CN114965438B - Laser plasma spectrum background removing method - Google Patents
Laser plasma spectrum background removing method Download PDFInfo
- Publication number
- CN114965438B CN114965438B CN202210412041.6A CN202210412041A CN114965438B CN 114965438 B CN114965438 B CN 114965438B CN 202210412041 A CN202210412041 A CN 202210412041A CN 114965438 B CN114965438 B CN 114965438B
- Authority
- CN
- China
- Prior art keywords
- spectrum
- intensity
- spectral
- background
- decomposition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001228 spectrum Methods 0.000 title claims abstract description 196
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000003595 spectral effect Effects 0.000 claims abstract description 89
- 230000008569 process Effects 0.000 claims abstract description 11
- 230000000694 effects Effects 0.000 claims abstract description 10
- 238000000354 decomposition reaction Methods 0.000 claims description 82
- 238000005516 engineering process Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000003705 background correction Methods 0.000 claims description 6
- 238000011088 calibration curve Methods 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 210000002381 plasma Anatomy 0.000 description 14
- 238000002536 laser-induced breakdown spectroscopy Methods 0.000 description 5
- 230000009466 transformation Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 239000003574 free electron Substances 0.000 description 3
- 150000002500 ions Chemical class 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000005457 Black-body radiation Effects 0.000 description 1
- 230000005461 Bremsstrahlung Effects 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000295 emission spectrum Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
Classifications
-
- 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
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Plasma & Fusion (AREA)
- Operations Research (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Optics & Photonics (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses a method for removing a laser plasma spectrum background, which is used for determining the wavelength and the intensity of a peak value of a target element. According to the sequence of the element concentration in the sample, selecting the element with higher concentration and larger influence on the spectrum intensity of the peak value of the target element as the secondary element. The NIST spectral intensities of the secondary elements at the peak wavelengths of the target elements are determined. And respectively searching the wavelength positions of the NIST spectral intensities of the secondary elements and the same spectral intensities at the peak positions of the target elements at the two sides of the wavelength positions of the peak values of the target elements. And respectively selecting a first group of wavelength positions at two sides of the wavelength position of the peak value of the target element as a starting point and an ending point of the spectral range of the background removing process. Since the spectral intensities of the secondary element at the start and end points of the spectrum are the same as the intensities at the peaks of the target element, the effect of the secondary element on the spectral intensities at the peaks of the target element is removed. The invention improves the authenticity and stability of the spectrum.
Description
Technical Field
The invention relates to the technical field of laser-induced breakdown spectroscopy, in particular to a laser plasma spectrum background removing method.
Background
The laser-induced breakdown spectroscopy (laser-induced breakdown spectrosopy, LIBS) is an elemental composition analysis technique. The laser emits strong laser pulse, and atoms and molecules in a focusing area of the laser are ionized by multiple photons to generate a large number of free electrons. The free electrons are accelerated under the action of laser, and after the electron high-energy regions are accelerated, the electrons can further impact other atoms to form an avalanche effect, so that a large amount of plasmas composed of free electrons and ions are finally generated. The plasma expands continuously with the temperature drop, and the ions and atoms in a high energy state transition to a low energy state and emit photons with a specific frequency. The spectrometer collects photon information generated by plasma cooling expansion to form characteristic spectral line information, and the element types and concentration information contained in the experimental sample can be obtained after computer analysis. The LIBS technology has the advantages of long-distance detection, high detection speed, no need of sample pretreatment, capability of overcoming severe environment and the like, and has good application prospects in the aspects of plastic classification, steel smelting, space detection and the like.
The LIBS emission line is formed by a laser plasma forming process, a continuous spectrum forming process and a linear spectrum forming process representing atomic characteristics. The emission spectrum of laser plasma has a characteristic of a strong continuous background. The transition of electrons between the continuum and discrete energy levels forms a continuous spectrum. The continuous background is typically due to bremsstrahlung, loading radiation and blackbody radiation, especially in the early stages of plasma life (typically up to one microsecond). The magnitude of the continuous background is related to a variety of factors, such as instrument response and plasma temperature.
The intensity of the spectral signal consists of the background intensity of the spectral signal, the independent spectral line signal, the noise intensity of the spectral signal. The background intensity of the spectrum signal is mainly the low frequency coefficient of the spectrum, the independent spectral line signal is mainly the medium frequency coefficient, and the noise intensity of the spectrum signal is mainly the high frequency coefficient. The purpose of spectral background removal is to remove the low frequency coefficients of the spectral signal and preserve the intermediate frequency coefficients of the spectral signal. The wavelet transformation is a signal processing method, can perform time-frequency decomposition on signals, and can overcome the defects of local characteristics of signals in the time domain of Fourier transformation and fixed time-frequency division rate of short-time Fourier transformation. The frequency decomposition can be performed on the spectrum signal by utilizing wavelet transformation, the spectrum signal is decomposed into linear combination of a wavelet function and a scale function, and the processing of different frequency parts of the spectrum signal is facilitated.
