Tantalum lithium infrared accurate identification system
By designing a tantalum lithium infrared accurate identification system, including data acquisition, preprocessing, feature extraction and map verification units, the problem of baseline drift interference in the tantalum lithium infrared spectrum is solved, and more accurate feature absorption peak recognition and quantitative analysis are achieved.
Patent Information
- Application Number
- CN202510278072.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-10
- Publication Date
- 2025-06-13
AI Technical Summary
Due to environmental factors or sample inhomogeneity, baseline drift often occurs in the infrared spectrum of tantalum lithium, which interferes with the identification of characteristic absorption peaks.
A tantalum lithium infrared accurate identification system is designed, including a data acquisition unit, a data preprocessing unit, a feature extraction unit and a graph verification unit. Data were collected by infrared spectrometer, background spectrometry was subtracted, H2O and CO2 absorption peaks were eliminated in the environment, and the identification and quantitative analysis of characteristic absorption peaks were improved by removing baseline drift.
It effectively eliminates interference caused by environmental factors, and improves the accuracy of identification of characteristic absorption peaks in tantalum lithium infrared spectrum and the accuracy of quantitative analysis.
Smart Images
Figure CN120142218A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of crystal material identification, and particularly relates to a tantalum lithium infrared precise identification system. Background Art
[0002] Tantalum lithium (LiTaO3) is an important functional ceramic material with special physical properties and belongs to a kind of ferroelectric material. It is in the ferroelectric phase at room temperature, has significant piezoelectric effect, pyroelectric effect and nonlinear optical properties. Infrared spectroscopy can be used to detect chemical bonds in materials. Due to the unique chemical bond structure of tantalum lithium, its infrared absorption peak can be used as an identification basis. However, due to environmental factors (such as humidity and temperature changes) or the non-uniformity of the sample itself, baseline drift often occurs in the infrared spectrum, which will interfere with the identification of characteristic absorption peaks. Summary of the Invention
[0003] The present invention provides a tantalum lithium infrared precise identification system to solve the above problems.
[0004] To solve the above problems, the technical solution provided by the present invention is as follows: A tantalum lithium infrared precise identification system, which includes: a data acquisition unit, a data preprocessing unit, a feature extraction unit and a spectrum verification unit;
[0005] The data acquisition unit: performs background scanning through an infrared spectrometer to obtain a background spectrum, then scans the tantalum lithium sample to obtain an initial spectrum, and subtracts the background spectrum from the initial light source to obtain a corrected spectrum; through the corrected spectrum, the absorption peaks of H 2 O and CO 2 in the environment are eliminated; The data preprocessing: removes baseline drift from the corrected spectrum. By removing baseline drift, the background signal changes caused by non-target substances in the corrected spectrum are removed, and the identification and quantitative analysis of interfering characteristic absorption peaks are improved;
[0006] The feature extraction unit: finds the positions and intensities of the characteristic absorption peaks after data preprocessing through a peak detection algorithm, and performs normalization processing on the spectrum to obtain a prominent spectrum; The spectrum verification unit: compares the prominent spectrum with the known standard tantalum lithium spectrum to confirm whether the positions and intensities of the characteristic peaks are consistent.
[0007] Preferred technical solution; The calculation formula of the corrected spectrum:
[0008] C(x) = S(x) - B(x)
[0009] B(x) is the spectrum measured when no sample is placed;
[0010] S(x) is the spectrum measured when a sample is placed;
[0011] C(x) is the corrected spectrum after background subtraction;
[0012] x is the wave number or position.
[0013] Preferred technical solution; the steps for removing baseline drift from the calibration spectrum are as follows:
[0014] Obtain the calibrated spectral data C(x).
[0015] Select the polynomial order n according to the baseline trend.
[0016] Calculate the matrix A and the vector b, and solve the normal equations Aa = b to obtain the polynomial coefficients a.
[0017] Calculate the baseline p(x) using the obtained polynomial coefficients.
