CN111366573B - Evaluation method based on LIBS spectral component analysis result - Google Patents
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- 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
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- G01N21/718—Laser microanalysis, i.e. with formation of sample plasma
Abstract
The invention belongs to the technical field of component detection, and particularly relates to an evaluation method based on LIBS spectral component analysis results, wherein an LIBS detection instrument is adopted to detect a material to be detected, so that an original detection spectrum of the material to be detected is obtained; analyzing the measured full spectrum to obtain a spectrum weight; reconstructing the spectrum using the spectral weights; the original detected spectrum is compared to the reconstructed spectrum to evaluate the spectral analysis results. According to the invention, the measured spectrum is decomposed and reconstructed according to the component analysis model, and the reconstructed spectrum is compared with the original spectrum, so that the quantitative evaluation of the measured spectrum is realized, and a basis is provided for reasonably rejecting the spectrum; on the basis, the abnormal spectrum is removed, the effective spectrum is reserved, and the quantitative analysis precision of the LIBS is improved.
Description
Technical Field
The invention belongs to the technical field of component detection, and particularly relates to an evaluation method based on an LIBS spectral component analysis result.
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. When the device is applied, the aging of the device and the change of the detection environment cause the measured spectrum signal to obviously fluctuate, and the fluctuation influences the reliability of the analysis result and reduces the analysis precision. The influence of the signal fluctuation on an analysis result can be reduced by adopting high-performance and high-precision equipment, but the input components are too large, so that the invalid spectrum signals are removed by a software algorithm in the conventional means. Because the measured total intensity distribution of the spectrum signals conforms to the normal distribution rule, the prior art generally judges whether the spectrum signals are valid based on a statistical principle, namely, averages all the measured spectrum signals to obtain an average spectrum, then calculates the correlation coefficient of each spectrum and the average spectrum, and finally rejects invalid spectra according to an artificially set rejection ratio and retains valid spectra. In the judgment method, the spectral rejection result can be greatly changed by the correlation coefficient calculation method or the rejection ratio, the reliability of spectral rejection is poor, and the analysis precision and accuracy are seriously influenced.
In addition, in practical application, when the measuring instrument is applied to a high-temperature environment for a long time, problems such as lens distortion and grating displacement occur, so that spectrum deviation is caused, and a spectrum measuring result is influenced, so that the measuring instrument needs to be maintained regularly. However, the maintenance of the instrument needs manual off-line operation, the maintenance and correction are complex, the maintenance time is long, and the improvement of production organization and efficiency is seriously influenced.
Disclosure of Invention
The invention aims to provide an evaluation method based on an LIBS spectral component analysis result, which can improve the detection precision.
In order to realize the purpose, the invention adopts the technical scheme that: an evaluation method based on LIBS spectral component analysis results comprises the following steps:
step A: detecting the material to be detected by using an LIBS (laser induced breakdown spectroscopy) detector to obtain an original detection spectrum of the material to be detected;
and B, step B: analyzing the measured full spectrum to obtain a spectrum weight;
and C: reconstructing the spectrum using the spectral weights;
step D: the original detected spectrum is compared to the reconstructed spectrum to evaluate the spectral analysis results.
Compared with the prior art, the invention has the following technical effects: the measured spectrum is decomposed and reconstructed according to the component analysis model, and the reconstructed spectrum is compared with the original spectrum to realize the quantitative evaluation of the measured spectrum, so that a basis is provided for reasonably rejecting the spectrum; on the basis, the abnormal spectrum is removed, the effective spectrum is reserved, and the quantitative analysis precision of the LIBS is improved.
Drawings
The contents of the description and the references in the drawings are briefly described as follows:
FIG. 1 is a raw spectrum measured in the example;
FIGS. 2, 3 and 4 are characteristic spectra obtained by reconstruction in the embodiment;
FIG. 5 is a reconstructed residual in an embodiment;
FIGS. 6, 7 and 8 are the composition spectra obtained by the embodiment;
FIG. 9 is a graph of example reconstructed spectra s c 。
Detailed Description
The following description of the embodiments of the present invention will be made in detail with reference to the accompanying drawings. The abscissa and ordinate in each figure are wavelength and spectral intensity.