In order to remove the background signal of the spectrum signal and improve the spectrum quality, a scholars, such as 201810827112.2 patents, can realize the separation of spectrums with different polarization states by adding a polarizer as a spectrum continuous background removing device, but the spectrum acquisition process is complex and the processing time is long because a plurality of target spectral lines with different rotation angles of the polarizer are needed. In addition, the scholars of patent 201611249381.2 propose to obtain the minimum value of the spectrum intensity in the spectrum range, define a window function to order the minimum value from small to large, select a plurality of previous minimum value points, and finally estimate the continuous background through interpolation. However, since the minimum value point number of the window function has a large influence on the estimated continuous background, the continuous background overfitting is easy to occur in the complex spectrum, and the window function is difficult to apply in the complex spectrum.
Disclosure of Invention
In view of the above, in order to solve the above problems in the prior art, the present invention provides a method for removing the background of a laser plasma spectrum, which can better remove the background intensity of the spectrum, improve the authenticity and stability of the spectrum, improve the fitting coefficient of a calibration curve, and better restore the linear relationship between the concentration of a target element and the spectrum intensity.
The invention solves the problems by the following technical means:
a method for removing a laser plasma spectrum background comprises the following steps:
Determining the wavelength position lambda 0 and the spectral intensity I 1 of the spectral intensity peak value of the target element;
Sequencing the concentration of all elements in the sample from high to low, and selecting the element with the highest concentration as a secondary element;
observing the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located;
If the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located is extremely low, selecting the element with the second highest concentration as the secondary element, and re-determining the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 until the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located is higher, so that the spectral intensity of the target element is greatly influenced;
searching a plurality of wavelength positions with the spectrum intensity of the secondary element equal to that of the spectrum intensity I 0 on the left side and the right side of the wavelength position lambda 0 respectively to form an alternative set of the spectrum range;
Respectively selecting a first group of wavelength positions lambda 1 and lambda 2 as a starting point and an ending point of a spectrum range at the left side and the right side of a wavelength position lambda 0 where a target element peak value is positioned, wherein the number of decomposition layers of wavelet decomposition is 5-8, the decomposition coefficients are db2-db20, and different decomposition combinations are formed by different decomposition layers and decomposition coefficients to form a wavelet decomposition coefficient combination;
selecting a first group of decomposition coefficients and the number of decomposition layers for the spectrum signals with the determined spectrum range, and performing wavelet decomposition on the first group of decomposition coefficients and the number of decomposition layers to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer;
Setting the high-frequency coefficient of the spectrum signal to zero, and reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer to serve as a background signal of the first fitting;
Performing iterative operation on the background signal fitted for the first time, and performing wavelet decomposition on the background signal again to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer;
setting the high-frequency coefficient of the spectrum signal to zero, and reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer to serve as a background signal after iteration;
Sequentially iterating according to the above processes, and obtaining a real background signal I 2 after proper iteration times;
Subtracting the actual background signal intensity I 2 from the spectrum intensity I 1 of the target element to obtain the spectrum intensity I 1-I2 after the background is corrected, and distinguishing whether the spectrum after the background is corrected is reasonable or not; judging whether the absolute value of the maximum value of the corrected spectrum intensity is more than five times of the absolute value of the minimum value of the spectrum intensity; if the absolute value of the maximum value of the corrected spectrum intensity is five times larger than the absolute value of the minimum value of the spectrum intensity, the spectrum background correction processing is considered to be effective, otherwise, the spectrum background correction processing is considered to be ineffective;
Similarly, the spectrum intensity I 3 and the real background intensity I 4 of the matrix element are obtained, the spectrum intensity I 3-I4 after the background is corrected is obtained, and whether the spectrum after the background is corrected is reasonable or not is judged;
dividing the target element and the matrix element to obtain the normalized strength of the target element;
According to the normalized relation between the intensity of the target element and the concentration of the target element, performing linear fitting:
Wherein R 2 is the goodness of fit, n is the number of the calibration samples, c i and The standard concentration and the predicted concentration of the i-th sample analysis element respectively,Is the average value of the standard concentration of the nth sample analysis element;
Obtaining a fitting goodness according to the formula (1), and analyzing a fitting effect; re-selecting different wavelet decomposition coefficient combinations of the spectrum signals in the same spectrum range, and performing wavelet decomposition on the spectrum signals until all the decomposition combinations are traversed to obtain a fitting goodness R 2 of the different wavelet decomposition coefficient combinations;
After all unreasonable wavelet decomposition coefficient combinations are removed, comparing the fitting goodness R 2 obtained by different wavelet decomposition coefficient combinations, and taking the highest fitting goodness R 2 as the fitting goodness R 2 of the spectrum range;
Re-selecting lambda 1 and lambda 2 as the starting point and the ending point of the next spectrum range, and repeating the steps to obtain the fitting goodness R 2 of different spectrum ranges;
traversing each spectrum range in sequence to respectively obtain fitting goodness R 2 of different spectrum ranges, comparing the fitting goodness of different spectrum ranges, and taking the spectrum range with the largest fitting goodness R 2 as the spectrum range of the best background removing treatment;
calculate the relative standard deviation RSD:
The relative standard deviation was found according to equation (2) and the stability of the spectral data after correction for background was analyzed.