[0018] Baseline correction: Subtract the baseline p(x) from the calibrated spectrum to obtain the finally calibrated baseline spectrum;
[0019] where the function of the baseline p(x) is: p(x) = a 0 + a 1 x + a 2 x 2 + …… a n x n a 0 Constant term, representing the value of the baseline at x = 0x = 0;
[0020] a 1 x first-order term, representing the part where the baseline changes linearly with the wave number;
[0021] a 2 x 2 : Second-order term, representing the part where the baseline changes quadratically with the wave number;
[0022] a n x n: nth-order term, representing the part where the baseline changes nn times with the wave number.
[0023] Preferred technical solution; construct the normal equations by calculating the matrix A and the vector b, so as to solve the optimal polynomial coefficients; through the coefficients, fit a baseline curve that is closest to the actual data, and subtract this baseline from the original spectrum to remove baseline drift;
[0024] Its objective function;
[0025]
[0026] By taking the partial derivative of the objective function S with respect to each coefficient aj and setting it to zero, the normal equations can be obtained:
[0027] Aa = b
[0028] C(x i ) is the actual spectral intensity at the i-th wavenumber position;
[0029] p(x i ) is the fitted baseline intensity at the i-th wavenumber position.
[0030] Preferred technical solution; Matrix A:
[0031]
[0032] j and k: Row and column indices of matrix A, corresponding to different powers of the polynomial respectively;
[0033] (j + k)-th power at the i-th wavenumber position;
[0034] Over all wavenumber positions Sum of
[0035] Vector b
[0036]
[0037] j: Element index of vector b, corresponding to different powers of the polynomial;
[0038] C(x i ) : The actual spectral intensity at the i-th wavenumber position;
[0039] j-th power at the i-th wavenumber position;
[0040] Over all wavenumber positions Sum of.
[0041] ; The feature extraction unit: Find the positions and intensities of the characteristic absorption peaks after data preprocessing through a peak detection algorithm; The steps are as follows:
[0042] Calculate the first derivative; Calculate the first derivative of the baseline spectral data to find the zero-crossing points: Search the baseline spectral data after calculating the first derivative to obtain the maximum value
[0043] Extract the absorption peak positions and intensities; Extract the maximum value to obtain the positions and intensities of the characteristic absorption peaks.
[0044] Preferred technical solution; Calculate the first derivative of the baseline spectral data, and the formula is as follows:
[0045]
[0046] yi′ represents the first derivative value at the i-th point;
[0047] yi+1 represents the value of the data point adjacent to the right of the i-th point, and the intensity value of the baseline spectral data to the right of the i-th point;
[0048] yi-1 represents the value of the data point adjacent to the left of the i-th point, and the intensity value of the baseline spectral data to the left of the i-th point;
[0049] Δx is the interval between adjacent data points.
[0050] Preferred technical solution; finding zero-crossing points: searching the baseline spectral data after calculating the first derivative to obtain the extreme values; the steps are as follows
[0051] Searching for compliance changes: Searching the first derivative values, finding the positions where the signs change, and recording all positions where the signs change;
[0052] Determining local maxima: When the first derivative changes from positive to negative (i.e., yi′>0 and yi+1′<0),
[0053] then the i-th point is a local maximum, that is, a characteristic absorption peak;
[0054] When the first derivative changes from positive to negative (i.e., yi′>0 and yi+1′>0), then the i-th point is a local maximum, that is, it is not used as a peak.
[0055] Preferred technical solution; extracting the peak position and intensity of absorption; extracting the maximum value to obtain the position and intensity of the characteristic absorption peak; according to the position where the sign changes, determining the index of each local maximum point, mapping these indices to the wavenumber values of the corrected spectral data to obtain the peak position; corresponding to the index of each local maximum point, extracting the intensity value in the corrected spectral data to obtain the peak intensity.
[0056] The beneficial effect compared with the prior art is that by adopting the above solution, the present invention eliminates the absorption peaks of H 2 O and CO 2 in the environment by subtracting the background spectrum from the initial spectrum; through the data preprocessing unit: removing the baseline drift of the corrected spectrum, and by removing the baseline drift, removing the background signal changes caused by non-target substances in the corrected spectrum, and improving the recognition and quantitative analysis of interfering characteristic absorption peaks. Brief Description of the Drawings
[0057] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0058] Figure 1 It is a schematic flowchart of the present invention; Detailed implementation mode
[0059] To facilitate the understanding of the present invention, the present invention will be described in more detail below with reference to the accompanying drawings and specific embodiments. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present invention more thorough and comprehensive.
