A method for quickly and adaptively matching spectral background by spectral peak fitting

Abstract

This invention belongs to the technical field of laser probe elemental analysis and provides a method for rapid adaptive matching of spectral background using peak fitting. The method includes: LIBS spectral acquisition to obtain plasma spectral data; selecting the peak intensities and widths of multiple peaks within the band containing the peak to be matched, obtaining a fitting equation; using this equation to obtain the width of the peak to be matched, while simultaneously setting the number of single-sided scans, background length, and step size; scanning the background noise to the left and right sides with the set background length and step size; selecting the background spectral segment with the smallest noise value from all obtained background noise values ​​as the background of that peak, using this value as the noise value of that peak, and using the average intensity of the spectral segment as the background value; and calculating the signal-to-noise ratio and signal-to-background ratio of each peak based on the peak intensity. This invention automatically estimates the peak width through peak fitting to achieve rapid adaptive identification and matching of the peak background, improving the analytical efficiency of laser probe technology.

Description

Technical Field

[0001] This invention belongs to the field of laser probe spectral analysis, and more specifically, relates to a method for rapidly and adaptively matching spectral background using spectral peak fitting. Background Technology

[0002] Laser probe technology, also known as laser-induced breakdown spectroscopy (LIBS), is now widely used in industrial production, deep-sea exploration, and other fields.

[0003] The basic principle of LIBS (Laser-Induced Plasma Optical Blot) technology is that the content of a specific element in a sample is usually proportional to the intensity of its laser-induced plasma emission spectrum. Therefore, when using LIBS for qualitative or quantitative analysis, the spectral intensity of the analyte element, or the spectral intensity of analytical lines for multiple elements, must first be extracted. Furthermore, the quality of the analytical lines must be evaluated to ensure their characterization performance. The quality of the analytical lines can generally be characterized by indicators such as spectral intensity, signal-to-noise ratio (SNR), and signal-to-background ratio (SPR). Typically, in a known spectrum, the spectral intensity of a specific peak is constant, but its SNR and SPR depend on the selection of the background band. Moreover, almost every analyst will select different lengths, intensities, and ranges, which introduces considerable uncertainty into the selection and evaluation of analytical lines, hindering the standardization and generalization of analytical models.

[0004] Currently, whether it is qualitative or quantitative analysis, when selecting analytical lines, the background is usually selected manually for evaluation and calculation, or a specific distance and number of pixels next to the spectral peak are roughly specified as the spectral peak background. Although the former can ensure accuracy, the efficiency is not satisfactory; although the latter can achieve automatic selection, the accuracy is difficult to guarantee. Summary of the Invention

[0005] To address the shortcomings of existing laser probe spectral analysis techniques, this invention provides a method for rapid adaptive matching of spectral background using peak fitting. Peak fitting is used to automatically estimate the peak width, thereby achieving rapid adaptive identification and matching of the laser probe spectral background.

[0006] This invention provides a method for rapid adaptive matching of spectral background using spectral peak fitting, comprising:

[0007] Step S1: Collect LIBS spectra to obtain the original plasma spectrum;

[0008] Step S2: Select multiple spectral peak positions within the band to be matched, obtain the spectral peak intensity value and spectral peak width value for each spectral peak position, and fit the spectral peak intensity value and spectral peak width value for multiple spectral peak positions to obtain the fitting equation;

[0009] Step S3: Obtain the peak intensity value of the peak to be matched, obtain the peak width value of the peak to be matched according to the fitting equation, and set the single-sided scanning number, background length and step size according to the peak width value of the peak to be matched.

[0010] Step S4: Determine the scanning start points to the left and right based on the wavelength index value and peak width of the spectral peak to be matched. From the scanning start points, scan the background noise to the left and right sides of the original spectrum with the set background length and step size.

[0011] Step S5: Among all the background noise obtained by scanning, select the background spectral segment with the smallest noise value as the background of the spectral peak to be matched, take the smallest noise value as the noise value of the spectral peak to be matched, and take the average spectral intensity of the background spectral segment as the background value of the spectral peak to be matched.

[0012] Step S6: Calculate the signal-to-noise ratio and signal-to-background ratio of the peak to be matched based on the background value of the peak to be matched.

[0013] Based on the above technical solution, the present invention can also be improved as follows.

