CN118169110A - Spectral analysis method, sample component analysis method and device, equipment and medium - Google Patents
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- 238000004458 analytical method Methods 0.000 title claims abstract description 49
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Abstract
The invention provides a spectrum analysis method, a sample component analysis method, a device, equipment and a medium, wherein the spectrum characteristic peak analysis method comprises the following steps: dividing the wavelength range of the spectrometer into a plurality of intervals according to the preset fitting window width, and fitting the spectral data to be processed in the intervals respectively to obtain a base line; splicing the baselines to obtain a full spectrum baseline; changing the width of a fitting window and repeating the steps to obtain a plurality of groups of full spectrum baselines; dividing the wavelength range of the spectrometer into a plurality of window intervals according to the preset baseline comparison window width, and calculating correlation indexes of a plurality of groups of full spectrum baselines in each window interval so as to obtain window intervals in which characteristic peaks possibly exist; and combining the standard spectral line database to obtain the characteristic peak position. The method solves the problem that the corresponding characteristic peak cannot be extracted due to the fact that the characteristic peak with the current peak broadening and weak signal is removed by a baseline correction algorithm in the spectrum preprocessing stage, and improves the accuracy of quantitative analysis.
Description
Technical Field
The present invention relates to the field of spectrum analysis technologies, and in particular, to a spectrum analysis method, a sample component analysis method, a device, equipment, and a medium.
Background
The material components in the industries such as metallurgy and the like are core parameters of process control and evaluation indexes of product quality, and component detection plays a very important role in various technical and economic indexes such as smelting degree of smelting products, quality of the smelting products and recovery rate of metals. The online real-time detection is one of the difficult problems of intelligent improvement and upgrading of smelting, especially the online detection of the components of high-temperature melt. At present, the detection of the material components in the smelting process adopts an off-line laboratory detection mode, so that the problems of insufficient real-time performance, insufficient reliability, insufficient guidance, insufficient safety and the like exist, the real-time monitoring, fine control and process improvement of the material components are not facilitated, and the intelligent upgrading of corresponding process nodes cannot be supported. The component analyzer based on Laser Induced Breakdown Spectroscopy (LIBS) can detect the components of the process materials, has the characteristics of no sampling, no sample preparation, no contact and no radiation, and helps to realize closed-loop control based on real-time component guiding process regulation, thereby realizing accurate process control, optimizing process connection efficiency and supporting intelligent construction.
The laser-induced breakdown spectroscopy is an emerging atomic emission spectroscopy analysis technology, and qualitative and quantitative analysis of substance components is realized by detecting the position and intensity information of spectral peaks, and the identification of the position of the spectral peaks and the fitting effect of spectral line intensity directly influence the accuracy of LIBS analysis. The conventional peak searching algorithm comprises a direct comparison method, a derivative method, a curve fitting method and the like, but for characteristic peaks with peak broadening and weak signals, the condition that the characteristic peaks are judged to be background spectra and removed by a baseline correction algorithm easily occurs in a spectrum preprocessing stage, so that the condition that the corresponding characteristic peaks cannot be extracted and the characteristic peak areas cannot be calculated is caused, and the quantitative accuracy is influenced.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention provides a spectrum analysis method, a sample component analysis method, a device, and a medium, so as to solve the technical problem that the characteristic peak of a weak signal is difficult to determine the position of a spectrum peak.
To achieve the above and other related objects, the present invention provides a method for analyzing spectral characteristic peaks, comprising the steps of: dividing the wavelength range of the spectrometer into a plurality of intervals according to the preset fitting window width, and fitting the spectral data to be processed in the intervals respectively to obtain a base line; splicing the baselines to obtain a full spectrum baseline; changing the width of the fitting window and repeating the steps to obtain a plurality of groups of full spectrum baselines; dividing the wavelength range of the spectrometer into a plurality of window intervals according to the preset baseline comparison window width, calculating a plurality of groups of correlation indexes of the full spectrum baselines in each window interval, and obtaining window intervals with possible characteristic peaks according to the correlation indexes and a preset correlation threshold; and obtaining the position of the characteristic peak according to the window interval in which the characteristic peak possibly exists and the standard spectral line database.
