Accurate standard-free quantification method based on automatic identification and matching of different matrixes

Abstract

The invention relates to the technical field of elemental analysis and detection, in particular to an accurate non-standard quantification method based on automatic identification and matching of different matrixes. Acquiring actual fluorescence intensity and an actual spectrogram, and determining the types of main dark substances; determining a map corresponding to the current detection sample and selecting a corresponding non-standard quantitative model; acquiring a simulation map; and calculating the similarity between the simulated spectrum and the actual spectrum, if the similarity is greater than a similarity threshold value, outputting each content in the sample, if the similarity is less than or equal to the similarity threshold value, outputting new content of each element, and carrying out full-spectrum matching on the simulated spectrum and the actual spectrum again until a matching result is converged. By constructing the atlas database and comparing the change trend of the actually measured atlas and the change trend of the simulated atlas, the situation that the substances are judged to be different substances when the same substances and different contents are caused by pure similarity comparison is avoided, errors caused by using wrong models can be avoided, and therefore the accuracy of non-standard quantification is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of element analysis and detection, and in particular to an accurate standard-free quantitative method based on automatic recognition and matching of different matrices. Background Art

[0002] X-ray fluorescence (XRF) analysis technology is widely used in fields such as materials science, geological exploration, environmental monitoring, and industrial quality control due to its advantages such as rapidity, non-destructiveness, and multi-element detection. With the increasing complexity of industrial detection scenarios, higher demands are being placed on the real-time performance and standard-free detection capabilities of XRF technology. Traditional quantitative methods rely on standard samples to establish calibration curves, but in practical applications, they often face problems such as difficulty in preparing standard samples and low matrix matching. Standard-free quantitative technology, due to its advantage of not relying on standard samples, has gradually become an important tool in industrial testing, environmental monitoring, and geological exploration.

[0003] In related technologies, although some studies have attempted to improve by expanding the database or introducing machine learning, there are still limitations such as low matrix matching accuracy, rough dark matter compensation, and inaccurate background subtraction, and the accuracy of the detection results cannot be guaranteed. Summary of the Invention

[0004] The purpose of the present invention is to provide an accurate label-free quantitative method based on automatic identification and matching of different matrices, so as to solve the problem that the accuracy of the detection results cannot be guaranteed in the existing technology, although some studies have attempted to improve it by expanding the database or introducing machine learning. However, there are still limitations such as low matrix matching accuracy, rough dark matter compensation, and inaccurate background subtraction.

[0005] The present invention provides an accurate label-free quantitative method based on automatic identification and matching of different matrices, comprising:

[0006] Test samples and pre-process test data;

[0007] Obtain the actual fluorescence intensity and actual spectrum of the substance being tested, and determine the type of the main dark matter based on the actual spectrum;

[0008] Obtain preset spectra in the spectrum library, calculate the similarity of the change trend of each preset spectrum and the actual spectrum respectively, select the preset spectrum with the highest change trend similarity as the spectrum corresponding to the current test sample and select the corresponding standard-free quantitative model;

[0009] Inputting the main dark matter species and experimental parameters into a standard-free quantitative model to obtain theoretical fluorescence spectra of each element, and fitting the theoretical fluorescence spectra to obtain simulated spectra;

[0010] Performing full spectrum matching on the simulated spectrum and the actual spectrum and calculating similarity, outputting the content of each element in the sample in response to the similarity being greater than a similarity threshold, and outputting new content of each element in response to the similarity being less than or equal to the similarity threshold, and re-performing full spectrum matching on the simulated spectrum and the actual spectrum until the matching results converge;

[0011] The experimental parameters include the voltage and current of the X-ray tube and the measurement time.

[0012] As a preferred technical solution for an accurate, standardless quantitative method based on automatic identification and matching of different matrices, the determination of the type of the main dark matter includes:

[0013] Obtain the scattering peak in a specific energy range in the actual spectrum;

[0014] The characteristic scattering peak is compared with a pre-established dark matter characteristic peak database to determine the type of the main dark matter.

[0015] As an optimal technical solution for an accurate, standard-free quantitative method based on automatic identification and matching of different matrices, the dark matter characteristic peak database includes characteristic scattering peak energy and intensity information corresponding to various dark substances under different conditions.

