X-ray fluorescence spectrum quantitative analysis method based on LM-BP neural network

Abstract

The invention provides an X-ray fluorescence spectrum quantitative analysis method based on an LM-BP neural network. The method comprises the following steps: acquiring an X-ray fluorescence spectrum of a training set sample, performing spectrum data treatment, and extracting intensity values of a plurality of data points in a single spectral line after the treatment; measuring the content of corresponding target elements of the training set sample; with the intensity values as input layer data and the content of the corresponding target elements as output layer data, acquiring hidden layer data according to a transfer function of an input layer and a hidden layer so as to build a BP neural network model; training the BP neural network model by an LM algorithm; and acquiring the content of the corresponding target element of a prediction set sample by virtue of the trained LM-BP neural network model. The method can be used for performing quantitative analysis on elements by virtue of the X-ray fluorescence spectrum based on the LM-BP neural network.

Description

technical field [0001] The invention relates to the technical field of X-ray fluorescence spectrum detection, in particular to an X-ray fluorescence spectrum quantitative analysis method based on an LM-BP neural network. Background technique [0002] As a kind of atomic emission spectroscopy, X-ray Fluorescence Spectroscopy (XRF) is widely used in element determination. X-ray fluorescence spectroscopy uses X-rays to excite the outer electrons of elements, uses a spectrometer to obtain the fluorescence spectrum during electronic transitions, and conducts qualitative and quantitative analysis based on the energy and intensity of the spectral lines. [0003] X-ray fluorescence spectroscopy detection technology has the advantages of fast analysis speed, wide range of detection elements, simple pre-treatment, and non-destructive testing. It has been widely used in the detection of heavy metals in metallurgy, geology, petroleum and other fields, and has achieved extensive social a...

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Problems solved by technology

However, due to the constraints of the matrix effect and self-absorption effect, the accuracy of the prediction model is especially reflected in heavy metals such as Cr, and no satisfactory results have been achieved.

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