Fourier infrared spectroscopic analysis method, system and equipment and storage medium

Abstract

The invention discloses a Fourier infrared spectrum analysis method, system and equipment and a storage medium, and relates to the technical field of spectrum analysis. The Fourier infrared spectroscopic analysis method comprises the following steps: acquiring first data of a sample to be detected; wherein the first data is Fourier infrared spectrum data, detected by a spectrum detection instrument, of a to-be-detected sample; performing decomposition preprocessing on the first data according to a preset decomposition algorithm to determine second data; and performing Fourier infrared spectroscopic analysis according to the second data. According to the Fourier infrared spectroscopic analysis method provided by the invention, the Fourier infrared spectroscopic data of the to-be-detected sample is decomposed and preprocessed according to the preset decomposition algorithm, so that nonlinear noise can be removed, interference of instrument noise and impurities on the to-be-detected sample is reduced, and the accuracy of Fourier infrared spectroscopic analysis is improved.

Description

Technical Field

[0001] The present application relates to the field of spectral analysis technology, and in particular to a Fourier transform infrared spectral analysis method, system, device and storage medium. Background Art

[0002] Fourier transform infrared spectroscopy (FTIR) is an important method for qualitative analysis of substances. Its analysis can provide extensive information about functional groups, helping to determine some or all molecular types and structures. Its advantages include high specificity, short analysis time, small sample volumes, no sample damage, and convenient measurement. However, when analyzing FTIR spectra, interference from instrument noise or impurity gases (such as water vapor) can easily cause nonlinear noise to be superimposed on the spectral curve, complicating qualitative analysis. Summary of the Invention

[0003] Purpose of the invention: The embodiments of the present application provide a Fourier transform infrared spectroscopy analysis method, system, device and storage medium to reduce the interference of instrument noise and impurities on the sample to be tested and improve the accuracy of Fourier transform infrared spectroscopy analysis.

[0004] Technical solution: The Fourier transform infrared spectroscopy analysis method described in the embodiment of the present application includes:

[0005] Acquire first data of the sample to be tested; wherein the first data is Fourier transform infrared spectrum data of the sample to be tested detected by a spectrum detection instrument;

[0006] Decomposing and preprocessing the first data according to a preset decomposition algorithm to determine second data;

[0007] Fourier transform infrared spectroscopy analysis is performed based on the second data.

[0008] In some embodiments, the step of decomposing and preprocessing the first data according to a preset decomposition algorithm to determine the second data includes:

[0009] determining peak values and valley values of the first data;

[0010] determining a first envelope and a second envelope of the first data according to the peak value and the valley value;

[0011] determining a residual of the first data according to the first envelope, the second envelope, and the first data;

[0012] determining a plurality of intrinsic modal components according to the residual, the first data, and a preset iterative algorithm;

[0013] The second data is determined based on the multiple intrinsic modal components and a preset filtering algorithm.

[0014] In some embodiments, determining the peak and valley values of the first data includes:

[0015] Differentiating the first data to obtain differential spectrum data;

[0016] Determining the peak value according to the differential spectrum data and a preset peak value judgment condition;

[0017] The valley value is determined according to the differential spectrum data and a preset valley value judgment condition.

[0018] In some embodiments, determining a first envelope and a second envelope of the first data according to the peak value and the valley value includes:

[0019] A cubic spline difference is performed based on the peak value and the valley value to obtain the first envelope curve and the second envelope curve.

[0020] In some embodiments, the calculation formula for determining the residual of the first data based on the first envelope, the second envelope, and the first data is:

[0021] x1=x0-(f x +t x ) / 2;

[0022] Wherein, x1 is the residual; x0 is the first data; f x is the first envelope; t x is the second envelope.

[0023] In some embodiments, determining a plurality of intrinsic modal components based on the residual, the first data, and a preset iterative algorithm includes:

[0024] In a case where the residual is an intrinsic modal component, the residual is recorded, a current value of the first data is set to be equal to a difference between the first data and the residual, and the operation of determining the peak and valley values of the first data is repeated.