In an embodiment of the present invention, the step of changing the width of the fitting window and repeating the above steps to obtain a plurality of groups of full spectrum baselines includes: sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than the upper limit of the wavelength range, so as to obtain a plurality of groups of full spectrum baselines.
In an embodiment of the present invention, the preset fitting window width is len min, and the maximum value of the fitting window width is len max; sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than the upper limit of the wavelength range, and obtaining a plurality of groups of full spectrum baselines comprises the following steps: sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than len max, so as to obtain a plurality of groups of full spectrum baselines.
In an embodiment of the present invention, the step of obtaining a window interval in which a characteristic peak may exist according to the correlation index and a preset correlation threshold value includes: if the correlation threshold of the window interval is smaller than the preset correlation threshold, the window interval is a window interval with possible characteristic peaks, otherwise, the window interval is a window interval without characteristic peaks.
In an embodiment of the present invention, the step of obtaining the position of the characteristic peak according to the window interval where the characteristic peak may exist and the standard spectral line database includes: and calculating the intersection of the wavelength range corresponding to the window interval in which the characteristic peak possibly exists and the characteristic peak wavelength of each substance contained in the standard spectral line database to obtain the characteristic peak position.
In an embodiment of the present invention, the step of obtaining the position of the characteristic peak further includes the following steps according to the window interval where the characteristic peak may exist and the standard spectral line database: and carrying out multimodal fitting on the characteristic peaks to obtain characteristic peak parameter information.
To achieve the above and other related objects, the present invention also provides a method for analyzing a sample component, comprising the steps of: laser is sent out to ablate a sample to be detected to form plasma; acquiring characteristic spectrums emitted by the plasmas to obtain spectrum data; analyzing the spectrum data by utilizing the spectrum characteristic peak analysis method to obtain the element types of the sample to be detected and the characteristic peak positions of each element; performing multimodal fitting on the characteristic peaks to obtain the spectral line intensity of each element; and analyzing and obtaining the element content of each element according to the spectral line intensity.
In an embodiment of the present invention, the step of analyzing the element content of each element according to the spectral line intensity includes: inputting the spectral line intensities of the detected element and the internal standard element into a preset concentration curve to obtain the element content of the detected element; repeating the steps to obtain the element content of each element.
To achieve the above and other related objects, the present invention also provides a sample component analysis apparatus comprising: the laser is used for emitting laser to ablate the sample to be detected to form plasma; the spectrometer is used for collecting characteristic spectrums emitted by the plasmas to obtain spectrum data; the characteristic peak analysis module is used for analyzing the spectrum data by utilizing the spectrum characteristic peak analysis method to obtain the element types of the sample to be detected and the characteristic peak positions of each element; the multimodal fitting module is used for multimodal fitting the characteristic peaks to obtain the spectral line intensity of each element; and the quantitative analysis module is used for analyzing and obtaining the element content of each element according to the spectral line intensity.
To achieve the above and other related objects, the present invention also provides an electronic device including a processor, a memory, and a communication bus; the communication bus is used for connecting the processor and the memory; the processor is configured to execute a computer program stored in the memory to implement a method as provided in any one of the embodiments above.
To achieve the above and other related objects, the present invention also provides a computer-readable storage medium having stored thereon a computer program for causing a computer to perform the method provided in any one of the above embodiments.
