CN111351778B - LIBS spectrum-based component analysis method - Google Patents

LIBS spectrum-based component analysis method Download PDF

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CN111351778B
CN111351778B CN202010228345.8A CN202010228345A CN111351778B CN 111351778 B CN111351778 B CN 111351778B CN 202010228345 A CN202010228345 A CN 202010228345A CN 111351778 B CN111351778 B CN 111351778B
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CN111351778A (en
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郭杰
潘从元
张兵
徐勇
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Hefei Gstar Intelligent Control Technical Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
    • G01N21/718—Laser microanalysis, i.e. with formation of sample plasma
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

Abstract

The invention belongs to the technical field of component detection, and particularly relates to a component analysis method based on LIBS spectrum. Detecting the material to be detected by using an LIBS detector to obtain a detection spectrum of the material to be detected; preprocessing the detection spectrum to obtain a standard spectrum; and analyzing the full spectrum of the standard spectrum to obtain the content weight of each element in the material to be detected. The spectrum obtained by the LIBS instrument is a mixed spectrum formed by overlapping the spectrum signals of a plurality of elements and the noise signals, the analysis method carries out component analysis according to the full spectrum of the elements, and compared with the prior art that a single spectral line in the full spectrum is selected for analysis, the analysis accuracy of the components is higher, and the application range is wider.

