Quantitative analysis method of xrf elements based on pca-svr

Abstract

The invention discloses an XRF element quantitative analysis method based on PCA-SVR. The method includes reading element peak information and content information; determining the input and output of the PCA-SVR model; calculating correlation coefficients and unit eigenvectors; and calculating principal components; Construct a classification hyperplane, transform the optimal classification hyperplane problem into a quadratic programming model; train the PCA-SVR model through parameter optimization, and quantitatively predict the element content; select the optimal number of principal components; calculate the coefficient of determination, and evaluate PCA-SVR Model predictions. The invention has a simple operation process, scientific and reasonable, simple process, easy operation, high prediction accuracy, intuitive results, easy to understand, can solve the problems of X-ray fluorescence energy spectrum peak overlap interference, and inaccurate measurement methods of traditional instruments, etc., and reduces environmental problems. The influence of the background reduces the error caused by statistical fluctuations, and can effectively and quickly make quantitative predictions on the elements contained in the analyte.

Description

XRF element quantitative analysis method based on PCA‑SVR technical field The present invention relates to element detection field, particularly a kind of XRF element quantitative analysis method based on PCA-SVR. Background technique [0002] With the gradual development of energy spectrum scientific research, online qualitative and quantitative detection technology has become a new development trend. through After more than ten years of perfect and extended research, the use of X-ray fluorescence (XRF) spectroscopy to analyze elemental content has become a new type of This method is widely used in metallurgy, building materials, geology and mining, commodity inspection, environmental protection, food hygiene, non-ferrous metals, etc. field. The method has the advantages of rapid analysis, no damage to sample properties, wide analysis range, stable and reliable results, and rapid realization of multiple Simultaneous analysis of elements and ease of operation are man...

AI Technical Summary

Problems solved by technology

[0003] The traditional method mainly uses XRF spectrometer to conduct accurate qualitative and quantitative analysis of trace elements, which is prone to problems such as peak count overlap between element spectral lines, uncertainty of element information, and high detection limit of elements. Under the situation that improves the accuracy of element quantitative analysis result, has become the focus of the present invention's research

Therefore, the principal component analysis-support vector regression (PCA-SVR) algorithm is applied to the quantitative analysis of elements, which solves the problems of inaccurate calculation and lack of data inspection of traditional X-ray fluorescence spectrometer, and aims to provide a basis for the quantitative analysis of X-ray fluorescence spectrometer results. An Alternative Test Method

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