[0014] Optionally, step S2, selecting multiple spectral peak positions within the band to be matched, and obtaining the spectral peak intensity and spectral peak width at each position, includes:

[0015] Based on the spectral band where the peak to be matched is located, select multiple peak positions within the spectral band that have relatively complete and symmetrical peak shapes. Extract the spectral intensity value of each peak position, as well as the number of pixels from the center wavelength position of the peak to the leftmost lowest point of the peak and the number of pixels from the center wavelength position of the peak to the rightmost lowest point of the peak. Record these as the left half-peak width and the right half-peak width. The smaller value between the left half-peak width and the right half-peak width is taken as the width value of the highest peak.

[0016] Optionally, based on the spectral band where the peak to be matched is located, select multiple peak positions within the spectral band that have relatively complete and symmetrical peak shapes, including:

[0017] Based on the spectral band where the peak to be matched is located, select the positions of the largest peak, the middle peak, and the smallest peak within the spectral band that have relatively complete and symmetrical peak shapes.

[0018] Optionally, based on the spectral band where the peak to be matched is located, select multiple peak positions within the spectral band that have relatively complete and symmetrical peak shapes, including:

[0019] Based on the spectral band where the peak to be matched is located, select the positions of the largest peak, the middle peak, and the smallest peak within the spectral band that have relatively complete and symmetrical peak shapes.

[0020] Optionally, the fitting equation can be a linear equation, a quadratic or higher polynomial equation, an exponential function equation, a logarithmic function equation, or a composite function equation.

[0021] Optionally, step S3, obtaining the peak intensity value of the peak to be matched, obtaining the peak width value of the peak to be matched according to the fitting equation, and setting the single-sided scanning number, background length, and step size based on the peak width value of the peak to be matched, includes:

[0022] Step S31: Substitute the peak intensity value of the peak i to be matched into the fitting equation to obtain the peak width value W of the peak i to be matched. i Spectral peak width value W i This serves as the starting point for adaptive search background;

[0023] Step S32, based on the peak width value W i Set the number of single-sided scans K, background length L, and step size S for the spectral peak i to be matched, where the value of K should be greater than or equal to the peak width W. i The maximum value is the sum of the background length L.

[0024] Optionally, step S4, which determines the scanning start points to the left and right based on the wavelength index value and peak width of the spectral peak to be matched, and scans the background noise to the left and right sides of the original spectrum from the scanning start points with the set background length and step size, includes:

[0025] The wavelength index value minus the peak width value Wi of the peak to be matched is taken as the actual adaptive starting point on the left side of the peak, and the wavelength index value plus the peak width value Wi of the peak to be matched is taken as the actual adaptive starting point on the right side of the peak. With a background length of L and a step size of S, the background noise is scanned (K - Wi) times to the left and right respectively, resulting in Nscan = 2*(K - Wi) background noise values.

[0026] Optionally, step S5, selecting the background spectral segment with the smallest noise value from all the background noise obtained by scanning as the background of the peak to be matched, using the smallest noise value as the noise value of the peak to be matched, and using the average spectral intensity of the background spectral segment as the background value of the peak to be matched, includes:

[0027] In the obtained N scan Within a given background noise value, select the background spectral band S with the lowest noise value. min The background of the spectral peak to be matched is its noise value N. minThe noise value of the spectral peak to be matched is the average intensity value B of the background spectral band. min This is the background value.

[0028] Optionally, step S6, calculating the signal-to-noise ratio and signal-to-background ratio of the spectral peak to be matched based on the background value of the peak, includes:

[0029] Based on the intensity value I of the peak to be matched m Calculate the signal-to-noise ratio (SNR) and signal-to-background ratio (SNR) of the spectral peaks to be matched, where the SNR = I m / S min SBR = I m / B min .

[0030] The present invention provides a method for rapid adaptive matching of spectral background using spectral peak fitting, which has the following advantages and effects:

[0031] (1) The method proposed in this invention can identify and match the most suitable spectral background in the vicinity of LIBS spectra in batches, and the background length, search range, search efficiency and other parameters can be directly controlled by relevant parameters, which are convenient and quick to adjust.

[0032] (2) Through automatic and rapid background identification and matching, it is possible to quickly calculate LIBS spectral peak quality assessment parameters such as noise, background, signal-to-noise ratio and signal-to-background ratio.