[0025] In a case where the residual is not the intrinsic modal component, setting the current value of the first data equal to the residual, and returning to repeatedly perform the operation of determining the peak value and the valley value of the first data;

[0026] By returning and repeating the operation of determining the peak values and valley values of the first data, a plurality of intrinsic modal components are obtained.

[0027] In some embodiments, determining whether the residual is the intrinsic modal component comprises:

[0028] (x1-x0) 2 / x0 2<ε or abs(f(x1)+t(x1)-p(x1))<1;

[0029] Among them, x1 is the residual; x0 is the first data; ε is the threshold; f(x1) is the number of peak points of x1; t(x1) is the number of valley points of x1; p(x1) is the number of zero-crossing points of x1; abs means taking the absolute value.

[0030] In some embodiments, the calculation formula for determining the second data based on the multiple connotation modal components and the preset filtering algorithm is:

[0031]

[0032] Among them, x0 ′ is the second data; x0 is the first data; x k is the intrinsic modal component obtained at the kth iteration.

[0033] In some embodiments, the Fourier transform infrared spectroscopy analysis method further includes: establishing third data of a standard sample; wherein the third data is Fourier transform infrared spectroscopy data of the standard sample;

[0034] The validity of the qualitative analysis of the second data is verified based on the first data, the second data, the third data and a preset similarity algorithm.

[0035] In some embodiments, verifying the validity of the qualitative analysis of the second data based on the first data, the second data, the third data, and a preset similarity algorithm includes:

[0036] determining a first similarity value between the third data and the first data according to the preset similarity algorithm;

[0037] determining a second similarity value between the third data and the second data according to the preset similarity algorithm;

[0038] The validity of the second data qualitative analysis is verified according to the first similarity value and the second similarity value.

[0039] Accordingly, the embodiment of the present application further provides a Fourier transform infrared spectroscopy analysis system, comprising:

[0040] An acquisition module is used to acquire first data of the sample to be tested; wherein the first data is Fourier transform infrared spectrum data of the sample to be tested detected by a spectrum detection instrument;

[0041] a determination module, configured to perform decomposition and preprocessing on the first data according to a preset decomposition algorithm to determine second data;

[0042] An analysis module is used to perform Fourier transform infrared spectrum analysis based on the second data.

[0043] Correspondingly, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the Fourier transform infrared spectroscopy analysis method as described above when executing the computer program.

[0044] Accordingly, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the Fourier transform infrared spectroscopy analysis method as described above is implemented.

[0045] Beneficial Effects: Compared to the prior art, the Fourier transform infrared spectroscopy analysis method, system, device, and storage medium of the embodiments of the present application include: obtaining first data of a sample to be tested; wherein the first data is Fourier transform infrared spectroscopy data of the sample to be tested detected by a spectral detection instrument; decomposing and preprocessing the first data according to a preset decomposition algorithm to determine second data; and performing Fourier transform infrared spectroscopy analysis based on the second data. The Fourier transform infrared spectroscopy analysis method provided in the present application, by decomposing and preprocessing the Fourier transform infrared spectroscopy data of the sample to be tested according to a preset decomposition algorithm, can remove nonlinear noise, reduce interference of instrument noise and impurities on the sample to be tested, and thus improve the accuracy of Fourier transform infrared spectroscopy analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 This is a flow chart of a Fourier transform infrared spectroscopy analysis method provided in an embodiment of the present application;

[0048] Figure 2 is a flow chart of another Fourier transform infrared spectroscopy analysis method provided in the embodiments of the present application;

[0049] Figure 3 Schematic diagram of the Fourier transform infrared spectrum of an acetone standard sample provided in the examples of the present application;

[0050] Figure 4 Schematic diagram of a Fourier transform infrared spectrum of a sample to be tested provided in an embodiment of the present application;

[0051] Figure 5This is a schematic diagram of a Fourier transform infrared spectrum of a sample to be tested after decomposition pretreatment provided in an embodiment of the present application;

[0052] Figure 6 This is a principle structure block diagram of a Fourier transform infrared spectroscopy analysis system provided in an embodiment of the present application;

[0053] Figure 7 It is a structural diagram of an electronic device provided in an embodiment of the present application.

