Multi-matrix soil heavy metal element detection method based on LIBS

Abstract

The invention provides an LIBS (laser-induced breakdown spectroscopy)-based multi-matrix soil heavy metal element detection method. The LIBS-based multi-matrix soil heavy metal element detection method comprises the following steps: detecting an element characteristic spectrum Selement and a matrix information spectrum Sall of target soil based on double spectrometers; inputting the matrix information spectrum Sall into a soil type identification model, determining a target soil type, and substituting the element characteristic spectrum Selection into an element content quantitative model to obtain an element predicted concentration Cpredict; and according to a soil type identification result, substituting the Cpredict into a corresponding error correction model to obtain a corrected element prediction concentration Ccorrect. The problems that the measurement precision is not high and the technical applicability is poor due to the matrix effect in the existing LIBS technology, and the actual application of LIBS is limited due to the fact that high sensitivity and wide spectrum detection of a single-spectrometer LIBS system are difficult to consider at the same time are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy metal element detection, and in particular to a multi-matrix soil heavy metal element detection method based on LIBS. Background Art

[0002] Heavy metal pollution in soil not only affects the growth of crops, but also poses a long-term threat to human health. Therefore, soil heavy metal detection has become an important research direction in environmental monitoring, agricultural production and pollution control. Traditional heavy metal detection methods, such as atomic absorption spectroscopy (AAS), inductively coupled plasma mass spectrometry (ICP-MS) and X-ray fluorescence (XRF) technologies, usually require complex sample pretreatment, and the detection process is time-consuming, making it difficult to achieve rapid on-site detection.

[0003] In recent years, laser induced breakdown spectroscopy (LIBS) technology has received widespread attention in the field of soil heavy metal detection due to its advantages such as no need for sample preparation, simple operation, fast detection speed, and simultaneous measurement of multiple elements. LIBS technology uses laser to excite the sample surface to form plasma, and analyzes the spectral line intensity in the spectral signal to obtain element content information. However, the promotion of LIBS technology faces a key challenge - the matrix effect. The matrix effect refers to the differences in the physical and chemical properties of different substances, which leads to different plasma characteristics generated by the interaction between the laser and the sample, thereby affecting the intensity and accuracy of the spectral signal. This effect is particularly significant in soil heavy metal detection. Different types of soil matrices will affect the spectral response of heavy metal elements. Therefore, effective methods are needed to eliminate the influence of the matrix effect and ensure the accuracy of the test results.

[0004] At present, the methods to eliminate matrix effects mainly include sample processing and device improvement. Sample processing is a common method to reduce the influence of matrix effects. The physical and chemical properties of samples are changed by physical or chemical means to make the samples more uniform, thereby reducing the interference of matrix effects on spectral signals. Tian Ye et al. proposed a surface film-assisted LIBS technology, which uniformly mixes the sample powder to be analyzed with the oily substance and applies it to the surface of the substrate to form a uniform film. This method reduces the influence of sample surface heterogeneity on laser-sample interaction. After obtaining the spectral data of the sample, the internal standard element is used for correction to weaken the influence of the matrix effect. In addition, the accuracy of the spectral signal can be improved by improving the detection equipment. Zhang Deng et al. proposed a matrix effect elimination method by adding an ICCD camera to the LIBS system to take plasma images. This method obtains plasma images of different matrices, compares the characteristic differences of plasma under different matrices, and combines image information for analysis and spectral correction, effectively eliminating the influence of matrix effects on spectral signals between different types of samples. This method uses spatial information in plasma images to assist analysis, has a good correction effect, and can provide strong support for some complex matrix samples.

[0005] The existing technologies have the following main disadvantages in solving the matrix effect problem in soil heavy metal detection:

[0006] First, the sample treatment method can only reduce the matrix effect to a certain extent, and the operation is complicated and time-consuming. The pretreatment operation changes the original morphology of the soil sample, affecting the authenticity of the test results. In addition, this method increases the complexity of the detection process and is not suitable for rapid on-site detection and large-scale promotion and application. Secondly, the device improvement method improves the quality of the spectral signal by adding an ICCD camera, but the ICCD camera is expensive, the equipment operation is complicated, the maintenance is difficult, and it is necessary to establish a complex correction relationship between the plasma image and the spectrum, which is difficult to meet the requirements of actual applications. Summary of the invention

[0007] The present invention provides a multi-matrix soil heavy metal element detection method based on LIBS, which is used to solve the defects in the prior art such as low measurement accuracy and poor technical applicability caused by the matrix effect, and the difficulty in balancing high sensitivity and wide spectrum detection faced by a single spectrometer LIBS system, which further limits the practical application of LIBS.