Description

LIBS spectrum-based component analysis method
Technical Field
The invention belongs to the technical field of component detection, and particularly relates to a component analysis method based on LIBS spectrum.
Background
The Laser Induced Breakdown Spectroscopy (LIBS) analysis technology can realize qualitative and quantitative analysis of chemical elements of substances, and has the characteristics of no need of sample preparation, direct and rapid analysis and the like, thereby becoming a research hotspot. In the prior art, LIBS spectral analysis finds a characteristic spectral position of an element to be analyzed by searching an element characteristic spectral library, then extracts spectral intensity of the position as a basis for qualitative and quantitative analysis, and completes element component analysis by means of a machine learning algorithm, such as multivariate analysis, partial least squares, BP algorithm, and the like. The analysis method is separated from the actual production process, the obtained model has poor interpretability, and the accuracy of component analysis cannot meet the requirements of production and application.
Disclosure of Invention
The invention aims to provide a component analysis method based on LIBS spectrum, which can improve detection precision.
In order to realize the purpose, the invention adopts the technical scheme that:
and B: detecting the material to be detected by using an LIBS detector to obtain a detection spectrum of the material to be detected;
and C: preprocessing the detection spectrum to obtain a standard spectrum;
step D: and analyzing the full spectrum of the standard spectrum to obtain the content weight of each element in the material to be detected.
Compared with the prior art, the invention has the following technical effects: the spectrum obtained by the LIBS instrument is a mixed spectrum formed by overlapping the spectrum signals of a plurality of elements and the noise signals, the analysis method carries out component analysis according to the full spectrum of the elements, and compared with the prior art that a single spectral line in the full spectrum is selected for analysis, the analysis accuracy of the components is higher, and the application range is wider.
Drawings
The contents of the description and the references in the drawings are briefly described as follows:
FIG. 1 is a schematic LIBS spectrum of blister copper;
FIG. 2 is a LIBS spectrum of matte;
fig. 3 is a schematic LIBS spectrum of copper concentrate;
FIG. 4 is a LIBS spectrum of slag;
FIGS. 5, 6 and 7 are characteristic spectra obtained in the examples;
fig. 8 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made in detail with reference to the accompanying drawings.
In the analysis method, the detection and analysis steps of the operator are as follows:
and B: and detecting the material to be detected by using an LIBS detector to obtain a detection spectrum of the material to be detected. Recording the detected spectrum as sc＝(s1，s2，…，sL)TWherein s is1，s2，…，sLThe method comprises the steps of obtaining an original measurement spectrum signal for a detection instrument to detect a material to be detected, and obtaining the length of spectrum data for measurement.
And C: and preprocessing the detection spectrum to obtain a standard spectrum. Recording the standard spectrum as
Figure BDA0002428414540000021
Wherein
Figure BDA0002428414540000022
The standard spectral signal obtained is preprocessed.
In this embodiment, the preprocessing operation sequentially includes band-pass filtering, mean value removal, and variance normalization. The core processing operation is band-pass filtering, which is used for removing the background trend term of low frequency and the noise interference term of high frequency, and removing the nonnegative constraint of spectrum. The spectrum measurement signals obtained by the LIBS detection instrument are all non-negative signals, and after filtering processing, the standard spectrum comprises negative signals. The averaging is to remove the influence of the dc component. The variance is normalized by dividing by the spectral variance to keep the amplitude of the spectrum consistent. The processing flows are executed in sequence, so that the consistency of the spectrum processing can be ensured.
Step D: and analyzing the full spectrum of the standard spectrum to obtain the content weight of each element in the material to be detected.
The term "analyzing the full spectrum of the standard spectrum" as used herein refers to the analysis of each standard spectrum signal contained in the standard spectrum
Figure BDA0002428414540000031
All the wavelength values are analyzed, namely, the measured values of all the wavelengths of the material to be measured are analyzed. Wherein, the wavelength range and the detection precision are related to the LIBS detection instrument.
The specific analysis step is to use the element feature matrix W pair
Figure BDA0002428414540000032
Performing component analysis to obtain spectral weight vector
Figure BDA0002428414540000033
Wherein c iskIs the content weight of the kth element. c, the strong correlation relationship which corresponds to the component content in the material to be detected one by one, and the type and the quality of the material to be detected can be judged according to the value of each content weight and the correlation relationship thereof.
The element characteristic matrix W is an analysis matrix and can be obtained through theoretical derivation calculation or sampling analysis.
Figure BDA0002428414540000034
Wherein k is the element type contained in the material to be measured, L is the length of the spectral data obtained by measurement, and wkIs the feature vector of the k-th element.
Regarding the element feature matrix W, inverting the element feature matrix W to obtain an element feature spectrum matrix E ═ W-1＝(e1，e2，…，ek) And then spectrum reconstruction is carried out by combining the spectrum weight vector c to obtain a reconstructed standard spectrum
Figure BDA0002428414540000035
The 3 characteristic spectrums shown in the attached figures 5-7 can be obtained, the abscissa of the characteristic spectrum is the wavelength, the ordinate is the spectrum intensity, wherein the theoretical spectrums of the attached figures 5 and Cu, the theoretical spectrums of the attached figures 6 and Fe, and the theoretical spectrums of the attached figures 7 and Si have high correlation respectively, namely the curves of the attached figures and the theoretical spectrums of the corresponding elements are similar and have the same characteristic spectral line, and accordingly, the w corresponding to the attached figures can be judgedkThe feature vector of which element.
In this embodiment, taking copper smelting as an example, the copper smelting process involves 4 kinds of materials to be analyzed, including matte, blister copper, copper concentrate and slag, which mainly contain elements such as copper, iron and silicon, i.e. the materials to be analyzed in this embodiment include 4 materials, and the materials have various compositions. The smelting field has a severe environment, namely the detection environment is complex. In order to ensure the component analysis result under the conditions of various material components and complex detection environment, the element characteristic matrix W of the embodiment is obtained in the following manner:
step A1: collecting samples of various materials to be analyzed related to the smelting process, wherein the materials to be analyzed comprise original mineral materials, process products and finished products in the metal smelting process, and the total number of the collected samples is N.
A large number of samples of various related materials are adopted for analysis and learning, the influence of spectral noise can be effectively reduced, and the obtained projection vector is smoother and more reliable, so that the robustness and the precision of component analysis are effectively improved. Since there are differences in the component contents of the same analyte material, it is preferable to collect multiple samples for each analyte material.
Step A2: under the same experiment condition, detecting the material to be detected by using an LIBS detection instrument to obtain the detection spectrum s of each samplei＝(si1，si2，…，siL)T。
LIBS spectra were collected for each sample several times. Preferably, b spectra are collected at a different positions for each sample, and a total of N-N · a · b spectra are obtained. Then siIs the ith detection spectrum and i is 1, …, n, L is the data length of the spectrum, siLThe signal is detected for the spectrum at L for the ith detection spectrum.
In this embodiment, each sample is subjected to a tabletting process to obtain 10 sample wafers to be analyzed having the same size and shape, the detection instrument collects the LIBS spectrum of each sample wafer under the same environmental conditions, each sample wafer collects 100 spectra, and N is 1000N spectra in total. The spectral morphology of each analyte material is shown in fig. 1-4, where the abscissa is wavelength and the ordinate is measured spectral intensity.
Step A3: for the detection spectrum siPreprocessing the spectrum to obtain a standard spectrum
Figure BDA0002428414540000051
The preprocessing operation in this step should be consistent with the preprocessing operation in step C, which includes at least filtering.
Combining the standard spectra of the samples
Figure BDA0002428414540000052
Obtaining a spectrum matrix S, wherein the height of the spectrum matrix S is L, and the width of the spectrum matrix S is n:
Figure BDA0002428414540000053
step A4: analyzing the components of the spectral matrix S by adopting a blind source separation algorithm, and separating to obtain an element characteristic vector w1，w2，…，wkAnd k is the number of the element types obtained by analysis, and the element feature vectors are combined to obtain an element feature matrix W.
In this embodiment, the ICA independent component analysis method is used to perform component analysis on the spectral matrix S, and the specific analysis steps are,
let the optimized direction projection vector of the k-th component be wkThe objective of the component analysis is to solve for wkMake it
Figure BDA0002428414540000054
Maximum of the absolute value of kurtosis, i.e.
Figure BDA0002428414540000055
And the gradient of the absolute value of the kurtosis is
Figure BDA0002428414540000061
Optimization of w by Newton iteration method1Then at w1Finding w in orthogonal space2So that
Figure BDA0002428414540000062
Maximize, and so on find all w1，…，wkProjecting all optimized directions onto a vector wkCombining to obtain element characteristic matrix, and recording as W ═ W1，w2，…，wk)T。