[0016] As a preferred technical solution for an accurate label-free quantitative method based on automatic identification and matching of different matrices, the full spectrum matching of the simulated spectrum with the actual spectrum includes:

[0017] The least squares method was used to calculate the similarity between the simulated and actual spectra;

[0018] Obtaining the correlation between each element and the similarity, generating a priority sequence for the elements from high to low according to the correlation, and adjusting the element content in sequence according to the priority sequence;

[0019] Obtain the theoretical fluorescence intensity of each substance, generate a simulated spectrum and repeat the above steps;

[0020] In response to the similarity gradually increasing and the increase amplitude becoming smaller and smaller, the change amplitude is compared with the amplitude threshold, and if the change amplitude is smaller than the amplitude threshold, it is determined that the detection is completed.

[0021] As an optimal technical solution for an accurate standardless quantitative method based on automatic recognition and matching of different matrices, the element contents are adjusted in sequence, and the content of each element is adjusted using a gradient descent algorithm, and the adjustment step size is adaptively adjusted according to the difference in similarity.

[0022] As an optimal technical solution for an accurate label-free quantitative method based on automatic identification and matching of different matrices, the spectrum library stores spectra of various samples, as well as the main components of dark matter, scattering peak characteristic parameters and corresponding label-free quantitative model optimization parameters of the samples corresponding to the spectra. The optimization parameters include normalization factor, background correction coefficient and spectral peak fitting offset.

[0023] As a preferred technical solution for an accurate label-free quantitative method based on automatic identification and matching of different matrices, the calculation of the similarity between the change trends of the preset spectrum and the actual spectrum further includes the following steps:

[0024] The preset spectrum and the actual spectrum are segmented according to energy intervals to form a number of corresponding sub-spectra and recorded as a comparison group;

[0025] Use symbolic trend coding to analyze the changing trend in each sub-spectrum graph and obtain the corresponding symbol sequence;

[0026] Calculating the intra-group similarity of the symbol sequences in the comparison group, and determining the weight coefficient of the comparison group according to the length of the energy interval;

[0027] The change trend similarity is calculated based on the intra-group similarity and the weight coefficient.

[0028] As an optimal technical solution for an accurate label-free quantitative method based on automatic identification and matching of different matrices, the label-free quantitative model is configured as a relationship model between experimental parameters, dark matter types and theoretical fluorescence intensities of different elements. The experimental parameters and dark matter types are input, and the theoretical fluorescence intensity of each substance is output.

[0029] As a preferred technical solution for an accurate label-free quantitative method based on automatic recognition and matching of different matrices, the label-free quantitative model includes a segmented multi-parameter background compensation algorithm to eliminate background noise generated by incident X-ray scattering, detector Compton escape effect, and incomplete charge collection effect;

[0030] The compensation algorithm includes one or more combinations of polynomial fitting, cubic spline interpolation, B-spline interpolation and Snip algorithm.

[0031] As a preferred technical solution for an accurate, standardless quantitative method based on automatic identification and matching of different matrices, the segmented multi-parameter background compensation is divided into multiple compensation segments according to energy intervals or element characteristic peak positions. Each compensation segment independently selects a background compensation algorithm and sets parameters. During the compensation process, the contribution of each segment's background noise to the element peak fitting is calculated in real time, and the compensation weight is dynamically adjusted.

[0032] Compared with the prior art, the beneficial effect of the present invention lies in that, by constructing a spectrum library, the present invention compares the measured spectrum with the spectrum in the spectrum library, and compares the change trend of the measured spectrum with the simulated spectrum, thereby avoiding the situation where the same content of substances is judged as different substances due to simple similarity comparison. Because the most suitable model is automatically matched (such as a carbon model for coal and an oxide model for ore), errors caused by using the wrong model can be avoided, thereby increasing the accuracy of standard-free quantification. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flowchart of the steps of an accurate label-free quantification method based on automatic identification and matching of different matrices according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0035] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0036] See also Figure 1 As shown, it is a flowchart of the steps of an accurate label-free quantitative method based on automatic identification and matching of different matrices according to an embodiment of the present invention, including:

[0037] Step S1, testing the sample and preprocessing the test data;