Further, standard concentration c i and predicted concentration of the i-th sample analysis elementCalculated by a calibration curve.
Further, the wavelength position lambda 0 of the target element spectral intensity peak and the spectral intensity I 1 are determined by referring to the line standard of the national institute of standards and technology NIST.
Further, the spectral intensity I 0 of the secondary element at the wavelength position λ 0 where the peak of the target element is located is observed by referring to the line standard of NIST of the national institute of standards and technology.
Further, the wavelength position λ 0 = 383.829nm of the target element spectral intensity peak is determined.
Further, the concentration of all elements in the sample is ordered from high to low, namely Mg, cu, si, fe, zn and Ti respectively, and Cu elements are selected as secondary elements; observing that the spectral intensity of the minor element at the wavelength position λ 0 = 383.829nm is extremely low, is not suitable as the minor element; selecting Si element as a secondary element; observing that the spectral intensity of the minor element at the wavelength position λ 0 = 383.829nm is extremely low, is not suitable as the minor element;
And (3) re-determining the Fe element as a secondary element of the wavelength, observing that the spectral intensity of the Fe element at the wavelength position lambda 0 = 383.829nm is higher, and greatly influencing the spectral intensity of the target element, and determining the Fe element as the secondary element.
Further, a plurality of wavelength positions with the spectral intensities of the secondary elements equal to that of I 0 are respectively searched for on the left and right sides of the wavelength position lambda 0 = 383.829nm
370.49nm,371.043nm,371.674nm,372.462nm,373.012nm,375.366nm,375.444nm,376.929nm,379.42nm,380.041nm,380.506nm,380.739nm,381.203nm,384.365nm,385.594nm,387.736nm,387.965nm,388.499nm,388.956nm,390.248nm,390.324nm An alternative set of spectral ranges is composed.
Further, wavelength positions λ 1 = 370.49nm and λ 2 = 384.365nm are selected as the start point and end point of the spectral range.
Further, the spectral signals in the spectral range of 370.49nm-390.76nm are subjected to wavelet decomposition by selecting the decomposition coefficient db2 and the decomposition layer number 5 layers, so that the high-frequency coefficient of each layer and the low-frequency coefficient of the highest layer are obtained.
Further, after 50 iterative operations, a real background signal I 2 is obtained.
Compared with the prior art, the invention has the beneficial effects that at least:
1. the method can effectively remove the influence of secondary elements on the spectrum intensity and improve the spectrum stability;
2. The invention scientifically determines the spectrum range and improves the objectivity and scientificity of spectrum range selection;
3. The method is simple to operate, and the fitting goodness between the element concentration and the spectrum intensity is improved rapidly.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a laser plasma spectral background removal method of the present invention;
FIG. 2 is a diagram showing the effect of the background removal treatment of Mg element in the invention;
FIG. 3 is a view showing the effect of the background removal treatment of the Al element of the present invention;
FIG. 4 is a plot of goodness-of-fit after background treatment for different spectral ranges in accordance with the present invention;
fig. 5 is a graph of the effect of fitting after the background removal process of the present invention.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, the following detailed description of the technical solution of the present invention refers to the accompanying drawings and specific embodiments. It should be noted that the described embodiments are only some embodiments of the present invention, and not all embodiments, and that all other embodiments obtained by persons skilled in the art without making creative efforts based on the embodiments in the present invention are within the protection scope of the present invention.
As shown in fig. 1, the invention provides a method for removing a laser plasma spectrum background, which comprises the following steps:
S1, determining the wavelength position lambda 0 and the spectral intensity I 1 of the spectral intensity peak value of the target element by consulting the spectral line standard of NIST of the national institute of standards and technology.
S2, sorting the concentration of all elements in the sample from high to low, and selecting the element with the highest concentration as a secondary element.
S3, observing the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located by consulting the spectral line standard of NIST of the national institute of standards and technology.