[0054] Reference numerals:

[0055] 101 - acquisition module; 102 - determination module; 103 - analysis module; 100 - Fourier transform infrared spectroscopy analysis system. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0057] It should be understood that although the terms first, second, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below could be referred to as the second component without departing from the teachings of the present invention. As used herein, the term "and / or" includes any one and all combinations of one or more of the associated listed items.

[0058] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of exemplary embodiments and may not be to scale. The modules or processes in the drawings are not necessarily required to implement the present application and therefore cannot be used to limit the scope of protection of the present application.

[0059] The applicant's research has revealed that Fourier transform infrared spectroscopy (FTIR) is an important method for qualitative identification of substances. Its analysis can provide extensive information about functional groups, helping to determine some or all molecular types and structures. Its advantages include high specificity, short analysis time, small sample volumes, no sample destruction, and convenient measurement. FTIR spectroscopy is primarily used for qualitative identification of substances, specifically determining the identity of an unknown substance by comparing its spectrum with a set of known reference samples.

[0060] With the development of qualitative analysis methods for Fourier transform infrared spectroscopy (FTIR), various methods have emerged for qualitative analysis of FTIR spectra, such as Constrained Least Squares (CLS), Partial Least Squares (PLS), and Artificial Neural Network (ANN). However, these methods all have drawbacks when performing qualitative analysis of infrared spectra. For example, when using CLS for component identification, absorbance data for all spectra in the investigated band must first be selected. However, all spectral data ultimately influences the algorithm's identification results. Furthermore, CLS analysis cannot address the multicollinearity problem of spectra in the spectral database. Furthermore, when using PLS modeling for qualitative analysis, the emergence of new components can reduce identification accuracy. Artificial neural network algorithms have developed rapidly in recent years, but these algorithms require complex training processes, and single ANN algorithms can only qualitatively identify a single component, making them incapable of rapid qualitative identification analysis.

[0061] At the same time, as the number of infrared spectra in infrared spectral databases increases, the collinearity of absorption spectral characteristics increases, making it difficult for existing infrared spectroscopy qualitative analysis methods to address this issue. Therefore, for real-time online applications, it is currently difficult to accurately and quickly perform qualitative analysis of the infrared spectra of samples to be tested from large infrared spectral databases.

[0062] When analyzing Fourier transform infrared spectra using related technologies, the spectral curve is easily superimposed with nonlinear noise due to interference from measuring instrument noise or impurity gases (such as water vapor), making qualitative analysis difficult.

[0063] In view of this, the embodiments of the present application provide a Fourier transform infrared spectroscopy analysis method, system, device and storage medium. The present application can remove nonlinear noise, reduce the interference of instrument noise and impurities on the sample to be tested, and thus improve the accuracy of Fourier transform infrared spectroscopy analysis by decomposing and preprocessing the Fourier transform infrared spectroscopy data of the sample to be tested according to a preset decomposition algorithm.

[0064] Figure 1 This is a flow chart of a Fourier transform infrared spectroscopy analysis method provided in an embodiment of the present application. This method can be applied to a spectroscopy analysis system to improve the accuracy of Fourier transform infrared spectroscopy analysis of a sample to be tested. This method can be performed by a Fourier transform infrared spectroscopy analysis system, which can be implemented by software and / or hardware, and can be configured in a processor or controller of the spectroscopy analysis system. Figure 1 , the method comprises the following steps:

[0065] Step 110: Acquire first data of the sample to be tested; wherein the first data is Fourier infrared spectrum data of the sample to be tested detected by a spectrum detection instrument.

[0066] The sample to be tested is a gas or liquid to be tested, such as acetone, etc., which can be set according to actual conditions and is not specifically limited here.

