LIBS spectrometer wavelength calibration method based on standard substance

Abstract

The invention discloses an LIBS (laser-induced breakdown spectroscopy) spectrometer wavelength calibration method based on a standard substance, which comprises the following steps of: dividing a plurality of sub-wavebands according to wavelength orientation, and selecting a corresponding standard substance; exciting the standard substance by adopting an LIBS (Laser-induced Breakdown Spectroscopy) technology, and collecting an atomic emission spectral line; carrying out baseline correction on the screened spectral data by adopting airPLS; performing cubic spline interpolation on the window data, and reconstructing a spectrum curve; a two-stage Lorentz fitting algorithm is applied to realize precise modeling, and the central wavelength, the intensity, the half-peak width and the baseline offset value of each characteristic peak are extracted; and constructing a pixel-wavelength mapping model by combining the characteristic peak density distribution and the adaptive piecewise polynomial fitting strategy of the fitting residual error, and writing the pixel-wavelength mapping model into a pixel-wavelength mapping lookup table of the spectrograph. By exciting the standard substance to generate wide-spectrum and dense characteristic spectral lines and combining a two-stage Lorentz fitting algorithm and a self-adaptive piecewise polynomial fitting technology, the wavelength calibration precision of the spectrometer in a full-wave band range is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spectrometer calibration, and particularly relates to a LIBS spectrometer wavelength calibration method based on standard substances. BACKGROUND

[0002] In the prior art, a LIBS (Laser-Induced Breakdown Spectroscopy) spectrometer usually adopts a standard light source method for wavelength calibration, such as a mercury argon lamp, corresponding to characteristic spectral lines of 253.65 nm, 365.01 nm, 696.54 nm, such as a neon lamp, corresponding to characteristic spectral lines of 540.06 nm, 585.25 nm, and the spectral line coefficient, resulting in limited wavelength calibration accuracy. In this wavelength calibration method, the spectral lines are sparse, and depend on standard light sources such as mercury argon lamps and neon lamps, which require stable light source conditions in a specific environment as a reference. However, gas discharge light sources are easily affected by factors such as temperature changes, power fluctuations and use aging, and are prone to spectral line wavelength drift or intensity attenuation, resulting in unstable calibration results. When using a standard light source for calibration, the number of available characteristic spectral lines is limited, especially in the ultraviolet band (200-400 nm), which is sparse and difficult to cover the entire detection band. This uneven distribution of calibration points makes the overall fitting model susceptible to local data, resulting in insufficient fitting accuracy in some bands, limiting the quantitative accuracy of high-resolution spectrometers in the full wavelength range.

[0003] In the Chinese patent document with the publication number CN111998944A, a wide-band spectrometer accurate calibration device and method based on multiple light sources are disclosed, including a standard light source, a light source controller, a light switch and a driving circuit; the working principle is as follows: the standard light source is controlled by the light source controller to generate spectral lines of a specific wavelength; the standard light source is connected to any channel of the light switch by an optical fiber, the light switch selects a channel according to the control instruction, and the output light of a selected standard light source passes through the adjustable optical attenuator; the adjustable optical attenuator adjusts the attenuation value of the output power of the standard light source, and the power enters the spectrometer; the optical signal is attenuated by the adjustable optical attenuator, enters the light switch, and the optical signal enters the standard light power meter, measures the real light power of the optical signal entering the spectrometer, and then obtains the polynomial coefficient according to the characteristic peak position matching and the fitting of the wavelength calibration curve. In the above disclosed technology, the wavelength calibration of the wide-band spectrometer is realized by the standard light source and the light source controller, but the problem of few spectral lines of the standard light source and uneven distribution of calibration points cannot be effectively overcome, the wavelength calibration accuracy of the high-resolution spectrometer in the full wavelength range is limited by the fluctuation of the spectral peak position and the accuracy of the fitting curve, and the overall fitting model is susceptible to local data. SUMMARY

[0004] The application aims to provide a LIBS spectrometer wavelength calibration method based on standard substance, which generates wide-spectrum and dense characteristic spectral lines by exciting standard substance, and improves the accuracy of wavelength calibration in the full waveband range of the spectrometer by combining an improved two-stage Lorentz fitting algorithm and an adaptive segmented polynomial fitting technology combining characteristic peak density distribution and fitting residual.

