A method to improve the accuracy of laser probe peak recognition using morphological fuzzy judgment

Abstract

The present invention belongs to the technical field related to laser probe element analysis, and discloses a method for improving the accuracy of laser probe spectral peak recognition by utilizing morphological fuzzy judgment. The method comprises the following steps: S1, LIBS spectrum acquisition, and obtaining plasma spectrum data; S2, setting the spectrum segment length, dividing the spectrum segments, and calculating the spectrum intensity difference between all adjacent points on both sides of the central wavelength in all spectrum segments (subtracting the previous value from the next point), and obtaining the intensity difference matrix of the left and right sides; S3, judging whether all elements in the intensity difference matrix of the left (right) sides are all greater than (less than) zero, and obtaining the difference state bit matrix of the left and right sides respectively; S4, establishing a fuzzy criterion, and calculating the sum of the difference state bits of the left and right sides respectively, first using the fuzzy criterion to identify symmetrical spectrum peaks and asymmetrical spectrum peaks, and then further identifying the single spur spectrum peak, double spur spectrum peak and multi-spur spectrum peak in the asymmetric spectrum peak; S5, outputting the wavelengths and spectral intensities of all identified spectrum peaks.

Description

Technical Field

[0001] The present invention belongs to the technical field related to laser probe spectral analysis, and more specifically, relates to a method for improving the accuracy of laser probe spectral peak recognition by utilizing morphological fuzzy judgment. Background Art

[0002] Laser probe technology, also known as laser-induced breakdown spectroscopy (LIBS), or LIBS for short, is a modern detection technology that uses high-energy-density lasers to ablate the sample surface and utilizes the plasma emission spectrum generated to perform elemental analysis.

[0003] When using laser probe technology for elemental analysis, the sample spectrum is typically first identified. A specific number of peak wavelengths are then selected from the identified peaks as analytical lines. The spectral intensity characteristics of the selected analytical lines are then extracted from the raw data, and an analytical model is established to achieve qualitative or quantitative analysis. Traditional peak detection methods include manual and automatic methods. The former offers high accuracy but requires manual inspection and selection, resulting in relatively low efficiency. The latter, on the other hand, utilizes specific algorithms to automatically identify all peaks in a spectrum, offering high efficiency and is generally used for qualitative analysis in laser probe technology and analytical line selection in multiple regression quantitative analysis. Traditional automatic peak detection methods include wavelet transform, second-order derivative, and image feature line selection. For example, in 2014, Jilin University published a paper on peak detection based on continuous wavelet transform, titled "An Automatic Peak Detection Method for LIBS Spectra Based on Continuous Wavelet Transform" (Spectroscopy and Spectral Analysis, Vol. 34, No. 7, pp. 1969-1972). The study published its recognition results of a total of 300 pixels in the 382.2747~385.3477nm spectrum of Fe3O4 samples, but the accuracy and quality of its spectral peak recognition (such as its spectral peak intensity and signal-to-noise ratio) need to be further improved.

[0004] Based on this, the present invention aims to provide a method for further improving the accuracy of identifying various types of spectral peaks in laser probe spectroscopy based on the spectral peak morphology. Summary of the Invention

[0005] In view of the shortcomings of the existing laser probe spectral analysis technology, the problem to be solved by the present invention is to achieve accurate and rapid identification of various spectral peaks through fuzzy judgment of spectral peak morphology, thereby improving the quality of the analysis line in the laser probe technology.

