Portable human body rubidium trace element detector and spectrum detection method

Abstract

The invention relates to the technical field of spectral measurement, in particular to a portable human body rubidium microelement detector and a spectral detection method. Converting the spectral data to obtain an absorbance curve; carrying out data segmentation on the absorbance curve, and calculating to obtain a baseline drift degree of the absorbance curve based on a segmentation result; performing baseline correction on the absorbance curve based on the baseline drift degree in combination with an iterative adaptive baseline correction algorithm to obtain an absorbance curve after baseline correction; and comparing the absorbance curve after the baseline correction with a standard absorbance-concentration curve to obtain the concentration of the rubidium element. According to the method, for the phenomenon that the content of the rubidium element is relatively low, the baseline drift degree of the spectral data is analyzed in a targeted manner, and the baseline drift degree is set as an iteration termination condition of an iteration self-adaptive baseline correction algorithm, so that a baseline drift part in the spectral data is separated, and the accurate content of the rubidium element in a sample is obtained on the basis of conversion.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral measurement, and particularly relates to a portable detector for rubidium trace elements in human body and a spectral detection method. Background Art

[0002] At present, a portable detector for rubidium trace elements can be combined with spectral detection technology, so that the detection of trace elements can be carried out without being restricted by laboratory conditions, and the content of rubidium elements in human body can be determined quickly and accurately. Usually, the portable detector integrates spectral detection technology inside, which can monitor the changes of rubidium trace elements in human body in real time. Combining with the precise analysis of spectral technology helps researchers better understand the role and influence of rubidium trace elements in human body.

[0003] However, the portable detector usually uses atomic absorption spectrometry to determine the content of rubidium elements in human samples. However, due to the instability of the working environment of the portable device, the spectral data is prone to baseline drift. But in the prior art, the iterative adaptive baseline correction algorithm is used to correct the baseline of the spectral data, and the setting of its convergence criterion is often a general quantity, which cannot effectively adapt to the use environment of the portable detector. Therefore, it has a great influence on the result of baseline correction and cannot accurately measure the content of rubidium elements in human body. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a portable detector for rubidium trace elements in human body and a spectral detection method to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows: In the first aspect, the present application provides a spectral detection method for rubidium trace elements in human body, including: Obtaining spectral data, where the spectral data is obtained by measuring rubidium elements in human samples using atomic absorption spectrometry; Converting the spectral data to obtain an absorbance curve; Segmenting the data of the absorbance curve and calculating the baseline drift degree of the absorbance curve based on the segmented result; Performing baseline correction on the absorbance curve based on the iterative adaptive baseline correction algorithm, and calculating the baseline drift degree once after each iteration of the baseline correction until the baseline drift degree is less than or equal to a first preset threshold, and then stopping the iteration to obtain the absorbance curve after baseline correction; Comparing the absorbance curve after baseline correction with a preset standard absorbance-concentration curve to obtain the concentration of rubidium elements in human samples.

[0005] In combination with the first aspect, in a possible implementation manner, segmenting the absorbance curve data and calculating the baseline drift degree of the absorbance curve based on the segmented result includes: Calculating the element performance degree of each absorption peak in the absorbance curve respectively according to a preset element performance degree calculation formula; Segmenting the absorbance curve based on the element performance degree of each absorption peak to obtain a plurality of background curves; Calculating the fluctuation trend degree of each background curve respectively; Calculating the fluctuation trend similarity between each background curve and its adjacent background curve based on all the fluctuation trend degrees; Calculating the baseline drift degree corresponding to the absorbance curve according to the fluctuation trend degree and the fluctuation trend similarity corresponding to each background curve.

[0006] In combination with the first aspect, in a possible implementation manner, segmenting the absorbance curve based on the element performance degree of each absorption peak to obtain a plurality of background curves includes: Judging one by one the magnitude relationship between the element performance degree of each absorption peak and a second preset threshold, and marking the absorption peaks with element performance degree greater than the second preset threshold to obtain marked peaks; Taking the positions of the minimum values among all the marked peaks as demarcation points, and dividing the absorbance curve into a plurality of the background curves based on all the demarcation points, and the element performance degree corresponding to each absorption peak in the background curve is less than the second preset threshold.