[0005] To solve the above technical problems, the embodiment of the application provides a technical solution as follows: a LIBS spectrometer wavelength calibration method based on standard substance, comprising the following steps: step S1: according to the detection capability of the spectrometer in the ultraviolet to near-infrared waveband, a plurality of sub-wavebands are divided, and standard substance capable of generating rich characteristic spectral lines in each sub-waveband is selected; step S2: the standard substance is excited by using the LIBS technology, the integral start time of the spectrometer is controlled by a time sequence controller, the delay is set to avoid the high-intensity continuous background radiation of the initial stage of the plasma, and stable and clear atomic emission spectral lines are collected as original spectral data; step S3: the collected original spectral data is screened, and the airPLS is used to correct the baseline of the screened spectral data; step S4: after the baseline correction, the main peak center pixel of each spectral line is taken as a base point, the data in the range of ±3-10 pixels is taken as window data, the window data is taken as an interpolation input interval, point interpolation is performed at a density of 2-5 times, cubic spline interpolation is performed, and the spectral curve is reconstructed; step S5: the spectral peak data after the reconstructed spectral curve is applied to an improved two-stage Lorentz fitting algorithm to realize accurate modeling: including the first stage of single-peak fitting, if the fitting residual exceeds the extreme value of 1.5 times the residual standard deviation, the second stage of double-peak fitting is triggered; the second peak parameter is dynamically generated from the residual, and the model complexity is controlled through the fitting residual drop rate, and finally the center wavelength, intensity, half-peak width and baseline offset value of each characteristic peak are extracted; step S6: a plurality of groups of spectral curves with a peak intensity saturation degree of 80%-90% are selected, the maximum half-peak width value in each group of spectral curves is extracted, and the average value is taken as a resolution evaluation index of the spectrometer; step S7: based on the extracted characteristic peak center wavelength and the corresponding pixel position, an adaptive segmented polynomial fitting strategy combining characteristic peak density distribution and fitting residual is used to construct a pixel-wavelength mapping model, including the following steps: based on the resolution, the characteristic peak is matched and locked, the pixel interval corresponding to the successfully matched characteristic spectral peak data is divided into a plurality of sub-sections, and the sub-sections are combined or subdivided according to the characteristic peak coverage rate and the resolution threshold; polynomial fitting is performed in each sub-section, and when the fitting residual RMSE exceeds the threshold, a regularization and / or model order promotion strategy is introduced to optimize the fitting stability; finally, each segment fitting parameter is written into the pixel-wavelength mapping lookup table of the spectrometer, and full-range high-precision wavelength calibration of the spectrometer is realized.

[0006] Further, in step S1, the standard substance is standard metal target copper and tin.

[0007] Further, in step S2, the original spectral data includes no less than 10 groups of spectral data repeatedly collected at the same excitation position for each standard substance.

[0008] Further, in step S3, the baseline correction of the screened spectral data by airPLS includes: initializing the baseline as the low-frequency trend of the original spectrum; and adjusting the weight coefficient by iteration to suppress the influence of the peak region on the baseline fitting, to obtain a smooth baseline signal and complete the baseline correction by deducting from the original spectral data.

[0009] Further, in step S5, the first-stage single-peak fitting includes establishing a single-peak model based on local window data, and the mathematical expression is: , wherein A is the peak intensity, is the center wavelength, is the half-peak width, and B is the baseline offset.

[0010] Further, in step S5, the second-stage double-peak fitting includes a double-peak Lorentz model fitting, and the mathematical expression is: ; wherein the second peak parameters are dynamically determined by the residual extreme value: the center wavelength initial value is taken from the position corresponding to the residual maximum value, the intensity initial value is set as about 2 times the residual peak value, and the half-peak width inherits from the main peak parameter.

[0011] Further, in step S6, the average value is calculated as the resolution evaluation index of the spectrometer, and the mathematical expression of the resolution is: ; wherein is the resolution, N is the total number of data groups, is the maximum half-peak width of the selected characteristic peak in the ith data group.

[0012] Further, in step S7, the characteristic peak matching and locking based on the resolution specifically includes searching for the center wavelength of each fitting peak in the standard spectral line library, and calculating the candidate matching items within the tolerance range according to the resolution matching threshold; wherein the resolution matching threshold is: An ordering function including an intensity weight coefficient 0.7 and a wavelength deviation weight coefficient 0.3 is constructed, and a strong peak signal is preferentially selected for matching and locking. The characteristic peak matching process implements bidirectional exclusive constraints, that is, each fitting peak uniquely corresponds to a standard wavelength and each standard wavelength is matched at most once.

[0013] Further, in step S7, the characteristic peak coverage criterion is: the entire effective pixel range is initially segmented into multiple candidate subsegments, and the number of effective standard spectral lines matched in each candidate subsegment is counted and dynamically set the minimum number of spectral lines covered according to the length of each segment , if merge the segment with one of its adjacent segments until the new sub-segment generated after the merging wherein, wherein: is the length of the current segment in pixels; is the average pixel distance of the characteristic spectral lines; is the reference spectral line density.

[0014] Further, in step S7, the fitting residual criterion in the adaptive segmented polynomial fitting strategy is: performing a cubic polynomial fitting on the sub-segment that meets the characteristic peak coverage requirement, and calculating the RMSE of the fitting result of the sub-segment, when the RMSE is greater than a set threshold, according to the pixel span of the sub-segment the following adjustment strategy is performed: if pixels, the sub-segment is evenly divided into two new sub-segments, and fitting is performed again respectively; otherwise, the original segment is maintained unchanged, the order of the fitting model is increased from cubic to quartic, and an L2 regularization term is introduced to constrain the fitting parameters.

