An adaptive spectral shift correction method for LIBS data

Abstract

This invention relates to an adaptive spectral drift correction method for LIBS data. The method includes the following steps: Step S1, adaptively segmenting the reference spectrum to separate significant characteristic peaks into different segments, obtaining the segmentation results of the reference spectrum; Step S2, applying the segmentation results of the reference spectrum in Step S1 to the spectrum to be corrected, obtaining the segmentation results of the spectrum to be corrected; Step S3, automatically comparing the distance between the most intense characteristic peaks in each segment of the reference spectrum and the spectrum to be corrected, adaptively correcting the segments that meet the distance constraints, thus completing the spectral drift correction of the LIBS data to be corrected. Compared with existing technologies, this invention uses an adaptive segmentation approach, reducing the high correlation between the correction results of various characteristic peaks in different segments, and improves the accuracy of spectral drift correction by locally adaptively correcting the peak position drift of characteristic peaks. It has advantages such as wide applicability to various data types, high classification accuracy, and good robustness of results.

Description

Technical Field

[0001] This invention relates to the field of spectral drift correction technology, and in particular to an adaptive spectral drift correction method for LIBS data. Background Technology

[0002] As laser-induced breakdown spectroscopy (LIBS) continues to play a vital role in planetary mineral composition analysis, high-precision data preprocessing has become a research hotspot. However, due to the complex planetary environment, the stress within the spectrometer changes when different payloads or the same payload acquire LIBS spectra of the same target under different detection environments. This causes a drift in the distribution of diffracted light on the CCD surface. This drift leads to differences in the positions of the same characteristic peaks in different LIBS data, resulting in inconsistent analysis results from multiple LIBS spectra of the same target during subsequent material composition analysis, thus affecting the accuracy of planetary surface material composition analysis.

[0003] Existing spectral drift correction methods have two main drawbacks:

[0004] 1) Purpose of Correction: Existing methods primarily aim to obtain the relationship between pixels and wavelengths, rather than correcting spectral drift. Although recalibration helps correct drift at individual pixel locations to some extent, the drift still exists. This is due to the limited resolution of the instrument, resulting in a significant wavelength interval between two pixels. This causes the positions of elemental characteristic peaks to typically be distributed between two pixels. Existing calibration methods only make the wavelengths corresponding to each pixel in the (Charge-coupled Device) CCD more accurate, but do not correct for the positions located between pixels.

[0005] 2) Standard Peak Selection Method: Existing correction methods are highly subjective in their selection of standard characteristic peaks, lacking general guidance. Furthermore, they fail to consider the uneven distribution of characteristic peaks within each channel of the LIBS data. For example, the applicability of averaging segmentation methods within channels with varying degrees of characteristic peak distribution will reduce the correction effect and accuracy.

[0006] Therefore, there is an urgent need to design a spectral drift correction method that takes into account the distribution of characteristic peak positions and adaptively selects standard peak positions. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide an adaptive spectral drift correction method for LIBS data.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] According to a first aspect of the present invention, an adaptive spectral drift correction method for LIBS data is provided, the method comprising the following steps:

[0010] Step S1: Adaptively segment the reference spectrum to separate significant characteristic peaks in the spectrum into different segments and obtain the segmentation results of the reference spectrum;

[0011] Step S2: Apply the segmentation results of the reference spectrum in step S1 to the laser-induced breakdown spectrum (LIBS) to be corrected, and obtain the segmentation results of the LIBS to be corrected.

[0012] Step S3: Automatedly compare the distance between the largest characteristic peaks in each segment of the reference spectrum and the spectrum to be corrected, and adaptively correct the segments that meet the distance constraints to complete the spectral line drift correction of the laser-induced breakdown spectrum (LIBS) data to be corrected.

[0013] Preferably, the adaptive segmentation in step S1 includes element peak finding and peak merging.