S4, if the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located is extremely low, selecting the element with the second highest concentration as the secondary element, and redefining the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 until the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located is higher, so that the spectral intensity of the target element is greatly influenced.
S5, searching a plurality of wavelength positions with the spectrum intensity of the secondary element equal to that of the spectrum intensity I 0 on the left side and the right side of the wavelength position lambda 0 by consulting the spectrum line standard of NIST of the national institute of standards and technologies to form an alternative set of the spectrum range.
S6, respectively selecting a first group of wavelength positions lambda 1 and lambda 2 at the left side and the right side of a wavelength position lambda 0 where a target element peak value is located as a starting point and an ending point of a spectrum range, wherein the number of decomposition layers of wavelet decomposition is 5-8, the decomposition coefficients are db2-db20, and different decomposition combinations are formed by different decomposition layers and decomposition coefficients to form a wavelet decomposition coefficient combination.
S7, selecting a first group of decomposition coefficients and the number of decomposition layers for the spectrum signal with the determined spectrum range, and performing wavelet decomposition on the spectrum signal to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer.
S8, after the high-frequency coefficient of the spectrum signal is set to zero, reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer, and taking the new spectrum signal as a background signal of the first fitting.
S9, performing iterative operation on the background signal subjected to the first fitting, and performing wavelet decomposition on the background signal again to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer.
S10, after the high-frequency coefficient of the spectrum signal is set to zero, reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer, and taking the new spectrum signal as a background signal after iteration.
S11, sequentially iterating according to the process, and obtaining a real background signal I 2 after proper iteration times.
S12, subtracting the actual background signal intensity I 2 from the spectrum intensity I 1 of the target element to obtain the spectrum intensity I 1-I2 after the background is corrected, and judging whether the absolute value of the maximum value of the spectrum intensity after the correction is greater than five times the absolute value of the minimum value of the spectrum intensity in order to judge whether the spectrum after the background is reasonable or not; if the absolute value of the corrected maximum value of the spectrum intensity is five times greater than the absolute value of the minimum value of the spectrum intensity, the spectrum processing is considered to be effective, otherwise the spectrum processing is considered to be ineffective.
S13, the same principle is adopted to obtain the spectrum intensity I 3 of the matrix element and the real background intensity I 4, obtain the spectrum intensity I 3-I4 after the background is corrected, and distinguish whether the spectrum after the background is corrected is reasonable or not.
S14, dividing the target element and the matrix element to obtain the normalized strength of the target element.
S15, performing linear fitting according to the normalized relationship between the intensity of the target element and the concentration of the target element:
Wherein R 2 is the goodness of fit, n is the number of scaled (inspected) samples, c i and The standard concentration and the predicted concentration (calculated by a calibration curve) of the i-th sample analysis element,Is the average of the standard concentrations of the n-th sample analysis element.
S16, according to the formula (1), obtaining a fitting goodness and analyzing a fitting effect; and (3) reselecting different wavelet decomposition coefficient combinations of the spectrum signals in the same spectrum range, and performing wavelet decomposition on the spectrum signals until all decomposition combinations are traversed, so as to obtain the fitting goodness (R 2) of the different wavelet decomposition coefficient combinations.
S17, after all unreasonable wavelet decomposition coefficient combinations are removed, comparing the fitting goodness (R 2) obtained by different wavelet decomposition coefficient combinations, and taking the highest fitting goodness (R 2) as the fitting goodness (R 2) of the spectrum range.
S18, re-selecting lambda 1 and lambda 2 as a starting point and an ending point of the next spectrum range, and repeating the steps to obtain the fitting goodness (R 2) of different spectrum ranges.
And S19, traversing each spectrum range in sequence to respectively obtain fitting goodness (R 2) of different spectrum ranges, comparing the fitting goodness of different spectrum ranges, and taking the spectrum range with the largest fitting goodness (R 2) as the spectrum range of the optimal background removing treatment.
S20, calculating a relative standard deviation RSD:
And S21, obtaining a relative standard deviation according to the formula (2), and analyzing the stability of the spectrum data after the background correction.
The present invention will be specifically described below.
As shown in fig. 2-5, the method for removing the spectral background of the laser plasma specifically comprises the following steps:
S1, determining the wavelength position lambda 0 = 383.829nm of the spectrum intensity peak value of the target element and the spectrum intensity I 1 by consulting the spectrum line standard of NIST of the national institute of standards and technology.
S2, sorting the concentration of all elements in the sample from high to low, wherein the elements are Mg, cu, si, fe, zn and Ti respectively, and Cu elements are selected as secondary elements.
