Mass spectrum data spectrogram signal calibration method and device

Abstract

The invention discloses a mass spectrum data spectrogram signal calibration method. The method comprises the following steps: acquiring an original spectrogram; establishing a one-to-one mapping relationship between the standard sampling time points and the mass spectrum data; constructing a two-dimensional wavelet transformation matrix X (gamma, mu), and completing spectrum peak identification and peak position marking; establishing an overlapping peak splitting mathematical model, identifying overlapping peaks in the spectral peaks and completing splitting; optimizing the central coordinatesand the half-peak width of a spectral peak by using an NM algorithm, and performing Gaussian fitting on each signal peak after the optimization is completed; searching a mark peak, and establishing amapping relationship between the accurate mass number of the mark peak and the actual corresponding sampling time; recording the corresponding delta Pi when the delta Pi is not overlapped with the delta Pi; and correcting the mass spectrum data of the corresponding subsection Si (x) by using the displacement delta Pi of each subsection to obtain an alignment spectrum S (x) under a target scale. The method has the remarkable advantages of simplicity and clearness in operation, high analysis efficiency, accuracy in calibration and the like, and the accuracy and reliability of spectrogram data processing are effectively improved.

Description

【Technical field】

[0001] The invention relates to the technical field of mass spectrometry analysis, in particular to a calibration method and device for spectrogram signals of mass spectrometry data. 【Background technique】

[0002] During the mass spectrometry system experiment, the spectrum collected by the system will be interfered by many factors such as ambient temperature, sampling rate, electromagnetic interference, system noise, etc. These interferences will cause distortion, drift and other distortions in the collected spectrum , even for the same sample, the spectra measured under different instruments and conditions are not the same. At the same time, many random noises will be added to the spectrogram, which can reduce the signal-to-noise ratio of the effective signal and adversely affect the identification. In the process of system application, due to different factors such as sampling time and environment, there are differences in the spectra collected in each...

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