[0014] Preferably, the elemental peak finding specifically involves:

[0015] Input the reference spectrum S and the number of peaks to search N;

[0016] The method of finding the maximum value by second-order difference is used to output the characteristic peak CP of the top N intensities in the reference spectrum S.

[0017] Preferably, the peak merging includes: 1) obtaining the merging interval; 2) peak merging.

[0018] Preferably, obtaining the merged interval specifically involves: finding the characteristic peak CP. i The pixel position P where the intensity on the left and right sides first falls below the set intensity threshold T. i R, P i L, obtain the characteristic peak CP i Merged interval U i .

[0019] Preferably, the peak merging is a cyclic traversal process, which identifies the currently unmerged feature peak CP with the highest intensity based on its intensity. i Then, the remaining unmerged characteristic peaks CP are judged sequentially. j Does it fall within the merged interval U? i If it falls into the middle; then CP will be... j Merging into characteristic peak CP i In the segmentation, if the peaks are not fused, then no merging is performed; after traversing and merging all characteristic peaks, the final number of segments and the range of each segment are obtained.

[0020] Preferably, the correction judgment for the segments that satisfy the distance constraint in step S3 is as follows: after finding the characteristic peak position Pstr with the largest intensity in the i-th segment of the reference spectrum S, the possible existence interval Ppos = [Pstr-M, Pstr+M] of the corresponding characteristic peak in the data to be corrected is generated; where M is the distance threshold.

[0021] If the feature peak Sstr with the strongest intensity in the i-th segment of the data to be corrected falls into the possible interval Ppos, then that segment needs to be corrected; otherwise, it cannot be corrected.

[0022] Preferably, the adaptive correction in step S3 specifically includes:

[0023] 1) Calculate the distance D between Pstr and Sstr within the segment and the pixel position distance d;

[0024] 2) By comparing the magnitude of D with 0, determine the translation correction direction of Sstr. If D is less than 0, shift the i-th segment of the data to be corrected to the left by d pixels; if D is greater than 0, shift the i-th segment of the data to be corrected to the right by d pixels.

[0025] D = Pstr – Sstr

[0026] d=|D|

[0027] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the methods described above.

[0028] According to a third aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] 1) This invention adopts an adaptive segmentation method, which reduces the high correlation between the correction results of each characteristic peak in different segments, and improves the accuracy of spectral drift correction by locally adaptively correcting the peak position drift of characteristic peaks. It has the advantages of wide applicability to data types, high classification accuracy, and good robustness of results.

[0031] 2) This invention can achieve adaptive correction of spectral drift in different resolution channels based on the positional distribution of characteristic peaks in LIBS data;

[0032] 3) The method of the present invention can not only correct the differences between LIBS data at different times of the same instrument, but also eliminate the inconsistency of LIBS data between different instruments, and has a wide range of applications. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the process of the present invention;

[0034] Figure 2 These are the reference spectrum and the spectrum used for correction in the embodiments of the present invention;

[0035] Figure 3 This is the adaptive segmentation result of the reference spectrum in the first channel (240-340nm) of the instrument in the embodiment of the present invention;

[0036] Figure 4 This is the adaptive segmentation result of the reference spectrum in the second channel (340-540nm) of the instrument in the embodiment of the present invention;

[0037] Figure 5 This is the adaptive segmentation result of the reference spectrum in the third channel (540-850nm) of the instrument in the embodiment of the present invention;

[0038] Figure 6 This is a schematic diagram showing the comparison of the peak positions of the maximum intensity characteristic peak in the first channel of the spectrum to be corrected and the reference spectrum before and after correction in an embodiment of the present invention.

[0039] Figure 7 This is a schematic diagram showing the comparison of the peak positions of the maximum intensity characteristic peak in the second channel of the spectrum to be corrected and the reference spectrum before and after correction in an embodiment of the present invention.