[0006] To achieve the above object, the present invention provides a method for improving the accuracy of laser probe spectrum peak recognition by using morphological fuzzy judgment, the method comprising the following steps:

[0007] S1, perform LIBS spectrum acquisition to obtain plasma spectrum data;

[0008] S2, set the spectrum segment length, divide the spectrum segment, and calculate the spectral intensity difference between all adjacent points on both sides of the central wavelength in all spectrum segments, and obtain the intensity difference matrix on the left and right sides of all spectrum segments;

[0009] S3, using the intensity difference matrices on the left and right sides of all spectral segments to obtain the difference state bit matrices on the left and right sides of all spectral segments respectively;

[0010] S4, summing all elements in the state bit matrices on the left and right sides of each spectrum segment to obtain the sum of the state bits of the corresponding spectrum segment. The sum of the state bits of all spectrum segments constitutes the state bit sum matrix of the entire spectrum; establishing a fuzzy criterion, first using the fuzzy criterion to identify symmetrical spectrum peaks and asymmetrical spectrum peaks, and then further identifying single glitch spectrum peaks, double glitch spectrum peaks, and multiple glitch spectrum peaks in the asymmetric spectrum peaks;

[0011] S5, outputs the wavelengths and spectral intensities of all identified spectral peaks.

[0012] Furthermore, step S2 specifically includes the following sub-steps:

[0013] S21, taking all wavelengths of the full spectrum as the center wavelength, setting the spectrum length, and specifying the spectrum range for each center wavelength;

[0014] S22, calculating the difference between all adjacent points on the left side of the central wavelength in the spectrum segment, subtracting the spectral intensity of the first point on the left side from the spectral intensity of the central wavelength to obtain the first intensity difference on the left side, then subtracting the spectral intensity of the second point on the left side from the spectral intensity of the first point on the left side to obtain the second intensity difference on the left side, and so on until the left boundary of the spectrum segment, and storing all the obtained intensity differences on the left side into the left difference matrix;

[0015] Calculate the difference of all adjacent points to the right of the central wavelength in the spectrum band. Subtract the spectral intensity of the central wavelength from the spectral intensity of the first point to the right of the central wavelength to obtain the first intensity difference on the right. Subtract the spectral intensity of the first point on the right from the spectral intensity of the second point on the right to obtain the second intensity difference on the right. Repeat this process until the right boundary of the spectrum band is reached. Then, store all the intensity differences on the right side into the right difference matrix.

[0016] Furthermore, step S3 specifically includes the following sub-steps:

[0017] Each element in the left difference matrix of each spectrum segment is judged. If the element is greater than zero, the state bit of the element is set to 1, otherwise it is set to 0. Finally, the left difference state bit matrix corresponding to all spectrum segments is obtained;

[0018] Each element in the right difference matrix of each spectral segment is judged. If the element is less than zero, the state bit of the element is set to 1, otherwise it is set to 0. Finally, the right difference state bit matrix corresponding to all spectral segments is obtained.

[0019] Furthermore, step S4 specifically includes the following sub-steps:

[0020] S41, summing all elements in the state bit matrices on the left and right sides of each spectrum segment to obtain the sum of the state bits of the corresponding spectrum segment. The sum of the state bits of all spectrum segments constitutes the state bit sum matrix of the entire spectrum.

[0021] S42, using the spectrum peak identification criterion to sequentially judge each element in the state bit sum matrix of the entire spectrum, to achieve fuzzy identification of all spectrum peaks in the entire spectrum;

[0022] The peak identification criteria are as follows: if the element is equal to the number of pixels in the corresponding spectrum segment minus 1, it is determined that there is only one symmetrical spectrum peak in the spectrum segment; if the element is equal to the number of pixels in the spectrum segment minus 2, and the maximum spectral intensity in the spectrum segment is not greater than the intensity of the central wavelength, it is determined that there is a single glitch spectrum peak with a span of one pixel in the spectrum segment; if the element is equal to the number of pixels in the spectrum segment minus 3, and the maximum spectral intensity in the spectrum segment is not greater than the intensity of the central wavelength, it is determined that there is a two-glitch spectrum peak with a span of one pixel in the spectrum segment, or a single glitch spectrum peak with a span of two pixels.

[0023] S43: Output the wavelengths and spectral intensities of all identified spectral peaks.

