A Maldi Mass Spectrum Peak Detection Method Based on Partial Differential Equation

Abstract

The invention discloses a MALDI mass spectrum peak detection method based on partial differential equations. The method first performs baseline correction on the MALDI mass spectrum signal, then uses the derivative spectrum method to enhance the signal, and then uses the time-space fractional diffusion equation to solve the denoising to obtain the final The smoothed MALDI mass spectrum signal, and finally choose the amplitude threshold and local maximum method for peak recognition to get the MALDI spectrum peak list, which has better peak preservation and denoising effect, and is easy 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...

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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

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