A method for detecting nanomaterials based on surface-enhanced Raman spectroscopy

Abstract

The present invention relates to the field of optical detection technology, and particularly relates to a detection method of nanomaterials based on surface-enhanced Raman spectroscopy, including: obtaining spectral data at different acquisition times based on the nanomaterials, generating a spectral curve, and performing preprocessing; analyzing the preprocessed spectral curve to obtain the initial shape feature as the peak factor, and constructing a particle set; calculating the fluctuation influence factor through the particle set, obtaining the overlap parameter of the particles according to the preprocessed spectral curve, and combining the fluctuation influence factor with the overlap parameter to determine the ultimate shape feature of the particles; performing clustering analysis on the ultimate shape feature of the particles to obtain several clustering clusters, calculating the degree of feature change of each clustering cluster, screening according to the degree of feature change, removing redundant peaks, and correcting the spectral curve after removal; performing quantitative analysis based on the corrected spectral curve, so as to have stronger adaptability to data noise, redundant information and external environment changes, and the result is more robust.

Description

Technical Field

[0001] The present invention relates to the technical field of optical detection, and particularly relates to a detection method for nanomaterials based on surface-enhanced Raman spectroscopy. Background Art

[0002] Surface-enhanced Raman spectroscopy (SERS, Surface-Enhanced Raman Spectroscopy) is a highly sensitive, high-resolution and non-destructive spectroscopic detection technology based on the surface enhancement effect. It can directly detect the molecule itself and provide detailed molecular information without destroying the sample. Therefore, it has been widely used in the detection field of metal nanomaterials.

[0003] The characteristic peaks of Raman spectroscopy are closely related to the chemical structure of the molecule. The spectrum obtained by SERS can provide rich qualitative and quantitative information for molecular structure analysis and component identification. Nanomaterials with specific sizes and morphologies, such as metal nanoparticles, nanotubes, nanorods, etc., can enhance the local electromagnetic field through the surface plasmon resonance (SPR, Surface Plasmon Resonance) phenomenon, thereby enhancing the Raman scattering signal; using SERS technology can deeply analyze the morphology, size, lattice structure and surface chemical reactions of metal nanostructures. It is not only a highly sensitive and non-destructive detection method, but also can deeply reveal the surface properties, chemical composition and molecular interactions of nanomaterials, providing strong support for the research and application of nanoscience and technology.

[0004] Due to the non-uniformity of metal nanomaterials in morphology, size and surface structure, when performing surface plasmon resonance (SPR), a mixed molecule adsorption situation may occur, which will lead to the enhancement of multiple Raman signals, and these signals may originate from different surface or interface states. Therefore, multiple overlapping peaks will appear in the spectrum; for the redundant peaks that appear in the spectral curve, the existing methods usually eliminate them through particle filtering (PF, Particle Filtering) and independent component analysis (ICA, Independent Component Analysis). Among them, particle filtering is a non-linear and non-Gaussian system estimation technology based on the Monte Carlo method. However, when multiple peaks overlap and the signal intensities are similar, due to the overlapping of the characteristic signals of multiple molecules or molecular groups, the formed redundant peaks cannot be effectively separated only by particle filtering, resulting in difficulty in distinguishing effective information from noise interference; in addition, if the signal overlap is too complex, or the independence of the signals is weak, such as the case where the peak widths or intensities are close, independent component analysis may not be able to effectively separate the overlapping peaks, resulting in difficulty in eliminating redundant peaks. Summary of the Invention

[0005] To solve the technical problem that the prior art cannot effectively separate and eliminate redundant peaks, the purpose of the present invention is to provide a method for detecting nanomaterials based on surface-enhanced Raman spectroscopy, and the specific technical solution adopted is as follows:

[0006] Based on the nanomaterials, spectral data at different acquisition times are obtained, a spectral curve is generated, and preprocessing is performed;

[0007] Analyze the preprocessed spectral curve to obtain the initial shape feature as the peak factor, define the peak factor as particles, and construct a particle set;

[0008] Calculate the fluctuation influence factor through the particle set, obtain the overlap parameter of the particles according to the preprocessed spectral curve, and combine the fluctuation influence factor with the overlap parameter of the corresponding particles to determine the ultimate shape feature of the particles;

[0009] Perform cluster analysis on the ultimate shape feature of the particles to obtain several clusters, calculate the degree of change in the characteristics of each cluster, and perform screening according to the degree of change in the characteristics to eliminate redundant peaks and correct the spectral curve after elimination;

[0010] Perform quantitative analysis based on the corrected spectral curve.

