Improved method for extracting Fourier transformation infrared spectrum characteristic variable of multi-component gas by aid of TR (Tikhonov regularization)

Abstract

The invention discloses an improved method for extracting Fourier transformation infrared spectrum characteristic variable of multi-component gas by aid of TR (Tikhonov regularization). The method includes resolving a characteristic variable extracting model into weight sum of the difference of multiple spectral line values; converting an original TR objective function into an objective function based on the model; then adding a bound term of the difference of spectral line positions in the objective function based on the model; realizing optimal functional solution by means of an LASSO (least absolute shrinkage and selection operator) arithmetic based on an Engl's criterion so as to obtain the optimal value of a regressive vector; and obtaining the characteristic variable capable of overcoming interferences caused by spectrum baseline deviation. The accuracy of online multi-component gas analysis results can be improved by the aid of the improved method. The improved method for extracting Fourier transformation infrared spectrum characteristic variable of multi-component gas by aid of TR can be used for multi-component gas quantitative spectrometric analysis application in the fields of gas logging for petroleum and natural gas exploration, quality control and fault diagnosis of products, hardware, chemical engineering and environmental protection.

Description

technical field [0001] The invention relates to the field of spectral quantitative analysis, in particular to a method for extracting characteristic variables in Fourier transform infrared spectral analysis of multi-component gases. Background technique [0002] Characteristic variable extraction is sometimes called characteristic spectrum selection, which is one of the important contents of spectral analysis. Selecting appropriate characteristic variables can help improve the signal-to-noise ratio of gas spectral analysis and reduce analysis errors. Commonly used feature variable extraction methods include forward selection, piecewise regression (SWR), genetic algorithm, simulated annealing, etc. Most of these methods are optimized based on calibration data bias or validation data bias. Optimizing only based on the deviation criterion of the calibration data will lead to overfitting, while optimizing only based on the deviation criterion of the validation data will lead to...

AI Technical Summary

Problems solved by technology

Although there are related baseline correction methods at present, due to the nonlinear relationship of baseline drift, these methods can only perform overall baseline correction, and there is still a certain drift in a local range, which will bring a greater impact on the analysis results. error

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