[0024] In general, the present invention has the following advantages and effects:

[0025] 1. The method proposed in this invention can realize accurate, rapid and automatic identification of laser probe spectrum peaks, thereby improving the analysis efficiency and accuracy of laser probe technology;

[0026] 2. The method proposed in this invention can identify various types of asymmetric spectral peaks, such as single-burr spectral peaks, double-burr spectral peaks, and multiple-burr spectral peaks, through a single state bit interpretation. There are no strict restrictions on the shape and location of the burrs, achieving the effect of fuzzy recognition of laser probe spectral peaks.

[0027] 3. Compared with the traditional wavelet transform method, the method proposed in the present invention improves the quality of the identified spectral peaks, such as spectral intensity, signal-to-noise ratio and position accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for improving the accuracy of laser probe spectral peak recognition by utilizing morphological fuzzy judgment provided by the present invention;

[0029] Figure 2 is the average spectrum of two igneous rock samples;

[0030] Figure 3 Schematic diagram of common spectral peak categories of optical probes;

[0031] Figure 4 Taking the local spectrum segment of 385.0-415.0 nm in the average spectrum as an example, a schematic diagram comparing the spectrum peaks identified by the method of the present invention and the traditional wavelet transform method is shown. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is now described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0033] The method of the present invention mainly comprises the following specific steps:

[0034] Step 1: Obtain the original spectrum and average spectrum. Use the laser probe system to collect the laser probe spectrum and obtain the original spectrum data of the sample to be tested, which is recorded as N mⅹn , where m and n are the number of rows and columns in the original number excluding the wavelength column, and then the average spectrum is calculated based on this, recorded as M mⅹ2 ;

[0035] Step 2: Obtain the intensity difference matrix.

[0036] Step 2.1 Divide the spectrum into segments. Set the length of the spectrum segment to N seg , and each wavelength in the average spectrum is taken as the central wavelength. Then the full spectrum of m pixels is divided into m spectrum segments, and each spectrum segment is recorded as the matrix Seg(N seg ⅹ1), the length of each spectrum segment is N seg pixels, then there are N pixels on both sides of the central wavelength. side =(N seg -1) / 2 pixels;

[0037] Step 2.2 Calculate the intensity difference matrix I on the left and right sides of the central wavelength left (mⅹN side ) and I right (mⅹN side ). Take a random spectral segment in the spectrum as an example, subtract the spectral intensity of the first point on the left from the spectral intensity of its central wavelength, and then subtract the spectral intensity of the second point on the left from the spectral intensity of the first point on the left, and so on until the left boundary of the spectral segment, and then all the intensity differences of the spectral segment are obtained and stored in a single left difference matrix Ileft (1ⅹN side ), similarly, the left intensity difference matrix I of all other spectral segments can be obtained left (mⅹN side ) and the intensity difference matrix I on the right right (mⅹN side ). The intensity difference between the left and right boundary points is approximated as -1.

[0038] Step 3: Obtain the difference state bit matrix.

[0039] Step 3.1 First determine the left intensity difference matrix I left (mⅹN side ) is greater than 0, if so, the state bit of the element is 1, otherwise it is 0, and finally the left difference state bit matrix S of all spectrum segments is obtained. left (mⅹN side );

[0040] Step 3.2 Then determine whether each element of the right intensity difference matrix is less than 0. If so, the state bit of the element is 1, otherwise it is 0. Finally, the right difference state bit matrix S of all spectral segments is obtained. right (mⅹN side );

[0041] Step 4: Spectral peak fuzzy judgment.