[0015] Compared with the prior art, the LIBS spectrometer wavelength calibration method based on a standard substance provided by the present application generates dense characteristic spectral lines covering the ultraviolet to near-infrared waveband by exciting the standard substance, ensures that there are enough calibration points in each sub-waveband, effectively solves the problems of sparse characteristic spectral lines in the ultraviolet waveband, difficulty in covering the entire detection waveband, and uneven distribution of calibration points in the prior art, and significantly improves the quantitative accuracy of a high-resolution spectrometer in the entire waveband range; by using the method of combining cubic spline interpolation and improved Lorentz fitting, the volatility of the spectral peak position is effectively reduced, the continuity and stability of the spectral data are improved, the difficulty in positioning the peak position caused by the volatility of the spectral peak position, the variety of peak shapes and the uncertainty of the morphology in the prior art is effectively solved, and the precision and reliability of the wavelength calibration of the spectrometer are significantly improved; by using the adaptive segmented polynomial fitting technology based on the joint criterion of characteristic peak density and fitting residual, combined with the cubic or quartic polynomial model and the L2 regularization optimization mechanism, the fitting error caused by local nonlinearity is effectively reduced, and the consistency and accuracy of the wavelength calibration of the spectrometer in the entire waveband range are improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] One or more embodiments are illustrated by way of example in the figures that form a part of this patent document, these example are not intended to limit the embodiments, elements having the same reference numbers in the figures represent similar elements, unless otherwise indicated, the figures in the drawings do not constitute a proportional limitation.

[0017] Figure 1 Schematic diagram of the steps of the wavelength calibration method of LIBS spectrometer based on standard substances in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the LIBS device in an embodiment of the present invention; Figure 3 Schematic diagram of the comparison results of the spectral peaks of the characteristic peaks of the copper element before and after interpolation in an embodiment of the present invention; Figure 4 Schematic diagram of the single peak fitting result of the Lorentzian function of the characteristic peak of the copper element in an embodiment of the present invention; Figure 5 Schematic diagram of multi-peak Lorentz fitting results in an embodiment of the present invention; Figure 6 It is a schematic diagram of the overall polynomial fitting result in the prior art; Figure 7 Schematic diagram of the adaptive piecewise polynomial fitting result in an embodiment of the present invention; Figure 8 Schematic diagram of the comparison curve between the segmented fitting residual in the embodiment of the present invention and the overall fitting residual in the prior art. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the present invention more apparent, various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will appreciate that many technical details are provided in various embodiments of the present invention to facilitate a better understanding of the present application. However, even without these technical details and the various variations and modifications based on the following embodiments, the technical solutions claimed in the claims of this application can be implemented.

[0019] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0020] like Figure 1 As shown, one embodiment of the present invention relates to a LIBS spectrometer wavelength calibration method based on a standard substance, comprising the following steps: Step S1: According to the detection capability of the spectrometer in the ultraviolet to near infrared band, a plurality of sub-bands are divided, and standard substances capable of generating rich characteristic spectral lines in each sub-band are selected to realize the coverage and calibration support of the entire working waveband of the spectrometer. In an example, the standard substances selected are metal targets of copper and tin. The metal targets of copper and tin can generate dense characteristic spectral lines in multiple sub-bands under the excitation of LIBS technology. The problems of sparse characteristic spectral lines in the ultraviolet band, difficulty in covering the entire detection waveband, and uneven distribution of calibration points in the traditional method are effectively solved, which significantly improves the quantitative accuracy of the high-resolution spectrometer in the full waveband range, reduces the dependence on specific external standard light sources, and reduces the cost and inconvenience caused by light source replacement and maintenance.

[0021] Step S2: When the standard substance is excited by LIBS technology, the integration start time of the spectrometer is controlled by a time sequence controller, and a delay is set to avoid the high-intensity continuous background radiation of the initial plasma, so that stable and clear atomic emission spectral lines are collected as original spectral data; preferably, each standard substance is repeatedly collected for not less than 10 groups of spectral data at the same excitation position to enhance statistical stability and suppress random errors.

[0022] Step S3: The original spectral data collected are screened, and the screened spectral data are baseline corrected by airPLS. Step S4: After baseline correction, the main peak center pixel of each spectral line is taken as a base point, the data in the range of ±3-10 pixels are taken as window data, the window data are taken as an interpolation input interval, point interpolation is performed at 2-5 times the density, cubic spline interpolation is performed, and the spectral curve is reconstructed. Step S5: The spectral peak data after the reconstructed spectral curve are applied to an improved two-stage Lorentz fitting algorithm to realize accurate modeling: including a first stage for single peak fitting, if there is an extreme value in the fitting residual that exceeds 1.5 times the standard deviation, a second stage of double peak fitting is triggered; the second peak parameter is dynamically generated from the residual, and the model complexity is controlled by the fitting residual drop rate, and finally the center wavelength, intensity, half peak width, baseline offset value of each characteristic peak are extracted; Step S6: A plurality of groups of spectral curves with peak intensity saturation of 80%-90% are selected, the maximum half peak width value in each group of spectral curves is extracted, and the average value is calculated as the resolution evaluation index of the spectrometer. Step S7: Based on the extracted characteristic peak center wavelength and corresponding pixel position, combined with the adaptive segmented polynomial fitting strategy of characteristic peak density distribution and fitting residual, the pixel-wavelength mapping model is constructed, including the following steps: based on the above resolution, the characteristic peak matching lock is performed, the pixel interval corresponding to the successfully matched characteristic peak data is preliminarily divided into several subsegments, and the segment merging or subdivision is performed according to the characteristic peak coverage rate and the resolution threshold; the polynomial fitting is performed in each subsegment, and when the fitting residual RMSE exceeds the threshold, the regularization and / or model order promotion strategy is introduced to optimize the fitting stability; finally, the fitting parameters of each segment are written into the pixel-wavelength mapping lookup table of the spectrometer, and the full-segment high-precision wavelength calibration of the spectrometer is realized.