S3, observing that the spectrum intensity of the minor element at the wavelength position lambda 0 = 383.829nm is extremely low by consulting the spectrum line standard of NIST of the national institute of standards and technologies, and the minor element is not suitable for being used as the minor element; selecting Si element as a secondary element; the spectral intensity of the minor element at the wavelength position λ 0 = 383.829nm was observed to be extremely low, and it was not suitable as the minor element.
S4, re-determining the Fe element as a secondary element of the wavelength, observing that the spectral intensity of the Fe element at a wavelength position lambda 0 = 383.829nm is higher, greatly influencing the spectral intensity of the target element, determining the Fe element as the secondary element, and recording that the spectral intensity of the secondary element at the wavelength position lambda 0 is I 0.
S5, searching a plurality of wavelength positions with the spectrum intensity of the secondary element equal to that of I 0 on the left side and the right side of the wavelength position lambda 0 = 383.829nm by consulting the spectrum line standard of NIST of the national institute of standards and technologies
370.49nm,371.043nm,371.674nm,372.462nm,373.012nm,375.366nm,375.444nm,376.929nm,379.42nm,380.041nm,380.506nm,380.739nm,381.203nm,384.365nm,385.594nm,387.736nm,387.965nm,388.499nm,388.956nm,390.248nm,390.324nm An alternative set of spectral ranges is composed.
S6, wavelength positions λ 1 = 370.49nm and λ 2 = 384.365nm are selected as the start point and end point of the spectral range.
S7, selecting a decomposition coefficient of db2 and a decomposition layer number of 5 for the spectrum signals in the spectrum range of 370.49nm-390.76nm, and carrying out wavelet decomposition to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer.
S8, after the high-frequency coefficient of the spectrum signal is set to zero, reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer, and taking the new spectrum signal as a background signal of the first fitting.
S9, performing iterative operation on the background signal subjected to the first fitting, and performing wavelet decomposition on the background signal again to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer;
S10, after the high-frequency coefficient of the spectrum signal is set to zero, reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer to serve as a background signal after iteration;
S11, sequentially iterating according to the process, and obtaining a real background signal I 2 after performing iterative operation for 50 times.
S12, subtracting the actual background signal intensity I 2 from the spectrum intensity I 1 of the target element to obtain the spectrum intensity I 1-I2 after the background is corrected, and judging whether the absolute value of the maximum value of the spectrum intensity after the correction is greater than five times the absolute value of the minimum value of the spectrum intensity in order to judge whether the spectrum after the background is reasonable or not; if the absolute value of the corrected maximum value of the spectrum intensity is five times greater than the absolute value of the minimum value of the spectrum intensity, the spectrum processing is considered to be effective, otherwise the spectrum processing is considered to be ineffective.
S13, the same principle is adopted to obtain the spectrum intensity I 3 of the matrix element and the real background intensity I 4, obtain the spectrum intensity I 3-I4 after the background is corrected, and distinguish whether the spectrum after the background is corrected is reasonable or not.
S14, dividing the target element and the matrix element to obtain the normalized strength of the target element.
S15, performing linear fitting according to the normalized relationship between the intensity of the target element and the concentration of the target element:
Wherein R 2 is the goodness of fit, n is the number of scaled (inspected) samples, c i and The standard concentration and the predicted concentration (calculated by a calibration curve) of the i-th sample analysis element,Is the average of the standard concentrations of the n-th sample analysis element.
S16, according to the formula (1), obtaining a fitting goodness and analyzing a fitting effect; and (3) selecting different wavelet decomposition modes again, selecting a decomposition coefficient of db3 and 5 decomposition layers of spectrum signals in the same spectrum range, performing wavelet decomposition on the spectrum signals, and repeating the steps.
S17, after all unreasonable wavelet decomposition combinations are removed, the fitting goodness (R 2) obtained by the wavelet decomposition modes of different combinations is compared, and the highest fitting goodness (R 2) is taken as the fitting goodness (R 2) of the spectrum range.
S18, λ 1 = 370.49nm and λ 2 = 385.594nm are newly selected as the starting point and the ending point of the next spectral range, and the above operation is repeated.
And S19, traversing each spectrum range in sequence to respectively obtain fitting goodness (R 2) of different spectrum ranges, comparing the fitting goodness of different spectrum ranges, and taking the spectrum range with the largest fitting goodness (R 2) as the spectrum range of the optimal background removing treatment.
S20, calculating a relative standard deviation RSD:
And S21, obtaining a relative standard deviation according to the formula (2), and analyzing the stability of the spectrum data after the background correction.