[0042] Step 4.1 Merge the state bit matrices on the left and right sides horizontally and sum them to get the state bit and matrix S of the full spectrum. sum (mⅹ1);

[0043] Step 4.2 Establish the spectrum peak fuzzy criterion:

[0044] a. When the i-th element S in the sum matrix sum (i) = N seg When -1, the spectrum segment has only one symmetrical peak;

[0045] b. When the i-th element S in the sum matrix sum (i) = N seg -2, and the maximum value of all intensity values in the spectral segment is less than the intensity of the central wavelength, then there is a single glitch peak with a span of 1 pixel in the spectral segment;

[0046] c. When the i-th element S in the sum matrix sum (i) = N seg -3, and the maximum value of all intensity values in the spectral segment is less than the intensity of the central wavelength, then the spectral segment has a single glitch peak with a span of two pixels, or two double glitch peaks with a span of one pixel;

[0047] d. Based on this theory, we can deduce the criteria for determining multiple burr peaks;

[0048] Step 4.3 uses the spectrum peak judgment criteria obtained in step 4.2 to realize automatic fuzzy recognition of all spectrum peaks in the entire spectrum by sequentially judging the state bits and all elements in the matrix.

[0049] Step 5: Output the wavelength and spectral intensity of all identified spectral peaks.

[0050] Example

[0051] The embodiment of the present invention provides a method for improving the accuracy of laser probe spectral peak recognition by using morphological fuzzy judgment, which mainly includes the following steps:

[0052] Step 1: Collect the original spectrum and average spectrum of the igneous rock sample. In this example, 12 igneous rock samples are collected for spectrum collection. There is one sample for each type. Ten points are collected at different positions of each sample. Ten spectra are collected at each point. The number of pixels of each spectrum is 4096. Therefore, the original spectrum data matrix N is obtained. 4096ⅹ1200 , whose average spectrum is M 4094ⅹ2 ,like Figure 1 shown.

[0053] Step 2: Obtain the intensity difference matrix.

[0054] Step 2.1 Assume the spectral length N of the symmetrical and asymmetrical peaks seg 9 and 11 pixels respectively.

[0055] Step 2.2 takes the spectrum segment with 268.51nm as the center wavelength as an example. The starting wavelengths of the spectrum segment are 268.33-268.7nm, and the spectral intensities are [304.33, 302.48, 336.15, 362.07, 397.35, 326.36, 316.44, 285.69, 274.47]. The spectral intensity of the center wavelength is subtracted from the spectral intensity of the first point on the left of the center wavelength, 362.07. Then, the spectral intensity of the first point on the left, 362.07, is subtracted from the spectral intensity of the second point on the left, 336.15. This process is repeated until the left boundary of the spectrum segment. Then, all the intensity differences of the spectrum segment are obtained and stored in a single left difference matrix I. left (1×4)=[-1.85, 33.67, 25.92, 35.28]. Similarly, the left and right intensity difference matrices of all other spectral segments can be obtained, which are:

[0056]

[0057] Step 3: Obtain the difference state bit matrix.

[0058] Step 3.1 First determine the left intensity difference matrix I left Is the value of each element in (1ⅹ4)=[-1.85,33.67,25.92,35.28] greater than 0? If so, the state bit of the element is 1, otherwise it is 0. Then the left difference state bit matrix of the spectrum segment is [0,1,1,1]. Finally, the left difference state bit matrix S of all spectrum segments is obtained. left (4094ⅹ4);

[0059] Step 3.2 Then determine whether the element of the right intensity difference matrix is less than 0. If it is, the state bit of the element is 1, otherwise it is 0. Then the right difference state bit matrix of the spectrum segment is [1,1,1,1]. Finally, the right difference state bit matrix S of all spectrum segments is obtained. right (4094ⅹ4), among which,

[0060]

[0061] Step 4: Fuzzy judgment of spectrum peaks.