[0023] Through the above steps, the LIBS spectrometer wavelength calibration method based on standard substance realizes high-density calibration point coverage in the whole working waveband and accurate analysis of overlapping spectral lines, and solves the problems of sparse spectral line dependence, insufficient detection stability and limited fitting accuracy of complex spectral lines in traditional methods.

[0024] In one embodiment, a LIBS spectrometer wavelength calibration method based on standard substance is involved, wherein the working full waveband wavelength range of the spectrometer is 200-900 nm, based on the spectral line coverage density, experimental operability and chemical stability requirements, the working full waveband is divided into multiple sub wavebands and standard substances are selected, the standard substances include metal targets copper and tin, and nitrogen and oxygen elements contained in air; as shown in Table 1, the sub waveband and standard substance element, characteristic peak corresponding record table: Table 1 Sub waveband and standard substance element, characteristic peak corresponding record table In one embodiment, a LIBS spectrometer wavelength calibration method based on standard substance is involved, which excites standard substance to generate plasma by LIBS (laser-induced breakdown spectroscopy) technology, and collects corresponding spectral data based on the detection working waveband and characteristics of the spectrometer to be calibrated. As shown in Figure 2As shown, the LIBS apparatus comprises a laser excitation unit, which generates high-energy pulsed laser light to induce plasma on the sample surface. The unit includes a laser, a reflector, and a focusing lens assembly; an optical acquisition unit, which collects the plasma emission light and transmits it to a spectrometer. In this embodiment, optical fiber transmission is used; a spectrometer, which disperses the plasma light into a spectrum and detects it; a computer, which is used for data processing and control, such as spectral analysis and quantitative modeling; and a timing controller, which is responsible for precisely coordinating the timing of laser pulses, spectrum acquisition, detector gating, and environmental control. During the acquisition of raw spectral data, the plasma excitation intensity and signal-to-noise ratio are optimized by adjusting the laser energy, focused spot size, and detector integration time. A digital delayed trigger mechanism is used to control the integration start time, avoiding the continuous background radiation in the initial plasma stage and acquiring stable and clear atomic emission spectra. For the spectrometer to be calibrated, copper and tin target samples are collected separately to ensure stable signal intensity and a satisfactory signal-to-noise ratio for the spectral data. In practice, the surfaces of the copper and tin targets must be flat and free of oxide layers or impurities to improve excitation efficiency and data quality. For each standard substance, no less than 10 sets of spectral data are repeatedly collected at the same excitation position to ensure the statistical reliability of the data. This method of collecting multiple sets of data can not only reduce the impact of random errors, but also provide more reference data points for subsequent calibration, thereby improving the stability of the calibration results. By optimizing the laser excitation parameters, such as adjusting the laser energy and the focus spot size, the plasma excitation intensity is adapted to the linear response range of the spectrometer to avoid signal saturation or insufficient signal-to-noise ratio. After the laser pulse is triggered, the integration start time of the spectrometer is controlled by a digital delay generator, and a delay is set to avoid the high-intensity continuous background radiation in the early stage of the plasma. The background radiation at this stage usually masks the characteristic spectral lines, resulting in a decrease in data quality. By setting an appropriate delay, it can be ensured that stable atomic emission lines are collected, thereby improving the reliability of the spectral data and the calibration accuracy.

[0025] After the data collection is completed, the collected spectral data is preliminarily screened to ensure that the signal intensity and signal-to-noise ratio of each group of data are within a preset reasonable range. By comparing the intensity changes of the spectral characteristic peaks under different excitation conditions, the best excitation parameter combination is selected to ensure the stability and accuracy of the subsequent calibration process. For the screened spectral data, the airPLS (Adaptive Iteratively Reweighted Penalized Least Squares) method is used for baseline correction. This method dynamically distinguishes the effective peak signal and the baseline background in the spectral data through an adaptive iterative weighting strategy, and gradually fits the baseline profile. The specific process includes: initializing the baseline as the low-frequency trend of the original spectrum, adjusting the weight coefficient through iteration to suppress the influence of the peak region on the baseline fitting, and finally obtaining a smooth baseline signal and subtracting it from the original data, effectively eliminating the interference of instrument dark current, stray light and continuous background radiation.