[0033] (3) The method proposed in this invention can accurately and quantitatively identify, match and evaluate the spectral background of specific spectral peaks, greatly reducing the manual intervention process, thus ensuring the reliability and uniqueness of the analysis model during LIBS qualitative and quantitative analysis. Attached Figure Description

[0034] Figure 1 A flowchart of a method for rapid adaptive matching of spectral background using spectral peak fitting provided by the present invention;

[0035] Figure 2 Average spectral bands and main peak positions of the igneous rock samples to be identified;

[0036] Figure 3 Fitting points for spectral peak intensity and width, and the resulting fitting equations. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined with each other to form feasible technical solutions. Such combinations are not constrained by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0038] Figure 1 A flowchart of a method for rapid adaptive matching of spectral background using spectral peak fitting provided by the present invention is shown below. Figure 1 As shown, the method includes:

[0039] Step S1: Collect LIBS spectra to obtain the original plasma spectrum.

[0040] Understandably, LIBS spectroscopy is first performed to obtain plasma spectral data, denoted as N. mⅹn n=2, where the first column is the wavelength column, the second column is the spectral intensity value corresponding to each wavelength, and m represents the number of wavelengths.

[0041] Step S2: Select multiple spectral peak positions within the band to be matched, obtain the spectral peak intensity value and spectral peak width value for each spectral peak position, and fit the spectral peak intensity value and spectral peak width value for multiple spectral peak positions to obtain the fitting equation.

[0042] Step S21: Within the spectral band where the peak to be matched is located, select the highest peak with a relatively complete and symmetrical shape, extract its spectral intensity, and the number of pixels from the center wavelength of the highest peak to the lowest point of the leftmost (rightmost) peak. Record this as the peak width, and take the smaller of the width values ​​on the left and right sides as the final width value. Then, extract the intensity and width values ​​of the intermediate peaks and the smaller peaks in the same way.

[0043] Specifically, in step S21, the maximum, intermediate, and minimum values ​​of the selected spectral peak intensities (P... max P mid and P min The three points are denoted as A, B, and C, respectively. Their peak widths can be obtained from the original spectrum and denoted as W. max W mid W minThen the coordinates of the three points are (P) max W max ), (P mid W mid ), (P min W min ).

[0044] By fitting the coordinates of three or more points, a fitting equation is obtained, which represents the functional relationship between the intensity and width of the spectral peak.

[0045] Specifically, in step S21, the independent variables selected for fitting within the spectral band can be the intensity of the spectral peak, or the area, intensity ratio, half-width, and contour shape of the spectral peak; the extracted spectral peak intensity can be the intensity value and width of the three points of maximum, intermediate, and minimum values, or other spectral peaks that conform to the intensity and width variation rules, which can be three points or more than three points, and the same applies to other independent variables.

[0046] Step S22: Establish a fitting equation based on the changing trends of the intensity and width values ​​of the maximum, intermediate, and smaller spectral peaks. The resulting equation is denoted as F(X).

[0047] Specifically, in step S22, the equation used to fit the trend of spectral peak intensity and width changes can be a linear equation, or a polynomial, exponential function, logarithmic function, or composite function of the second or higher degree; it can be a single fitting equation within a spectral segment, or it can be a piecewise fitting of multiple fitting equations within a spectral segment based on the background conditions of the spectral segment.

[0048] Step S3: Obtain the peak intensity value of the peak to be matched, obtain the peak width value of the peak to be matched according to the fitting equation, and set the single-side scan number, background length and step size according to the peak width value of the peak to be matched.

[0049] Specifically, step S3 includes:

[0050] Step S31: Using the obtained fitting equation F(X), substitute the intensity value of the peak to be matched into the fitting equation to calculate the width value of the peak to be matched, where the width value of the i-th peak to be matched is denoted as W. i This width value serves as the starting point for the adaptive search background.

[0051] Step S32, based on the obtained peak width W i Set the number of single-sided scans K, background length L, and step size S for each spectral peak, where the value of K should be greater than or equal to the spectral peak width W. i The maximum value is the sum of the background length L.

[0052] Step S4: Determine the scanning start points to the left and right based on the wavelength index value and peak width of the peak to be matched. Scan the background noise to the left and right sides of the original spectrum from the scanning start points with the set background length and step size.

