Variance analysis method and system for mass spectrum imaging data

Abstract

The invention relates to a variance analysis method and a variance analysis method system for mass spectrum imaging data. The method comprises the following steps of: selecting a region needing variation analysis and determining the shape and the size of a minimum region to obtain a mass spectrum data set of a tested sample; analyzing the variation existing between the selected minimum regions to obtain a variation coefficient; and sequencing according to the variation coefficient, expanding by taking the selected minimum region as a core and judging whether the variation degree between the expanded regions meets a preset condition or not. Through the method and the system, the research personnel can accurately and precisely analyze the obvious variation between the imaging data images of different samples, the sample region of the variation, the type of a material and the content variation.

Description

technical field [0001] The invention relates to the field of mass spectrometry analysis, in particular to a method and system for differential analysis of mass spectrometry imaging data. Background technique [0002] The method of mass spectrometry is to ionize material particles (atoms, molecules) into ions, and separate them according to the space position and time sequence through an appropriate stable or changing electric field or magnetic field to achieve mass-to-charge ratio separation, and detect their intensity for qualitative, Analytical methods for quantitative analysis. Due to the nature of direct measurement and the characteristics of high sensitivity, high resolution, high throughput and high applicability, mass spectrometer and mass spectrometry technology play an important role in modern science and technology. With the development of life sciences, environmental sciences, medical sciences, nanomaterials and other disciplines, as well as the needs of food saf...

AI Technical Summary

Problems solved by technology

The problem with the first method is: a lot of irrelevant information is added during the comparison, which conceals or weakens the real difference between different samples, which is easy to affect the judgment, it is not easy to find out the material difference, and it is impossible to find out the spatial difference; the second method The problem is that if the ROS is selected to be large, the problem of the first method will appear. If the ROS is selected to be small, there will be problems with the size, shape, and location of the selected ROS. At the same time, data consistency in the A-type ROS is required. Good, the data consistency in the B-type ROS is also good, the data in the A-type ROS is obviously different from the data in the B-type ROS

Therefore, it is difficult to give the material difference between the two types of samples, and it is even more difficult to give the area where the difference is located

When analyzing two different samples, it is difficult to find out the material difference as follows: 1. The difference between the same samples is too large, which is easy to cover up the difference between different samples; 2. The difference caused by chemical noise or local pollution, etc. can be misjudged as a difference

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