[0008] In one aspect, the present invention provides a multi-matrix soil heavy metal element detection method based on LIBS, comprising:

[0009] The element characteristic spectrum S_element and matrix information spectrum S_all of the target soil are detected based on the dual spectrometer;

[0010] The matrix information spectrum S_all is input into the soil type identification model to determine the target soil type, and the element characteristic spectrum S_element of the target soil is substituted into the target element quantitative model to obtain the element predicted concentration C_predict;

[0011] According to the target soil type, the predicted element concentration C_predict is input into the error correction model corresponding to the target soil type to obtain the corrected predicted concentration C_correct, and the corrected predicted concentration C_correct is used as the final prediction result.

[0012] Preferably, when establishing the target element content calibration curve, it includes:

[0013] S1. Obtain a set of soil samples with different target element content characteristics;

[0014] S2, using a dual spectrometer to collect a LIBS spectrum set of the soil sample set;

[0015] S3. Establishing a quantitative model for target element content according to the LIBS spectrum set.

[0016] Preferably, the step of obtaining a soil sample set having different target element content characteristics comprises:

[0017] A standard soil sample was prepared, and a set of test samples containing different target element contents were prepared using the standard addition method.

[0018] Preferably, when the error correction model is established, it includes:

[0019] S4, obtaining a new type of soil sample subset, and determining an actual content value set of a target element in the new type of soil sample subset;

[0020] S5. Using a dual spectrometer to collect spectral data of the new type of soil sample subset, and substituting the characteristic spectrum of the target element therein into a quantitative model of the target element content to obtain a predicted content value set;

[0021] S6, fitting the actual content value set and the predicted content value set by a linear regression model, and constructing an error correction model for the predicted content according to the above;

[0022] S7. Repeat steps S4-S6 to establish error correction models for different types of soil for a variety of new types of soil samples.

[0023] Preferably, the error correction model for predicting the content is expressed by the following formula:

[0024] C correct =S×C predict +B

[0025] In the formula, C predict is the predicted content of the target element, C correct is the corrected predicted content of the target element, and S and B are constants.

[0026] Preferably, when the soil type identification model is established, it includes:

[0027] A soil type recognition model is constructed based on machine learning algorithm combined with soil matrix information spectrum.

[0028] Preferably, the soil type identification model is constructed based on a machine learning algorithm combined with soil matrix information spectrum, including:

[0029] Support vector machine was used as the machine learning algorithm to construct the soil type identification model;

[0030] The Gaussian kernel function is selected as the kernel function type to fit the complex mapping relationship between soil type and spectral characteristics;

[0031] The penalty coefficient C and kernel function parameter Gamma of the soil type identification model are determined by grid optimization method.

[0032] Preferably, when the dual spectrometer detects the element characteristic spectrum S_element and the soil matrix information spectrum S_all of the target soil, it includes:

[0033] Wavelet transform is used to eliminate noise information in spectral data;

[0034] Model-free algorithm is used to remove background information from spectral data;

[0035] Area normalization is used to eliminate data fluctuations caused by different sampling points.

[0036] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any of the LIBS-based multi-matrix soil heavy metal element detection methods described above.

[0037] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the LIBS-based multi-matrix soil heavy metal element detection methods described above.

[0038] On the other hand, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned LIBS-based multi-matrix soil heavy metal element detection methods.

[0039] The present invention provides a multi-matrix soil heavy metal element detection method based on LIBS. By utilizing the coordinated work of a high-sensitivity monochromator and a wide-spectrum spectrometer, a dual spectrometer is used to collect the spectrum of the test sample and establish a quantitative model of the element content, and the actual content is obtained by combining ICP-MS or AAS technology, and an error correction model and a soil type identification model are established. A series of operations solve the problems of low measurement accuracy and poor technical applicability of the existing LIBS technology due to matrix effects, and the difficulty of high sensitivity and wide-spectrum detection in a single spectrometer LIBS system, which leads to limited practical application of LIBS, and achieves the following beneficial effects:

[0040] 1. Improve detection accuracy: Through the collaborative work of dual spectrometers, the high-sensitivity monochromator focuses on the characteristic spectral lines of the target elements, and the wide-spectrum spectrometer obtains matrix information for correction, which effectively reduces the interference of matrix effects on the detection results and improves the measurement accuracy.