[0053] Specifically, the wavelength index value of the spectral peak to be matched is subtracted (or added) by the width W. i As the actual adaptive starting point to the left (right) of the spectral peak, with a background length of L and a step size of S, scan to the left (right) (K - W) i ) background noise, to obtain N scan = 2*(K - W i () background noise values.

[0054] Step S5: Among all the background noise obtained by scanning, select the background spectral segment with the smallest noise value as the background of the spectral peak to be matched, take the smallest noise value as the noise value of the spectral peak to be matched, and take the average spectral intensity of the background spectral segment as the background value of the spectral peak to be matched.

[0055] Specifically, in the obtained N scan Within a given set of background noise values, select the background spectral band S with the lowest noise value. min The background for the spectral peak to be matched is its noise value N. min The noise value of the spectral peak to be matched is the average intensity value B of the background spectral band. min This is the background value.

[0056] Step S6: Calculate the signal-to-noise ratio and signal-to-background ratio of the peak to be matched based on the background value of the peak to be matched.

[0057] Specifically, based on the intensity value I of the spectral peak to be matched m Including its noise value and background paper, calculate the signal-to-noise ratio (SNR) and signal-to-background ratio (SNR) of the spectral peak to be matched, where the SNR = I m / S min SBR = I m / B min .

[0058] The following specific embodiment illustrates the method for rapid adaptive matching of spectral background using spectral peak fitting provided by the present invention, which mainly includes the following steps:

[0059] Step 1: Acquire plasma spectra. In this example, a segment of the average spectrum from 12 igneous rock samples is used as an example to perform rapid adaptive matching of peaks and background. The selected spectral range is 334.53 - 363.68 nm, and the spectral data matrix N... mⅹn = N 679ⅹ2The spectral peaks to be matched for background analysis are 42 peaks within this spectral band, covering most of the more significant peaks in this band, such as... Figure 2 As shown.

[0060] Step two: Establish the functional relationship between spectral peak intensity and spectral peak width. Figure 2 Within the shown spectral range, three peaks—the maximum, median, and minimum values—are selected and denoted as points A, B, and C, with coordinates (3904.8, 8), (2468.1, 4), and (1453.9, 3), respectively. Based on the distribution trend of these three points, a linear fitting method is used for fuzzy fitting (high fitting accuracy is not required; a general trend is sufficient), resulting in the fitting equation F(X) = 0.002X – 0.45. Figure 3 As shown.

[0061] Step 3: Calculate background scan parameters. First, based on the fitting equation obtained in Step 2, input the intensity values ​​I = [3904.8, 1672.3,…, 1358.9] of all the peaks to be matched, and calculate the peak width value W = [7, 2, 5,…, 2] corresponding to each peak. This width value is used as the starting point for the adaptive background search. Simultaneously, set the number of single-side scans K for each peak. temp = 70, background length is 7 pixels, step size is 1 pixel.

[0062] Step four: Scan all spectral background on both sides of the center wavelength of the spectral peak. Table 1 shows the background matching results and spectral peak quality assessment table obtained using the method of this invention.

[0063] Table 1 Background matching results and spectral peak quality assessment obtained using the method of this invention.

[0064]

[0065] Table 1 or Figure 2Taking the first spectral peak λ = 334.88 nm as an example, the index value of this peak wavelength is 9 (i.e., it is located in the 9th pixel of the entire spectrum), and its peak width W1 = 7. Therefore, the actual adaptive starting position of the right-side scan should be 16, the background length is 7, and the step size is 1. First, scan the background noise to the right K1 = (70 – 7) = 63 times, moving one step to the right each time with the background length, and calculate the noise value of this background spectral segment. Similarly, scan the left side of the center wavelength of the spectral peak. Since the left side is close to the spectral segment boundary, it can only be scanned once according to the above configuration. Therefore, the left and right sides of the spectral peak are scanned a total of 64 times, resulting in 64 background noise values. (Note 1: When the left and right sides are close to the spectral segment boundary, the background spectral segment is selected based on the actual length to the boundary. If it is less than one background length, it is calculated as one approximation. Note 2: If it is not close to the spectral segment boundary, the left and right sides should be scanned a total of N times.) scan =2*(70 - 7) = 126 times).