[0011] Preferably, based on the nanomaterials, spectral data at different acquisition times are obtained, a spectral curve is generated, and preprocessing is performed, including:

[0012] Obtain the nanomaterials to be detected, use a spectrometer to obtain spectral data at different acquisition times, define the Raman shift as the horizontal axis and the Raman scattering intensity as the vertical axis to generate a spectral curve, and perform preprocessing on the spectral curve through moving average filtering to obtain a denoised spectral curve.

[0013] Preferably, analyze the preprocessed spectral curve to obtain the initial shape feature as the peak factor, define the peak factor as particles, and construct a particle set, including:

[0014] Perform peak detection on the preprocessed spectral curve, and extract the signal intensity and frequency position of the Raman peak;

[0015] Calculate the initial shape feature of the Raman peak, denoted as the peak factor;

[0016] Define the peak factor as particles, determine the peak factors of all Raman peaks in the spectral curve, and construct a particle set.

[0017] Preferably, calculate the initial shape feature of the Raman peak, and the corresponding calculation formula is:

[0018]

[0019] where t iDenote the peak factor of the i-th Raman peak on the preprocessed spectral curve; W i Denote the full width at half maximum of the i-th Raman peak; W i,l Denote the Raman shift corresponding to the spectral data of the left half part in the i-th Raman peak; W i,r Denote the Raman shift corresponding to the spectral data of the right half part in the i-th Raman peak; exp() represents the exponential function with the natural constant e as the base.

[0020] Preferably, calculate the fluctuation influence factor through the particle set, and the corresponding calculation formula is:

[0021]

[0022] where, a i Denote the fluctuation influence factor of the i-th particle; t i,τ Denote the peak factor corresponding to the spectral curve of the i-th particle at the τ-th acquisition moment; t i+1,τ Denote the peak factor corresponding to the spectral curve of the (i + 1)-th particle at the τ-th acquisition moment; l i,i+1 Denote the distance between the i-th particle and the (i + 1)-th particle; M represents the number of all spectral curves obtained based on the acquisition moments; norm represents the normalization function.

[0023] Preferably, obtain the overlap parameter of the particle according to the preprocessed spectral curve, and the corresponding calculation formula is:

[0024]

[0025] where, q i Denote the overlap parameter of the i-th particle; λ′ i Denote the total peak width of the i-th particle; l i,u Denote the Raman shift of the u-th Raman peak in the i-th particle; l i,u+1 Denote the Raman shift of the (u + 1)-th Raman peak in the i-th particle; N represents the number of Raman peaks included in the particle set.

[0026] Preferably, combine the fluctuation influence factor with the overlap parameter of the corresponding particle to determine the ultimate shape feature of the particle, and the corresponding calculation formula is:

[0027] δ j =a i ×q i

[0028] where, δ j Denote the ultimate shape feature of the i-th particle currently analyzed; a i Denote the fluctuation influence factor of the i-th particle; q i Denote the overlap parameter of the i-th particle.

[0029] Preferably, perform clustering analysis on the ultimate shape features of the particles to obtain several clustering clusters, calculate the degree of feature change of each clustering cluster, and perform screening according to the degree of feature change, eliminate redundant peaks, and correct the spectral curve after elimination, including:

[0030] Perform clustering analysis on the ultimate shape features of the particles to obtain several clustering clusters, and calculate the degree of feature change of each clustering cluster;

[0031] Set a screening threshold. When the degree of feature change of the clustering cluster is greater than the screening threshold, the current clustering cluster contains redundant peaks and is eliminated;

[0032] Use Gaussian fitting to reconstruct the peak shape of the spectral curve after eliminating redundant peaks, obtain the correction parameters of each Raman peak after reconstruction, and correct the spectral curve through the correction parameters.

[0033] Preferably, calculate the degree of feature change of each clustering cluster, and the corresponding calculation formula is:

[0034]

[0035] where D j represents the degree of feature change of the j-th clustering cluster; δ j represents the ultimate shape feature of the i-th particle in the j-th clustering cluster; nj represents the number of clustering clusters; C j represents the geometric center of the j-th clustering cluster; p i,j represents the i-th particle in the j-th clustering cluster; ||p i,j -C j || 2 represents the distance from the i-th particle in the j-th clustering cluster to the geometric center of the j-th clustering cluster; m represents the number of particles contained in the clustering cluster.