[0038] Step S2, obtaining the actual fluorescence intensity and actual spectrum of the substance being tested, and determining the type of the main dark matter based on the actual spectrum;

[0039] Step S3, obtaining preset spectra in the spectrum library, calculating the similarity of the change trend between each preset spectrum and the actual spectrum, selecting the preset spectrum with the highest change trend similarity as the spectrum corresponding to the current test sample and selecting the corresponding standard-free quantitative model;

[0040] Step S4: Input the main dark matter species and experimental parameters into the standard-free quantitative model to obtain the theoretical fluorescence spectrum of each element, and fit the theoretical fluorescence spectrum to obtain the simulated spectrum;

[0041] Step S5: Full spectrum matching is performed between the simulated spectrum and the actual spectrum, and similarity is calculated. In response to the similarity being greater than a similarity threshold, the content of each element in the sample is output. In response to the similarity being less than or equal to the similarity threshold, new content of each element is output, and the simulated spectrum and the actual spectrum are re-matched until the matching results converge.

[0042] Experimental parameters include: X-ray tube voltage, current and measurement time.

[0043] Specifically, the present invention constructs a spectrum library to compare the measured spectrum with the spectrum in the spectrum library, and compares the changing trends of the measured spectrum with the simulated spectrum, thereby avoiding the situation where the same substances with different contents are judged as different substances due to simple similarity comparison. Because the most suitable model is automatically matched (such as a carbon model for coal and an oxide model for ore), errors caused by using the wrong model can be avoided, thereby increasing the accuracy of standard-free quantification.

[0044] Furthermore, determining the type of the main dark matter includes the following steps:

[0045] Obtain the scattering peak in a specific energy range in the actual spectrum;

[0046] Different types of dark matter will produce characteristic scattering peaks at specific energies. The characteristic scattering peaks are compared with a pre-established dark matter characteristic peak database to determine the type of main dark matter.

[0047] Furthermore, when the element content reaches a certain level, the ratio of the analytical line intensity to the scattered radiation intensity is basically independent of the matrix. Based on this characteristic, the scattered peak of dark matter can be used to participate in quantification, thereby achieving accurate quantification of the target element, thereby avoiding the problem of incorrect normalization calculation caused by dark matter, and further increasing the accuracy of the standardless quantitative detection results.

[0048] In detail, the dark matter characteristic peak database was established by collecting and analyzing XRF spectra of a large number of standard samples with known dark matter components. The database includes the characteristic scattering peak energy and intensity information corresponding to various dark matter under different conditions.

[0049] Specifically, the dark matter characteristic peak database of the embodiment of the present invention includes spectra of 200 standard samples of known components (including coal, minerals, and polymers), and the spectra are marked with their corresponding dark matter types (such as carbon and oxygen). The data can be directly obtained through big data or a limited number of experiments.

[0050] Furthermore, in step S5, the simulated spectrum is fully matched with the actual spectrum, including:

[0051] Step S51, using the least squares method to calculate the similarity between the simulated spectrum and the actual spectrum;

[0052] Step S52: Obtain the correlation between each element and the similarity, generate a priority sequence for the elements from high to low according to the correlation, and adjust the element content in sequence according to the priority sequence;

[0053] Step S53, obtaining the theoretical fluorescence intensity of each substance, generating a simulated spectrum and repeating the above steps;

[0054] Step S54 , in response to the similarity gradually increasing and the increase amplitude becoming smaller and smaller, the change amplitude is compared with the amplitude threshold, and if the change amplitude is smaller than the amplitude threshold, it is determined that the detection is completed.

[0055] In detail, the magnitude of the correlation can be determined based on the degree of fluctuation of the similarity when different elements are adjusted by the same amplitude. For example, when the iron content increases by 1%, the intensity of the simulation spectrum at a certain key energy point is closer to the actual value, and the similarity increases greatly, indicating that the correlation between iron and the similarity is greater; conversely, when the silicon content increases by 1%, the intensity of the simulation spectrum at a certain key energy point changes little or increases negatively, indicating that the correlation between silicon and the similarity is smaller.

[0056] Furthermore, by comparing the correlation between different elements and similarity, a priority sequence can be generated for the elements according to the correlation from high to low, and the elements with greater influence are adjusted first, thereby accelerating the convergence speed and increasing the detection speed.