[0067] The first data is Fourier transform infrared spectroscopy data of the sample to be tested obtained by direct detection by a spectral detection instrument, i.e., data before preprocessing. The spectral detection instrument may be a Fourier transform infrared spectrometer (FTIR), etc., and may be configured according to actual conditions and is not specifically limited here.

[0068] Step 120: Decompose and pre-process the first data according to a preset decomposition algorithm to determine second data.

[0069] The second data is Fourier infrared spectrum data obtained by decomposing and preprocessing the first data.

[0070] The preset decomposition algorithm is an algorithm based on Empirical Mode Decomposition (EMD).

[0071] Specifically, first data of a sample to be tested is obtained. Then, the first data is decomposed and preprocessed according to a preset decomposition algorithm to obtain second data. This effectively removes nonlinear noise from the spectral curve and reduces interference from instrument noise and / or impurities on the Fourier transform infrared spectrum of the sample to be tested. This facilitates subsequent qualitative analysis based on the preprocessed Fourier transform infrared spectrum data, thereby improving the accuracy of the qualitative analysis using Fourier transform infrared spectroscopy.

[0072] In some embodiments, performing decomposition and preprocessing on the first data according to a preset decomposition algorithm to determine the second data includes the following steps:

[0073] Step 1: Determine the peak and valley values of the first data.

[0074] For example, let the peak value of the first data be (f1, f2, ..., f n ), the valley value is (t1,t2,…,t n ).

[0075] In some embodiments, determining the peak and valley values of the first data includes: differentiating the first data to obtain differential spectral data; determining the peak value based on the differential spectral data and preset peak judgment conditions; and determining the valley value based on the differential spectral data and preset valley judgment conditions.

[0076] Specifically, first, the Fourier transform infrared spectrum data of the sample to be tested (ie, the first data) is differentiated to obtain differential spectrum data x_diff. The specific differential calculation formula is:

[0077] x_diff[i]=x0[i+1]-x0[i];

[0078] Where x_diff[i] represents the differential value of the i-th point in the spectral data, x0[i+1] represents the value of the i+1-th point in the spectral data; x0[i] represents the value of the i-th point in the spectral data; where i=0, 1, 2,…, n-1, and n is the length of x0.

[0079] Then, traverse the differential spectrum data, and the peak judgment condition is: if

[0080] x_diff[i]*x_diff[i+1]<=0 and x_diff[i+1]<0;

[0081] Then x0[i+1] is the peak value. Where x_diff[i+1] represents the difference value of point i+1 in the spectrum data.

[0082] The valley value judgment condition is: if

[0083] x_diff[i]*x_diff[i+1]<=0 and x_diff[i+1]>0;

[0084] Then x0[i+1] is the valley value.

[0085] Step 2: Determine a first envelope and a second envelope of the first data according to the peak value and the valley value.

[0086] In some embodiments, determining the first envelope and the second envelope of the first data according to the peak values and the valley values includes: performing a cubic spline difference based on the peak values and the valley values to obtain the first envelope and the second envelope.

[0087] The first envelope is an upper envelope, and the second envelope is a lower envelope.

[0088] Specifically, after determining the peak and valley values of the first data x0, cubic spline interpolation is performed based on the peak and valley values to calculate the upper envelope f of the first data x0. x and the lower envelope t x .

[0089] Step three: determining the residual of the first data according to the first envelope, the second envelope, and the first data.

[0090] In some embodiments, a calculation formula for determining the residual of the first data based on the first envelope, the second envelope, and the first data (i.e., a residual calculation formula) is:

[0091] x1=x0-(f x +t x ) / 2;

[0092] Among them, x1 is the residual; x0 is the first data; f x is the first envelope; t x is the second envelope.

[0093] Step 4: Determine multiple intrinsic modal components based on the residual, the first data, and a preset iterative algorithm.