[0060] It should be noted that when an element is referred to as "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "fixed", "integrally formed", "left", "right" and similar expressions used in this specification are only for the purpose of illustration. In the drawings, units with similar structures are labeled with the same reference numerals.
[0061] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in this specification in the description of the present invention are only for the purpose of describing specific embodiments and are not used to limit the present invention.
[0062] As Figure 1 shown, an embodiment of the present invention is: a tantalum lithium infrared precise identification system, which includes: a data acquisition unit, a data preprocessing unit, a feature extraction unit and a spectrum verification unit; the data acquisition unit: obtains a background spectrum through background scanning by an infrared spectrometer, and then scans a tantalum lithium sample to obtain an initial spectrum. The initial spectrum subtracts the background spectrum to obtain a corrected spectrum; through the corrected spectrum, the absorption peaks of H 2 O and CO 2 in the environment are eliminated; the data preprocessing unit: removes the baseline drift of the corrected spectrum. By removing the baseline drift, the background signal change caused by non-target substances in the corrected spectrum is removed, and the identification and quantitative analysis of interference characteristic absorption peaks are improved;
[0063] The feature extraction unit: finds the positions and intensities of the characteristic absorption peaks after data preprocessing through a peak detection algorithm, and normalizes the spectrum to obtain a prominent spectrum;
[0064] The spectrum verification unit: compares the prominent spectrum with a known standard tantalum lithium spectrum to confirm whether the positions and intensities of the characteristic peaks are consistent.
[0065] The calculation formula of the corrected spectrum:
[0066] C(x) = S(x) - B(x)
[0067] The spectrum measured when no sample is placed, B(x);
[0068] The spectrum measured when a sample is placed, S(x);
[0069] The corrected spectrum after background subtraction, C(x);
[0070] x is the wave number or position.
[0071] It should be noted that: in order to accurately identify tantalum lithium through infrared, the present invention first obtains the background spectrum and the initial spectrum of the tantalum lithium sample respectively through the data acquisition unit, and then subtracts the background spectrum in the initial spectrum to obtain the corrected spectrum, so as to obtain the corrected spectrum of the pure sample without the background spectrum, eliminating the absorption peaks of H 2 O and CO 2 in the environment, reducing the complexity and improving the effect when performing data preprocessing on the corrected spectrum later;
[0072] The infrared spectrometer here is a Fourier transform infrared spectrometer (FTIR), and the set wave number range is 400 - 4000 cm^-1.
[0073] Furthermore, in order to improve the signal-to-noise ratio, the background and the sample are scanned multiple times and the average value is taken, which can reduce the influence of random noise and instantaneous environmental changes, thereby ensuring the accuracy and comprehensiveness of the spectrum.
[0074] Example 2: Due to the baseline drift caused by the change of the background signal caused by non-target substances, the baseline drift usually shows a slow-changing trend. If not corrected, it will interfere with the identification and quantitative analysis of characteristic absorption peaks. In order to process the remaining baseline drift problem in the corrected spectrum, it is necessary to perform baseline correction on the corrected spectrum. Therefore, this solution processes the baseline drift problem in the corrected spectrum through the data preprocessing unit, thereby solving the problem of removing the slowly changing baseline component from the initial spectral data and retaining the true characteristic absorption peak information:
[0075] The steps for removing baseline drift from the corrected spectrum are as follows:
[0076] Obtain the corrected spectral data C(x).
[0077] Select the polynomial order n according to the baseline trend.
[0078] Calculate the matrix A and the vector b, and solve the normal equation system Aa = b to obtain the polynomial system
[0079] coefficient a.
[0080] Calculate the baseline p(x) using the obtained polynomial coefficients.