The invention determines the wavelength and intensity of the peak value of the target element by consulting the line standard of NIST of national institute of standards and technology. Then according to the sequence of the element concentration in the sample, selecting the element with higher concentration and larger influence on the spectrum intensity of the peak value of the target element as the secondary element. The NIST spectral intensity of the secondary element at the peak wavelength of the target element is determined by consulting national institute of standards and technology NIST spectral line standards. On both sides of the wavelength position of the peak of the target element, the wavelength positions of the NIST spectral intensities of the secondary elements and the same spectral intensities at the peak position of the target element are respectively searched, and the alternative set of the spectral ranges is determined. And respectively selecting a first group of wavelength positions at two sides of the wavelength position of the peak value of the target element as a starting point and an ending point of the spectral range of the background removing process. Since the spectral intensities of the secondary element at the start and end points of the spectrum are the same as the intensities at the peaks of the target element, the effect of the secondary element on the spectral intensities at the peaks of the target element is removed. The number of decomposition layers of wavelet decomposition is 5-8, the decomposition coefficient is db2-db20, and different decomposition combinations are formed by different decomposition layers and decomposition coefficients to form wavelet decomposition coefficient combinations. To distinguish whether the corrected spectrum is reasonable or not, it is determined whether the absolute value of the maximum value of the corrected spectrum intensity is greater than five times the absolute value of the minimum value of the spectrum intensity. If the absolute value of the corrected maximum value of the spectrum intensity is greater than five times the absolute value of the minimum value of the spectrum intensity, the spectrum processing is considered to be effective, otherwise the spectrum processing is considered to be ineffective. By traversing different wavelet decomposition coefficient combinations, the goodness-of-fit (R 2) obtained for the wavelet decomposition coefficient combinations of the different combinations is compared, and the highest goodness-of-fit (R 2) is taken as the goodness-of-fit (R 2) for that spectral range. By traversing each spectrum range, the fitting goodness (R 2) of different spectrum ranges is obtained respectively, the fitting goodness of different spectrum ranges is compared, and the spectrum range with the largest fitting goodness (R 2) is taken as the spectrum range of the optimal background removing treatment. The method can quickly determine the spectrum range of effective background removal treatment, can well remove the background intensity of the spectrum, improve the authenticity and stability of the spectrum, improve the fitting goodness of a calibration curve, and well restore the linear relation between the concentration of target elements and the spectrum intensity.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The laser plasma spectrum background removing method is characterized by comprising the following steps of:
Determining the wavelength position lambda 0 and the spectral intensity I 1 of the spectral intensity peak value of the target element;
Sequencing the concentration of all elements in the sample from high to low, and selecting the element with the highest concentration as a secondary element;
observing the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located;
If the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located is extremely low, selecting the element with the second highest concentration as the secondary element, and re-determining the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 until the spectral intensity I 0 of the secondary element at the wavelength position lambda 0 where the peak value of the target element is located is higher, so that the spectral intensity of the target element is greatly influenced;
searching a plurality of wavelength positions with the spectrum intensity of the secondary element equal to that of the spectrum intensity I 0 on the left side and the right side of the wavelength position lambda 0 respectively to form an alternative set of the spectrum range;
Respectively selecting a first group of wavelength positions lambda 1 and lambda 2 as a starting point and an ending point of a spectrum range at the left side and the right side of a wavelength position lambda 0 where a target element peak value is positioned, wherein the number of decomposition layers of wavelet decomposition is 5-8, the decomposition coefficients are db2-db20, and different decomposition combinations are formed by different decomposition layers and decomposition coefficients to form a wavelet decomposition coefficient combination;
selecting a first group of decomposition coefficients and the number of decomposition layers for the spectrum signals with the determined spectrum range, and performing wavelet decomposition on the first group of decomposition coefficients and the number of decomposition layers to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer;
Setting the high-frequency coefficient of the spectrum signal to zero, and reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer to serve as a background signal of the first fitting;
Performing iterative operation on the background signal fitted for the first time, and performing wavelet decomposition on the background signal again to obtain a high-frequency coefficient of each layer and a low-frequency coefficient of the highest layer;
setting the high-frequency coefficient of the spectrum signal to zero, and reconstructing a new spectrum signal together with the low-frequency coefficient of the highest layer to serve as a background signal after iteration;