[0036] Preferably, perform quantitative analysis based on the corrected spectral curve, including:

[0037] Perform signal processing on the corrected spectral curve to generate a three-dimensional spectrogram, display the position and intensity changes of the characteristic peaks, and perform quantitative analysis to generate quantifiable data, and obtain the analysis results in any form of table and picture.

[0038] The present invention has the following beneficial effects:

[0039] By optimizing the existing particle filtering technology, the random sampling and weighting mechanism of particle filtering enables this application to extract meaningful signals from complex dynamic processes, combine with clustering algorithms to process spectral data, identify and eliminate redundant peaks, effectively cope with various complex situations that may occur in experiments, make the existing detection means have stronger adaptability to data noise, redundant information and external environment changes, and the obtained data analysis results are more robust. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 FIG. is a flowchart of the steps of a method for detecting nanomaterials based on surface-enhanced Raman spectroscopy provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features and effects of a method for detecting nanomaterials based on surface-enhanced Raman spectroscopy proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] The following will specifically describe the specific solution of a method for detecting nanomaterials based on surface-enhanced Raman spectroscopy provided by the present invention with reference to the accompanying drawings.

[0045] Please refer to Figure 1 , which shows a flowchart of the steps of a method for detecting nanomaterials based on surface-enhanced Raman spectroscopy provided by an embodiment of the present invention. The method includes:

[0046] Step S1: Obtain spectral data at different acquisition times based on the nanomaterials, generate a spectral curve, and perform preprocessing;

[0047] Step S2: Analyze the preprocessed spectral curve to obtain the initial shape feature as the peak factor, define the peak factor as a particle, and construct a particle set;

[0048] Step S3: Calculate the fluctuation influence factor through the particle set, obtain the overlap parameter of the particles according to the preprocessed spectral curve, and combine the fluctuation influence factor with the overlap parameter of the corresponding particles to determine the ultimate shape characteristics of the particles;

[0049] Step S4: Conduct cluster analysis on the ultimate shape characteristics of the particles to obtain several clusters, calculate the degree of characteristic change of each cluster, and perform screening according to the degree of characteristic change, eliminate redundant peaks, and correct the spectral curve after elimination;

[0050] Step S5: Conduct quantitative analysis based on the corrected spectral curve.

[0051] For better illustration, surface-enhanced Raman spectroscopy (SERS) is a spectroscopic analysis technique based on the Raman scattering effect. By using a rough metal surface or nanostructure to enhance the Raman signal, high-sensitivity detection of the molecular structure of substances can be achieved; its principle involves surface plasmon resonance. When a laser irradiates the surface of a metal nanostructure, a local electromagnetic field enhancement effect will be generated on the surface, thus greatly amplifying the Raman scattering signal of the molecule; it is suitable for trace analysis and can enhance the Raman signal by a factor of 10 6 to 10 12 times, making the originally weak signals that are difficult to detect become clearly distinguishable.

[0052] In surface-enhanced Raman scattering (SERS) experiments, the spectral signals obtained are often easily interfered by various factors, including but not limited to the noise generated by the equipment itself, the continuous change of environmental conditions, and the randomness during the adsorption process, etc. These interference factors may cause redundancy or noise in the experimental data, affecting the quality of the spectral data; the generation of redundant particles is often caused by some unimportant or inaccurate signal states, such as incorrect selection of adsorption sites or inappropriate desorption processes; if these redundant data are not effectively identified and removed, it will have a negative impact on the accuracy of signal analysis and ultimately affect the reliability of the detection results of nanomaterials.

[0053] A Raman peak refers to a characteristic peak on the spectral curve, indicating a specific energy state of molecular vibration or rotation. The position, shape, and intensity of the Raman peak can provide important information about the molecular structure, chemical bond type, and intermolecular interaction.

[0054] As an optional implementation manner, in this embodiment, the nanomaterial refers to a metal nanostructure, that is, a metal material at the nanoscale, which can significantly enhance the Raman scattering signal and make the detection process more sensitive and efficient.

[0055] Furthermore, in step S1, it includes:

[0056] Obtain the nanomaterials to be detected, use a spectrometer to obtain spectral data at different acquisition times, define the Raman shift as the horizontal axis and the Raman scattering intensity as the vertical axis to generate a spectral curve, and preprocess the spectral curve through moving average filtering to obtain a denoised spectral curve.

[0057] Optionally, in this embodiment, the spectrometer is a confocal micro-Raman spectrometer, which is a high-precision spectral analysis device that enables the excitation light source, the sample, and the detector to be in the same focal plane, greatly improving the collection efficiency and resolution of the spectral signal. Through its confocal optical system, it only collects the scattered light from a tiny area on the surface of the nanomaterials, effectively reducing the interference of background noise, enhancing the signal-to-noise ratio, and being able to quickly respond and accurately record the intensity of the Raman scattered light, providing a reliable basis for subsequent data analysis.