[0062] Step 4.1: Merge the state bit matrices on the left and right sides of the spectrum segment horizontally to obtain the matrix [0, 1, 1, 1, 1, 1, 1]. The horizontal summation gives the sum of the state bits of the spectrum segment to be 7. Similarly, the state bits and matrix S of the full spectrum can be obtained for the remaining spectrum segments. sum (4094ⅹ1)=[3,2,2,…,6];

[0063] Step 4.2: Establish fuzzy criteria:

[0064] a. When the i-th element S in the sum matrix sum When (i) = 8, the spectrum segment has only one symmetrical peak;

[0065] b. When the i-th element S in the sum matrix sum When (i) = 7, and the maximum value of all intensities in the spectral segment is less than the intensity of the central wavelength, then there is a single glitch peak with a span of 1 pixel in the spectral segment;

[0066] c. When the i-th element S in the sum matrix sum (i) = 6, and the maximum value of all intensity values in the spectral segment is less than the intensity of the central wavelength, then the spectral segment has a single glitch peak with a span of two pixels, or two double glitch peaks with a span of one pixel;

[0067] d. Based on this theory, we can deduce the criteria for determining multiple burr peaks;

[0068] Step 4.3 uses the fuzzy criterion obtained in step 4.2. Since the spectrum segment satisfies S sum(i) = 9 - 2 = 7, and the maximum values of all intensities within this spectral band, excluding the central wavelength, are less than the intensity of the central wavelength, as shown in the spectral band intensity matrix [304.33, 302.48, 336.15, 362.07, 397.35, 326.36, 316.44, 285.69, 274.47]. Therefore, this spectral band contains a single glitch peak spanning one pixel. Automatic fuzzy identification of all peaks within the entire spectrum is achieved by sequentially evaluating the remaining status bits and all elements in the matrix.

[0069] Step 5: Output the wavelengths and spectral intensities of all identified spectral peaks, as shown in Table 1.

[0070] Table 1 Wavelengths and spectral intensities of all spectral peaks identified using the method of the present invention

[0071]

[0072] Based on the above embodiments, the present invention provides a method for improving the accuracy of laser probe spectrum peak recognition by using morphological fuzzy judgment (hereinafter referred to as fuzzy judgment method) and a traditional spectrum peak recognition method based on wavelet transform (hereinafter referred to as wavelet transform method). All spectrum peaks they identify are as follows: Figure 4 The peak quality comparison results are shown in Table 2:

[0073] Table 2 Comparison of the number and quality of spectral peaks identified by the method of the present invention and the traditional wavelet transform method

[0074]

[0075] Depend on Figure 4 It can be seen that both methods can automatically identify the vast majority of significant peaks in the entire spectrum, and as can be seen from Table 2, the number of identical peaks identified by the two methods is 86, accounting for approximately 58.1% of the total number of 148. The remaining 62 are their own unique peaks, i.e., inconsistent peaks identified by the two methods. After calculation of the three commonly used spectral quality indicators of the average spectral intensity, average signal-to-noise ratio, and average signal-to-background ratio of the 62 unique peaks, the average spectral intensity, average signal-to-noise ratio, and average signal-to-background ratio of the unique peaks of the fuzzy judgment method proposed in the present invention are 2400.6, 181.6, and 1.5, respectively, which are significantly higher than the 2046.1, 157.3, and 1.1 of the wavelet transform method. Therefore, its spectral peak quality is better than the traditional wavelet transform method. In addition, by Figure 4Comparison of the local details of the identified peaks reveals that many of the peaks identified by the traditional wavelet transform method are located in non-peak positions, as shown by the five-pointed star peak positions indicated by the arrows near wavelengths such as 385.0nm, 405.0nm, and 412.5nm in the figure. This situation does not exist in the method of the present invention. In summary, the peak identification accuracy of the method of the present invention is significantly superior to that of the traditional wavelet transform method.

