SFG spectrum advanced fitting analysis method and system
By employing advanced fitting analysis methods, the problems of peak overlap, fitting robustness, and automation in SFG spectral analysis have been solved, achieving efficient and accurate spectral deconvolution and quantitative analysis, which is suitable for data processing in the fields of materials science and surface chemistry.
Patent Information
- Application Number
- CN202511077303.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-01
- Publication Date
- 2025-11-14
AI Technical Summary
SFG spectral analysis suffers from problems such as peak overlap and deconvolution difficulties, poor fitting robustness and stability, the influence of weak signals and background noise, and a lack of automation and standardization, resulting in complex and inefficient analysis.
Advanced fitting analysis methods are employed, including data preprocessing, intelligent initial parameter estimation, nonlinear least squares optimization algorithm fitting, and post-processing optimization. By generating initial amplitude and center position estimates of Gaussian peaks, the fitting boundary is dynamically calculated, and peak merging and refinement are performed to generate fitting parameter reports and interactive spectral fitting plots.
It achieves accurate, stable, and automated deconvolution and quantitative analysis of SFG spectra, improving analysis efficiency, eliminating subjective errors, and ensuring the objectivity and repeatability of fitting results. It is suitable for large-scale experimental data processing in the fields of materials science and surface chemistry.
Smart Images
Figure CN120950880A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of sum-frequency generation vibrational spectra (SFG) fitting technology, specifically to an advanced fitting analysis method and system for SFG spectra. Background Technology
[0002] Sum-frequency vibrational spectroscopy (SFG), as a nonlinear optical technique, possesses interface selectivity and high sensitivity, giving it unique advantages in studying the structure and orientation of material surfaces, interfaces, thin films, and biomolecules. SFG spectroscopy can provide vibrational information of molecules at interfaces, thereby revealing important physicochemical processes such as molecular structure, arrangement, intermolecular forces, and chemical reactions.
[0003] However, the analysis process of SFG spectra is typically complex and challenging. The main problems are as follows:
[0004] 1. Peak overlap and deconvolution difficulties: SFG spectra often contain multiple overlapping vibrational peaks, such as CH stretching and OH stretching regions. These overlaps make it difficult to accurately identify and quantify the contribution of individual components. Traditional manual or semi-automatic fitting methods require a lot of experience and are easily affected by subjective factors.
[0005] 2. Poor robustness and stability of fitting: Multi-Gaussian peak fitting is a nonlinear optimization problem, which is highly sensitive to initial parameters. Inappropriate initial guesses, overly broad or narrow parameter constraints may lead to fitting failure, convergence to a local optimum, or the production of fitting results that do not conform to physicochemical significance.
[0006] 3. Weak signal and background noise: SFG spectra often have weak signals, and the presence of background noise or stray signals further increases the difficulty of accurate fitting.
[0007] 4. Lack of automation and standardization: Existing analytical tools typically require users to manually set peak parameters and adjust the fitting region, lacking a standardized automated workflow. This makes it difficult to achieve efficient batch data processing and reproducible results. This severely restricts work efficiency when processing large numbers of samples or conducting long-term monitoring.
[0008] In summary, the current challenge in SFG spectral analysis lies in achieving accurate, stable, and automated peak deconvolution in complex spectra. Therefore, developing an SFG spectral fitting and analysis system that can overcome the aforementioned limitations and provide efficient, objective, and intelligent performance has significant theoretical and practical application value. Summary of the Invention
[0009] Purpose of the invention: The purpose of this invention is to provide an advanced fitting analysis method and system for SFG spectra, aiming to overcome the problems of strong subjectivity, poor fitting robustness, and low efficiency in existing SFG spectral analysis.
[0010] Technical solution: To achieve the above-mentioned objectives, the present invention adopts the following technical solution:
[0011] An advanced fitting analysis method for SFG spectra includes the following steps:
[0012] Spectral data is loaded from the original SFG data file, and preprocessing is performed, including background subtraction, signal normalization, and data extrapolation in the low-frequency region.
[0013] Based on the preprocessed spectral data, according to the predefined peak groups and the nominal centers of each target peak, within a specified search tolerance range, the initial amplitude and center position estimates of the Gaussian peaks are automatically generated by combining the local maximum intensity of the data. Based on the initial amplitude and center position estimates and the preset width, the fitting boundaries of the amplitude, center position and width of each Gaussian peak to be fitted are dynamically calculated and set. Within the fitting boundaries, a nonlinear least squares optimization algorithm is used to simultaneously fit multiple Gaussian peaks, and the parameters of the successfully fitted Gaussian peaks are refined. The refinement includes merging the fitted peaks that meet the specified conditions.
[0014] Based on the refined and merged Gaussian peaks, generate a fitting parameter report and / or a detailed interactive SFG spectral fitting plot.
[0015] Preferably, the method for generating the estimated center position of the initial amplitude of the Gaussian peak includes:
[0016] For each target peak, within the preset search wavenumber tolerance range centered on its nominal center, the local maximum intensity point in the search data is used as the initial amplitude estimate and the initial center position estimate.
[0017] If no obvious peak is found in the search area or the peak intensity is lower than the preset threshold, the system will revert to using the default weak amplitude as the initial amplitude estimate.
[0018] Preferably, after generating the initial amplitude and center position estimate of the Gaussian peak, and before performing Gaussian fitting, the process further includes:
[0019] The initial estimates are filtered based on the preset minimum peak spacing, and the priority of peaks is determined according to the type and amplitude of the peaks. The initial estimates of peaks that meet the specified priority requirements are then fitted.
[0020] Preferably, when calculating the fitting boundary, the peak type, nominal value, preset displacement tolerance, and full width at half maximum (FWHM) range are comprehensively considered, and the following rules are followed:
[0021] The lower limit of amplitude is not less than the minimum amplitude threshold; the upper limit of amplitude is obtained by multiplying the initial amplitude estimate by the amplification factor of the corresponding peak type, and the upper limit of amplitude is less than the absolute maximum amplitude threshold.
[0022] The center position boundary is set around the initial evaluation center and the nominal center of the peak. The allowable displacement depends on the preset absolute displacement tolerance of the peak type and the fractional offset related to the half-width at half-maximum, where the displacement tolerance of non-primary target peaks is lower than that of other types of peaks.
[0023] The width boundary is limited to the boundary values corresponding to the preset half-width range of the target peak.
[0024] Preferably, the specified conditions include priority determination based on one or more of the following: peak spacing, peak amplitude, and peak type.
[0025] Preferably, merging fitted peaks that meet specified conditions includes:
[0026] Identify peak pairs whose center distance between two fitted peaks is less than the preset minimum fitted peak spacing;
[0027] Based on the type and fitting amplitude of each peak in the peak pair, a higher priority winning peak is determined;
[0028] Lower priority peaks are marked as merged and their fitting magnitude is cleared to zero, so that only the winning peaks are presented in the final report.
[0029] Preferably, the winning peak with higher priority is determined according to the following rules:
[0030] Prioritize retaining non-filled peaks and discard filled peaks;
[0031] Under the same filled / unfilled conditions, the main target peak should be retained first, and the non-main target peaks should be discarded.
[0032] Given equal importance, peaks with larger amplitudes should be retained first;
[0033] If the amplitudes are similar, the peak whose fitting center is closer to its nominal center should be retained first.
[0034] An advanced fitting analysis system for SFG spectra includes:
[0035] The data preprocessing module is used to load spectral data from the original SFG data file and perform preprocessing, including background subtraction, signal normalization, and data extrapolation in the low-frequency region.
[0036] The intelligent fitting and optimization module is used to automatically generate the initial amplitude and center position estimates of Gaussian peaks based on preprocessed spectral data, according to predefined peak groups and the nominal centers of each target peak, within a specified search tolerance range, and in combination with the local maximum intensity of the data. Based on the initial amplitude and center position estimates and the preset width, the module dynamically calculates and sets the fitting boundaries for the amplitude, center position, and width of each Gaussian peak to be fitted. Within the fitting boundaries, a nonlinear least squares optimization algorithm is used to simultaneously fit multiple Gaussian peaks, and the parameters of the successfully fitted Gaussian peaks are refined. The refinement includes merging fitted peaks that meet specified conditions.
[0037] The report feedback module is used to generate a fitting parameter report and / or a detailed interactive SFG spectral fitting plot based on the refined and merged Gaussian peaks.
[0038] The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the steps of the advanced fitting analysis method for SFG spectra as described above.
[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the advanced fitting analysis method for SFG spectra as described above.
[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0041] (1) By introducing intelligent initial parameter guessing, dynamic constraint fitting boundary, and post-processing optimization mechanisms, accurate, stable, and automated deconvolution and quantitative analysis of SFG spectra are achieved, providing objective, repeatable, and deeply physicochemically significant data for materials science, surface chemistry, and other fields. The entire analysis process is highly automated; users only need to load the raw data to automatically complete the entire process from preprocessing and fitting to result reporting, greatly improving the efficiency of SFG data analysis and making it suitable for batch processing of large-scale experimental data.
[0042] (2) By using intelligent initial parameter guessing and dynamic boundary constraints, the convergence and stability of nonlinear fitting are greatly improved. Even in areas with weak signals or complex overlap, accurate fitting results that conform to physicochemical meaning can be obtained. The introduced post-processing peak merging mechanism can effectively identify and eliminate false peaks caused by overfitting or noise, so that the final peak position more accurately reflects the actual molecular vibration mode and simplifies the interpretation of the results.
[0043] (3) It eliminates subjective errors caused by human intervention, ensuring the objectivity, repeatability, and consistency of the fitting results. All fitting parameters and process data can be digitally output, facilitating archiving, traceability, and further quantitative research. Attached Figure Description
[0044] Figure 1 This is a flowchart of the process described in this invention.
[0045] Figure 2 The results are obtained by analyzing the raw SFG spectral data of the random CH segment using the method of this invention.
[0046] Figure 3 The results are obtained by analyzing the raw SFG spectral data in the random OH segment using the method of this invention. Detailed Implementation
[0047] To provide a clearer understanding of the features and advantages of the technical solution of the present invention, the composition and implementation of the specific solution are described below in conjunction with the accompanying drawings.
[0048] Reference Figure 1 This invention also proposes an advanced fitting analysis system for SFG spectra, comprising: a data preprocessing module for loading raw data and performing signal extraction and processing; an intelligent fitting and optimization module for first performing intelligent initial parameter estimation, then performing dynamic constraint multi-peak fitting using a nonlinear least squares method, and then performing post-processing optimization through wind merging, parameter refinement, and visualization checks; and a report feedback module for generating a fitting parameter report, as well as generating a final report and charts; the updated fitting parameters are fed back to the signal extraction and processing unit of the data preprocessing module, and the report is fed back to the data loading unit of the data preprocessing module.
[0049] Based on the advanced fitting analysis system for SFG spectra described above, this embodiment of the invention also proposes an advanced fitting analysis method for SFG spectra, comprising the following steps: data preprocessing, loading raw data and performing signal extraction and processing; intelligent fitting and optimization, first performing intelligent initial parameter estimation, then performing dynamic constraint multi-peak fitting through nonlinear least squares method, and then performing post-processing optimization through wind merging, parameter refinement, visualization inspection, and other operations; report feedback, generating a fitting parameter report, and generating a final report and charts.
[0050] The system is deployed as software on computer equipment or other electronic devices. The execution of the proposed advanced fitting analysis method for SFG spectra is based on the system. It can be understood that the functions of each functional module of the system correspond to the steps of the method. For clarity and convenience, the specific implementation of the present invention will be described in detail below from the perspective of the method.
[0051] Step S1: Data loading and preprocessing.
[0052] Spectral data is loaded from the original SFG data file, and preprocessing is performed, including background subtraction, signal normalization, and data extrapolation in the low-frequency region. Specifically,
[0053] (1) Load spectral data: Load the file and enter all the data points in the file.
[0054] (2) Background subtraction: Automatically find the baseline of the spectrum and subtract the baseline data from the sum-frequency signal data in the spectrum.
[0055] (3) Signal normalization: Detect visible light (Vis) and infrared light (IR) signals, and normalize the target signal by Signal*10000 / (Vis*IR), where Signal is the target signal, i.e., the sum-frequency signal intensity, Vis is the visible light signal intensity, and IR is the infrared light signal intensity.
[0056] (4) Low-frequency data extrapolation: To ensure the peak integrity of the broad spectral band at the low wavenumber end and prevent inaccurate baseline fitting due to data truncation, this method conceptually includes a low-frequency data extrapolation step. This step extends the existing data trend near the low wavenumber end of the spectral data (e.g., wavenumbers below a specified threshold) using a linear or polynomial function, generating an extrapolated data point that smoothly transitions to the baseline. This step ensures that the subsequent fitting model can handle the complete peak shape, especially the low-frequency wings of the broad peak, thereby improving the physical realism and accuracy of the fitting results.
[0057] Step S2, intelligent initial parameter estimation.
[0058] According to an embodiment of the present invention, the intelligent initial parameter estimation method is as follows: The maximum intensity value and its corresponding wavenumber in the data are searched within a specified search window as the initial amplitude and center of the peak; based on the type of the peak (e.g., main target peak, filling peak) and the initial evaluation intensity, a priority is set for whether it is selected for fitting; after all initial estimates are generated, peaks with excessively close center positions are pre-filtered, and the optimal peak is selected for fitting based on the peak's attributes (e.g., whether it is a main target peak, amplitude).
[0059] In this embodiment of the invention, the system presets detailed information for multiple target peaks, such as 2855, 2870, 2890, 2910, 2935, 2960, and 2975 in the CH region and 3390, 3580, 3650, and 3685 in the OH region. Each peak includes its nominal center, region type (CH / OH), initial full width at half maximum (FWHM) guess, and allowable FWHM range. For each target peak, the system first searches for a local maximum intensity point in the data within the wavenumber range of the allowable peak search tolerance near its nominal center, using this as the initial amplitude guess and center guess of the peak, and combines it with the preset FWHM guess. If no obvious peak is found in the region, or the peak intensity is lower than the set initial amplitude threshold factor multiplied by the maximum intensity of the entire region, the system will revert to using the default weak initial amplitude and nominal center as the initial parameter estimate. This process ensures that even when a peak is not prominent, it can still be included in the fit, giving the system a chance to attempt a fit. Finally, before passing all initial estimates to the fitting function, the system filters these estimates based on the minimum peak separation initial guess (i.e., the minimum peak spacing initial guess). If the initial guess centers of two peaks are too close, the system selects one as the fitting object according to a preset priority (e.g., non-filled peaks are preferred over filled peaks, main target peaks are preferred over non-main target peaks, and higher amplitude peaks are preferred over lower amplitude peaks), avoiding fitting difficulties or non-convergence caused by overly dense initial estimates.
[0060] Step S3: Dynamically constrained multi-Gaussian peak fitting.
[0061] This invention uses a multi-Gaussian function model and a nonlinear least squares optimization algorithm to fit SFG spectral data and obtain the fitting parameters for each Gaussian peak.
[0062] Based on the initial parameter estimates obtained in step S2 and the preset width, the fitting boundaries for the amplitude, center position, and width of each Gaussian peak to be fitted are dynamically calculated and set. The calculation of the fitting boundaries comprehensively considers the peak type, nominal value, preset displacement tolerance, and full width at half maximum (FWHM).
[0063] Using standard Gaussian functions A single vibrational peak is described by A, where A is the amplitude, χ is the spectral data point, C is the center position, and σ is the Gaussian standard deviation (convertible to and from the full width at half maximum (FWHM) via a conversion factor). The lower limit of amplitude is not less than 0.0001; the upper limit of amplitude is based on the initial estimated amplitude and multiplied by the amplification factor corresponding to the peak type (max_amplitude_fit_primary_factor for primary target peaks and max_amplitude_fit_secondary_factor for non-primary target peaks), and is limited by the absolute maximum amplitude max_amplitude_absolute. The peak center is set around the initial estimated center and the nominal center. The allowable displacement depends on the preset absolute displacement tolerance of the peak type (CH / OH) and the fractional offset related to the FWHM, which can be an absolute offset or a fractional offset related to the FWHM. For non-primary target peaks, a stricter non-primary center fit tolerance can be applied, with a narrower boundary range. Furthermore, a hard constraint can be imposed on the target peaks by defining a range from the 'minallowed center' to the 'max allowed center'. The peak width (Sigma) is strictly limited to the Sigma range corresponding to the preset half-width at half-maximum (FWHM) range for each target peak.
[0064] Step S4: Post-processing optimization.
[0065] According to an embodiment of the present invention, post-processing optimization includes refining the fitting parameters. The method is as follows: identifying peak pairs whose fitting center distance is less than a preset minimum fitted peak separation; determining a higher priority winning peak based on the type (filling peak, main target peak) and fitting amplitude of each peak in the peak pair; marking the lower priority peaks as merged and clearing their fitting amplitude to zero, so that only the winning peak is presented in the final report.
[0066] Specifically, when traversing the successfully fitted peaks, if the center distance between two fitted peaks is less than the minimum fitted peak separation (min_fitted_peak_separation, which can be set separately for the CH and OH regions), the system will merge them according to a set of priority rules:
[0067] a. Non-filled peaks are preferred over filled peaks, meaning that non-filled peaks are retained and filled peaks are discarded.
[0068] b. The main target peak is preferred over non-main target peaks. That is, under the same filling / unfilling conditions, the main target peak is retained first and the non-main target peaks are discarded.
[0069] c. Peaks with larger amplitudes are preferred over peaks with smaller amplitudes; that is, under the same importance (fill / primary target), peaks with larger amplitudes are retained first.
[0070] d. If the amplitudes are similar, select the peak whose fitted center is closer to its expected center.
[0071] Finally, the fitting amplitude of the merged peaks will be set to 0 and marked in the report as "Merged into [Winner_Peak_Name]", ensuring that the peak list in the final report is concise and physically meaningful.
[0072] Step S5: Visualize and report the fitting results, generating a spectral fitting plot and a fitting report.
[0073] Based on the fitting results, a detailed spectral fitting plot can be generated. As an example, a detailed spectral fitting plot includes:
[0074] ① Original SFG signal;
[0075] ② Normalized data, extrapolated data, fitting bus, and individual Gaussian peak components;
[0076] ③ Residual plot (the difference between the fitted bus and the normalized data);
[0077] ④ Peak position and peak parameter labeling.
[0078] Optionally, a magnified view of the local region can be intelligently selected and drawn based on peak activity, and peak parameters can be automatically labeled; optionally, a structured data report containing all fitting parameters, chemical interpretations, and statistical summaries can also be generated.
[0079] Optionally, based on the fitting, the intensity of specific key characteristic peaks can be automatically identified and calculated, enabling users to more intuitively understand the relationship between spectra and molecular structure / interface properties, thereby accelerating scientific discovery.
[0080] It can be understood that in the above description, steps S2, S3, and S4 correspond to the intelligent fitting and optimization steps described above. Accordingly, the intelligent fitting and optimization module includes an intelligent initial parameter estimation unit corresponding to step S2, a dynamically constrained multi-Gaussian peak fitting unit corresponding to step S3, and a post-processing optimization unit corresponding to step S4.
[0081] The calculation logic of each step in the method of this invention is clear, the calculation process is concise, and the calculation results can be clearly marked on the visual interface, facilitating further interpretation and use by the user. From the perspective of the software system, it facilitates modular development for functional deployment and use.
[0082] Figure 2 and Figure 3 These are typical results of processing randomly generated CH and OH SFG spectral data using the method of this invention. They excellently demonstrate several key beneficial effects of this invention:
[0083] (1) Accuracy and high goodness of fit: In both figures, the fitted curve (red solid line) highly coincides with the original data curve (orange-red solid line). The residual plot at the bottom (purple curve) shows that the fitting error fluctuates randomly around the zero line with very small amplitude, which intuitively proves that the fitting result is very accurate.
[0084] (2) Automated deconvolution capability: The green dashed lines in the figure represent individual Gaussian peak components automatically resolved from the overlapping spectral envelope. For example, in Figure 2 In the CH region, multiple closely connected CH vibration peaks are clearly separated, demonstrating the powerful deconvolution capability of this invention.
[0085] (3) Intelligent information annotation and visualization: The graph automatically annotates the center position and full width at half maximum (FWHM) of each fitted peak, and generates a detailed text box containing all fitted parameters and states. This intelligent visualization method greatly facilitates the interpretation and use of the results by users.
[0086] (4) Robustness: These figures are based on randomly generated simulated data that includes noise and peak shifts. The present invention can still successfully and accurately complete the fitting, demonstrating its good robustness to the complexity and uncertainty of real experimental data.
[0087] As can be seen from the above verification cases, the method of the present invention can perform in-depth analysis of SFG spectral data in a highly automated and standardized manner, and the results are objective, quantitative, and have extremely high application value.
[0088] The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the steps of the advanced fitting analysis method for SFG spectra as described above.
[0089] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the advanced fitting analysis method for SFG spectra as described above.
[0090] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus (systems), computer devices, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0091] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that each step in the flowchart and combinations thereof can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A device for a function specified in one or more processes.
[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.
[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more processes.
Claims
1. An advanced fitting analysis method for SFG spectra, characterized in that, Includes the following steps: Spectral data is loaded from the original SFG data file, and preprocessing is performed, including background subtraction, signal normalization, and data extrapolation in the low-frequency region. Based on the preprocessed spectral data, according to the predefined peak groups and the nominal centers of each target peak, within a specified search tolerance range, the initial amplitude and center position estimates of the Gaussian peaks are automatically generated by combining the local maximum intensity of the data. Based on the initial amplitude and center position estimates and the preset width, the fitting boundaries of the amplitude, center position and width of each Gaussian peak to be fitted are dynamically calculated and set. Within the fitting boundaries, a nonlinear least squares optimization algorithm is used to simultaneously fit multiple Gaussian peaks, and the parameters of the successfully fitted Gaussian peaks are refined. The refinement includes merging the fitted peaks that meet the specified conditions. Based on the refined and merged Gaussian peaks, generate a fitting parameter report and / or a detailed interactive SFG spectral fitting plot.
2. The method according to claim 1, characterized in that, Methods for generating the initial amplitude center position estimate of the Gaussian peak include: For each target peak, within the preset search wavenumber tolerance range centered on its nominal center, the local maximum intensity point in the search data is used as the initial amplitude estimate and the initial center position estimate. If no obvious peak is found in the search area or the peak intensity is lower than the preset threshold, the system will revert to using the default weak amplitude as the initial amplitude estimate.
3. The method according to claim 2, characterized in that, After generating the initial amplitude and center position estimates of the Gaussian peak, and before performing Gaussian fitting, the following steps are also included: The initial estimates are filtered based on the preset minimum peak spacing, and the priority of peaks is determined according to the type and amplitude of the peaks. The initial estimates of peaks that meet the specified priority requirements are then fitted.
4. The method according to claim 1, characterized in that, When calculating the fitting boundary, the type of peak, nominal value, preset displacement tolerance, and full width at half maximum (FWHM) range are comprehensively considered, and the following rules are followed: The lower limit of amplitude is not less than the minimum amplitude threshold; the upper limit of amplitude is obtained by multiplying the initial amplitude estimate by the amplification factor of the corresponding peak type, and the upper limit of amplitude is less than the absolute maximum amplitude threshold. The center position boundary is set around the initial evaluation center and the nominal center of the peak. The allowable displacement depends on the preset absolute displacement tolerance of the peak type and the fractional offset related to the half-width at half-maximum, where the displacement tolerance of non-primary target peaks is lower than that of other types of peaks. The width boundary is limited to the boundary values corresponding to the preset half-width range of the target peak.
5. The method according to claim 1, characterized in that, The specified conditions include priority judgment based on one or more of the following: peak spacing, peak amplitude, and peak type.
6. The method according to claim 5, characterized in that, Merge fitted peaks that meet specified conditions, including: Identify peak pairs whose center distance between two fitted peaks is less than the preset minimum fitted peak spacing; Based on the type and fitting amplitude of each peak in the peak pair, a higher priority winning peak is determined; Lower priority peaks are marked as merged and their fitting magnitude is cleared to zero, so that only the winning peaks are presented in the final report.
7. According to the method of claim 6, the winning peak with higher priority is determined according to the following rules: Prioritize retaining non-filled peaks and discard filled peaks; Under the same filled / unfilled conditions, the main target peak should be retained first, and the non-main target peaks should be discarded. Given equal importance, peaks with larger amplitudes should be retained first; If the amplitudes are similar, the peak whose fitting center is closer to its nominal center should be retained first.
8. An advanced fitting analysis system for SFG spectra, characterized in that, include: The data preprocessing module is used to load spectral data from the original SFG data file and perform preprocessing, including background subtraction, signal normalization, and data extrapolation in the low-frequency region. The intelligent fitting and optimization module is used to automatically generate the initial amplitude and center position estimates of Gaussian peaks based on preprocessed spectral data, according to predefined peak groups and the nominal centers of each target peak, within a specified search tolerance range, and in combination with the local maximum intensity of the data. Based on the initial amplitude and center position estimates and the preset width, the module dynamically calculates and sets the fitting boundaries for the amplitude, center position, and width of each Gaussian peak to be fitted. Within the fitting boundaries, a nonlinear least squares optimization algorithm is used to simultaneously fit multiple Gaussian peaks, and the parameters of the successfully fitted Gaussian peaks are refined. The refinement includes merging fitted peaks that meet specified conditions. The report feedback module is used to generate a fitting parameter report and / or a detailed interactive SFG spectral fitting plot based on the refined and merged Gaussian peaks.
9. A computer device, characterized in that, include: One or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the steps of the advanced fitting analysis method for SFG spectra as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the advanced fitting analysis method for SFG spectra as described in any one of claims 1-7.