[0094] Specifically, the peak and valley values of the first data x0 are determined. Then, cubic spline interpolation is performed based on the peak and valley values to calculate the upper envelope f of the first data x0. x and the lower envelope t x Secondly, the residual of the first data is calculated according to the residual calculation formula. Finally, the intrinsic modal component is found through iteration based on the residual, the first data, and the prediction iteration algorithm, so that the intrinsic modal component can be subtracted subsequently, which is conducive to removing nonlinear noise.

[0095] In some embodiments, determining multiple intrinsic modal components based on the residual, the first data, and a preset iterative algorithm includes: when the residual is an intrinsic modal component, recording the residual, and making the current value of the first data equal to the difference between the first data and the residual, and returning to repeatedly executing the operation of determining the peak and valley values of the first data; when the residual is not an intrinsic modal component, making the current value of the first data equal to the residual, and returning to repeatedly executing the operation of determining the peak and valley values of the first data; by returning to repeatedly executing the operation of determining the peak and valley values of the first data, multiple intrinsic modal components are obtained.

[0096] Specifically, the parameter is determined to be an intrinsic modal component based on the criteria for determining intrinsic modal components. If the current residual x1 is an intrinsic modal component, the residual x1 is recorded, the current value of the first data x0 is set to x0-x1, and steps 1 through 4 are repeated. If the current residual x1 is not an intrinsic modal component, x0 is set to x1, and steps 1 through 4 are repeated. Multiple intrinsic modal components can be obtained in this manner.

[0097] In some embodiments, the judgment condition for determining whether the residual is an intrinsic modal component includes:

[0098] (x1-x0) 2 / x0 2 <ε or abs(f(x1)+t(x1)-p(x1))<1;

[0099] Where x1 is the residual; x0 is the first data; ε is the threshold; f(x1) is the number of peak points of x1; t(x1) is the number of valley points of x1; p(x1) is the number of zero-crossing points of x1; abs means taking the absolute value.

[0100] Step 5: Determine the second data based on the multiple intrinsic modal components and the preset filtering algorithm.

[0101] In some embodiments, the calculation formula for determining the second data based on multiple connotation modal components and a preset filtering algorithm is:

[0102]

[0103] Among them, x0′ is the second data; x0 is the first data; x k is the intrinsic modal component obtained at the kth iteration.

[0104] For example, the value of k is 6, which can be set according to actual conditions and is not specifically limited here.

[0105] Step 130: Perform Fourier transform infrared spectroscopy analysis based on the second data.

[0106] Among them, the second data is the Fourier transform infrared spectrum data after the first data is decomposed and preprocessed, which can effectively remove the nonlinear noise on the spectrum curve, reduce the interference of instrument noise and / or impurity gas on the sample to be tested, and thus improve the accuracy of subsequent Fourier transform infrared spectrum qualitative analysis.

[0107] It can be understood that the Fourier transform infrared spectroscopy analysis method provided in the present application can remove nonlinear noise, reduce the interference of instrument noise and impurities on the sample to be tested, and thus improve the accuracy of Fourier transform infrared spectroscopy analysis by decomposing and preprocessing the Fourier transform infrared spectroscopy data of the sample to be tested according to a preset decomposition algorithm.

[0108] Figure 2 This is a flow chart of another Fourier transform infrared spectroscopy analysis method provided in the embodiments of this application. In some embodiments, please refer to Figure 2 , the Fourier transform infrared spectroscopy analysis method also includes:

[0109] Step 210: Create third data of the standard sample; wherein the third data is Fourier transform infrared spectrum data of the standard sample.

[0110] The standard sample is a standard sample corresponding to the sample to be tested. For example, if the sample to be tested is acetone, the standard sample is an acetone standard sample.

[0111] Step 220: Acquire first data of the sample to be tested; wherein the first data is Fourier transform infrared spectrum data of the sample to be tested detected by a spectrum detection instrument.

[0112] Step 230: Decompose and pre-process the first data according to a preset decomposition algorithm to determine second data.

[0113] Step 240: Perform Fourier transform infrared spectroscopy analysis based on the second data.