[0026] In one embodiment, a LIBS spectrometer wavelength calibration method based on standard substances is involved. In the LIBS spectral wavelength calibration process, due to factors such as laser pulse instability, plasma spatial distribution difference, optical system perturbation, and detector sampling accuracy, even if the same standard substance is repeatedly collected at the same position, slight fluctuations in the spectral peak center position often occur. Although this fluctuation is small, it still introduces an error that cannot be ignored in high-precision wavelength calibration scenarios. The stability of the spectral line peak position directly affects the accuracy of the fitting result. To improve the reconstruction accuracy of the spectral line and reduce the interference of the above fluctuations, and to provide a high-resolution data basis for subsequent Lorentz fitting, the embodiment provided in the present application introduces a cubic spline interpolation process after baseline correction of the spectral data. This method uses a cubic spline function with first and second derivative continuity to reconstruct a continuous curve from the original discrete spectrum, significantly improving the data density while preserving the spectral peak profile. Specifically, it includes: spectral peak window selection: taking the center pixel of the main peak of each spectral line as a reference, the data within ±3-10 pixels of the main peak are selected as the interpolation input interval; constructing a spline function: based on the wavelength and corresponding intensity points in the selected interval, a cubic spline curve is constructed to ensure local smoothness and physical reasonableness; high-density interpolation: the original wavelength interval is interpolated with 2-5 times the accuracy (density) to construct a more continuous and smooth spectral curve.

[0027] Figure 3The results show a comparison of the copper characteristic peak (236.989 nm) before and after interpolation. The left figure shows the original spectrum collected five times. The peak shape exhibits some deviation, with an RSD of 0.01% for the main peak. The right figure shows the spectrum after cubic spline interpolation. The peak edge transitions are smoother, the main peak positions of multiple repeated spectra are highly consistent, and the RSD significantly decreases to 0.0004%, effectively improving the consistency and symmetry of the peak shape. Cubic spline interpolation not only improves the accuracy of spectral reconstruction and suppresses peak fluctuations caused by sampling errors, but also provides a more reliable basis for subsequent Lorentz function fitting, further promoting the improvement of wavelength calibration accuracy.

[0028] One embodiment involves a wavelength calibration method for a LIBS spectrometer based on reference materials. In complex spectra, the presence of overlapping peaks can lead to errors in the calculation of the central wavelength. This invention utilizes an improved Lorentz fitting algorithm to resolve and locate complex spectral peaks. Its technical features are primarily reflected in a dynamic parameter initialization mechanism, a residual-driven bimodal discrimination strategy, and an intensity-first bidirectional matching principle. The specific implementation process is as follows: Dynamic baseline calculation and adaptive threshold setting are used to coarsely locate characteristic peaks in collected spectral intensity data. The 25th percentile of the spectral intensity distribution is used as the baseline, and the significance threshold is dynamically calculated based on the difference between the 95th and 25th percentiles of the spectral intensity. This effectively overcomes the problem of missing weak peaks with traditional fixed thresholds. Peak position determination also involves simultaneous analysis of half-width (FWHM) characteristics, combined with a window edge protection mechanism to eliminate spurious peak signals caused by noise.

[0029] For each candidate peak region, a two-stage Lorentz fitting process is performed. First, a single-peak model is established based on the local window data, and its mathematical expression is: .

[0030] Where A is the peak intensity, is the central wavelength, is the half-peak width, and B is the baseline offset. Parameter initialization uses a dynamic calculation mechanism: the initial center wavelength is determined by statistical analysis of window data , baseline value Take the average of the intensity of the three points at the beginning and end of the window, and the half-peak width Automatically generated by detecting the wavelength span at 50% peak height in the raw data.

[0031] like Figure 4 Figure 2 shows the Lorentzian function single-peak fitting result for the copper characteristic peak (236.989 nm). After cubic spline interpolation and Lorentzian fitting, the fitting curve closely matches the actual spectrum, accurately restoring the peak position and morphological characteristics.

[0032] When there is a significant extreme value in the single-peak fitting residual that exceeds 1.5 times the residual standard deviation, a double-peak Lorentz model fitting is triggered, whose mathematical expression is: .

[0033] The second peak parameters are dynamically determined by the residual extreme value: the center wavelength initial value is taken from the position corresponding to the residual maximum, and the intensity initial value is set to be about 2 times the residual peak value, and the half-peak width inherits from the main peak parameters. By constraining the double-peak fitting residual sum of squares to be reduced by more than about 20% compared to the single-peak, the model complexity is self-optimized.

[0034] As Figure 5 shown in the comparison chart of the multi-peak Lorentz fitting results realized based on the above strategy. In the figure, taking the two overlapping characteristic peaks of copper element—224.262 nm and 224.7 nm as an example, the original spectral data and the separation fitting results of the double-peak Lorentz model are shown. Through double-peak fitting, two closely adjacent peaks are successfully separated, effectively solving the fitting error and peak position deviation problem caused by peak overlap in single-peak fitting. The fitting curve is highly consistent with the experimental data, further verifying the accuracy and robustness of the method in complex spectral line analysis, providing a reliable peak parameter basis for subsequent quantitative analysis. Through this step, we get the basic parameters of each candidate peak, including peak intensity, center wavelength, half-peak width, and baseline offset. These parameters provide an important basis for further accurate analysis of the spectrum. In particular, the half-peak width (FWHM), which not only reflects the width of the spectral peak, but also can be used as an important evaluation index of resolution. By measuring the half-peak width of different peaks, we can intuitively understand the resolution capability of the instrument. The higher the resolution, the smaller the half-peak width. This index has important significance in practical application, especially when the spectral peaks are close to each other, it can provide a basis for judging whether they can be effectively distinguished.

