XRF element quantitative analysis method based on PCA-SVR

Abstract

The invention discloses an XRF element quantitative analysis method based on PCA-SVR. The method comprises the following steps of reading element peak information and content information, determining input and output of the PCA-SVR model, calculating a correlation coefficient and a unit feature vector, calculating a principal component, constructing a classification hyperplane, and converting an optimal classification hyperplane problem into a quadratic programming model, training a PCA-SVR model through parameter optimization, and quantitatively predicting the element content, selecting the optimal number of the principal component, and calculating a decision coefficient, and evaluating the prediction effect of the PCA-SVR model. The method is simple, scientific and reasonable in operation process, simple in flow, convenient to operate, high in prediction accuracy, intuitive in result, popular and easy to understand, can solve the problems of X fluorescence energy spectrum peak value overlapping interference, inaccurate traditional instrument measurement method and the like, reduces the influence of environmental background, reduces errors caused by statistical fluctuation, and effectively and quickly conducts quantitative prediction on the elements contained in the to-be-detected object.

Description

technical field [0001] The invention relates to the field of element detection, in particular to a PCA-SVR-based XRF element quantitative analysis method. Background technique [0002] With the gradual development of energy spectrum scientific research, online qualitative and quantitative detection technology has become a new development trend. After more than ten years of perfect and extended research, the use of X-ray fluorescence (XRF) spectroscopy to analyze element content has become a new type of analysis technology. This method is widely used in metallurgy, building materials, geological mines, commodity inspection, environmental protection, food hygiene, Various fields such as non-ferrous metals. This method has many advantages such as rapid analysis, no damage to sample properties, wide range of analysis, stable and reliable results, rapid simultaneous analysis of multiple elements, and easy operation. [0003] The traditional method mainly uses XRF spectrometer t...

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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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