Example one
An evaluation method based on LIBS spectral component analysis results comprises the following steps:
step A: and (3) detecting the material to be detected by using an LIBS detector to obtain an original detection spectrum of the material to be detected, wherein the spectrum image is shown in the attached figure 1. Noting the original detection spectrum as s c ＝(s 1 ，s 2 ，…，s L ) T Wherein s is 1 ，s 2 ，…，s L The measured spectral signal obtained for the instrument measurement, and L is the length of the spectral data obtained for the measurement.
And B, step B: and analyzing the measured full spectrum to obtain the spectrum weight.
Step B1: and preprocessing the original detection spectrum to obtain a standard spectrum. Recording the standard spectrum as
Wherein
The standard spectral signal obtained is preprocessed.
The preprocessing at least comprises filtering the detection spectrum, wherein the detection spectrum is non-negative signals, and the standard spectrum obtained by filtering comprises negative signals. In this embodiment, the preprocessing operation sequentially includes band-pass filtering, mean value removal, and variance normalization. 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.
And step B2: and analyzing the full spectrum of the standard spectrum to obtain the spectrum weight.
Using element feature matrices W pairs
Performing component analysis to obtain spectral weight
Wherein c is k Is 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 feature matrix W as the component analysis matrix has a height k and a width L, and can be obtained by theoretical derivation calculation or sampling analysis.
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 w k Is the feature vector of the k-th element.
Step C: reconstructing the spectrum using the spectral weights;
inverting the element characteristic matrix W to obtain an element characteristic spectrum matrix E = W -1 ＝(e 1 ，e 2 ，…，e k ) Then, the spectrum is reconstructed by combining the spectrum weight c,obtaining a reconstructed standard spectrum
K =3 in this example, according to e 1 The resulting spectral image is plotted as shown in FIG. 2, according to e 2 The resulting spectral image is plotted as shown in FIG. 3, according to e 3 The resulting spectral image is shown in fig. 4. Comparing the theoretical spectra of the elements with the theoretical spectra of the elements in the attached figures 2, 3 and 4, the corresponding e can be judged k 、w k To which element it relates.
Step D: the original detected spectrum is compared to the reconstructed spectrum to evaluate the spectral analysis results.
Step D1: c, performing reverse processing on the reconstructed standard spectrum obtained in the step C to obtain a reconstructed original spectrum s g ＝(s′ 1 ，s′ 2 ，…，s′ L ) T S therein' L And the reconstructed spectrum signal of the material to be detected at the position L.
The reconstructed original spectrum obtained in this example is shown in fig. 9. In addition, the reconstruction algorithm can also be applied to each component spectrum c i e i Are separately reconstructed as shown by c in FIG. 6 1 e 1 And (4) obtaining the full spectrum of the 1 st element in the material to be detected through reconstruction and reverse treatment. And so on, and the attached figure 7 is a full spectrum for measuring the 2 nd element in the material to be measured; FIG. 8 is a full spectrum of the 3 rd element in the material to be measured. The curves of fig. 6, 7, 8 are superimposed to obtain fig. 9.
The inverse processing in step D1 is provided corresponding to the preprocessing in step B1, the order of each processing item is the inverse of each processing item in the preprocessing, and the operation of each processing item is the inverse of the operation in the corresponding processing item in the preprocessing.
Step D2: the original detected spectrum is compared to the reconstructed original spectrum to evaluate the spectral analysis results. Subtracting the original detection spectrum and the reconstructed original spectrum to obtain a reconstructed residual res = s c -s g In this embodiment, a spectral image formed by the reconstructed residual is shown in fig. 5. Obtaining spectral scores from reconstructed residuals for interpretationThe results of the spectral analysis are cut off and the score is calculated as follows:
the score range of the spectrum score is 0-100, and 100 points represent that the original detection spectrum can be perfectly decomposed and reconstructed by a component analysis model; the lower the score is, the larger the reconstructed residual value is, which means that the effect of decomposing the original detection spectrum is worse, the reconstructed residual is usually caused by detection noise or spectrum offset, the detection noise cannot be avoided, and the spectrum offset can be corrected.