[0035] In one embodiment, a LIBS spectrometer wavelength calibration method based on standard substances is involved. The half-peak width (FWHM) of each candidate peak, the FWHM data can be used to quantify the resolution of the spectrometer. In order to ensure the accuracy of the measurement results, 5 sets of test data of each standard sample must be selected, and the conditions of these test data should be consistent, especially the peak intensity saturation degree should be controlled in a moderate range (for example, 80%-90%). This range ensures that the spectral line will not be distorted due to excessive saturation, thereby affecting the evaluation of the resolution.

[0036] After obtaining the spectral lines with obvious characteristics in the standard sample, the half-peak width of each spectral line can be further analyzed. The half-peak width refers to the width when the spectral line intensity reaches half of the maximum value, which directly reflects the degree of detail of the spectral line. In order to quantify the resolution, statistical analysis can be performed on the half-peak width values in multiple data sets. Among the half-peak width values of the selected characteristic spectral lines in each data set, we should focus on the maximum half-peak width value, because the maximum half-peak width value will represent the worst case of resolution in that data set. That is, the maximum half-peak width value of each data set reflects the resolution capability of that test. The following formula can be used to calculate the resolution: .

[0037] where, is the resolution, N is the total number of data sets, is the maximum half-peak width of the selected characteristic peak in the i-th data set. By this formula, by calculating the average of these maximum half-peak widths, we obtain the average resolution of the spectrometer under that condition. The calculation of the resolution of the instrument provides a reliable basis for further analysis of characteristic peaks, and the smaller the value of the resolution, the higher the resolution of the instrument, which can effectively distinguish closer spectral peaks. Therefore, by calculating the resolution, the resolution capability of the instrument can be accurately evaluated, and a reference is also provided for characteristic spectral line matching.

[0038] In one embodiment, a LIBS spectrometer wavelength calibration method based on standard substances is disclosed. In order to improve the fitting accuracy and robustness of the spectrometer wavelength calibration, the present application proposes an adaptive segmented polynomial fitting strategy based on the joint criterion of characteristic peak density and fitting residual in the pixel-wavelength mapping relationship construction stage, which is used to replace the traditional overall cubic polynomial fitting scheme. As shown in Figure 6 the traditional overall polynomial fitting result, the fitting curve is relatively smooth but the fitting effect in the local nonlinear region is poor, and the overall RMSE is high.

[0039] As shown in Figure 7 the adaptive segmented polynomial fitting result of the present application. The spectrometer wavelength calibration method provided by the present application ensures the continuity of global fitting, and adaptively segments according to the peak density and fitting residual, locally uses cubic or quartic polynomial fitting, significantly improves the local fitting accuracy, significantly reduces the RMSE, enhances the adaptability to local nonlinear sections, and is particularly suitable for complex spectral systems with uneven spectral line density or dramatic response curve fluctuations. The specific process is as follows: Characteristic peak matching: For each fitted peak center wavelength, search for candidate matching items within the tolerance range calculated according to the resolution index in the standard spectral line library. According to the calculated resolution matching threshold: .

[0040] An intensity-driven prioritization strategy was employed, with a ranking function including an intensity weighting factor of 0.7 and a wavelength deviation weighting factor of 0.3, prioritizing strong peaks for matching and locking. A bidirectional exclusive constraint was implemented during the matching process to ensure that each detected peak uniquely corresponded to a standard wavelength and that each standard wavelength was matched at most once, eliminating the problem of multiple mappings in dense spectral regions.

[0041] Characteristic peak coverage criterion: First, the successfully matched characteristic peak data are sorted according to their corresponding pixel positions as the basic data for fitting. Then, the entire valid pixel range is preliminarily divided into multiple candidate sub-segments, initially set to 100-300 pixels per segment. The number of valid standard spectral lines matched in each segment is counted, and the minimum spectral line coverage number is dynamically set according to the length of each segment: .

[0042] in: is the pixel length of the current segment; is the average pixel spacing of the characteristic spectral lines; It is the reference spectral line density, which can be set according to the instrument characteristics, such as 0.01–0.02 / pixel; get Finally, count the number of valid standard spectral lines matched in each segment ,like , indicating that the data of this segment is not sufficient to support a stable polynomial fitting. In this case, merge this segment with the one with the larger number of spectral lines in its left or right neighboring sub-segment until the total number of spectral lines after merging is not less than .