The invention has the beneficial effects that: according to the spectrum analysis method, the sample component analysis device, the sample component analysis equipment and the sample component analysis medium, the spectrum analysis method divides different intervals, then the fitted baselines of the intervals are spliced into the full spectrum baselines, the correlation of the full spectrum baselines obtained by fitting the different intervals in the width of the comparison window of the baselines is calculated, so that whether the widened weak peaks exist in the window is determined, and finally the accurate peak positions can be obtained according to the standard spectral lines, thereby solving the problems that the corresponding characteristic peaks cannot be extracted and the characteristic peak areas cannot be calculated due to the fact that the characteristic peaks with weak signals are removed through a baseline correction algorithm in the spectrum preprocessing stage at present, identifying and fitting the widened weak peaks, and improving the accuracy of quantitative analysis.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a first spectral feature peak analysis method according to an embodiment of the present invention;
FIG. 2 is a spectrum diagram of spectral data to be processed according to an embodiment of the present invention;
FIG. 3 is an enlarged schematic view of a portion of the bands of FIG. 2;
FIG. 4 is a flowchart of a second spectral feature peak analysis method according to an embodiment of the present invention;
FIG. 5 is a flow chart of a first sample component analysis method according to an embodiment of the present invention;
FIG. 6 is a flow chart of a second method for analyzing components of a sample according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a sample component analysis apparatus according to an embodiment of the present invention;
FIG. 8 is a graph showing the concentration of a sample quantitatively analyzed according to the conventional peak searching method according to an embodiment of the present invention;
FIG. 9 is a graph showing the concentration of the peak searching method according to FIG. 1 according to the present invention;
Fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Reference numerals illustrate: 101. a laser; 102. a spectrometer; 103. a characteristic peak analysis module; 104. a multimodal fitting module; 105. a quantitative analysis module; 201. a processor; 202. a memory.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which is to be read in light of the following specific examples. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. In addition to the specific methods, devices, materials used in the embodiments, any methods, devices, and materials of the prior art similar or equivalent to those described in the embodiments of the present invention may be used to practice the present invention according to the knowledge of one skilled in the art and the description of the present invention.
It is to be understood that the terminology used in the examples of the invention is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in some of which well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Laser-induced breakdown spectroscopy is an emerging atomic emission spectroscopy analysis technology, and qualitative and quantitative analysis of material components is realized by detecting the position and intensity information of spectral peaks. In the actual detection spectrum, peak broadening and characteristic peaks with weak signals, such as characteristic peaks at a wavelength of 656nm in fig. 2, often occur, and the possible reasons for the occurrence of the characteristic peaks are caused by overlapping of a plurality of hydrogen element peaks. The characteristic peaks are very easy to appear in the condition of judging background spectrum and removing the characteristic peaks through a baseline correction algorithm in a spectrum preprocessing stage, so that the condition that corresponding characteristic peaks cannot be extracted and the area of the characteristic peaks cannot be calculated is caused, and the quantitative accuracy is influenced. The spectrum characteristic peak analysis method provided by the invention can well solve the problem.
For example, fig. 2 shows spectrum data to be processed, fig. 3 shows broadened weak peaks in a certain wave band (651-662) in fig. 2, where a peak actually exists, but the signal is weak, and is easy to remove by the existing peak searching method.
Referring to fig. 1, fig. 1 is a spectrum characteristic peak analyzing method according to an embodiment of the invention, which includes the following steps: s301, dividing a wavelength range of a spectrometer into a plurality of intervals according to a preset fitting window width, and fitting spectral data to be processed in the intervals respectively to obtain a base line. It can be appreciated that each spectrometer has its own performance parameters including a wavelength range, a wavelength resolution, a noise equivalent power, a dynamic range, etc., where a wavelength range refers to a range of wavelengths of light waves that can be measured by the spectrometer, different types of spectrometers may cover different wavelength bands from ultraviolet light to infrared light, and the selection of the wavelength range depends on the test requirements of the user; wavelength resolution is the ability of a spectrometer to resolve two close wavelengths, typically expressed in terms of a minimum resolvable wavelength difference, and a high wavelength resolution spectrometer is capable of distinguishing very close spectral lines, which is important for applications requiring accurate measurements.
It will be appreciated that, for the spectral data to be processed, we can acquire the wavelength range of the spectrometer that collects the spectral data, and when the interval division is performed, each parameter can be in units of wavelength or in units of band number.
When the preset window width is taken as a wavelength unit, the number of the intervals is calculated by dividing the wavelength range by the preset window width. For the convenience of calculation, in an embodiment of the present invention, the total number of bands of the spectrometer is recorded as N (i.e. the ratio of the wavelength range to the wavelength resolution) in units of the number of bands, the spectrum abscissa is marked as 1 to N, when the fitting window width is len, the number of intervals is roundup (N/len), wherein roundup () is rounded up, and it is understood that if N is not divisible by len, the last interval is (roundup (N/len) -1) xlen to N.