[0066] Step 5: Select the band corresponding to the minimum noise value as the target background band. Among all the background noise values ​​obtained in Step 4, select the one with the smallest value as the actual background noise corresponding to that spectral peak. Taking the spectral peak with λ = 334.88 nm as an example, its minimum noise value N... min = 21.3, the background band S corresponding to this noise value min = 336.55 -336.81 nm, and then taking the average intensity of this background band as the background value, then its background value B min = 1450.7.

[0067] Step six: Calculate the spectral peak evaluation index. Combine this with the spectral peak intensity I. m Calculate the signal-to-noise ratio (SNR) and signal-to-background ratio (SNR) for each spectral peak, where SNR = I m / S min SBR = I m / B min In this embodiment, the intensity value I of the spectral peak at λ = 334.88 nm is... m = 3904.8. From step five, we have obtained that its noise value and background value are 21.3 and 1450.7, respectively. Therefore, according to the signal-to-noise ratio and signal-to-background ratio formulas, its signal-to-noise ratio and signal-to-background ratio are 183.4 and 2.7, respectively. The calculation results of its spectral peak evaluation index are shown in Table 2. Table 2 takes the first three spectral peaks in Table 1 as examples and uses the traditional manual method to match the background and spectral peak quality evaluation table.

[0068] Table 2. Background and spectral peak quality assessment table using traditional manual methods.

[0069]

[0070] Taking the first three spectral peaks in Table 1 as examples, traditional manual background matching was used for peak background matching. As can be seen from Tables 1 and 2, the matching results for the first two peaks differ significantly from those of the method provided in this invention, mainly because the selected background spectral segments are not the same. The signal-to-noise ratio and signal-to-background ratio obtained by the manual method are significantly weaker than those obtained by the method of this invention. This is because, when using the manual method, a relatively flat background spectral segment around the peak is generally selected based on the visual effect of the spectrum; however, the visual effect of the spectrum is not necessarily the optimal effect within a specific range. Furthermore, the matching results may vary from person to person when using manual matching, thus introducing uncertainty. Moreover, for the third spectral peak (336.11 nm) in Table 2, since there is a relatively flat background spectral segment in the vicinity of this peak, the visual effect is more obvious, and therefore the matching results using the method of this invention and the manual method are consistent. However, there is still a significant difference in matching efficiency between the two methods. Manual background matching for specific spectral peaks takes approximately several minutes (this time is slightly reduced when several peaks share the same background, but the average time is still in the minutes range). In contrast, the method of this invention for batch adaptive matching requires only a few seconds. Taking the 42 peaks in this embodiment as an example, the automated program execution time is only about 6-7 seconds, and the matching model and program are universal. Therefore, the method for rapid adaptive matching of spectral backgrounds using peak fitting provided by this invention has significant advantages in matching accuracy and efficiency compared to traditional manual background matching methods.

[0071] This invention provides a method for rapid adaptive matching of spectral background using peak fitting. LIBS spectral acquisition yields plasma spectral data. Within the band containing the peak to be matched, multiple peaks are selected based on their intensities and widths to obtain a fitting equation. This equation is used to determine the width of the peak to be matched, while simultaneously setting the number of single-sided scans, background length, and step size. Background noise is scanned to the left and right sides using the set background length and step size. Among all the obtained background noise values, the background spectral segment with the smallest noise value is selected as the background for that peak, and its value is used as the noise value of that peak. The average intensity of the spectral segment is used as the background value. Combining the peak intensities, the signal-to-noise ratio (SNR) and signal-to-background ratio (SPR) of each peak are calculated. This invention automatically estimates the peak width through peak fitting, achieving rapid adaptive identification and matching of the peak background, thus improving the analytical efficiency of laser probe microanalysis.