Step E: when the spectrum score is greater than or equal to a set value, marking the original detection spectrum as an effective spectrum; when the spectrum score is smaller than a set value, correcting the spectrum shift to obtain an optimal score; setting the spectral shift as tau and substituting the spectral shift into the steps B, C and D to obtain an objective function score (tau), solving the tau to maximize the value of the objective function score (tau), and optimizing by using a Newton iteration method to obtain the spectral shift tau opt And an optimal score opt 。
Step F: when the best score opt When the original detection spectrum is larger than or equal to the set value, marking the original detection spectrum as an offset effective spectrum; when the optimal score is score opt And when the spectrum is smaller than the set value, rejecting the corresponding original detection spectrum.
And E, setting the set value in the step F as an evaluation standard value, and setting the set value by a user according to experience and detection precision requirements, wherein the set value can be adjusted according to requirements.
Example two
An evaluation method based on LIBS spectral component analysis results comprises the following steps:
step A: and detecting the material to be detected by using an LIBS detector to obtain an original detection spectrum of the material to be detected. Noting the original detection spectrum as s c ＝(s 1 ，s 2 ，…，s L ) T Wherein s is 1 ，s 2 ，…，s L Measurement spectrum signals obtained for instrumental measurements, L spectra obtained for measurementsThe length of the data.
And B: and analyzing the full spectrum of the original detection spectrum to obtain the spectrum weight.
Using element feature matrices W to s c Performing component analysis to obtain the spectrum weight c = W.s c ＝(c 1 ，c 2 ，…，c k ) T Wherein c is k Is the content weight of the kth element.
And C: reconstructing the spectrum using the spectral weights;
inverting the element characteristic matrix W to obtain an element characteristic spectrum matrix E = W -1 ＝(e 1 ，e 2 ，…，e k ) And then spectrum reconstruction is carried out by combining the spectrum weight c to obtain a reconstructed original spectrum s g ＝(s′ 1 ，s′ 2 ，…，s′ L ) T 。
Step D: the original detected spectrum is compared to the reconstructed original spectrum to evaluate the spectral analysis results. Subtracting the original detection spectrum and the reconstructed original spectrum to obtain a reconstructed residual res = s c -s g And obtaining a spectrum score according to the reconstructed residual error to judge a spectrum component analysis result, wherein the spectrum score is calculated according to the following formula:
in example two, neither the original detected spectrum is pre-processed in step B, nor the reconstructed spectrum is processed in step D. In other embodiments, the raw detection spectrum may be optionally preprocessed in step B to improve the accuracy of the spectral analysis, but the reconstructed spectrum is not processed in step D to improve the evaluation speed.
The element feature matrix W in this embodiment is obtained as follows:
step 1: and collecting samples of each material to be analyzed related to the component analysis, wherein the total number of the collected samples is N. For example, if the analysis result of the composition is used for monitoring the metal smelting process and quality, the material to be analyzed should include the original mineral aggregate, the products of each process and the finished product in the metal smelting process,
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 2: 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 sample i ＝(s i1 ，s i2 ，…，s iL ) T 。
LIBS spectra were collected several times for each sample. Preferably, b spectra are collected at a different positions for each sample, resulting in a total of N = N · a · b spectra. Then s i Is the ith detection spectrum and i =1, \ 8230;, n, L is the data length of the spectrum, s iL The 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, and each sample wafer collects 100 spectra, so that N =1000N spectra are obtained.
And 3, step 3: preprocessing the detected spectrum to obtain a standard spectrum
The preprocessing operation in this step should be identical to the preprocessing operation in step B, which includes at least filtering.
Combining the standard spectra of the samples
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:
if the original detection spectrum is not preprocessed in the step B, the detection spectrum is directly combined without being preprocessed in the step B i A spectral matrix S composed of the detected spectral data is obtained.
And 4, step 4: analyzing the components of the spectral matrix S by adopting a blind source separation algorithm, and separating to obtain an element characteristic vector w 1 ，w 2 ，…，w k And 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, an ICA independent component analysis method is used to perform component analysis on the spectral matrix S, and the specific analysis step is to set the projection vector of the k-th component in the optimized direction as w k The objective of the composition analysis is to solve for w k Make it
Maximum of the absolute value of kurtosis, i.e.