[0043] Fitting residual criterion: Perform cubic polynomial fitting on each sub-segment that meets the characteristic peak coverage requirements, and use the least squares method to determine the fitting coefficients , , , , and calculate the RMSE (Root Mean Square Error) of the fitting result of this segment. When the RMSE is less than the threshold, the wavelength value corresponding to the pixel is calculated based on the fitting result to form a pixel-wavelength mapping; when the RMSE is greater than the threshold, the pixel span of the sub-segment is calculated based on the wavelength value corresponding to the pixel. Execute the following adjustment strategy: If If the subsegment is evenly divided into two new subsegments, the fitting is repeated for each subsegment. Otherwise, the original subsegment remains unchanged, the fitting model order is increased from cubic to quadratic, and an L2 regularization term (hyperparameter λ can be set to 0.0001 to 0.001) is introduced to constrain the fitting parameters to improve stability. The RMSE threshold is preferably set to 0.002-0.005.

[0044] Segmented polynomial fitting: After the above process is completed, all sub-segments are judged, merged and subdivided, and finally determined as valid fitting segments. A pixel-wavelength mapping model is independently constructed in each segment, usually using the following cubic polynomial expression: .

[0045] in is the pixel position, is the fitting wavelength value, , , , For the After the fitting is completed, the boundary pixels of each segment, the corresponding polynomial coefficients and the pixel interval index are written into the spectrometer's pixel-wavelength mapping lookup table (LUT) to achieve high-precision conversion from the original pixel position to the physical wavelength value. Figure 8 As shown in FIG, the comparison curve of the overall fitting residual and the segmented fitting residual is shown. The segmented fitting residual used in the spectrometer wavelength calibration method provided by the present invention has a smaller fluctuation range and is closer to zero, which verifies the superior performance of the method.

[0046] The wavelength calibration method of the LIBS spectrometer based on the standard substance provided in the embodiment of the application generates wide-spectrum dense characteristic spectral lines covering the ultraviolet to near-infrared band by exciting the standard substance, ensures that there are enough calibration points in each sub-band, effectively solves the problems of sparse characteristic spectral lines in the ultraviolet band, difficulty in covering the entire detection band and uneven distribution of calibration points in the prior art, significantly improves the quantitative accuracy of the high-resolution spectrometer in the full-band range, reduces the dependence on specific external standard light sources, and reduces the cost and inconvenience caused by light source replacement and maintenance; the method combining cubic spline interpolation and improved Lorentz fitting is adopted, wherein the cubic spline interpolation effectively reduces the volatility of the spectral peak position, improves the continuity and stability of the spectral data; the improved Lorentz fitting can accurately obtain the peak shape of the spectral peak, ensure that the fitting result truly reflects the physical form of the spectral peak, accurately determine the key parameters such as the peak position, intensity and half-peak width, effectively solve the difficulty in positioning the peak position caused by the volatility of the spectral peak position, the diversity of the peak shape and the uncertain form in the prior art, and significantly improve the accuracy and reliability of the wavelength calibration of the spectrometer; the two-stage fitting technology is adopted, that is, the single-peak model parameters are dynamically initialized first, and then the double-peak model fitting is triggered based on residual analysis, effectively processes the overlapping spectral lines in the spectrum, accurately locks the primary and secondary peak center wavelengths, significantly improves the analysis accuracy of the complex spectrum, enhances the accuracy and stability of the calibration, and effectively solves the problem of decreased calibration accuracy caused by spectral peak overlap in the prior art; the adaptive segmented fitting strategy based on the joint criterion of characteristic peak density and fitting residual is adopted, combined with the cubic or quartic polynomial model and the L2 regular optimization mechanism, the fitting error caused by local nonlinearity is effectively reduced, the overall calibration accuracy and consistency are improved, and the problem of limited calibration accuracy caused by factors such as grating dispersion nonlinearity, detector edge response distortion and optical path geometry deviation in the prior art is solved; by constructing the one-to-one mapping lookup table (LUT) of the pixel position and the physical wavelength and writing it into the device parameter unit, the programmatic calling and real-time compensation of the calibration model are realized, the wavelength conversion in the subsequent spectral detection process is ensured to be efficient and accurate, and the seamless integration of calibration and detection is promoted.

[0047] Although the present application has been disclosed with the preferred embodiments as above, it is not intended to limit the present application, and any person skilled in the art can make some modifications and improvements without departing from the spirit and scope of the present application, therefore the protection scope of the present application shall be defined by the claims.