[0072] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0073] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0074] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for rapid adaptive matching of spectral background using spectral peak fitting, characterized in that, include: Step S1: Collect LIBS spectra to obtain the original plasma spectrum; Step S2: Select multiple spectral peak positions within the band to be matched, obtain the spectral peak intensity value and spectral peak width value for each spectral peak position, and fit the spectral peak intensity value and spectral peak width value for multiple spectral peak positions to obtain the fitting equation; Step S3: Obtain the peak intensity value of the peak to be matched, obtain the peak width value of the peak to be matched according to the fitting equation, and set the single-sided scanning number, background length and step size according to the peak width value of the peak to be matched. Step S4: Determine the scanning start points to the left and right based on the wavelength index value and peak width of the spectral peak to be matched. From the scanning start points, scan the background noise to the left and right sides of the original spectrum with the set background length and step size. Step S5: Among all the background noise obtained by scanning, select the background spectral segment with the smallest noise value as the background of the spectral peak to be matched, take the smallest noise value as the noise value of the spectral peak to be matched, and take the average spectral intensity of the background spectral segment as the background value of the spectral peak to be matched. Step S6: Calculate the signal-to-noise ratio and signal-to-background ratio of the peak to be matched based on the background value of the peak to be matched.

2. The method according to claim 1, characterized in that, Step S2 involves selecting multiple peak positions within the spectral band to be matched, and obtaining the peak intensity and peak width at each peak position, including: Based on the spectral band where the peak to be matched is located, select multiple peak positions within the spectral band that have relatively complete and symmetrical peak shapes. Extract the spectral intensity value of each peak position, as well as the number of pixels from the center wavelength position of the peak to the leftmost lowest point of the peak and the number of pixels from the center wavelength position of the peak to the rightmost lowest point of the peak. Record these as the left half-peak width and the right half-peak width, and use the smaller value between the left half-peak width and the right half-peak width as the width value of the peak position.

3. The method according to claim 2, characterized in that, Based on the spectral band where the peak to be matched is located, select multiple peak positions within the spectral band that have relatively complete and symmetrical peak shapes, including: Based on the spectral band where the peak to be matched is located, select the positions of the largest peak, the middle peak, and the smallest peak within the spectral band that have relatively complete and symmetrical peak shapes.

4. The method according to claim 1, characterized in that, The fitted equation is a linear equation, a quadratic or higher polynomial equation, an exponential function equation, a logarithmic function equation, or a composite function equation.

5. The method according to claim 1, characterized in that, Step S3 involves obtaining the peak intensity value of the peak to be matched, obtaining the peak width value of the peak to be matched according to the fitting equation, and setting the single-sided scanning number, background length, and step size based on the peak width value of the peak to be matched, including: Step S31: Substitute the peak intensity value of the peak i to be matched into the fitting equation to obtain the peak width value W of the peak i to be matched. i Spectral peak width value W i This serves as the starting point for adaptive search background; Step S32, based on the peak width value W i Set the number of single-sided scans K, background length L, and step size S for the spectral peak i to be matched, where the value of K is greater than or equal to the peak width W. i The maximum value is the sum of the background length L, and the step size is the number of pixels moved each time when scanning to the left or right.

6. The method according to claim 5, characterized in that, Step S4 involves determining the left and right scanning starting points based on the wavelength index value and peak width of the spectral peak to be matched, and scanning the background noise to the left and right sides of the original spectrum from the scanning starting points with the set background length and step size, including: The wavelength index value minus the peak width value Wi of the peak to be matched is taken as the actual adaptive starting point on the left side of the peak, and the wavelength index value plus the peak width value Wi of the peak to be matched is taken as the actual adaptive starting point on the right side of the peak. With a background length of L and a step size of S, the background noise is scanned (K - Wi) times to the left and right respectively, resulting in Nscan = 2×(K - Wi) background noise values.

7. The method according to claim 6, characterized in that, Step S5, selecting the background spectral segment with the smallest noise value from all the background noise obtained by scanning as the background of the peak to be matched, using the smallest noise value as the noise value of the peak to be matched, and using the average spectral intensity of the background spectral segment as the background value of the peak to be matched, includes: In the obtained N scan Within a given set of background noise values, select the background spectral band S with the lowest noise value. min The background for the spectral peak to be matched is its noise value N. min The noise value of the spectral peak to be matched is the average intensity value B of the background spectral band. min This is the background value.

8. The method according to claim 7, characterized in that, Step S6, which calculates the signal-to-noise ratio and signal-to-background ratio of the spectral peak to be matched based on the background value of the peak, includes: Based on the intensity value I of the peak to be matched m Calculate the signal-to-noise ratio (SNR) and signal-to-background ratio (SNR) of the spectral peaks to be matched, where the SNR = I m / S min SBR = I m / B min .

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