[0081] Baseline correction: Subtract the baseline p(x) from the corrected spectrum to obtain the finally corrected baseline spectrum;
[0082] Among them, the function of the baseline p(x) is: p(x) = a 0 + a 1 x + a 2 x 2 + …… a n x n
[0083] a 0 Constant term, representing the value of the baseline at x = 0;
[0084] a 1 First-order term of x, representing the part where the baseline changes linearly with the wavenumber;
[0085] a 2 x 2 : Second-order term, representing the part where the baseline changes quadratically with the wavenumber;
[0086] a n x n: nth-order term, representing the part where the baseline changes n times with the wavenumber.
[0087] Construct the normal equations by calculating the matrix A and the vector b, so as to solve the optimal polynomial coefficients; through the coefficients, fit a baseline curve that is closest to the actual data, and subtract this baseline from the original spectrum to remove baseline drift; its objective function;
[0088]
[0089] By taking the partial derivative of the objective function S with respect to each coefficient aj and setting it to zero, the normal equations can be obtained:
[0090] Aa = b
[0091] C(x i ) is the actual spectral intensity at the ith wavenumber position;
[0092] p(x i ) is the fitted baseline intensity at the ith wavenumber position.
[0093] Matrix A:
[0094]
[0095] j and k: Row and column indices of matrix A, corresponding to different powers of the polynomial respectively;
[0096] The (j + k)th power at the ith wavenumber position;
[0097] At all wavenumber positions The sum of
[0098] Vector b
[0099]
[0100] j: The element index of vector b, corresponding to different powers of the polynomial;
[0101] C(x i ): The actual spectral intensity at the i-th wavenumber position;
[0102] The j-th power at the i-th wavenumber position;
[0103] At all wavenumber positions The sum of
[0104] It should be noted that: In the polynomial order, if the selected polynomial order is too high, the model may overfit the data, capturing unnecessary noise and details, resulting in unstable fitting results; if the selected polynomial order is too low, the model may not be able to accurately describe the trend of the baseline, resulting in insufficient baseline correction. Therefore, choosing an appropriate polynomial order requires finding a balance between fitting accuracy and the risk of overfitting; and how to choose an appropriate
[0105] Polynomial order n:
[0106] Observe the spectral characteristics
[0107] By observing the shape of the spectral data and the characteristics of baseline drift, initially estimate the appropriate polynomial order. For example:
[0108] ● If the baseline shows a linear or approximately linear trend, a first-order polynomial
[0109] (n = 1) can be selected.
[0110] ● If the baseline shows a parabolic or slightly curved trend, a second-order polynomial (n = 2) can be selected.
[0111] ● If the baseline has an obvious multi-bending trend, a higher order can be selected.
[0112] Cross-validation
[0113] Use the method of cross-validation to evaluate the fitting effects of polynomials of different orders. The specific steps are as follows:
[0114] ● Divide the dataset into a training set and a validation set.
[0115] ● Fit different polynomial orders and evaluate the fitting error on the validation set.
[0116] · Select the polynomial order with the smallest fitting error.
[0117] Visual inspection
[0118] In practical applications, visual inspection can be performed by plotting the fitting results and the original spectrum to determine whether the fitting effect is reasonable. For example:
[0119] · If the fitting result is too smooth and fails to capture the main trend of the baseline, the order may be too low.
[0120] · If the fitting result is too complex and contains unnecessary details, the order may be too high.
[0121] For example, generate an integer array from 0 to the spectrum length to represent the wavenumber positions; take the polynomial coefficients as 1; calculate the difference between the fitted polynomial and the actual spectrum; calculate the value of the polynomial p(x) at the given wavenumber positions, solve for the optimal polynomial coefficients using the objective function, calculate the baseline using the obtained optimal polynomial coefficients, subtract the baseline from the corrected spectrum to obtain the final baseline spectrum. Each term in the polynomial fitting formula corresponds to a different variation trend of the baseline, and the optimal polynomial coefficients are found by minimizing the sum of the squared residuals, thereby achieving baseline correction.
[0122] Example 3; the feature extraction unit: finds the positions and intensities of the characteristic absorption peaks after data preprocessing through a peak detection algorithm; the steps are as follows:
[0123] Calculate the first derivative; perform the calculation of the first derivative on the data of the baseline spectrum to find the zero-crossing points: search the data of the baseline spectrum after calculating the first derivative to obtain the maximum values and extract the positions and intensities of the absorption peaks; extract the maximum values to obtain the positions and intensities of the characteristic absorption peaks.