The spectral characteristic peak analysis method further comprises the following steps: s302, splicing the baselines to obtain a full spectrum baseline. S303, changing the width of the fitting window and repeating the steps to obtain a plurality of groups of full spectrum baselines. The base line is obtained by fitting or is spliced, which is the conventional operation in spectrum data processing, except that the invention obtains different full spectrum base lines by adopting fitting window widths with different sizes, and the group number of the full spectrum base lines is equal to the number of values taken by the fitting window widths. For example, in one embodiment, the fit window width takes a total of P values, and then we can obtain P sets of full spectrum baselines.
The spectral characteristic peak analysis method further comprises the following steps: s304, dividing the wavelength range of the spectrometer into a plurality of window intervals according to the preset baseline comparison window width, calculating correlation indexes of a plurality of groups of full spectrum baselines in each window interval, and obtaining the window interval with possible characteristic peaks according to the correlation indexes and the preset correlation threshold. If the baseline contrast window width is M, and the unit is the same number of bands, the total number of bands is N, and can be divided into roundup (N/M) window sections. Then, the correlation of a plurality of groups of full spectrum baselines is required to be compared in 1-round dup (N/M) window intervals, each window outputs a correlation index, and then the correlation index is compared with a preset correlation threshold value, and the threshold value is required to be obtained by parameter adjustment. By comparing the window interval with a preset threshold value, whether a broadened weak peak exists in each window interval can be judged. It should be noted that in this step, the result of the judgment is obtained by processing the spectrum data, and the judgment is further performed in conjunction with the actual situation area, so that the "window area where the characteristic peak may exist" is obtained.
The spectral characteristic peak analysis method further comprises the following steps: and S305, obtaining the position of the characteristic peak according to the window interval where the characteristic peak possibly exists and a standard spectral line database. A standard spectral line database is an information base that contains spectral data of a large number of known substances, typically containing a large amount of spectral information of atoms, molecules or other substances, which is critical for scientific research and industrial applications. In short, for the window intervals in which the characteristic peaks may exist, which are calculated in step S304, each window interval corresponds to a wavelength range, and if the wavelength range does not include any spectral peaks of known substances, it is indicated that the window interval should be discarded.
In the embodiment, different intervals are divided, the baselines fitted by the intervals are spliced into a full spectrum baseline, the correlation of the full spectrum baselines obtained by fitting the different intervals in the width of a comparison window of the baselines is calculated, so that whether a widened weak peak exists in the window is determined, and finally, the accurate peak position can be obtained according to a standard spectral line, the problems that the corresponding characteristic peak cannot be extracted and the area of the characteristic peak cannot be calculated due to the fact that the characteristic peak with the current peak type is widened and the characteristic peak with the weak signal is removed by a baseline correction algorithm in a spectrum preprocessing stage are solved, the widened weak peak is identified and fitted, and the accuracy of quantitative analysis is improved.
In one embodiment of the present invention, step S303 includes: sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than the upper limit of the wavelength range, so as to obtain a plurality of groups of full spectrum baselines. The adjustment of the width of the fitting window is realized by sequentially increasing the preset value, so that the fitting window is easier to realize in terms of program. Alternatively, the fitting window width may be adjusted in a sequentially decreasing manner.
In a specific embodiment of the present invention, the preset fitting window width is len min, and the maximum value of the fitting window width is len max; sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than the upper limit of the wavelength range, and obtaining multiple groups of full spectrum baselines comprises the following steps: sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than len max, so as to obtain a plurality of groups of full spectrum baselines. The introduction parameters len min and len max correspond to setting a lower limit and an upper limit for the variation range of len. It will be appreciated that len min takes a minimum of 1 (in practice, this is not the case and typically len min≥△N）,lenmax takes a maximum of N.