[0041] 2. Enhanced technical applicability: No complicated pretreatment of soil samples is required, and the original samples can be directly tested, which simplifies the operation process and reduces human errors. At the same time, the error correction model and soil type identification model established in combination with the machine learning algorithm are applicable to various types of soils, enhancing the versatility and adaptability of the test.

[0042] 3. Improve application convenience: The method of the present invention does not require the modification of the traditional LIBS optical path. It only needs to adopt a side-axis acquisition method to connect a spectrometer device for obtaining soil matrix information. The device is very simple to build and suitable for use in industrial environments. And when the soil type increases, it only needs to update the soil type discrimination model to realize the heavy metal detection of the new type of soil. It does not require complex data processing procedures, has high reliability, and can be effectively applied to actual industrial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of a multi-matrix soil heavy metal element detection method based on LIBS provided in an embodiment of the present invention;

[0045] Figure 2 The dual-spectrometer LIBS acquisition system of the present invention;

[0046] Figure 3 The embodiment of the present invention provides Figure 2The element information spectrum S_element of the collected soil sample;

[0047] Figure 4 The embodiment of the present invention provides Figure 2 The matrix information spectrum S_all of the collected soil samples;

[0048] Figure 5 Provide prediction results of Cr element quantitative model for the embodiment of the present invention;

[0049] Figure 6 Provide uncorrected prediction results of the influence of Cr element matrix effect on the embodiment of the present invention;

[0050] Figure 7 Provides a prediction result after correction of the influence of the Cr element matrix effect for the embodiment of the present invention;

[0051] Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Combine the following Figure 1-Figure 7 The present invention describes the LIBS-based multi-matrix soil heavy metal element detection method.

[0054] Figure 1 This is one of the flow charts of the multi-matrix soil heavy metal element detection method based on LIBS provided in an embodiment of the present invention.

[0055] like Figure 1 As shown, the LIBS-based multi-matrix soil heavy metal element detection method provided by the embodiment of the present invention can be executed by an electronic device or computer system with corresponding computing and processing capabilities, and the method mainly includes the following steps:

[0056] 101. Detect the element characteristic spectrum S_element and soil matrix information spectrum S_all of the target soil based on the dual spectrometer;

[0057] 102. Input the matrix information spectrum S_all into the soil type identification model to determine the target soil type, and substitute the element characteristic spectrum S_element of the target soil into the target element content quantitative model to obtain the element predicted concentration C_predict;

[0058] 103. According to the target soil type, the element predicted concentration C_predict is input into the error correction model corresponding to the target soil type to obtain the corrected predicted concentration C_correct, and the corrected predicted concentration C_correct is used as the final prediction result.

[0059] This method is a soil heavy metal element detection method that uses dual spectrometers to collaboratively analyze and correct the LIBS matrix effect. This method does not require the modification of the traditional LIBS optical path. It only needs to use a side-axis acquisition method to connect an external spectrometer device for obtaining soil matrix information. The device is very simple to build and is suitable for use in industrial environments. Secondly, this method does not require complex sample pretreatment. Only the traditional LIBS sample pretreatment method is needed to obtain the LIBS spectral intensity and element content calibration curve. The sample pretreatment method is simple and suitable for use in industrial environments. In addition, when the soil type increases, this method only needs to update the soil type discrimination model to realize the heavy metal detection of the new type of soil. It does not require a complex data processing process and is suitable for use in industrial environments. Therefore, this method has high reliability and can be effectively applied to actual industrial scenarios.

[0060] Based on the above scheme, this embodiment further describes the soil heavy metal element detection method of dual spectrometer collaborative analysis and correction of LIBS matrix effect in more detail:

[0061] like Figure 1 The detection method comprises:

[0062] Step 1. Prepare standard soil samples and use the standard addition method to prepare a set of test samples containing different target element contents.

[0063] Step 2. Use a dual spectrometer LIBS system (such as Figure 2 Spectrometer 1 is responsible for collecting characteristic spectra of target elements S_element, and spectrometer 2 is responsible for collecting spectra of soil matrix information S_all.