[0007] In combination with the first aspect, in a possible implementation manner, calculating the fluctuation trend degree of each background curve respectively includes: Dividing the background curve by a sliding window to obtain a plurality of sub-curves; Performing linear regression fitting on each sub-curve respectively to obtain the fitting line slope corresponding to each sub-curve; Calculating the variance value based on all the fitting line slopes; Calculating the average value of the element performance degrees based on the element performance degrees corresponding to all the absorption peaks in the background curve; Calculating the fluctuation trend degree of the absorption peak curve according to the variance value and the average value of the element performance degrees.

[0008] In combination with the first aspect, in a possible implementation manner, the method for obtaining a preset standard absorbance-concentration curve includes: Preparing several rubidium element standard solutions with different concentrations; Use atomic absorption spectrometry to measure rubidium in each rubidium element standard solution respectively, and obtain the standard spectral data corresponding to each element standard solution; Perform the above data segmentation, baseline drift degree calculation, and baseline correction on each standard spectral data respectively to obtain the standard absorbance curve corresponding to each element standard solution; Based on the concentration corresponding to each element standard solution and the rubidium element absorption peak in the standard absorbance curve, establish a standard absorbance-concentration curve.

[0009] Combined with the first aspect, in a possible implementation, the first preset threshold is 0.1. In a second aspect, the present application also provides a portable detector for human rubidium trace elements, including: An acquisition module for obtaining spectral data, which is obtained by measuring rubidium in a human sample using atomic absorption spectrometry; A graphic conversion module for converting the spectral data into an absorbance curve; A graphic segmentation module for segmenting the absorbance curve and calculating the baseline drift degree of the absorbance curve based on the segmented results; A curve correction module for performing baseline correction on the absorbance curve based on the iterative adaptive baseline correction algorithm, and calculating the baseline drift degree once after each iteration of the baseline correction until the baseline drift degree is less than or equal to the first preset threshold and then stopping the iteration to obtain the baseline-corrected absorbance curve; A content calculation module for comparing the baseline-corrected absorbance curve with a preset standard absorbance-concentration curve to obtain the concentration of rubidium in the human sample.

[0010] Combined with the second aspect, in a possible implementation, the graphic segmentation module includes: A first calculation module for calculating the element manifestation degree of each absorption peak in the absorbance curve respectively according to a preset element manifestation degree calculation formula; A background division module for segmenting the absorbance curve based on the element manifestation degree of each absorption peak to obtain several background curves; A second calculation module for calculating the fluctuation trend degree of each background curve respectively; A third calculation module for calculating the fluctuation trend similarity between each background curve and its adjacent background curve based on all the fluctuation trend degrees; A fourth calculation module for calculating the baseline drift degree corresponding to the absorbance curve according to the fluctuation trend degree and the fluctuation trend similarity corresponding to each background curve.

[0011] In combination with the second aspect, in a possible implementation, the background division module includes: A logic module for judging one by one the magnitude relationship between the element manifestation degree of each absorption peak and a second preset threshold, and marking the absorption peaks with an element manifestation degree greater than the second preset threshold to obtain marked peaks; A segmentation module for using the positions of the minimum values among all the marked peaks as demarcation points, and dividing the absorbance curve into several background curves based on all the demarcation points, where the element manifestation degree corresponding to each absorption peak within the background curve is less than the second preset threshold.

[0012] In combination with the second aspect, in a possible implementation, the second calculation module includes: A sliding division module for performing sliding window division on the background curve to obtain several sub-curves; A fitting module for performing linear regression fitting on each sub-curve respectively to obtain the fitting straight line slope corresponding to each sub-curve; A fifth calculation module for calculating a variance value based on all the fitting straight line slopes; A sixth calculation module for calculating an average element manifestation degree based on the element manifestation degrees corresponding to all absorption peaks within the background curve; A seventh calculation module for calculating the fluctuation trend degree of the absorption peak curve according to the variance value and the average element manifestation degree.