It will be appreciated that in this embodiment, the fitting window width is sequentially increased, so that the initial value of len is len min, and if the increased preset value is Δn, then it is equivalent to that len takes { len min,lenmin+△N,lenmin+2△N,lenmin +3 Δn, … } respectively, until the value of len is greater than len max, and each len corresponds to a section dividing method, and the finally obtained full spectrum baseline group number p= rounddown ((len max-lenmin)/Δn) +1 is obtained, where rounddown () is a downward rounding.
In a specific embodiment of the present invention, the step of obtaining a window interval in which a characteristic peak may exist according to the correlation index and a preset correlation threshold value includes: if the correlation threshold of the window interval is smaller than the preset correlation threshold, the window interval is a window interval with possible characteristic peaks, otherwise, the window interval is a window interval without characteristic peaks. By comparing the correlation threshold value with a preset correlation threshold value, each window interval can be conveniently judged to be possible or not to exist.
In one embodiment of the present invention, step S305 includes: and calculating the intersection of the wavelength range corresponding to the window interval in which the characteristic peak possibly exists and the characteristic peak wavelength of each substance contained in the standard spectral line database to obtain the position of the characteristic peak. It can be understood that, for the convenience of calculation, the unit of the window interval where the characteristic peak may exist is the number of bands corresponding to the spectrum abscissa, it is necessary to convert the window interval into the wavelength range first, there are a plurality of window intervals where the characteristic peak may exist, and the wavelength ranges corresponding to the window intervals are regarded as the first set. And then regarding the characteristic peak wavelength of each substance contained in the standard spectral line database as a second set, and solving the intersection of the first set and the second set to obtain the position of the characteristic peak.
Referring to fig. 4, in some cases, in addition to the position of the characteristic peak, more information of the characteristic peak needs to be obtained, so the following steps are further included after step S305: s400', carrying out multimodal fitting on the characteristic peaks to obtain characteristic peak parameter information. The characteristic peak parameter information may be, for example, parameters such as characteristic peak intensity and width; multimodal fitting is also a more common operation in the spectral domain, for example lorentz or voigt peak-type fitting can be used, the principle being that each peak is assumed to be generated by one or more exciton states, each exciton state can represent its energy and lifetime by a complex number, and then the best fit parameters can be solved by using the least squares method, and can be used to describe a single or multiple characteristic peaks of the emission spectrum, thereby obtaining characteristic peak parameter information. And respectively calculating peak areas of the fitted characteristic peak parameters as the spectral line intensity, and summing the characteristic peaks containing a plurality of measured elements or internal standard elements to obtain the spectral line intensity.
Referring to fig. 5, fig. 5 is a sample component analysis method according to an embodiment of the invention, which includes the following steps: s100, laser ablation is sent out to ablate a sample to be detected to form plasma; s200, acquiring a characteristic spectrum emitted by plasma to obtain spectrum data; s300, analyzing the spectrum data by utilizing the spectrum characteristic peak analysis method to obtain the element types of the sample to be detected and the characteristic peak positions of each element; s400, carrying out multimodal fitting on characteristic peaks to obtain the spectral line intensity of each element; s500, analyzing and obtaining the element content of each element according to the spectral line intensity.
In step S500, quantitative calculation is performed on the element content according to the spectral line intensity, and two common quantitative methods in chromatographic analysis are an internal standard method and an external standard method. The internal standard method is an indirect or relative calibration method, and the content of the detected component is calculated according to the mass ratio of the detected component and the internal standard and the ratio of the chromatographic peak area thereof by adding a certain amount of pure substance (internal standard) into an accurately weighed sample; the method can effectively offset errors caused by sample preparation and instrument fluctuation, and improves the accuracy of analysis results; the internal standard method is suitable for cases where high precision analysis is required, especially when the matrix of the sample is complex or difficult to accurately measure. The external standard method is a direct calibration method, and a standard curve is manufactured by using a pure product of a component to be measured as a reference substance; measuring the peak area or peak height of the sample, and finding out the corresponding concentration on a standard curve; the external standard method is simple to operate, convenient to calculate, free from measuring correction factors, and suitable for automatic analysis and rapid detection; it requires a better repeatability of the instrument and a higher stability of the operating conditions.