[0064] Step 3. Based on the element characteristic spectrum S_element and the heavy metal content data in the soil, a quantitative model of the target element content is obtained.

[0065] Step 4. Collect a small amount of new type of soil sample and use ICP-MS or AAS technology to measure the actual content C_actual of the target element in the sample.

[0066] Step 5. Use the dual-spectrometer LIBS system to collect the spectra S_element and S_all of the sample to be tested, and substitute the spectrum S_element of the sample to be tested into the established element quantitative model to obtain the predicted content C_predict of the target element of the sample to be tested.

[0067] Step 6. Use the linear regression model to fit the actual content C_actual and the predicted content C_predict of the target element, and establish an error correction model C_correct=S*C_predict+B for the predicted content of the element.

[0068] Step 7. Collect new types of soil samples, repeat the above steps 4-6, and establish prediction error correction models for different types of soil.

[0069] Step 8. Use machine learning algorithm combined with soil matrix information spectrum S_all to establish a soil type recognition model.

[0070] Step 9. Collect S_element and S_all of the spectrum of the soil sample to be tested, substitute S_element into the calibration curve to obtain the element predicted concentration C_predict, substitute S_all into the soil type identification model to determine the soil type of the sample to be tested, substitute the predicted concentration C_predict into the corresponding error correction model according to the soil type, obtain the corrected predicted concentration C_correct, and use it as the final prediction result.

[0071] Preferably, the spectrum collected above can be improved by using spectrum preprocessing technology. Wavelet transform is used to eliminate noise information in the spectrum data; Model-free algorithm is used to remove background information in the spectrum data; area normalization is used to eliminate data fluctuations caused by different sampling points.

[0072] Preferably, the soil type identification model may adopt a support vector machine (SVM), wherein the kernel function type adopts a Gaussian kernel function, and the penalty coefficient C and kernel function parameter Gamma of the model are determined by a grid optimization method.

[0073] In order to better illustrate the present invention, in view of the matrix effect problem faced by the detection of soil heavy metal elements based on LIBS, the soil heavy metal element detection method of the dual spectrometer collaborative analysis and correction of LIBS matrix effect is adopted to realize the correction of LIBS matrix effect. The following example is given:

[0074] Example 1: The soil types selected in this example are standard soil (No. GBW07405), yellow-brown soil and black soil. Each type of soil is prepared using the standard addition method with different chromium (Cr) content heavy metal soil samples.

[0075] Table 1 Cr element content in standard soil, yellow brown soil and black soil (wt.%)

[0076]

[0077]

[0078] First, weigh 1g of soil sample and put it into a 25mm diameter sample pressing mold. Press it for 1 minute under a hydraulic press to get a flake soil sample. Then put it into Figure 2 In the LIBS detection device shown in FIG. 1 , after the laser emits a pulsed laser to ablate the sample to be detected, the dual spectrometer simultaneously obtains the spectral information S_element and S_all of each soil sample (such as Figure 3 and Figure 4 As shown). The laser used in this experiment is a Nd:YAG pulse laser with a wavelength of 532nm. Spectrometer 1 uses a Shamrock500i spectrometer produced by Andor, with a grating of 3600l / mm, a slit width of 200um, a delay time of 2us, and a gate width of 2us. S_element is obtained through spectrometer 1. Spectrometer 2 uses an AvaSpecMulti-ChannelSpectronmeter spectrometer produced by AVATES, with a delay time of 2us and a gate width of 9us. S_all is obtained through spectrometer 2.

[0079] According to the experimental standard soil spectral data S_element and Cr element content data, a quantitative prediction model of Cr element is established, such as Figure 5 As shown, the evaluation parameters (correlation coefficient (R2), root mean square error (RMSE), and mean relative error (MRE)) of the quantitative model prediction results of the Cr element content of the standard sample are all very good.

[0080] When the quantitative model was used to predict the Cr content in yellow-brown soil and black soil, the RMSE and MRE of the prediction results were very poor. Figure 6 As shown in the figure. After the results were corrected by the correction error model, the RMSE and ARE of the prediction results were significantly improved, R2 increased from incalculable to above 0.98, RMSE decreased by 89.14% and 94.73%, and MRE decreased by 88.06% and 87.38%. The influence of soil matrix effect on the prediction results was significantly eliminated.