Sequentially iterating according to the above processes, and obtaining a real background signal I 2 after proper iteration times;
Subtracting the actual background signal intensity I 2 from the spectrum intensity I 1 of the target element to obtain the spectrum intensity I 1-I2 after the background is corrected, and distinguishing whether the spectrum after the background is corrected is reasonable or not; judging whether the absolute value of the maximum value of the corrected spectrum intensity is more than five times of the absolute value of the minimum value of the spectrum intensity; if the absolute value of the maximum value of the corrected spectrum intensity is five times larger than the absolute value of the minimum value of the spectrum intensity, the spectrum background correction processing is considered to be effective, otherwise, the spectrum background correction processing is considered to be ineffective;
Similarly, the spectrum intensity I 3 and the real background intensity I 4 of the matrix element are obtained, the spectrum intensity I 3-I4 after the background is corrected is obtained, and whether the spectrum after the background is corrected is reasonable or not is judged;
dividing the target element and the matrix element to obtain the normalized strength of the target element;
According to the normalized relation between the intensity of the target element and the concentration of the target element, performing linear fitting:
Wherein R 2 is the goodness of fit, n is the number of the calibration samples, c i and The standard concentration and the predicted concentration of the i-th sample analysis element respectively,Is the average value of the standard concentration of the nth sample analysis element;
Obtaining a fitting goodness according to the formula (1), and analyzing a fitting effect; re-selecting different wavelet decomposition coefficient combinations of the spectrum signals in the same spectrum range, and performing wavelet decomposition on the spectrum signals until all the decomposition combinations are traversed to obtain a fitting goodness R 2 of the different wavelet decomposition coefficient combinations;
After all unreasonable wavelet decomposition coefficient combinations are removed, comparing the fitting goodness R 2 obtained by different wavelet decomposition coefficient combinations, and taking the highest fitting goodness R 2 as the fitting goodness R 2 of the spectrum range;
Re-selecting lambda 1 and lambda 2 as the starting point and the ending point of the next spectrum range, and repeating the steps to obtain the fitting goodness R 2 of different spectrum ranges;
traversing each spectrum range in sequence to respectively obtain fitting goodness R 2 of different spectrum ranges, comparing the fitting goodness of different spectrum ranges, and taking the spectrum range with the largest fitting goodness R 2 as the spectrum range of the best background removing treatment;
calculate the relative standard deviation RSD:
The relative standard deviation was found according to equation (2) and the stability of the spectral data after correction for background was analyzed.
2. The method for removing background of laser plasma spectrum according to claim 1, wherein standard concentration c i and predicted concentration of the i-th sample analysis elementCalculated by a calibration curve.
3. The method according to claim 1, wherein the wavelength position λ 0 and the spectral intensity I 1 of the spectral intensity peak of the target element are determined by referring to the spectral line standard of NIST of national institute of standards and technology.
4. The method of claim 1, wherein the spectral intensity I 0 of the secondary element at the wavelength location λ 0 where the peak of the target element is located is observed by looking up the line standard of NIST in the national institute of standards and technology.
5. The method of claim 1, wherein the wavelength position λ 0 = 383.829nm of the spectral intensity peak of the target element is determined.
6. The method for removing the spectral background of the laser plasma according to claim 5, wherein the concentrations of all elements in the sample are ranked from high to low, respectively Mg, cu, si, fe, zn, ti, and Cu element is selected as a secondary element; observing that the spectral intensity of the minor element at the wavelength position λ 0 = 383.829nm is extremely low, is not suitable as the minor element; selecting Si element as a secondary element; observing that the spectral intensity of the minor element at the wavelength position λ 0 = 383.829nm is extremely low, is not suitable as the minor element;
And (3) re-determining the Fe element as a secondary element of the wavelength, observing that the spectral intensity of the Fe element at the wavelength position lambda 0 = 383.829nm is higher, and greatly influencing the spectral intensity of the target element, and determining the Fe element as the secondary element.
7. The method of claim 6, wherein a plurality of wavelength positions having a spectral intensity equal to I 0 of the minor element are found on both sides of a wavelength position λ 0 = 383.829nm
370.49nm,371.043nm,371.674nm,372.462nm,373.012nm,375.366nm,375.444nm,376.929nm,379.42nm,380.041nm,380.506nm,380.739nm,381.203nm,384.365nm,385.594nm,387.736nm,387.965nm,388.499nm,388.956nm,390.248nm,390.324nm, An alternative set of spectral ranges is composed.
8. The method according to claim 7, wherein the wavelength positions λ 1 = 370.49nm and λ 2 = 384.365nm are selected as the start point and the end point of the spectral range.
9. The method according to claim 8, wherein the spectral signal in the spectral range of 370.49nm to 390.76nm is subjected to wavelet decomposition with a decomposition coefficient db2 and a decomposition layer number 5, and the high frequency coefficient of each layer and the low frequency coefficient of the highest layer are obtained.