[0013] The present invention has the following beneficial effects: By specifically analyzing the baseline drift degree of the spectral data that constitutes the absorbance curve, and setting the baseline drift degree as the iteration termination condition of the iterative adaptive baseline correction algorithm, the iterative adaptive baseline correction algorithm can gradually approach and accurately separate the baseline drift part in the spectral data of the human body sample. The effective correction of the signal is realized, so that the fluctuation of trace elements in the spectral data is distinguished from the baseline drift fluctuation, and the accurate baseline drift degree is obtained, thereby providing a more accurate and stable correction effect, and finally realizing the accurate determination of rubidium element in the human body. The above spectral detection method is of great significance for obtaining the accurate rubidium element content in the human body. In view of the relatively low content of rubidium element in the human body, the present invention distinguishes the fluctuation of trace elements in the absorbance curve, thereby obtaining the baseline drift degree and performing correction, so as to reduce the influence of baseline drift on the determination of rubidium element content. Description of the Drawings

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0015] Figure 1 It is a schematic flowchart of a method for spectral detection of rubidium trace elements in the human body provided by Embodiment 1 of the present invention; Figure 2 It is a schematic flowchart of step S3 provided by Embodiment 1 of the present invention; Figure 3 It is a schematic flowchart of step S5 provided by Embodiment 1 of the present invention; Figure 4 It is a schematic structural diagram of a portable detector for rubidium trace elements in the human body described in Embodiment 2 of the present invention; Figure 5 It is a schematic structural diagram of the graphic segmentation module described in Embodiment 2 of the present invention; Figure 6 It is a schematic structural diagram of the background division module described in Embodiment 2 of the present invention; Figure 7 It is a schematic structural diagram of the second calculation module described in Embodiment 2 of the present invention. Detailed Embodiments

[0016] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of a method for spectral detection of rubidium trace elements in the human body proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. Embodiment

[0018] The following specifically describes the specific solution of a method for spectral detection of rubidium trace elements in the human body provided by the present invention in combination with the accompanying drawings.

[0019] Please refer to Figure 1 , which shows the flowchart of the method for spectral detection of rubidium trace elements in the human body provided by an embodiment of the present invention. Specifically, in this embodiment, it includes step S1, step S2, step S3, step S4, and step S5.

[0020] Step S1: Obtain spectral data, which is obtained by measuring rubidium in a human sample using atomic absorption spectrometry.

[0021] Among them, atomic absorption spectrometry is a technique for quantitatively analyzing the content of an element based on measuring the absorption degree of light by ground-state atoms of a specific element. In this embodiment, first, pretreatment such as digestion or ashing of the human sample is performed, then the rubidium element is converted into a solution state, and subsequently, the sample solution is measured using atomic absorption spectrometry. The specific pretreatment process and measurement process are the content of the prior art and will not be elaborated in this application.

[0022] Step S2: Convert the spectral data to obtain an absorbance curve. Among them, the absorbance curve mentioned in this step has the wavelength as the abscissa and the absorbance as the ordinate, thereby obtaining a curve of absorbance data corresponding to different wavelengths.

[0023] Step S3: Segment the data of the absorbance curve and calculate the baseline drift degree of the absorbance curve based on the segmented results.

[0024] Specifically, the baseline drift degree in this application is used to characterize the deviation of the absorbance curve, which is mainly caused by influencing factors such as instrument aging, temperature fluctuation, light source intensity change, scattering and absorption characteristics of the sample itself, and the content of rubidium in the sample. Instrument aging and temperature fluctuation usually affect the accuracy of the optical system, resulting in optical path deviation or wavelength change. The instability of the light source intensity will cause fluctuations in the detector response. In addition, the scattering of undissolved particles in the sample and the absorption of the solvent or matrix will also cause systematic deviation of the baseline. This deviation has a significant impact on the accuracy of spectral analysis, directly interfering with the true measurement of absorbance, thereby affecting the sensitivity and repeatability of quantitative analysis. However, the application object of this application is to determine the content of rubidium in a human sample, and the content of rubidium in the human body is trace, so the impact of baseline drift on the determination of rubidium content is greater. Therefore, in this step, it includes Step S31, Step S32, Step S33, Step S34, and Step S35 to accurately calculate the baseline drift degree. And in this application, by combining the baseline drift degree with the iterative adaptive baseline correction algorithm, it can effectively prevent deviations caused by fluctuations in rubidium due to overcorrection or undercorrection.