[0057] In detail, the element contents are adjusted in sequence, and the gradient descent algorithm is used to adjust the content of each element. The adjustment step size is adaptively adjusted according to the similarity difference. In the embodiment of the present invention, taking iron as an example, when the similarity difference is greater than 20%, the iron content adjustment step size is 5%; when the similarity is less than or equal to 20%, the iron content adjustment step size is 1%. The adjustment step size for trace elements should be even smaller.

[0058] Specifically, the present invention increases the fitting speed by using large-step fast adjustment and small-step fine adjustment, while avoiding excessive adjustment or insufficient adjustment accuracy caused by using the same step size, thereby further increasing the accuracy of the non-standard quantitative detection results.

[0059] Furthermore, the spectrum library stores spectra of various samples, as well as the main components of dark matter of the samples corresponding to the spectra, scattering peak characteristic parameters and corresponding standardless quantitative model optimization parameters. The optimization parameters include normalization factor, background correction coefficient and spectrum peak fitting offset, among which: the determination and use of the values of normalization factor, background correction coefficient and spectrum peak fitting offset are all existing technologies and will not be elaborated here.

[0060] In detail, the construction of the atlas library includes XRF spectrum acquisition of standard samples, dark matter component calibration, and standard-free quantitative model training. The standard samples include typical matrix types in the target detection field, and each standard sample is associated with spectral data of at least three different concentration gradients.

[0061] Specifically, in step S3, calculating the similarity between the change trends of the preset spectrum graph and the actual spectrum graph further includes the following steps:

[0062] Step S31, segmenting the preset spectrum and the actual spectrum according to energy intervals to form a number of corresponding sub-spectra and recording them as a comparison group;

[0063] Step S32, using symbolic trend coding to analyze the change trend in each sub-spectrum to obtain a corresponding symbol sequence;

[0064] Step S33, calculating the intra-group similarity of the symbol sequences in the comparison group, and determining the weight coefficient of the comparison group according to the length of the energy interval;

[0065] Step S34: Calculate the change trend similarity based on the intra-group similarity and the weight coefficient.

[0066] Furthermore, the present invention cuts the spectrum into several segments (such as 0-5keV, 5-15keV) through symbolic trend coding, and each segment is represented by an arrow to indicate the trend. If the order of the arrows in two segments is the same, they are considered to be the same type of samples even if the concentrations are different. In this way, there is no need to worry about the specific values, but only to look at the shape trend, avoiding misjudgment caused by different concentrations, increasing the accuracy of selecting the preset spectrum graph, and providing an accurate basis for the subsequent similarity judgment, thereby further increasing the accuracy of the unlabeled quantitative detection results.

[0067] In detail, the standard-free quantitative model is configured as a relationship model between experimental parameters, dark matter types and theoretical fluorescence intensities of different elements. The experimental parameters and dark matter types are input, and the theoretical fluorescence intensity of each substance is output.

[0068] Furthermore, the standardless quantitative model includes a segmented multi-parameter background compensation algorithm to eliminate background noise generated by incident X-ray scattering, detector Compton escape effect, and incomplete charge collection effect;

[0069] The compensation algorithm includes one or more combinations of polynomial fitting, cubic spline interpolation, B-spline interpolation and Snip algorithm.

[0070] Furthermore, during the actual detection process, background can have a certain impact on the test results, especially on trace element results. The background influence comes from the scattering of the incident X-rays (Compton scattering and Rayleigh scattering), the Compton scattering escape effect of the detector, the incomplete charge collection effect of the detector, and the photoelectron and Auger electron escape effect. Therefore, during the quantitative calculation process using the fundamental parameter method, it is necessary to eliminate the influence of this background on the element peak fitting through back-scaling, thereby further improving the accuracy of the standardless quantitative detection results.

[0071] Specifically, the segmented background compensation is divided into multiple compensation segments according to the energy interval or the position of the element characteristic peak. Each compensation segment independently selects the background compensation algorithm and sets the parameters. During the compensation process, the contribution of each segment of background noise to the element peak fitting is calculated in real time and the compensation weight is dynamically adjusted. The segmented compensation process is all existing technology and will not be described in detail here.