[0114] Step 250: Verify the validity of the qualitative analysis of the second data based on the first data, the second data, the third data, and a preset similarity algorithm.

[0115] Among them, the calculation formula of the preset similarity algorithm is:

[0116]

[0117] Wherein, x is the test data (such as the first data or the second data); y is the standard sample database; and n is the data length.

[0118] In some embodiments, verifying the validity of the qualitative analysis of the second data based on the first data, the second data, the third data and a preset similarity algorithm includes: determining a first similarity value between the third data and the first data based on the preset similarity algorithm; determining a second similarity value between the third data and the second data based on the preset similarity algorithm; and verifying the validity of the qualitative analysis of the second data based on the first similarity value and the second similarity value.

[0119] Specifically, according to the calculation formula of the preset similarity algorithm, the first data is substituted into x in the calculation formula of the preset similarity algorithm, and the third data is substituted into y in the calculation formula of the preset similarity algorithm to calculate a first similarity value. Similarly, according to the calculation formula of the preset similarity algorithm, the second data is substituted into x in the calculation formula of the preset similarity algorithm, and the third data is substituted into y in the calculation formula of the preset similarity algorithm to calculate a second similarity value. Finally, the first similarity value is compared with the second similarity value to verify whether the denoising of the second data after the above-mentioned decomposition preprocessing is effective.

[0120] Figure 3 : is a schematic diagram of the Fourier transform infrared spectrum of an acetone standard sample provided in the examples of this application, Figure 4 is a schematic diagram of a Fourier infrared spectrum of a sample to be tested provided in an embodiment of the present application, Figure 5 This is a schematic diagram of a Fourier transform infrared spectrum of a sample to be tested after decomposition pretreatment provided in the embodiment of the present application. Figures 3 to 5 For example, taking the sample acetone as an example, Figure 4 The sample to be tested is at 1400cm-1 -1700cm -1 In the wave number band, due to the interference of instrument noise and impurity water vapor, its Fourier infrared spectrum shows obvious noisy signals. Figure 4 The Fourier transform infrared spectrum data of the acetone sample to be tested (ie the first data x0) and Figure 3 a first similarity value (95.3%) of the Fourier transform infrared spectrum data of the acetone standard sample (ie, the third data), and Figure 5 The Fourier transform infrared spectrum data (i.e., the second data) of the acetone sample to be tested after EMD decomposition pretreatment is compared with Figure 3 The second similarity value (98.6%) for the FTIR spectrum data of the acetone standard sample (i.e., the third data) shows that the similarity value increases from the first similarity value of 95.3% to the second similarity value of 98.6%. This verifies that the second data has been effectively denoised after the above decomposition preprocessing, which is beneficial for improving the accuracy of subsequent FTIR spectrum analysis.

[0121] Figure 6 This is a principle structure diagram of a Fourier infrared spectroscopy analysis system provided in the embodiment of the present application. Correspondingly, the embodiment of the present application also provides a Fourier infrared spectroscopy analysis system, please refer to Figure 6 The Fourier transform infrared spectroscopy analysis system 100 includes an acquisition module 101 for acquiring first data of a sample to be tested; wherein the first data is Fourier transform infrared spectroscopy data of the sample to be tested detected by a spectral detection instrument; a determination module 102 for decomposing and preprocessing the first data according to a preset decomposition algorithm to determine second data; and an analysis module 103 for performing Fourier transform infrared spectroscopy analysis based on the second data.

[0122] The technical solution of the embodiment of the present application provides a Fourier transform infrared spectroscopy analysis system, which can remove nonlinear noise, reduce the interference of instrument noise and impurities on the sample to be tested, and thus improve the accuracy of Fourier transform infrared spectroscopy analysis by decomposing and preprocessing the Fourier transform infrared spectroscopy data of the sample to be tested according to a preset decomposition algorithm.

[0123] In some embodiments, the determination module 102 is further configured to:

[0124] Differentiating the first data to obtain differential spectrum data;

[0125] Determining the peak value according to the differential spectrum data and a preset peak value judgment condition;

[0126] The valley value is determined according to the differential spectrum data and a preset valley value judgment condition.