[0124] Perform the calculation of the first derivative on the data of the baseline spectrum, and the formula is as follows:
[0125]
[0126] yi′ represents the value of the first derivative at the i-th point;
[0127] yi+1 represents the value of the data point adjacent to the right of the i-th point, the intensity value of the baseline spectrum data to the right of the i-th point;
[0128] yi-1 represents the value of the data point adjacent to the left of the i-th point, the intensity value of the baseline spectrum data to the left of the i-th point;
[0129] Δx is the interval between adjacent data points.
[0130] Finding zero-crossing points: Search the baseline spectral data after calculating the first derivative to obtain the limit values. The steps are as follows: Search for changes that meet the criteria: Search the first derivative values, find the positions where the signs change, and record all the positions where the changes meet the criteria.
[0131] Determine local maxima: When the first derivative changes from positive to negative (i.e., yi′>0 and yi + 1′<0),
[0132] then the i-th point is a local maximum, that is, a characteristic absorption peak.
[0133] When the first derivative changes from positive to negative (i.e., yi′>0 and yi + 1′>0), then i
[0134] the point is a local maximum, that is, it is not regarded as a peak.
[0135] Extract the positions and intensities of the absorption peaks; Extract the maxima to obtain the positions and intensities of the characteristic absorption peaks; According to the positions where the signs change, determine the indices of each local maximum point, map these indices to the wavenumbers of the calibrated spectral data to obtain the peak positions; Corresponding to the indices of each local maximum point, extract the intensity values in the calibrated spectral data to obtain the peak intensities, and normalize the peak intensities to obtain the prominent spectrum.
[0136] The spectrum verification unit: Compare the prominent spectrum with the known standard tantalum lithium spectrum to confirm whether the positions and intensities of the characteristic peaks are consistent.
[0137] It should be noted that: Directly compare the positions of the characteristic peaks in the experimental spectrum with the positions of the characteristic peaks in the standard tantalum lithium spectrum. Check whether the wavenumbers of each characteristic peak are the same or close. If the wavenumber of a certain characteristic peak in the prominent spectrum differs greatly from the wavenumber of a certain characteristic peak in the standard spectrum, it may be caused by reasons such as instrument error and sample purity.
[0138] If the wavenumber difference is small (for example, within the range of ±1 cm-1), then it can be considered that these two characteristic peaks match.
[0139] It should be noted that the above technical features continue to be combined with each other to form various embodiments not listed above, which are all regarded as within the scope recorded in the description of the present invention; and for those of ordinary skill in the art, they can be improved or transformed according to the above description, and all these improvements and transformations should fall within the protection scope of the appended claims of the present invention.
Claims
1. A tantalum-lithium infrared precision identification system, characterized in that; A tantalum-lithium infrared precise identification system, comprising: a data acquisition unit, a data preprocessing unit, a feature extraction unit and a spectrum verification unit; The data acquisition unit: performs background scanning by an infrared spectrometer to obtain a background spectrum, then scans the tantalum lithium sample to obtain an initial spectrum, and deducts the background spectrum from the initial light source to obtain a correction spectrum; the absorption peaks of H2O and CO2 in the environment are eliminated by the correction spectrum; the data preprocessing unit: removes the baseline drift of the correction spectrum, and by removing the baseline drift, removes the background signal changes caused by non-target substances in the correction spectrum, thereby improving the recognition and quantitative analysis of interfering characteristic absorption peaks; The feature extraction unit is used to find the position and intensity of the characteristic absorption peak after data preprocessing through a peak detection algorithm, and normalize the spectrum to obtain a prominent spectrum; the spectrum verification unit is used to compare the prominent spectrum with a known standard tantalum lithium spectrum to confirm whether the position and intensity of the characteristic peak are consistent.
2. According to claim 1, a tantalum-lithium infrared precise identification system is characterized in that: The calculation formula of the corrected spectrum is: C(x)=S(x)-B(x) B(x) is the spectrum measured without placing a sample; S(x) is the spectrum measured when the sample is placed; C(x) is the corrected spectrum after background subtraction; x is the wave number or position.