[0072] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. A person skilled in the art would be able to make other variations or modifications based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. An accurate label-free quantitative method based on automatic identification and matching of different matrices, characterized in that: include: Test samples and pre-process test data; Obtain the actual fluorescence intensity and actual spectrum of the substance being tested, and determine the type of the main dark matter based on the actual spectrum; Obtain preset spectra in the spectrum library, calculate the similarity of the change trend of each preset spectrum and the actual spectrum respectively, select the preset spectrum with the highest change trend similarity as the spectrum corresponding to the current test sample and select the corresponding standard-free quantitative model; Inputting the main dark matter species and experimental parameters into a standard-free quantitative model to obtain theoretical fluorescence spectra of each element, and fitting the theoretical fluorescence spectra to obtain simulated spectra; Performing full spectrum matching on the simulated spectrum and the actual spectrum and calculating similarity, outputting the content of each element in the sample in response to the similarity being greater than a similarity threshold, outputting new content of each element in response to the similarity being less than or equal to the similarity threshold, and re-performing full spectrum matching on the simulated spectrum and the actual spectrum until the matching results converge; The experimental parameters include the voltage and current of the X-ray tube and the measurement time.

2. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 1, characterized in that: The types of dark matter identified include: Obtain the scattering peak in a specific energy range in the actual spectrum; The characteristic scattering peak is compared with a pre-established dark matter characteristic peak database to determine the type of the main dark matter.

3. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 2, characterized in that: The dark matter characteristic peak database includes characteristic scattering peak energy and intensity information corresponding to various dark matter under different conditions.

4. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 1, characterized in that: Performing full spectrum matching on the simulated spectrum and the actual spectrum, including: The least squares method was used to calculate the similarity between the simulated and actual spectra; Obtaining the correlation between each element and the similarity, generating a priority sequence for the elements from high to low according to the correlation, and adjusting the element content in sequence according to the priority sequence; Obtain the theoretical fluorescence intensity of each substance, generate a simulated spectrum and repeat the above steps; In response to the similarity gradually increasing and the increase amplitude becoming smaller and smaller, the change amplitude is compared with the amplitude threshold, and if the change amplitude is smaller than the amplitude threshold, it is determined that the detection is completed.

5. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 4, characterized in that: The element contents are adjusted in sequence, and the content of each element is adjusted using a gradient descent algorithm, and the adjustment step size is adaptively adjusted according to the difference in similarity.

6. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 1, characterized in that: The spectrum library stores spectra of various samples, as well as the main components of dark matter of the samples corresponding to the spectra, scattering peak characteristic parameters and corresponding standardless quantitative model optimization parameters, the optimization parameters including normalization factor, background correction coefficient and spectrum peak fitting offset.

7. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 6, characterized in that: The calculating of the similarity between the change trends of the preset spectrum graph and the actual spectrum graph further comprises the following steps: The preset spectrum and the actual spectrum are segmented according to energy intervals to form a number of corresponding sub-spectra and recorded as a comparison group; Use symbolic trend coding to analyze the changing trend in each sub-spectrum graph and obtain the corresponding symbol sequence; Calculating the intra-group similarity of the symbol sequences in the comparison group, and determining the weight coefficient of the comparison group according to the length of the energy interval; The change trend similarity is calculated based on the intra-group similarity and the weight coefficient.

8. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 1, characterized in that: The standard-free quantitative model is configured as a relationship model between experimental parameters, dark matter types and theoretical fluorescence intensities of different elements. The experimental parameters and dark matter types are input, and the theoretical fluorescence intensity of each substance is output.

9. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 8, characterized in that: The standardless quantitative model includes a segmented multi-parameter background compensation algorithm for eliminating background noise generated by incident X-ray scattering, detector Compton escape effect, and incomplete charge collection effect; The compensation algorithm includes one or more combinations of polynomial fitting, cubic spline interpolation, B-spline interpolation and Snip algorithm.

10. The accurate label-free quantitative method based on automatic identification and matching of different matrices according to claim 9, characterized in that: The segmented multi-parameter background compensation is divided into multiple compensation segments according to energy intervals or element characteristic peak positions. Each compensation segment independently selects a background compensation algorithm and sets parameters. During the compensation process, the contribution of each segment's background noise to the element peak fitting is calculated in real time and the compensation weight is dynamically adjusted.

Images