Claims (5)

1. A composition analysis method based on LIBS spectrum comprises the following steps:
step A: sampling analysis to obtain element characteristic matrix
Figure FDA0003482644600000011
Wherein k is the element type contained in the material to be measured, L is the length of the spectral data obtained by measurement, and wkIs the feature vector of the k element;
the step A is to analyze the sampling material based on a blind source separation algorithm to obtain an element characteristic matrix W, and comprises the following steps:
step A1: collecting samples of various materials to be analyzed related to a smelting process, wherein the materials to be analyzed comprise original mineral materials, process products and finished products in the metal smelting process, and the total number of the collected samples is N;
step A2: under the same experiment condition, detecting the material to be detected by using an LIBS detection instrument to obtain the detection spectrum s of each samplei＝(si1，si2，…，siL)T，siThe detection frequency is 1, …, n, n is the detection frequency, and L is the length of the spectrum data obtained by measurement;
step A3: for the detection spectrum siPreprocessing the spectrum to obtain a standard spectrum
Figure FDA0003482644600000012
Combining the standard spectra of the samples
Figure FDA0003482644600000013
Obtaining a spectral matrix
Figure FDA0003482644600000014
Step A4: analyzing the components of the spectral matrix S by adopting a blind source separation algorithm, and separating to obtain an element characteristic vector w1，w2，…，wkK is the number of the element types obtained by analysis, and the element feature vectors are combined to obtain an element feature matrix W;
and B: detecting the material to be detected by using an LIBS detector to obtain a detection spectrum of the material to be detected, wherein the detection spectrum is sc＝(s1，s2，…，sL)TWherein s is1，s2，…，sLMeasuring spectral signals obtained for the instrument measurements;
and C: preprocessing the detected spectrum to obtain a standard spectrum, and recording the standard spectrum as
Figure FDA0003482644600000021
Wherein
Figure FDA0003482644600000022
A standard spectral signal obtained for pre-processing;
step D: analyzing the full spectrum of the standard spectrum to obtain the content weight of each element in the material to be detected;
using the element feature matrix W to the standard spectrum
Figure FDA0003482644600000023
Performing component analysis to obtain spectral weight vector
Figure FDA0003482644600000024
Wherein c iskIs the content weight of the kth element.
2. The method of claim 1, wherein the LIBS spectrum-based composition analysis comprises: in the step A4, an ICA independent component analysis method is adopted to perform component analysis on the spectral matrix S, and the specific analysis steps are,
let the optimized direction projection vector of the k-th component be wkThe objective of the component analysis is to solve for wkMake it
Figure FDA0003482644600000025
Maximum of the absolute value of kurtosis, i.e.
Figure FDA0003482644600000026
And the gradient of the absolute value of the kurtosis is
Figure FDA0003482644600000027
Optimization of w by Newton iteration method1Then at w1Finding w in orthogonal space2So that
Figure FDA0003482644600000028
Maximize, and so on find all w1，…，wkProjecting all optimized directions onto a vector wkCombining to obtain element characteristic matrix, and recording as W ═ W1，w2，…，wk)T。
3. The method of claim 1, wherein the LIBS spectrum-based composition analysis comprises: in the step C, the preprocessing comprises filtering the detection spectrum, wherein the detection spectrum is a non-negative signal, and the standard spectrum obtained by filtering comprises a negative signal.
4. The method of claim 3, wherein the LIBS spectrum-based composition analysis comprises: in the step C, the preprocessing operation sequentially comprises band-pass filtering, mean value removing and variance standardization.
5. The method of claim 1, wherein the LIBS spectrum-based composition analysis comprises: the preprocessing operation in step a3 is identical to the preprocessing operation in step C.
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