[0025] S31: Calculate the element manifestation degree of each absorption peak in the absorbance curve according to the preset element manifestation degree calculation formula; In this step, to accurately obtain the baseline drift degree of the absorbance curve, first, considering the performance degree of each absorption peak in the absorbance curve, the absorption peak with a high performance degree must be generated by a certain element. Then, to prevent errors in calculating the baseline drift degree caused by the fluctuations of trace elements, the absorbance curve needs to be segmented to analyze the baseline drift degree. The absorption peak with a high performance degree must not be the result of baseline drift, but the one with a low performance degree may be the fluctuation of trace elements or baseline drift. Therefore, first analyze the element performance degree of each peak to segment the curve. In an ideal situation, the absorption peak corresponding to the element should be symmetric; and compared with the fluctuation caused by baseline drift, the absorption peak of the element shows the characteristics of a large aspect ratio and concentration.

[0026] Specifically, the calculation formula for the element performance degree preset in this application is: , where, represents the element performance degree of the th absorption peak; represents the width of the th absorption peak, which is the distance between the nearest extreme points on both sides of the peak point of this absorption peak; represents the th wavelength within the th absorption peak; represents the wavelength corresponding to the peak value of the th absorption peak; represents the absorbance corresponding to the th wavelength within the th absorption peak.

[0027] In the above formula, represents the distance between the th wavelength within the th absorption peak and the wavelength corresponding to the peak value; represents the concentration degree of the th absorption peak. When this formula value is larger, it means it is closer to the peak position and higher in height, then the concentration degree of this absorption peak is larger, and its element performance degree is larger.

[0028] S32. Segment the absorbance curve based on the element performance degree of each absorption peak to obtain several background curves. Since in the absorbance curve, the absorption peak with a high element performance degree is generally a certain element, but the absorption peak with a low element performance degree may be the fluctuation of baseline drift or the absorption peak of a low-concentration element. Therefore, in this application, the absorption peak with a high element performance degree will be used as the division basis. Specifically, this step includes step S321 and step S322.

[0029] S321. Judging one by one the relationship between the element manifestation degree of each absorption peak and the second preset threshold, and marking the absorption peaks with the element manifestation degree greater than the second preset threshold to obtain marked peaks. Among them, the second preset threshold is preferably 0.8. Those skilled in the art can also select other values, and no specific limitation is made in this application.

[0030] S322. Taking the positions of the minimum values among all the marked peaks as demarcation points, and dividing the absorbance curve into several background curves based on all the demarcation points. The element manifestation degree corresponding to each absorption peak within the background curve is less than the second preset threshold.

[0031] S33. Calculating the fluctuation trend degree of each background curve respectively.

[0032] In this embodiment, to obtain the baseline drift degree of the absorbance curve, while subdividing the generation sources of weak fluctuations, it is also necessary to prevent the fluctuations of weak elements from being misidentified as the fluctuations of baseline drift, which ultimately leads to errors in the calculated baseline drift degree. Therefore, in this embodiment, it is necessary to analyze the fluctuation trend of each curve segment and make distinctions. In fact, the fluctuations caused by the baseline drift of the absorbance curve are continuous changes, while the spectral fluctuations of weak elements are instantaneous fluctuations. Therefore, the degree of their element manifestation is relatively high compared to the degree of baseline drift. When the element manifestation degree of the local fluctuations within each curve segment is smaller and the persistence of the fluctuation changes is greater, it is more likely to be the baseline drift fluctuation, and the greater the baseline drift degree of this curve segment. Therefore, in this step, it also includes step S331, step S332, step S333, step S334, and step S335.

[0033] S331. Dividing the background curve by a sliding window to obtain several sub-curves. Specifically, in this step, the sliding window method is adopted with a sliding window size of 3×3, and it is divided from the left end to the right end of each background curve.

[0034] S332. Performing linear regression fitting on each sub-curve respectively to obtain the fitting line slope corresponding to each sub-curve.

[0035] S333. Calculating the variance value based on all the fitting line slopes.

[0036] S334. Calculating the mean value of the element manifestation degree based on the element manifestation degrees corresponding to all absorption peaks within the background curve.

[0037] S335. Calculating the fluctuation trend degree of the absorption peak curve according to the variance value and the mean value of the element manifestation degree.