[0040] Figure 8 This is a schematic diagram showing the comparison of the peak positions of the maximum intensity characteristic peak in the third channel between the spectrum to be corrected and the reference spectrum before and after correction in an embodiment of the present invention. Detailed Implementation

[0041] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0042] Example

[0043] like Figure 1 As shown in the figure, this embodiment presents an adaptive spectral drift correction method for LIBS data, which specifically includes the following steps:

[0044] Step S1: Adaptively segment the reference spectrum to separate significant characteristic peaks in the spectrum into different segments and obtain the segmentation results of the reference spectrum;

[0045] Adaptive segmentation includes element peak finding and peak merging.

[0046] The elemental peak finding process is as follows: input the reference spectrum S and the number of peaks to be found N; and output the characteristic peaks CP of the top N intensities in the reference spectrum S by using the second-order difference method to find the maxima.

[0047] Peak merging includes:

[0048] 1) Obtain the merged interval;

[0049] Finding characteristic peaks CP i The pixel position P where the intensity on the left and right sides first falls below the set intensity threshold T. i R, P i L, obtain the characteristic peak CP i Merged interval U i .

[0050] 2) Peak merging:

[0051] Peak merging is performed as a loop traversal process, identifying the currently unmerged feature peak CP based on its intensity. i Then, the remaining unmerged characteristic peaks CP are judged sequentially. j Does it fall within the merged interval U? i If it falls into the middle; then CP will be... j Merging into characteristic peak CP i In the segmentation, if the peaks are not fused, then no merging is performed; after traversing and merging all characteristic peaks, the final number of segments and the range of each segment are obtained.

[0052] The reference spectrum and the spectrum to be corrected have the same wavelength range. During adaptive segmentation, the number of peaks sought is N = 20, and the intensity threshold is T = 0.001. Figure 3-5 The segmented results are for the reference spectrum.

[0053] Step S2: Apply the reference spectrum segmentation results from S1 to the spectrum to be corrected to obtain the spectrum segmentation results to be corrected.

[0054] Step S3: Automatedly compare the distance between the largest characteristic peaks in each segment of the reference spectrum and the spectrum to be corrected, and adaptively correct the segments that meet the distance constraints to complete the spectral line drift correction of the LIBS data to be corrected. Specifically:

[0055] The correction judgment is as follows: after finding the characteristic peak position Pstr with the largest intensity in the i-th segment of the reference spectrum S, the possible existence interval Ppos = [Pstr-M, Pstr+M] of the corresponding characteristic peak in the data to be corrected is generated; where M is the distance threshold.

[0056] If the feature peak Sstr with the strongest intensity in the i-th segment of the data to be corrected falls into the possible interval Ppos, then that segment needs to be corrected; otherwise, it cannot be corrected.

[0057] Adaptive correction, specifically:

[0058] 1) Calculate the distance D between Pstr and Sstr within the segment and the pixel position distance d;

[0059] 2) By comparing the magnitude of D with 0, determine the translation correction direction of Sstr. If D is less than 0, shift the i-th segment of the data to be corrected to the left by d pixels; if D is greater than 0, shift the i-th segment of the data to be corrected to the right by d pixels.

[0060] D = Pstr – Sstr

[0061] d=|D|

[0062] The threshold M for determining whether correction is possible is set to 5. The local spectral results before and after correction are as follows: Figure 6-8 As shown. Figure 2 The first two LIBS data points are shown before correction.

[0063] To compare the effectiveness of the corrected results, the average drift of several obvious elemental characteristic peaks before and after correction was selected as an evaluation index. The selected elemental peaks and their peak positions in the reference spectrum are shown in Table 1, and the statistical results before and after correction are shown in Table 2.