And the gradient of the absolute value of the kurtosis is
Obtaining w by using Newton iteration method optimization 1 Then at w 1 Finding w in orthogonal space 2 So that
Maximize, and so on find all w 1 ，…，w k Projecting all optimized directions onto a vector w k Combining to obtain an element characteristic matrix, and recording as W = (W) 1 ，w 2 ，…，w k ) T 。
Claims (6)
1. An evaluation method based on LIBS spectral component analysis results comprises the following steps:
step A: detecting the material to be detected by using an LIBS (laser induced breakdown spectroscopy) detector to obtain an original detection spectrum of the material to be detected;
noting the original detection spectrum as s c ＝(s 1 ，s 2 ，…，s L ) T Wherein s is 1 ，s 2 ，…，s L Measuring the obtained measurement spectral signal for the instrument;
and B: analyzing the measured full spectrum to obtain a spectrum weight;
step B1: preprocessing the original detection spectrum to obtain a standard spectrum, and recording the standard spectrum as
Wherein
The standard spectrum signal obtained by preprocessing is used as a reference spectrum signal;
and step B2: analyzing the full spectrum of the standard spectrum to obtain a spectrum weight;
obtaining an element characteristic matrix W according to theoretical derivation calculation or sampling analysis,
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 w k A feature vector of a kth element;
using element feature matrices W pairs
Performing component analysis to obtain spectral weight
Wherein c is k The content weight of the kth element, c and the component content in the material to be detected are in one-to-one correspondence strong correlation relationship, and the object to be detected can be judged according to the value of each content weight and the correlation relationship thereofThe type and quality of the material;
step C: reconstructing the spectrum using the spectral weights;
inverting the element characteristic matrix W to obtain an element characteristic spectrum matrix E = W -1 ＝(e 1 ，e 2 ，…，e k ) And then spectrum reconstruction is carried out by combining with the spectrum weight c to obtain a reconstructed standard spectrum
Step D: comparing the original detected spectrum with the reconstructed spectrum to evaluate a spectral analysis result;
step D1: c, the reconstructed standard spectrum obtained in the step C is subjected to reverse processing to obtain a reconstructed original spectrum s g ＝(s′ 1 ，s′ 2 ，…，s′ L ) T S therein' L A reconstructed spectrum signal of the material to be detected at the position L;
step D2: comparing the original detected spectrum with the reconstructed original spectrum to evaluate the spectral analysis result, and subtracting the original detected spectrum from the reconstructed original spectrum to obtain a reconstructed residual res = s c -s g And obtaining a spectrum score according to the reconstructed residual error to judge a spectrum component analysis result, wherein the spectrum score is calculated according to the following formula:
the score range of the spectrum score is 0-100, a score of 100 indicates that the original detection spectrum can be decomposed and reconstructed perfectly by the component analysis model, and the lower the score is, the larger the reconstruction residual value is, and the poorer the decomposition effect of the original detection spectrum is.
2. The LIBS spectral composition analysis result-based evaluation method according to claim 1, wherein: further comprising a step E: when the spectrum score is greater than or equal to a set value, marking the original detection spectrum as an effective spectrum; when the spectrum score is smaller than a set value, correcting the spectrum shift to obtain an optimal score; light settingThe spectrum shift is tau and substituted into the steps B, C and D to obtain an objective function score (tau), the target of the shift correction is to solve the tau to enable the value of the objective function score (tau) to be maximum, and the spectrum shift tau is obtained by utilizing the Newton iteration method to optimize opt And an optimal score opt 。
3. The LIBS spectral composition analysis result-based evaluation method according to claim 2, wherein: further comprising step F: when the optimal score is score opt When the original detection spectrum is larger than or equal to the set value, marking the original detection spectrum as a shifted effective spectrum; when the best score opt And when the spectrum is smaller than the set value, removing the corresponding original detection spectrum.
4. The LIBS spectral composition analysis result-based evaluation method according to claim 1, wherein: in the step B1, 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.
5. The LIBS spectral composition analysis result-based evaluation method according to claim 4, wherein: in step D1, the inverse process is provided in correspondence with the preprocessing in step B1, the order of each processing item is the inverse of each processing item in the preprocessing, and the operation of each processing item is the inverse of the operation in the corresponding processing item in the preprocessing.
6. The LIBS spectral composition analysis result-based evaluation method according to claim 4, wherein: in the step B1, the preprocessing operation sequentially comprises band-pass filtering, mean value removing and variance standardization.
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