[0038] Among them, the fluctuation trend degree in this step The calculation formula is as follows: , wherein, represents the fluctuation trend degree of the n-th background curve; represents the variance of the slopes of the fitting lines of each sub-curve obtained within the sliding window of the n-th background curve, reflecting the consistency of the fluctuation trend of this section of the background curve. When this formula is smaller, it indicates that the fluctuation trends of each sub-curve of this section of the background curve are highly consistent, indicating that the fluctuation change of this section of the background curve grows continuously in the same way, and it is more likely to be the fluctuation trend of baseline drift, and the fluctuation trend degree of this section of the background curve is larger; represents the mean value of the element manifestation degrees of all absorption peaks of the n-th background curve. When this value is smaller, it indicates that the element manifestation degrees of all absorption peaks of this section of the curve are low, and the possibility of the fluctuation caused by baseline drift is greater, and its fluctuation trend is larger.

[0039] S34. Calculate the fluctuation trend similarity between each of the background curves and the adjacent background curves based on all the fluctuation trend degrees. In fact, when the fluctuation trend of each section of the background curve is larger, its baseline drift degree is larger; at the same time, when the absorbance curve has baseline drift, the fluctuation transition between each section of the background curve is gentle, the consistency degree of the fluctuation trends of adjacent background curves is high, and the fluctuation trend similarity between adjacent background curves is high. Therefore, obtain the fluctuation trend similarity between the first or last background curve and the unilateral background curve, take the difference. When the difference is smaller, it indicates that the fluctuation trend similarity is higher. Therefore, in this step, the fluctuation trend similarity is to compare and analyze the fluctuation trends of each section of the background curve with the adjacent background curves on its left and right sides respectively.

[0040] Specifically, the calculation formula of the fluctuation trend similarity is as follows: , wherein, represents the fluctuation trend similarity between the n-th background curve and the adjacent background curve; represents the fluctuation trend of the n-th background curve; represents the fluctuation trend degree of the n-th background curve; represents the fluctuation trend degree of the n-th background curve; represents the fluctuation trend degree of the curve obtained by combining the n-th background curve with the adjacent background curves on the left and right, represents the The fluctuation trend of the i-th background curve within the neighborhood of the (i-1)-th background curve. Here, R is a positive integer and an even number. In this step, R can be 6, and those skilled in the art can also choose other values as the comparison neighborhood range.

[0041] In the above formula, represents the difference in the fluctuation trend between the i-th background curve and the (i-1)-th background curve adjacent to the left. The smaller this formula is, the higher the similarity of the fluctuation trend between these two background curves; the (i-1)-th background curve; represents the difference in the fluctuation trend between the i-th background curve and the (i + 1)-th background curve adjacent to the right. The smaller this formula is, the higher the similarity of the fluctuation trend between these two background curves; the (i + 1)-th background curve; represents the degree of difference between the fluctuation trend of the i-th background curve and the combined background curve formed by merging the left and right adjacent background curves and the fluctuation trend of each background curve. Baseline drift causes the fluctuation of the background curve to continuously increase or decrease, while the fluctuation of weak elements does not cause continuous changes in the background curve. The smaller this formula is, the smaller the degree of difference, the higher the similarity of the fluctuation trend between the background curves, and the higher the degree of baseline drift. It should be noted that the left and right mentioned above both refer to the positions on the absorbance curve. the i-th

[0042] S35. Calculate the baseline drift degree corresponding to the absorbance curve according to the fluctuation trend degree and the fluctuation trend similarity corresponding to each background curve.

[0043] Specifically, in this embodiment, to obtain the accurate baseline drift degree of the absorbance curve and prevent the problem of overestimation of the calculated baseline drift degree caused by the fluctuation of weak elements, first analyze the fluctuation trend and similarity of each curve segment to obtain a more accurate baseline drift degree. The above-mentioned fluctuation trend degree of each background curve and the fluctuation trend similarity between the background curve and the adjacent background curve are obtained. The greater the fluctuation trend degree of each background curve, the greater its baseline drift degree. At the same time, the greater the fluctuation trend similarity between each background curve and the adjacent background curve, the more it indicates that the curve is continuously fluctuating, and the greater the baseline drift degree of the curve. That is, in this application, the following baseline drift degree calculation formula is constructed to describe the baseline drift degree of the absorbance curve.