10. The method for removing the spectral background of the laser plasma according to claim 1, wherein the real background signal I 2 is obtained after 50 iterative operations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210412041.6A CN114965438B (en) | 2022-04-19 | 2022-04-19 | Laser plasma spectrum background removing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210412041.6A CN114965438B (en) | 2022-04-19 | 2022-04-19 | Laser plasma spectrum background removing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114965438A CN114965438A (en) | 2022-08-30 |
| CN114965438B true CN114965438B (en) | 2024-08-02 |
Family
ID=82977372
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210412041.6A Active CN114965438B (en) | 2022-04-19 | 2022-04-19 | Laser plasma spectrum background removing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114965438B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106814061A (en) * | 2016-12-13 | 2017-06-09 | 华中科技大学 | A kind of method for improving LIBS overlap peak accuracy of quantitative analysis |
| CN107632010A (en) * | 2017-09-22 | 2018-01-26 | 段忆翔 | A kind of quantitative approach of combination LIBS to steel samples |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7248370B2 (en) * | 2005-03-07 | 2007-07-24 | Caleb Brett Usa, Inc. | Method to reduce background noise in a spectrum |
| US11953427B2 (en) * | 2015-08-03 | 2024-04-09 | Endress+Hauser Optical Analysis, Inc. | Reconstruction of frequency registration for quantitative spectroscopy |
| CN109507120A (en) * | 2018-10-26 | 2019-03-22 | 重庆邮电大学 | Solution cathode glow discharging emission spectrographic analysis method based on standard addition method combination wavelet transformation |
| CN111398233A (en) * | 2020-04-07 | 2020-07-10 | 安徽理工大学 | Laser spectrum detection method for red wine quality |
| CN113970540B (en) * | 2021-09-09 | 2023-07-04 | 华南师范大学 | Elemental isotope analysis system and method based on laser-induced plasma |
-
2022
- 2022-04-19 CN CN202210412041.6A patent/CN114965438B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106814061A (en) * | 2016-12-13 | 2017-06-09 | 华中科技大学 | A kind of method for improving LIBS overlap peak accuracy of quantitative analysis |
| CN107632010A (en) * | 2017-09-22 | 2018-01-26 | 段忆翔 | A kind of quantitative approach of combination LIBS to steel samples |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114965438A (en) | 2022-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106814061B (en) | A method of improving laser induced breakdown spectroscopy overlap peak accuracy of quantitative analysis | |
| Yuan et al. | A partial least squares and wavelet-transform hybrid model to analyze carbon content in coal using laser-induced breakdown spectroscopy | |
| CN108169213B (en) | Automatic identification method for peak elements of laser-induced breakdown spectroscopy | |
| Guo et al. | Wavelet-based interference correction for laser-induced breakdown spectroscopy | |
| CN107037012B (en) | Echelle spectrometer dynamic correcting method for laser induced breakdown spectroscopy acquisition | |
| Wang et al. | Lens-to-sample distance effect on the quantitative analysis of steel by laser-induced breakdown spectroscopy | |
| Xie et al. | Accuracy improvement of quantitative LIBS analysis using wavelet threshold de-noising | |
| CN114813709A (en) | Soil component detection method, equipment and system | |
| Ruan et al. | A modified backward elimination approach for the rapid classification of Chinese ceramics using laser-induced breakdown spectroscopy and chemometrics | |
| CN106770192B (en) | Laser induced breakdown spectroscopy continuous background bearing calibration based on interpolation method | |
| CN114965438B (en) | Laser plasma spectrum background removing method | |
| CN115728289A (en) | Method for correcting LIBS spectral volatility through plasma image-spectrum fusion and application | |
| Ferreira et al. | Multiple response optimization of laser-induced breakdown spectroscopy parameters for multi-element analysis of soil samples | |
| CN108344729B (en) | Laser-induced breakdown spectroscopy rapid detection method based on multi-spectral-line internal calibration | |
| Lin et al. | Modified iterative wavelets for background removal in laser-induced breakdown spectroscopy based on fiber laser ablation | |
| CN105717094A (en) | Metal element analysis method based on large database recognition | |
| CN105758882A (en) | Fluorescence-detecting and spectrum pre-processing method for lubricating oil metal X-rays | |
| CN110398488B (en) | Nonlinear quantitative analysis method applied to laser-induced breakdown spectroscopy | |
| CN107941758A (en) | A kind of coal quality measurement of caloric value method based on Pretreated spectra | |
| Lv et al. | Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy | |
| Zhou et al. | Rapid quantitative analysis of multiple rare earth elements in NdFeB alloys based on laser-induced breakdown spectroscopy (LIBS) and random forest (RF) | |
| Bador et al. | Advances in low-energy electron-induced X-ray spectroscopy (LEEIXS) | |
| CN117609872A (en) | Method for improving accuracy of LIBS (laser induced breakdown spectroscopy) detection on content of calcium element in carbonate rock | |
| CN105717093A (en) | Cement characteristic analysis method based on large database recognition | |
| John et al. | Continuum removal and self-absorption correction incalibration-free laser-induced breakdownspectroscopy for efficient elemental analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |