method to enhance the signal, and then uses time-spatial fractional diffusion equation to solve and denoise to obtain the final smoothed MALDI mass spectrum signal, and the peak list of MALDI spectrum is obtained by choosing amplitude threshold and local maximum as peak recognition method, which has better peak-preserving and noise-removing effect and is convenient to use.

Description

technical field [0001] The invention relates to a mass spectrum peak detection method, in particular to a MALDI mass spectrum peak detection method based on partial differential equations. Background technique [0002] MALDI mass spectrometry (matrix-assisted laser desorption ionization mass spectrometry) peak detection is an important link in the peak signal processing, and the results of the peak detection will affect the subsequent analysis of the signal. Due to the influence of factors such as the experimental environment and the resolution level of the instrument, some low-amplitude spectral peaks and overlapping spectral peaks may be buried by noise, and chemical, ionization, and electronic noise often cause the curve to decline, resulting in baseline drift of the spectral peak signal. [0003] In recent years, with the rapid development of computer and information technology, signal processing technology has emerged and developed rapidly. A variety of signal processin...

AI Technical Summary

Problems solved by technology

[0006] Smoothing is the most important step in spectral peak detection. The simplest smoothing method is sliding mean filtering, which is to calculate the average of adjacent odd points to replace the original center point; Savitzky-Golay filtering is currently widely used. This method is a A generalized sliding mean filtering method, which performs least squares fitting on a small group of continuous data points, and takes the center point of the polynomial fitting curve as output. Compared with the sliding mean filtering method, this method has better peak protection effect, but the denoising effect is not good enough; Gaussian filtering is an improved method of sliding mean filtering smoothing window, using Gaussian function as the smoothing window, similarly, Kaiser proposed Kaiser filtering, using Kaiser window as the smoothing window, the efficiency of these two methods Higher, but its protective effect on the peak is not strong

Images