[0044] Among them, the baseline drift degree calculation formula is: , where B represents the baseline drift degree of the absorbance curve; N represents the total number of segments into which the absorbance curve is divided into background curves, that is, the total number of background curves; represents the The fluctuation trend degree of the background curve segment; Indicates the Similarity of the fluctuation trend between the background curve segment and the adjacent background curve; Table Indicates the Fluctuation amplitude of the background curve segment, which is the difference between the maximum value and the minimum value. Baseline drift will cause the background curve to shift, which can represent the curve offset difference caused by baseline drift. Indicates the normalization function.

[0045] In the above formula, Indicates the possibility of baseline drift of the background curve segment. When the fluctuation trend of each background curve segment is larger, excluding the influence of trace element fluctuations, and at the same time the similarity of the fluctuation trend between each background curve segment and the adjacent background curve is larger, the possibility of baseline drift is greater; Indicates the Degree of baseline drift of the background curve segment. According to the possibility of baseline drift and the fluctuation amplitude of this background curve segment, the degree of baseline drift is obtained. When this formula is larger, the degree of baseline drift of this background curve segment is greater; Indicates the mean value of the degree of baseline drift of each background curve segment in the entire absorbance curve, representing the degree of baseline drift of the absorbance curve. When this formula is larger, it indicates that the degree of baseline drift of the absorbance curve is greater. As Figure 2 Shown, this figure is a schematic flow diagram of step S3.

[0046] Step S4: Perform baseline correction on the absorbance curve based on the iterative adaptive baseline correction algorithm, and calculate the degree of baseline drift once after each iteration of the baseline correction until the degree of baseline drift is less than or equal to the first preset threshold, and then stop the iteration to obtain the absorbance curve after baseline correction. Regarding the iterative adaptive baseline correction algorithm, it is the content of the prior art, and the correction process will not be elaborated in this application. Among them, the first preset threshold is 0.1.

[0047] Step S5: Compare the absorbance curve after baseline correction with the preset standard absorbance-concentration curve to obtain the rubidium element concentration in the human sample.

[0048] Among them, the acquisition method of the preset standard absorbance-concentration curve mentioned in this embodiment is as follows: S51: Configure several rubidium element standard solutions with different concentrations.

[0049] S52: Use atomic absorption spectrometry to measure rubidium elements in each rubidium element standard solution respectively to obtain the standard spectral data corresponding to each element standard solution.

[0050] S53. For each standard spectral data, perform the above data segmentation, calculation of the baseline drift degree, and the above baseline correction to obtain the standard absorbance curve corresponding to each elemental standard solution.

[0051] S54. Based on the concentration corresponding to each elemental standard solution and the absorption peak of rubidium element in the standard absorbance curve, establish a standard absorbance-concentration curve. That is, through the steps of construction, obtain a curve with absorbance as the ordinate and rubidium element concentration as the abscissa.

[0052] And it should also be noted that in the process of comparing the absorbance curve after the above baseline correction with the preset standard absorbance-concentration curve in this application, the corresponding rubidium element concentration can be found on the standard absorbance-concentration curve according to the absorbance generated by the rubidium element in the human sample by means of interpolation or direct calculation, and then the actual content of the rubidium element in the human sample is determined. As Figure 3 shown, this figure is a schematic flow chart of step 5. Embodiment

[0053] As Figure 4 shown, this embodiment provides a portable detector for rubidium trace elements in the human body. The portable detector for rubidium trace elements in the human body includes: An acquisition module, configured to acquire spectral data, where the spectral data is obtained by measuring the rubidium element in a human sample using atomic absorption spectrometry; A graph conversion module, configured to convert the spectral data to obtain an absorbance curve; A graph segmentation module, configured to segment the absorbance curve and calculate the baseline drift degree of the absorbance curve based on the segmented result; A curve correction module, configured to perform baseline correction on the absorbance curve based on the iterative adaptive baseline correction algorithm, and calculate the baseline drift degree once after each iteration of the baseline correction until the baseline drift degree is less than or equal to a first preset threshold and then stop the iteration to obtain the absorbance curve after baseline correction; A content calculation module, configured to compare the absorbance curve after the baseline correction with the preset standard absorbance-concentration curve to obtain the rubidium element concentration in the human sample.

