Analysis data processing method and device

Abstract

Data for a plurality of samples collected by an LC / MS analysis, a GC / MS analysis, or other analyses are converted into a two-dimensional table format without losing information and with a small load, thereby allowing a multivariate analysis process to be efficiently performed. The LC / MS data measured on a plurality of samples are obtained and the respective extracted ion chromatograms (XICs) are created (steps S1, S2). After a holding time shift correction, waveform processing, etc. are performed (steps S3, S4), a one-dimensional table in which signal intensity values are arranged is then created for each of the XICs. Subsequently, a long one-dimensional table is created by linking the one-dimensional tables of the XICs of one sample in the order of m / z (step S5), after which a two-dimensional table is created by arranging the one-dimensional tables of the plurality of samples in another dimensional direction (step S6).

Description

technical field [0001] The present invention relates to a data processing method and device for processing data collected by an analysis device, and more specifically, to a data processing method and device for processing data collected by an analysis device that combines the following two methods, that is, Methods such as liquid chromatography, gas chromatography, and capillary electrophoresis to separate components in a sample in a one-dimensional direction with time as the separation factor, as well as mass analysis, infrared spectroscopy, and ultraviolet-visible spectroscopy for methods other than sample acquisition time A method of measuring the signal strength obtained by separating elements separated in more than one dimension. Background technique [0002] Analysis by LC / MS, GC / MS, CE / MS, etc., which combine component separation methods such as liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE) with mass spectrometry (MS) methods ...

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Problems solved by technology

[0007] However, all the information in the direction of mass-to-charge ratio is lost in TIC. Although information on one mass-to-charge ratio is preserved in XIC, all information on other mass-to-charge ratios is lost. It can be said that all the one-dimensional information in the direction of mass-to-charge ratio is essentially are missing

In this way, in the case of missing information in one of the dimensional directions of two-dimensional data, there is a problem that if the missing information includes important information that is characterized by the difference between multiple samples, even if the Multivariate analysis cannot obtain appropriate results for evaluating the similarity and difference of multiple samples, and cannot accurately compare samples

[0009] However, if you want to perform complex data processing calculations like this, the load on the hardware will increase, so you need to prepare a high-performance CPU and a large-capacity RAM, and the processing throughput will also decrease.

In addition, when performing data processing calculations as described above, calculation parameters prepared in advance are used, but these parameters may cause large differences in multivariate analysis results.

In addition, when performing peak extraction, etc., the original information is lost during this process, so the difference between samples may not be accurately reflected in the multivariate analysis results.

As a result, there are cases where accurate comparison of samples cannot be performed despite complicated data processing.

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