Method for improving signal-to-noise ratio of LC-MS data

Abstract

The invention provides a method for improving the signal-to-noise ratio of LC-MS data. The method for improving the signal-to-noise ratio of the LC-MS data comprises the following steps: (A1) obtaining a starting position w1 and an ending position w2 of a chromatographic peak s (t); (A2) dividing the chromatographic peak s (t) into a peak part s1 (t) and a non-peak part s2 (t), and entering the step (B1) and the step (C1) respectively; (B1) designing a primary function according to the chromatographic peak s (t); (B2) obtaining a first layer approximation coefficient a1 and a first layer detail coefficient d1 of the peak part s1 (t); (B3) obtaining a multilayer approximation coefficient an and detail coefficients d1, d2... dn of each layer of the peak part s1 (t); (B4) obtaining an approximate part s1a (t) and a detail part s1b (t) of the peak part s1 (t); performing EMD empirical mode decomposition on the detail part s1b (t); (B5) calculating the energy value of each modal component; (B6) obtaining denoised peak part information s' 1 (t) = s1a (t) + s' 1b (t); (C1) for the non-peak part s2 (t), obtaining an approximate part s2a (t) of the non-peak part s2 (t) according to the mode of the steps (B1)-(B4); and (2) integrating the peak part information s' 1 (t) and the approximate part s2a (t) to obtain the denoised chromatographic peak s' (t). The method has the advantages of good denoising effect and the like.

Description

technical field [0001] The present invention relates to chromatography, and in particular to methods for improving the signal-to-noise ratio of LC-MS data. Background technique [0002] Liquid chromatography-mass spectrometry (LC-MS) is an important tool for qualitative analysis of complex mixture samples. It combines the advantages of liquid chromatography and mass spectrometry when used alone, and has greater advantages. It is widely used in various fields such as environment, food, and geological detection. Raw LC-MS data is the basis for qualitative analysis of LC-MS, but it will contain interference from instrument noise, neutral particles, other compounds, etc. If the raw LC-MS data is directly used for qualitative analysis of mixture samples, the resulting Analysis results are not reliable. Additionally, the complexity of LC-MS systems often makes it difficult for analysts to meet method detection limits. Therefore, there is a need for an algorithm that can improve...

AI Technical Summary

Problems solved by technology

[0004] 1. Fourier filtering method, the Fourier filtering method has insufficient processing ability for non-stationary signals and cannot obtain the moment when each frequency appears

[0005] 2. The wavelet denoising method can deal with non-stationary signals, but it is difficult to select the threshold parameter of wavelet transform

[0006] 3. The polynomial smoothing algorithm is also called the SG filter method, which weights the data in the window and can smooth the noise, but it is not adaptive

[0007] In addition, the above algorithms all process the entire spectral line at the same time, which may cause signal distortion while filtering out noise

[0008] The signal of LC-MS data has the characteristics of local mutation. When the traditional denoising algorithm is used for processing, the tip of the signal is often removed while removing the noise, thereby reducing the response of the signal peak, which is very unfavorable for subsequent Analysis

Images