Chromatographic overlapping peak analytical method based on wavelet transform and random forest model

Abstract

The invention discloses a chromatographic overlapping peak analytical method based on wavelet transform and a random forest model. The chromatographic overlapping peak analytical method comprises thesteps that a plurality of chromatographic overlapping peak signals are generated in a simulation mode according to different parameters; for each of the overlapping peak signals, gaus1 wavelets are used for performing the wavelet transform to simulate a first-order derivative; a simulated first-order derivative curve is used for calculating and obtaining four curve inflection points of the original chromatographic overlapping peak signals; the curve inflection points are divided into a training set and a test set according to a certain proportion; the horizontal and vertical coordinates of thefour inflection points are used as the input, a sub-peak area ratio is used as the output, and the optimal parameter of a model is determined by using a cross-validation method in the training set; the random forest model is constructed and trained in a supervised mode according to the optimal parameter; and the test set is used for verifying the effect of the model; and the same method is used for detecting the inflection points of the actual overlapping peak signals, and the trained model is used for performing fitting calculation on the own sub-peak area ratio. The chromatographic overlapping peak analytical method improves the accuracy of the analytical result, and has the advantages of high model convergence speed, simple parameter adjustment and high training efficiency.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a chromatographic overlapping peak analysis method based on wavelet transform and random forest model. Background technique [0002] Chromatography, also known as chromatographic analysis and chromatography, is a technique for studying and solving the separation of mixtures. In chromatographic analysis, the qualitative and quantitative analysis of the samples used in the experiment is the most critical step. In the chromatographic curve, each chromatographic peak corresponds to a different component, and the composition of each substance can be calculated from the area of ​​the chromatographic peak. However, during the analysis of complex substances, chromatographic peaks often overlap, which brings some difficulties to chromatographic analysis. Therefore, the resolution of overlapping peaks has a great influence on the qualitative and quantitative analysis of chromatography. ...

AI Technical Summary

Problems solved by technology

Among them, the traditional Fourier transform and derivative methods are sensitive to noise, which reduces the signal-to-noise ratio and is not conducive to qualitative and quantitative analysis; geometric methods include the perpendicular method and the tangent method, which are simple in principle and fast in calculation speed, but for some There may be large errors in the accuracy of overlapping peak decomposition; the implementation process and operation of the curve fitting method are relatively complicated, and it is difficult to realize the real-time processing of the chromatographic curve, which has certain limitations in practical applications; although the wavelet transform method is simple in operation, it is widely used , but it is easy to cause the baseline drift of the transformed curve, resulting in inaccurate reconstructed signals; while the neural network method uses its strong nonlinear mapping ability to calculate the sub-peak area ratio, but its mathematical model is more complicated, and the network structure selection It can only be selected based on experience, and the neural network algorithm has a large amount of calculation, and the convergence speed of the solution network is also slow

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