[0064] Table 1. Top 6 characteristic peaks by intensity for each channel (unit: nanometers)

[0065]

[0066]

[0067] Table 2. Statistical results of characteristic peak shift before and after correction (unit: nanometers)

[0068]

[0069] like Figures 5-8As shown, the corrected data more closely matches the positions of the peaks in the reference data compared to the uncorrected data. The average drift of the characteristic peaks in each channel in Table 2 also decreased from 0.1064 nm, 0.1350 nm, and 0.4177 nm to 0.0074 nm, 0.0051 nm, and 0.0122 nm, respectively. Compared to before correction, the average drift of each channel decreased by two orders of magnitude. Particularly in the third channel with lower spectral resolution, the corrected average drift is approximately 3% of the uncorrected average drift. These results not only demonstrate that the method proposed in this invention can effectively correct spectral drift, but also show that the method has a significant drift correction effect even on low-resolution spectral data.

[0070] In summary, the results of a series of qualitative and quantitative experimental analyses demonstrate that the adaptive spectral drift correction method based on LIBS data proposed in this invention has significant advantages in accurately removing differences between data and achieving high-precision spectral drift correction applications.

[0071] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0072] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0074] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0075] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An adaptive spectral shift correction method for LIBS data, characterized in that, The method comprises the following steps: Step S1, adaptive segmentation is performed on the reference spectrum, and significant characteristic peaks in the spectrum are separated into different segments to obtain a segmented result of the reference spectrum; Step S2, the segmented result of the reference spectrum in step S1 is applied to the to-be-corrected laser-induced breakdown spectroscopy (LIBS) to obtain a segmented result of the to-be-corrected laser-induced breakdown spectroscopy (LIBS); Step S3, the spacing between the characteristic peaks with the largest intensity in each segment of the reference spectrum and the to-be-corrected spectrum is automatically compared, and the segments that meet the distance constraint are adaptively modified to complete the spectral line shift correction of the to-be-corrected laser-induced breakdown spectroscopy (LIBS) data; The adaptive segmentation in the step S1 comprises element peak searching and peak position merging; The element peak searching specifically comprises: Input reference spectrum S Number of peaks to find N ; The reference spectrum is output by a method of finding a maximum value by second-order difference S Medium intensity before N Characteristic peak CP ; The peak position merging comprises: 1) obtaining a merging interval; and 2) peak position merging; The acquisition merging interval is specifically: finding a characteristic peak CP i The left and right intensities are less than a set intensity threshold for the first time T of a pixel position P i R , P i L acquiring a characteristic peak CP i of a merging interval U i ; The peak merging process is a loop traversal, which identifies the characteristic peak with the highest intensity that has not yet been merged, based on its intensity. CP i Then, the remaining unmerged characteristic peaks are judged in turn. CP j Does it fall within the merged range? U i If it falls in, then... CP j Merging into characteristic peaks CP i In the segmentation, if the segmentation is not merging, then no merging is performed; after traversing and merging all feature peaks, the final number of segments and the range of each segment are obtained. The segment satisfying the distance constraint in the step S3, the correction judgment is specifically: finding a reference spectrum S In the first i Segment with the strongest intensity of the characteristic peak Pstr After that, the possible existence interval of the corresponding characteristic peak in the data to be corrected is generated Ppos =[ Pstr-M , Pstr+M ];Wherein, M is the distance threshold value; If the data to be corrected is number i The characteristic peak with the highest intensity in each segment Sstr Falling into a possible range Ppos If the middle section is correct, then that section needs to be corrected; otherwise, it cannot be corrected. The adaptive modification in the step S3 specifically comprises: 1) Calculate distance to satisfy Pstr within segment Sstr D distance to pixel position d ;​ 2) By judgment D Determine the value of 0. Sstr The direction of translation correction, if D If the value is less than 0, the data to be corrected will be... i Each segment is shifted to the left as a whole. d 1 pixel; if D If the value is greater than 0, then the data to be corrected will be... i Each segment is shifted to the right as a whole. d Pixels: D = Pstr – Sstr d = |D| 。 2.An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that, The processor executes the program to implement the method of claim 1.

3. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of claim 1.

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