Mass spectrometer lens parameter fitting method based on radial basis function network

Abstract

The invention provides a mass spectrometer lens parameter fitting method based on a radial basis function network, and the method comprises the steps: obtaining a fitting curved surface of lens parameters and peak intensity through the combination of the radial basis function network and a prior information set, and obtaining the corresponding lens parameter when the peak intensity is highest according to the fitting curved surface. According to the method, fitting is carried out on the mass spectrometer lens parameter-peak intensity curved surface, and all mass spectrometer lens parameter-peak intensity pairs do not need to be actually traversed; and after the curved surface fitting is completed, the maximum value of the peak intensity can be obtained according to the input-output explicit expression, and the optimal lens parameter is obtained according to the maximum value, so that the optimal lens parameter combination can be quickly obtained.

Description

technical field [0001] The invention belongs to the technical field of mass spectrometers, and in particular relates to a mass spectrometer lens parameter fitting method based on a radial basis function network. Background technique [0002] Triple quadrupole mass spectrometer is currently an important instrument for analysts to conduct qualitative and quantitative analysis of target compounds. It consists of four metal rods arranged in parallel, and a DC voltage and a radio frequency voltage applied to the rods generate a magnetic field, causing ions to move in a helical orbit along the axis between the metal rods. Depending on the voltage applied to the electrodes, ions of a certain m / z value will pass through the quadrupole, while other ions with higher or lower m / z values ​​will fly outward and fail to pass through the quadrupole. [0003] Before using the mass spectrometer, we need to obtain the optimal mass spectrometer lens parameters of the compound to be measured. ...

AI Technical Summary

Problems solved by technology

There are two disadvantages of automatic optimization. One is that the optimization time is usually relatively long because multiple iterative optimizations are required; the other is that the optimal lens parameters determined by the automatic optimization algorithm are often not globally optimal.

In summary, the current lens parameter optimization method has the defects of long optimization time, low efficiency, and difficulty in converging to the global optimal solution.

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