Referring to fig. 6, steps S300 to S600 are more detailed, and another embodiment of the sample component analysis method is described in detail in the related steps of the spectrum analysis method, which will not be described herein.
In a specific embodiment of the present invention, modeling is performed by an internal standard method, and a matrix element with relatively stable element content is determined from a sample to be detected as an internal standard element, I C and I R respectively represent the spectral line intensities of the detected element and the internal standard element, and linear regression can be performed on (I C/IR) and (C C) to establish a concentration curve C C=K*(IC/IR)+B,CC as reference concentrations obtained by other assay methods, so as to obtain values of K and B. At the time of detection, the values of K and B are known, and I C and I R can be obtained by step S400, so that the concentration of the element to be detected can be calculated from the formula.
In an embodiment of the present invention, step S500 includes: inputting the spectral line intensities of the detected element and the internal standard element into a preset concentration curve to obtain the element content of the detected element; repeating the steps to obtain the element content of each element.
Referring to fig. 7, fig. 7 is a sample component analysis device according to an embodiment of the invention, including: the laser is used for emitting laser to ablate the sample to be detected to form plasma; the spectrometer is used for collecting characteristic spectrums emitted by the plasmas to obtain spectrum data; the characteristic peak analysis module is used for analyzing the spectrum data by utilizing the spectrum characteristic peak analysis method to obtain the element types of the sample to be detected and the characteristic peak positions of each element; the multimodal fitting module is used for multimodal fitting of the characteristic peaks to obtain the spectral line intensity of each element; and the quantitative analysis module is used for analyzing and obtaining the element content of each element according to the spectral line intensity. It will be appreciated that the laser and spectrometer may be integrated and that the subject in fig. 7 is for illustration only.
The sample component analysis device of the present embodiment corresponds to the sample component analysis method described above, and the functional module in the sample component analysis device corresponds to the corresponding step in the sample component analysis method. The sample component analysis apparatus of the present embodiment can be implemented in conjunction with the sample component analysis method, that is, the related technical details mentioned in the sample component analysis method described above can be applied to the sample component analysis apparatus of the present embodiment without collision.
It should be noted that each of the above functional modules may be fully or partially integrated into one physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, some or all of the steps of the above methods, or the above functional modules, may be implemented by integrated logic circuits of hardware in the processor element or instructions in the form of software.
Taking the test of the content of H 2 O in alumina as an example, in the prior art, the peak type of the detected element hydrogen element in most samples is widened, the signal is weak, the detected element hydrogen element is judged to be a background spectrum in a spectrum pretreatment stage and is removed by a baseline correction algorithm, so that in fig. 8, the ratio (I C/IR) of the intensities of the detected element and the spectrum line of an internal standard element in most samples is 0, and the quantitative analysis concentration curve R2 is too low to perform quantitative analysis. After the weak peak analysis is carried out by adopting the method, the characteristic peak of the hydrogen element can be positioned, the intensity can be quantified, the quantitative analysis concentration curve is shown in figure 9, and the R2 reaches more than 0.97. The model is adopted to calculate, the deviation is shown in the following table, and the table shows that the accuracy of quantitative analysis is improved by the weak peak analysis method.
Table 1: concentration and deviation of quantitative analysis by peak searching method of the invention
| Sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Reference concentration | 0.027 | 0.037 | 0.038 | 0.057 | 0.086 | 0.084 | 0.088 | 0.081 | 0.080 | 0.107 |
| Measured concentration | 0.024 | 0.039 | 0.039 | 0.058 | 0.081 | 0.086 | 0.086 | 0.083 | 0.089 | 0.100 |
| Absolute deviation of | 0.004 | 0.002 | 0.002 | 0.001 | 0.005 | 0.002 | 0.001 | 0.002 | 0.009 | 0.006 |
Referring to fig. 10, fig. 10 is a schematic diagram showing an electronic device according to an embodiment of the present invention, including a processor, a memory, and a communication bus; the communication bus is used for connecting the processor and the memory; the processor is configured to execute a computer program stored in the memory to implement the spectral feature peak analysis method of any one of the above.
An embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program for causing a computer to execute the spectral feature peak analysis method according to any one of the above.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. The method for analyzing the spectral characteristic peak is characterized by comprising the following steps of:
dividing the wavelength range of the spectrometer into a plurality of intervals according to the preset fitting window width, and fitting the spectral data to be processed in the intervals respectively to obtain a base line;
Splicing the baselines to obtain a full spectrum baseline;
Changing the width of the fitting window and repeating the steps to obtain a plurality of groups of full spectrum baselines;
Dividing the wavelength range of the spectrometer into a plurality of window intervals according to the preset baseline comparison window width, calculating a plurality of groups of correlation indexes of the full spectrum baselines in each window interval, and obtaining window intervals with possible characteristic peaks according to the correlation indexes and a preset correlation threshold;
and obtaining the position of the characteristic peak according to the window interval in which the characteristic peak possibly exists and the standard spectral line database.
2. The method of claim 1, wherein the step of changing the width of the fitting window and repeating the above steps to obtain a plurality of groups of full spectrum baselines comprises:
Sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than the upper limit of the wavelength range, so as to obtain a plurality of groups of full spectrum baselines.
3. The spectral feature peak analysis method according to claim 2, wherein the preset fitting window width is len min, and the maximum value of the fitting window width is len max;
Sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than the upper limit of the wavelength range, and obtaining a plurality of groups of full spectrum baselines comprises the following steps:
Sequentially increasing the width of the fitting window by a preset value and repeating the steps until the width of the fitting window is larger than len max, so as to obtain a plurality of groups of full spectrum baselines.
4. The method for analyzing spectral characteristic peaks according to claim 1, wherein the step of obtaining a window section in which characteristic peaks may exist according to the correlation index and a preset correlation threshold value includes:
if the correlation threshold of the window interval is smaller than the preset correlation threshold, the window interval is a window interval with possible characteristic peaks, otherwise, the window interval is a window interval without characteristic peaks.
5. The method for resolving a spectral characteristic peak according to claim 1, wherein the step of obtaining the position of the characteristic peak according to a window interval in which the characteristic peak may exist and a standard spectral line database comprises:
and calculating the intersection of the wavelength range corresponding to the window interval in which the characteristic peak possibly exists and the characteristic peak wavelength of each substance contained in the standard spectral line database to obtain the characteristic peak position.
6. A method for analyzing a sample component, comprising the steps of:
Laser is sent out to ablate a sample to be detected to form plasma;
acquiring characteristic spectrums emitted by the plasmas to obtain spectrum data;
Analyzing the spectrum data by using the spectrum characteristic peak analysis method according to any one of claims 1-5 to obtain the element types of the sample to be detected and the characteristic peak positions of each element;
performing multimodal fitting on the characteristic peaks to obtain the spectral line intensity of each element;
And analyzing and obtaining the element content of each element according to the spectral line intensity.
7. The method according to claim 6, wherein the step of analyzing the element content of each element based on the spectral line intensities comprises:
inputting the spectral line intensities of the detected element and the internal standard element into a preset concentration curve to obtain the element content of the detected element;
repeating the steps to obtain the element content of each element.
8. A sample component analysis apparatus, comprising:
the laser is used for emitting laser to ablate the sample to be detected to form plasma;
The spectrometer is used for collecting characteristic spectrums emitted by the plasmas to obtain spectrum data;
The characteristic peak analyzing module is configured to analyze the spectral data by using the spectral characteristic peak analyzing method according to any one of claims 1 to 5, so as to obtain the element types of the sample to be detected and the characteristic peak positions of each element;
the multimodal fitting module is used for multimodal fitting the characteristic peaks to obtain the spectral line intensity of each element; and
And the quantitative analysis module is used for analyzing and obtaining the element content of each element according to the spectral line intensity.
9. An electronic device comprising a processor, a memory, and a communication bus; the communication bus is used for connecting the processor and the memory; the processor is configured to execute a computer program stored in the memory, so as to implement the method according to any one of claims 1 to 5.
10. A computer readable storage medium, having stored thereon a computer program for causing a computer to perform the method of any one of claims 1-5.
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