[0127] In some embodiments, the determination module 102 is further configured to:

[0128] A cubic spline difference is performed based on the peak value and the valley value to obtain the first envelope curve and the second envelope curve.

[0129] In some embodiments, the residual of the first data is calculated as follows:

[0130] x1=x0-(f x +t x ) / 2;

[0131] Wherein, x1 is the residual; x0 is the first data; f x is the first envelope; t x is the second envelope.

[0132] In some embodiments, the determination module 102 is further configured to:

[0133] In a case where the residual is an intrinsic modal component, the residual is recorded, a current value of the first data is set to be equal to a difference between the first data and the residual, and the operation of determining the peak and valley values of the first data is repeated.

[0134] In a case where the residual is not the intrinsic modal component, setting the current value of the first data equal to the residual, and returning to repeatedly perform the operation of determining the peak value and the valley value of the first data;

[0135] By returning and repeating the operation of determining the peak values and valley values of the first data, a plurality of intrinsic modal components are obtained.

[0136] In some embodiments, determining whether the residual is the intrinsic modal component comprises:

[0137] (x1-x0) 2 / x0 2 <ε or abs(f(x1)+t(x1)-p(x1))<1;

[0138] Among them, x1 is the residual; x0 is the first data; ε is the threshold; f(x1) is the number of peak points of x1; t(x1) is the number of valley points of x1; p(x1) is the number of zero-crossing points of x1; abs means taking the absolute value.

[0139] In some embodiments, the calculation formula for determining the second data based on multiple connotation modal components and a preset filtering algorithm is:

[0140]

[0141] Among them, x0 ′ is the second data; x0 is the first data; x kis the intrinsic modal component obtained at the kth iteration.

[0142] In some embodiments, the Fourier transform infrared spectroscopy analysis method further includes: establishing third data of a standard sample; wherein the third data is Fourier transform infrared spectroscopy data of the standard sample;

[0143] The validity of the qualitative analysis of the second data is verified based on the first data, the second data, the third data and a preset similarity algorithm.

[0144] In some embodiments, verifying the validity of the qualitative analysis of the second data based on the first data, the second data, the third data, and a preset similarity algorithm includes:

[0145] determining a first similarity value between the third data and the first data according to the preset similarity algorithm;

[0146] determining a second similarity value between the third data and the second data according to the preset similarity algorithm;

[0147] The validity of the second data qualitative analysis is verified according to the first similarity value and the second similarity value.

[0148] Figure 7 Schematic diagram of the structure of an electronic device provided in the embodiment of the present application. Correspondingly, the embodiment of the present application also provides an electronic device, please refer to Figure 7 The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the Fourier transform infrared spectroscopy analysis method are implemented. Since the Fourier transform infrared spectroscopy analysis method has been described in detail above, it will not be repeated here.

[0149] Accordingly, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-mentioned Fourier transform infrared spectroscopy analysis method are implemented. Since the Fourier transform infrared spectroscopy analysis method has been described in detail above, it will not be repeated here.

[0150] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0151] The above is a detailed introduction to the Fourier transform infrared spectroscopy analysis method, system, device and storage medium provided in the embodiments of the present application, and specific examples are used to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the technical solution and its core idea of the present application; ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solution of the embodiments of the present application.

Claims

1. A Fourier transform infrared spectroscopy analysis method, characterized in that: include: Acquire first data of the sample to be tested; wherein the first data is Fourier transform infrared spectrum data of the sample to be tested detected by a spectrum detection instrument; Decomposing and preprocessing the first data according to a preset decomposition algorithm to determine second data; Fourier transform infrared spectroscopy analysis is performed based on the second data.