[0081] Table 2 Comparison of Cr prediction results of yellow-brown soil and black soil samples before and after correction

[0082]

[0083] Therefore, this embodiment verifies that the correction method proposed in the present invention can effectively eliminate the influence of the soil matrix effect on the prediction results of the Cr content in the soil. The method shown can also be extended to other elements, such as cadmium (Cd), lead (Pb), copper (Cu), etc.

[0084] Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0085] like Figure 8 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the multi-matrix soil heavy metal element detection method based on LIBS.

[0086] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0087] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the LIBS-based multi-matrix soil heavy metal element detection method provided by the above-mentioned methods.

[0088] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the LIBS-based multi-matrix soil heavy metal element detection method provided by the above-mentioned methods.

[0089] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-matrix soil heavy metal element detection method based on LIBS, characterized in that: include: The element characteristic spectrum S_element and matrix information spectrum S_all of the target soil are detected based on the dual spectrometer; Input the matrix information spectrum S_all into the soil type identification model to determine the target soil type, and substitute the element characteristic spectrum S_element into the target element content quantitative model to obtain the element predicted concentration C_predict; According to the target soil type, the predicted element concentration C_predict is input into the error correction model corresponding to the target soil type to obtain the corrected predicted concentration C_correct, and the corrected predicted concentration C_correct is used as the final prediction result.

2. The multi-matrix soil heavy metal element detection method based on LIBS according to claim 1, characterized in that: The establishment of the quantitative model for the target element content includes: S1. Obtain a set of soil samples with different target element content characteristics; S2, using a dual spectrometer to collect a LIBS spectrum set of the soil sample set; S3. Establishing a quantitative model for target element content according to the LIBS spectrum set.

3. The multi-matrix soil heavy metal element detection method based on LIBS according to claim 2 is characterized in that: The method of obtaining a soil sample set having different target element content characteristics comprises: A standard soil sample was prepared, and a set of test samples containing different target element contents were prepared using the standard addition method.

4. The multi-matrix soil heavy metal element detection method based on LIBS according to claim 2, characterized in that: The establishment of the error correction model includes: S4, obtaining a new type of soil sample subset, and determining an actual content value set of a target element in the new type of soil sample subset; S5, using a dual spectrometer to collect spectral data of the new type of soil sample subset, and substituting the element characteristic spectra therein into a target element quantitative model to obtain a predicted content value set; S6. Fitting the actual content value set and the predicted content value set by a linear regression model, and constructing an error correction model for the predicted content; S7. Repeat steps S4-S6 to establish error correction models for different types of soil for a variety of new types of soil samples.

5. The multi-matrix soil heavy metal element detection method based on LIBS according to claim 4 is characterized in that: The error correction model for predicting the content is expressed by the following formula: C correct =S×C predict +B In the formula, C predict is the predicted content of the target element, C correct is the actual content of the target element, and S and B are constants.

6. The multi-matrix soil heavy metal element detection method based on LIBS according to claim 4, characterized in that: The establishment of the soil type identification model includes: A soil type recognition model is constructed based on machine learning algorithm combined with soil matrix information spectrum.

7. The LIBS-based multi-matrix soil heavy metal element detection method according to claim 6, characterized in that: The soil type identification model constructed based on the machine learning algorithm combined with the soil matrix information spectrum includes: Support vector machine was used as the machine learning algorithm to construct the soil type identification model; The Gaussian kernel function is selected as the kernel function type to fit the complex mapping relationship between soil type and spectral characteristics; The penalty coefficient C and kernel function parameter Gamma of the soil type identification model are determined by grid optimization method.

8. The multi-matrix soil heavy metal element detection method based on LIBS according to claim 1, characterized in that: When the dual spectrometer detects the element characteristic spectrum S_element and the soil matrix information spectrum S_all of the target soil, it includes: Wavelet transform is used to eliminate noise information in spectral data; Model-free algorithm is used to remove background information from spectral data; Area normalization is used to eliminate data fluctuations caused by different sampling points.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the multi-matrix soil heavy metal element detection method based on LIBS as described in any one of claims 1 to 8 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-matrix soil heavy metal element detection method based on LIBS as described in any one of claims 1 to 6 is implemented.

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