3. According to claim 2, a tantalum-lithium infrared precise identification system is characterized in that: The steps to correct the spectrum to remove baseline drift are as follows: Obtain corrected spectral data C(x); The polynomial order n is selected according to the baseline trend; Calculate the matrix A and vector b, solve the normal equation system Aa=b to obtain the polynomial coefficient a; Calculate the baseline p(x) using the obtained polynomial coefficients; Baseline correction: Subtract the baseline p(x) from the corrected spectrum to obtain the final corrected baseline spectrum; The function of the baseline p(x) is: p(x) = a0 + a1x + a2x 2 +……a n x n a0 constant term, which represents the value of the baseline when x=0x=0; a1x linear term, which represents the part of the baseline that changes linearly with the wave number; a2x 2 : quadratic term, which indicates the part of the baseline that changes quadratically with the wave number; a n x n: The nth-order term represents the part of the baseline that changes with the wave number n times.
4. According to claim 3, a tantalum-lithium infrared precise identification system is characterized in that: By calculating the matrix A and the vector b, a normal equation system is constructed to solve the optimal polynomial coefficients; through the coefficients, a baseline curve closest to the actual data is fitted, and this baseline is subtracted from the original spectrum to remove the baseline drift; Its objective function; By taking partial derivatives of the objective function S with respect to each coefficient aj and setting it to zero, we can obtain the normal equations: Aa=b C(x i ) is the actual spectral intensity at the ith wavenumber position; p(x i ) is the fitted baseline intensity at the ith wavenumber position.
5. According to claim 4, a tantalum-lithium infrared precise identification system is characterized in that: Matrix A: j and k: row and column indices of matrix A, corresponding to different powers of the polynomial; The (j+k)th power of the ith wavenumber position; At all wavenumber positions The sum of Vector b j: element index of vector b, corresponding to different powers of the polynomial; C(x i ): actual spectral intensity at the i-th wavenumber position; The jth power of the ith wave number position; At all wavenumber positions The sum of .
6. According to claim 5, a tantalum-lithium infrared precise identification system is characterized in that: The feature extraction unit: finds the position and intensity of the characteristic absorption peak after data preprocessing through a peak detection algorithm; the steps are as follows: Calculate the first derivative; Calculate the first derivative of the baseline spectrum data Find the zero crossing point: Find the baseline spectrum data after calculating the first-order derivative and get the maximum value Extract the absorption peak position and intensity; Extract the maximum value and obtain the position and intensity of the characteristic absorption peak.
7. A tantalum-lithium infrared precise identification system according to claim 6, characterized in that: The first-order derivative of the baseline spectrum data is calculated as follows: yi′ represents the first-order derivative value at the i-th point; yi+1 represents the value of the data point adjacent to the right of the i-th point, and the intensity value of the baseline spectrum data to the right of the i-th point; yi-1 represents the value of the data point adjacent to the left of the i-th point, and the intensity value of the baseline spectrum data on the left of the i-th point; Δx is the interval between adjacent data points.
8. The tantalum-lithium infrared precise identification system according to claim 7, characterized in that: Find the zero crossing point: Find the baseline spectrum data after calculating the first-order derivative to obtain the limit value; The steps are as follows Find coincident changes: Search for the first-order derivative value, find the location where the sign changes, and record all the locations where the sign changes; Determine the local maximum: When the first-order derivative changes from positive to negative (i.e., yi′>0 and yi+1′<0), point i is the local maximum, i.e., the characteristic absorption peak; When the first-order derivative changes from positive to negative (i.e., yi′>0 and yi+1′>0), point i is a local maximum, i.e., it is not a peak.
9. The tantalum-lithium infrared precise identification system according to claim 8, characterized in that: Extract the absorption peak position and intensity; extract the maximum value to obtain the position and intensity of the characteristic absorption peak; determine the index of each local maximum point according to the position of the sign change, map these indexes to the wave value value of the corrected spectral data to obtain the peak position; corresponding to the index of each local maximum point, extract the intensity value in the corrected spectral data to obtain the peak intensity.