2. The Fourier transform infrared spectroscopy analysis method according to claim 1, wherein Decomposing and preprocessing the first data according to a preset decomposition algorithm to determine second data includes: determining peak values and valley values of the first data; determining a first envelope and a second envelope of the first data according to the peak value and the valley value; determining a residual of the first data according to the first envelope, the second envelope, and the first data; determining a plurality of intrinsic modal components according to the residual, the first data, and a preset iterative algorithm; The second data is determined based on the multiple intrinsic modal components and a preset filtering algorithm.

3. The Fourier transform infrared spectroscopy analysis method according to claim 2, wherein Determining the peak value and the valley value of the first data includes: Differentiating the first data to obtain differential spectrum data; Determining the peak value according to the differential spectrum data and a preset peak value judgment condition; The valley value is determined according to the differential spectrum data and a preset valley value judgment condition.

4. The Fourier transform infrared spectroscopy analysis method according to claim 2, wherein The determining of a first envelope and a second envelope of the first data according to the peak value and the valley value includes: A cubic spline difference is performed based on the peak value and the valley value to obtain the first envelope curve and the second envelope curve.

5. The Fourier transform infrared spectroscopy analysis method according to claim 2, wherein The calculation formula for determining the residual of the first data according to the first envelope, the second envelope, and the first data is: x1=x0-(f x +t x ) / 2; Wherein, x1 is the residual; x0 is the first data; f x is the first envelope; t x is the second envelope.

6. The Fourier transform infrared spectroscopy analysis method according to claim 2, wherein The determining of a plurality of intrinsic modal components according to the residual, the first data, and a preset iterative algorithm includes: In a case where the residual is an intrinsic modal component, the residual is recorded, a current value of the first data is set to be equal to a difference between the first data and the residual, and the operation of determining the peak and valley values of the first data is repeated. In a case where the residual is not the intrinsic modal component, setting the current value of the first data equal to the residual, and returning to repeatedly perform the operation of determining the peak value and the valley value of the first data; By returning and repeating the operation of determining the peak values and valley values of the first data, a plurality of intrinsic modal components are obtained.

7. The Fourier transform infrared spectroscopy analysis method according to claim 6, wherein The judgment condition for determining whether the residual is the intrinsic modal component includes: (x1-x0) 2 / x0 2 <ε or abs(f(x1)+t(x1)-p(x1))<1; Among them, x1 is the residual; x0 is the first data; ε is the threshold; f(x1) is the number of peak points of x1; t(x1) is the number of valley points of x1; p(x1) is the number of zero-crossing points of x1; abs means taking the absolute value.

8. The Fourier transform infrared spectroscopy analysis method according to claim 2, wherein The calculation formula for determining the second data according to the multiple connotation modal components and the preset filtering algorithm is: Among them, x0 ′ is the second data; x0 is the first data; x k is the intrinsic modal component obtained at the kth iteration.

9. The Fourier transform infrared spectroscopy analysis method according to claim 1, wherein Also includes: Establishing third data of the standard sample; wherein the third data is Fourier transform infrared spectrum data of the standard sample; The validity of the qualitative analysis of the second data is verified based on the first data, the second data, the third data and a preset similarity algorithm.

10. The Fourier transform infrared spectroscopy analysis method according to claim 9, characterized in that: The verifying the validity of the qualitative analysis of the second data based on the first data, the second data, the third data, and a preset similarity algorithm includes: determining a first similarity value between the third data and the first data according to the preset similarity algorithm; determining a second similarity value between the third data and the second data according to the preset similarity algorithm; The validity of the second data qualitative analysis is verified according to the first similarity value and the second similarity value.

11. A Fourier transform infrared spectroscopy analysis system, characterized in that: include: An acquisition module is used to acquire first data of the sample to be tested; wherein the first data is Fourier transform infrared spectrum data of the sample to be tested detected by a spectrum detection instrument; a determination module, configured to perform decomposition and preprocessing on the first data according to a preset decomposition algorithm to determine second data; An analysis module is used to perform Fourier transform infrared spectrum analysis based on the second data.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the Fourier transform infrared spectroscopy analysis method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the Fourier transform infrared spectroscopy analysis method according to any one of claims 1 to 10 is implemented.

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