[0076] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for improving the accuracy of laser probe spectrum peak recognition by using morphological fuzzy judgment, characterized in that: The method comprises the following steps: S1, perform LIBS spectrum acquisition to obtain plasma spectrum data; S2, set the spectrum segment length, divide the spectrum segment, and calculate the spectral intensity difference between all adjacent points on both sides of the central wavelength in all spectrum segments, and obtain the intensity difference matrix on the left and right sides of all spectrum segments; S3, using the intensity difference matrices on the left and right sides of all spectral segments to obtain the difference state bit matrices on the left and right sides of all spectral segments respectively; S4, summing all elements in the state bit matrices on the left and right sides of each spectrum segment to obtain the sum of the state bits of the corresponding spectrum segment. The sum of the state bits of all spectrum segments constitutes the state bit sum matrix of the entire spectrum; establishing a fuzzy criterion, first using the fuzzy criterion to identify symmetrical spectrum peaks and asymmetrical spectrum peaks, and then further identifying single glitch spectrum peaks, double glitch spectrum peaks, and multiple glitch spectrum peaks in the asymmetric spectrum peaks; S5, outputs the wavelengths and spectral intensities of all identified spectral peaks.

2. The method for improving the accuracy of laser probe spectrum peak recognition by using morphological fuzzy judgment according to claim 1, characterized in that: Step S2 specifically includes the following sub-steps: S21, taking all wavelengths of the full spectrum as the center wavelength, setting the spectrum length, and specifying the spectrum range for each center wavelength; S22, calculating the difference between all adjacent points on the left side of the central wavelength in the spectrum segment, subtracting the spectral intensity of the first point on the left side from the spectral intensity of the central wavelength to obtain the first intensity difference on the left side, then subtracting the spectral intensity of the second point on the left side from the spectral intensity of the first point on the left side to obtain the second intensity difference on the left side, and so on until the left boundary of the spectrum segment, and storing all the obtained intensity differences on the left side into the left difference matrix; Calculate the difference of all adjacent points to the right of the central wavelength in the spectrum band. Subtract the spectral intensity of the central wavelength from the spectral intensity of the first point to the right of the central wavelength to obtain the first intensity difference on the right. Subtract the spectral intensity of the first point on the right from the spectral intensity of the second point on the right to obtain the second intensity difference on the right. Repeat this process until the right boundary of the spectrum band is reached. Then, store all the intensity differences on the right side into the right difference matrix.

3. The method for improving the accuracy of laser probe spectrum peak recognition by using morphological fuzzy judgment according to claim 1, characterized in that: Step S3 specifically includes the following sub-steps: Each element in the left difference matrix of each spectrum segment is judged. If the element is greater than zero, the state bit of the element is set to 1, otherwise it is set to 0. Finally, the left difference state bit matrix corresponding to all spectrum segments is obtained; Each element in the right difference matrix of each spectral segment is judged. If the element is less than zero, the state bit of the element is set to 1, otherwise it is set to 0. Finally, the right difference state bit matrix corresponding to all spectral segments is obtained.

4. The method for improving the accuracy of laser probe spectrum peak recognition by using morphological fuzzy judgment according to claim 1, characterized in that: Step S4 specifically includes the following sub-steps: S41, summing all elements in the state bit matrices on the left and right sides of each spectrum segment to obtain the sum of the state bits of the corresponding spectrum segment. The sum of the state bits of all spectrum segments constitutes the state bit sum matrix of the entire spectrum. S42, using the spectrum peak identification criterion to sequentially judge each element in the state bit sum matrix of the entire spectrum, to achieve fuzzy identification of all spectrum peaks in the entire spectrum; The peak identification criteria are as follows: if the element is equal to the number of pixels in the corresponding spectrum segment minus 1, it is determined that there is only one symmetrical spectrum peak in the spectrum segment; if the element is equal to the number of pixels in the spectrum segment minus 2, and the maximum spectral intensity in the spectrum segment is not greater than the intensity of the central wavelength, it is determined that there is a single glitch spectrum peak with a span of one pixel in the spectrum segment; if the element is equal to the number of pixels in the spectrum segment minus 3, and the maximum spectral intensity in the spectrum segment is not greater than the intensity of the central wavelength, it is determined that there is a two-glitch spectrum peak with a span of one pixel in the spectrum segment, or a single glitch spectrum peak with a span of two pixels. S43: Output the wavelengths and spectral intensities of all identified spectral peaks.

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