[0054] As Figure 5 shown, this figure is a schematic structural diagram of the graph segmentation module.

[0055] Based on the above embodiments, the graph segmentation module includes: A first calculation module, configured to calculate the element manifestation degree of each absorption peak in the absorbance curve respectively according to a preset element manifestation degree calculation formula; A background division module, configured to segment the absorbance curve based on the element expression degree of each absorption peak, so as to obtain a plurality of background curves; A second calculation module, configured to calculate the fluctuation trend degree of each background curve respectively; A third calculation module, configured to calculate the fluctuation trend similarity between each background curve and its adjacent background curve based on all the fluctuation trend degrees; A fourth calculation module, configured to calculate the baseline drift degree corresponding to the absorbance curve according to the fluctuation trend degree and the fluctuation trend similarity corresponding to each background curve.

[0056] Based on the above embodiments, the background division module includes: A logic module, configured to judge one by one the magnitude relationship between the element expression degree of each absorption peak and a second preset threshold, and mark the absorption peaks with the element expression degree greater than the second preset threshold to obtain marked peaks; A segmentation module, configured to use the positions of the minimum values among all the marked peaks as demarcation points, and based on all the demarcation points, segment the absorbance curve into a plurality of the background curves, and the element expression degree corresponding to each absorption peak within the background curve is less than the second preset threshold.

[0057] Based on the above embodiments, the second calculation module includes: A sliding division module, configured to perform sliding window division on the background curve to obtain a plurality of sub-curves; A fitting module, configured to perform linear regression fitting on each sub-curve respectively to obtain the fitting straight line slope corresponding to each sub-curve; A fifth calculation module, configured to calculate the variance value based on all the fitting straight line slopes; A sixth calculation module, configured to calculate the average value of the element expression degree based on the element expression degrees corresponding to all the absorption peaks within the background curve; A seventh calculation module, configured to calculate the fluctuation trend degree of the absorption peak curve according to the variance value and the average value of the element expression degree.

[0058] As Figure 6 shown, this figure is a schematic structural diagram of the background division module; as Figure 7 shown, this figure is a schematic structural diagram of the second calculation module.

[0059] It should be noted that regarding the portable human rubidium trace element detector in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0060] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A spectral detection method for rubidium trace elements in the human body, characterized in that, Including: Obtaining spectral data, which is obtained by measuring rubidium element in a human sample using atomic absorption spectrometry; Converting the spectral data to obtain an absorbance curve; Segmenting the data of the absorbance curve and calculating the baseline drift degree of the absorbance curve based on the segmented result; Performing baseline correction on the absorbance curve based on the iterative adaptive baseline correction algorithm, and calculating the baseline drift degree once after each iteration of the baseline correction until the baseline drift degree is less than or equal to a first preset threshold, then stopping the iteration to obtain the absorbance curve after baseline correction; Comparing the absorbance curve after baseline correction with a preset standard absorbance-concentration curve to obtain the concentration of rubidium element in the human sample.

2. The method for spectral detection of rubidium trace elements in the human body according to claim 1, wherein, Segmenting the data of the absorbance curve and calculating the baseline drift degree of the absorbance curve based on the segmented result, including: Calculating the element manifestation degree of each absorption peak in the absorbance curve respectively according to a preset element manifestation degree calculation formula; Segmenting the absorbance curve based on the element manifestation degree of each absorption peak to obtain several background curves; Calculating the fluctuation trend degree of each background curve respectively; Calculating the fluctuation trend similarity between each background curve and its adjacent background curve based on all the fluctuation trend degrees; Calculating the baseline drift degree corresponding to the absorbance curve according to the fluctuation trend degree and the fluctuation trend similarity corresponding to each background curve.

3. The method for spectral detection of rubidium trace elements in the human body according to claim 2, wherein Segmenting the absorbance curve based on the element manifestation degree of each absorption peak to obtain several background curves, including: Judging one by one the magnitude relationship between the element manifestation degree of each absorption peak and a second preset threshold, and marking the absorption peaks with element manifestation degree greater than the second preset threshold to obtain marked peaks; Taking the positions of the minimum values among all the marked peaks as demarcation points, and dividing the absorbance curve into several background curves based on all the demarcation points, and the element manifestation degree corresponding to each absorption peak in the background curve is less than the second preset threshold.