Claims

1. A LIBS spectrometer wavelength calibration method based on standard substances, characterized in that: The following steps are involved: Step S1: Divide the spectrum into multiple sub-bands based on the detection capability of the spectrometer in the ultraviolet to near-infrared band, and select a standard substance that can produce rich characteristic spectral lines in each sub-band; Step S2: Using LIBS technology to excite the standard substance, controlling the integration start time of the spectrometer through a timing controller, setting a delay to avoid the high-intensity continuous background radiation in the early stage of the plasma, and collecting stable and clear atomic emission lines as raw spectral data; Step S3: Filter the collected raw spectral data and perform baseline correction on the filtered spectral data using airPLS; Step S4: After baseline correction, the data within the range of ±3-10 pixels of the main peak center pixel of each spectral line is intercepted as the base point as the window data, and the window data is used as the interpolation input interval. Point interpolation is performed at 2-5 times the density, and cubic spline interpolation is performed to reconstruct the spectral curve; Step S5: Applying an improved two-stage Lorentz fitting algorithm to the peak data after reconstructing the spectral curve to achieve accurate modeling: including performing a single peak fitting in the first stage. If the fitting residual exceeds the extreme value of 1.5 times the residual standard deviation, the second stage of bimodal fitting is triggered; the second peak parameters are dynamically generated from the residual, and the model complexity is controlled by the fitting residual decrease rate, and finally the central wavelength, intensity, half-peak width, and baseline offset value of each characteristic peak are extracted; Step S6: Select multiple groups of spectral curves with peak intensity saturation between 80% and 90%, extract the maximum half-peak width in each group of spectral curves, and calculate the average value as the resolution evaluation index of the spectrometer; Step S7: Based on the extracted characteristic peak center wavelength and the corresponding pixel position, combined with the characteristic peak density distribution and the adaptive piecewise polynomial fitting strategy of the fitting residual, a pixel-wavelength mapping model is constructed, including the following steps: characteristic peak matching and locking are performed based on the resolution, the pixel interval corresponding to the successfully matched characteristic spectrum peak data is divided into several sub-segments, and the segments are merged or subdivided according to the characteristic peak coverage and the resolution threshold; polynomial fitting is performed in each sub-segment, and when the fitting residual RMSE exceeds the threshold, regularization and / or model order improvement strategy is introduced to optimize the fitting stability; finally, the fitting parameters of each segment are written into the pixel-wavelength mapping lookup table of the spectrometer to realize the full-band high-precision wavelength calibration of the spectrometer.

2. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 1, wherein In step S1, the standard substances are standard metal target materials copper and tin.

3. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 1, wherein In step S2, the original spectral data includes no less than 10 groups of spectral data repeatedly collected at the same excitation position for each standard substance.

4. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 1, wherein In step S3, the baseline correction of the filtered spectral data using airPLS includes: initializing the baseline to the low-frequency trend of the original spectrum; suppressing the influence of the peak area on the baseline fitting by iteratively adjusting the weight coefficient, obtaining a smooth baseline signal and subtracting it from the original spectral data to complete the baseline correction.

5. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 1, wherein In step S5, the first stage of performing unimodal fitting includes establishing a unimodal model based on the local window data, and its mathematical expression is: , where A is the peak intensity, is the central wavelength, is the half-peak width, and B is the baseline offset.

6. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 5, wherein In step S5, the second-stage bimodal fitting includes bimodal Lorentz model fitting, and its mathematical expression is: ; Among them, the second peak parameter is dynamically determined by the residual extreme value: the initial value of the central wavelength Take the position corresponding to the maximum residual value, the initial value of intensity Set to about 2 times the residual peak value, half-peak width Inherited from the main peak parameters.

7. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 1, wherein Step S6, the average value is calculated as the resolution evaluation index of the spectrometer, and the mathematical expression of the resolution is: Where is the resolution, N is the total number of data sets, is the maximum half-peak width of the characteristic peak selected in the i-th group of data.

8. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 1, wherein In step S7, the characteristic peak matching and locking based on the resolution specifically includes searching for each fitting peak center wavelength in the standard spectral line library and calculating candidate matching items within the tolerance range according to the resolution matching threshold; wherein the resolution matching threshold is: ; A sorting function with an intensity weight coefficient of 0.7 and a wavelength deviation weight coefficient of 0.3 was constructed, and strong peak signals were preferentially selected for matching and locking. A bidirectional exclusive constraint was implemented in the characteristic peak matching process, that is, each fitting peak uniquely corresponds to a standard wavelength and each standard wavelength is matched at most once.

9. The LIBS spectrometer wavelength calibration method based on a standard substance according to claim 8, wherein In step S7, the characteristic peak coverage criterion is: the entire effective pixel range is initially divided into multiple candidate sub-segments, and the number of effective standard spectral lines matched in each candidate sub-segment is counted. , and dynamically set the minimum number of spectral lines covered according to the length of each segment ,like , merge it with an adjacent segment until the new sub-segment generated by the merger ;in, , where: is the pixel length of the current segment; is the average pixel spacing of the characteristic spectral lines; is the reference spectral density.

10. The LIBS spectrometer wavelength calibration method based on standard substances according to claim 8, characterized in that: In step S7, the fitting residual criterion in the adaptive piecewise polynomial fitting strategy is: perform cubic polynomial fitting on the sub-segments that meet the characteristic peak coverage requirements, and calculate the RMSE of the sub-segment fitting results. When the RMSE is greater than the set threshold, the sub-segment pixel span is selected according to the pixel span of the sub-segment. Execute the following adjustment strategy: If If the pixel is equal to 0, the sub-segment is evenly divided into two new sub-segments and the fitting is performed again on each sub-segment; otherwise, the original segment is kept unchanged, the fitting model order is increased from cubic to quadratic, and the L2 regularization term is introduced to constrain the fitting parameters.

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