4. The method for spectral detection of rubidium trace elements in the human body according to claim 2, characterized in that, Calculating the fluctuation trend degree of each background curve respectively, including: Dividing the background curve by a sliding window to obtain several sub-curves; Performing linear regression fitting on each sub-curve respectively to obtain the fitting line slope corresponding to each sub-curve; Calculating the variance value based on the fitting line slopes of all of them; Calculating the average value of the element manifestation degree based on the element manifestation degrees corresponding to all the absorption peaks in the background curve; Calculating the fluctuation trend degree of the absorption peak curve according to the variance value and the average value of the element manifestation degree.

5. The method for spectral detection of rubidium trace elements in the human body according to claim 1, characterized in that, The obtaining method of the preset standard absorbance-concentration curve, including: Preparing several rubidium element standard solutions with different concentrations; Using atomic absorption spectrometry to measure rubidium element in each rubidium element standard solution respectively to obtain the standard spectral data corresponding to each element standard solution; Performing the data segmentation, baseline drift degree calculation and the baseline correction on each standard spectral data respectively to obtain the standard absorbance curve corresponding to each element standard solution; A standard absorbance-concentration curve is established based on the concentration corresponding to each elemental standard solution and the absorption peak of rubidium element in the standard absorbance curve.

6. The method for spectral detection of rubidium trace elements in the human body according to claim 1, characterized in that, The first preset threshold is 0.

1.

7. A portable detector for human rubidium trace elements, characterized in that, It includes: An acquisition module for obtaining spectral data, which is obtained by measuring the rubidium element in a human sample using atomic absorption spectrometry; A graph conversion module for converting the spectral data to obtain an absorbance curve; A graph segmentation module for segmenting the data of the absorbance curve and calculating the baseline drift degree of the absorbance curve based on the segmented results; A curve correction module for correcting the baseline of the absorbance curve based on the iterative adaptive baseline correction algorithm, and calculating the baseline drift degree once after each iteration of the baseline correction until the baseline drift degree is less than or equal to the first preset threshold and then stopping the iteration to obtain the absorbance curve after baseline correction; A content calculation module for comparing the absorbance curve after baseline correction with a preset standard absorbance-concentration curve to obtain the concentration of rubidium element in the human sample.

8. The portable human rubidium trace element detector according to claim 7, characterized in that, The graph segmentation module includes: A first calculation module for calculating the element manifestation degree of each absorption peak in the absorbance curve according to a preset element manifestation degree calculation formula; A background division module for segmenting the absorbance curve based on the element manifestation degree of each absorption peak to obtain several background curves; A second calculation module for calculating the fluctuation trend degree of each of the background curves; A third calculation module for calculating the fluctuation trend similarity between each background curve and its adjacent background curve based on all the fluctuation trend degrees; A fourth calculation module for calculating the baseline drift degree corresponding to the absorbance curve according to the fluctuation trend degree and the fluctuation trend similarity corresponding to each background curve.

9. The portable human rubidium trace element detector according to claim 8, characterized in that, The background division module includes: A logic module for successively judging the magnitude relationship between the element manifestation degree of each absorption peak and the second preset threshold, and marking the absorption peaks with an element manifestation degree greater than the second preset threshold to obtain marked peaks; A segmentation module for using the positions of the minimum values among all the marked peaks as demarcation points, and dividing the absorbance curve into several background curves based on all the demarcation points, where the element manifestation degree corresponding to each absorption peak in the background curve is less than the second preset threshold.

10. The portable human rubidium trace element detector according to claim 8, characterized in that, The second calculation module includes: A sliding division module for performing sliding window division on the background curve to obtain several sub-curves; A fitting module for performing linear regression fitting on each sub-curve respectively to obtain the fitting straight line slope corresponding to each sub-curve; A fifth calculation module for calculating the variance value based on all the fitting straight line slopes; A sixth calculation module for calculating the average value of the element manifestation degrees corresponding to all the absorption peaks in the background curve; A seventh calculation module for calculating the fluctuation trend degree of the absorption peak curve according to the variance value and the average value of the element manifestation degrees.

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