[0058] Specifically, use a confocal micro-Raman spectrometer to collect the spectral data of the metal nanomaterials to be detected within a preset time interval to obtain spectral data at different acquisition times. Take the Raman shift as the horizontal axis and the Raman scattering intensity as the vertical axis to plot and generate a spectral curve image, denoted as the Raman spectral curve of the nanomaterials; and set a variation step size based on the preset time interval to delimit different acquisition times. One acquisition time corresponds to one Raman spectral curve of the nanomaterials, and then obtain several Raman spectral curves of the nanomaterials; then select moving average filtering to perform smoothing signal processing on the Raman spectral curve of the nanomaterials, that is, to smooth the signal by calculating the average value of consecutive points in the spectral curve to obtain a denoised spectral curve, avoiding the presence of noise from masking or distorting important spectral features.

[0059] It can be understood that SERS is an analytical method that does not require the use of any markers. It mainly relies on the random adsorption of metal nanomaterials on the SERS substrate to achieve a significant enhancement of the signal; since the adsorption sites of molecules on the surface of metal nanomaterials are randomly distributed, the adsorption and desorption processes of molecules are dynamically changing at different time points, that is, the spectral data obtained from SERS experiments may contain fluctuations and variations from multiple sources, such as changes in the adsorption state of molecules, noise during the experimental process, etc. However, these raw data do not fully represent the true molecular information. Therefore, in order to accurately extract useful molecular information from these spectral data, particle filtering technology is usually used for screening. This application optimizes particle filtering and combines a clustering algorithm. By analyzing a series of spectral data in the time series and using the particle set after particle filtering, it can effectively eliminate the fluctuations caused by the adsorption and desorption processes to accurately remove redundant data, allowing the staff to perform more accurate material detection and analysis based on the corrected spectral curve.

[0060] Furthermore, in step S2, it includes:

[0061] Step S21: Perform peak detection on the preprocessed spectral curve, and extract the signal intensity and frequency position of the Raman peak.

[0062] It should be noted that in the spectral curve of metal nanomaterials, the adsorption differences of molecules at different times are reflected in the different peak characteristics of Raman peaks. By analyzing the peaks in the spectral data, information such as the corresponding signal intensity, frequency position, and shape characteristics can be obtained; then, by taking the geometric shape characteristics of the same peak at different times, that is, the geometric characteristics of the peak within the same Raman shift range as particles; it can be explained that the peaks in the Raman spectrum correspond to the vibration modes of specific chemical bonds inside the nanomaterials. Ideally, at different detection times, the peaks at the same position only have changes in scattering intensity, while the geometric shape and position of the peaks remain unchanged. Furthermore, a particle set is constructed, that is, the peak characteristics at different acquisition times are reflected through the particle set, providing a data basis for subsequent analysis or optimization.

[0063] Step S22: Calculate the initial shape characteristics of the Raman peak, denoted as the peak factor.

[0064] As an optional implementation manner, the initial shape characteristics refer to the symmetry degree of the Raman peak.

[0065] Specifically, using a sliding window, peak detection is performed on all spectral curves after smoothing filtering by judging the local maximum value within the window, and features such as the signal intensity and frequency position of the peak are extracted; in the spectral curve, the adsorption differences of nanomaterials are reflected in the shape characteristics of Raman peaks at different times. Among them, the full width at half maximum of the Raman peak reflects the vibration mode of the nanomaterials and is used to master the characteristics of the nanomaterials; the symmetry of the peak is used to describe the waveform concentration degree of the Raman peak, reflecting the structural characteristics of metal nanomaterial molecules at the current acquisition time. Then, the initial shape characteristics of the Raman peak, that is, the peak factor, are calculated by combining the full width at half maximum of the Raman peak with the spectral data.

[0066] Furthermore, in step S22, the calculation formula for the initial shape characteristics of the Raman peak is as follows:

[0067]

[0068] where t i represents the peak factor of the i-th Raman peak on the preprocessed spectral curve; W i represents the full width at half maximum of the i-th Raman peak; W i,l represents the Raman shift corresponding to the spectral data of the left half part of the i-th Raman peak; W i,r represents the Raman shift corresponding to the spectral data of the right half part of the i-th Raman peak; exp() represents the exponential function with the natural constant e as the base.

[0069] It is explained that the shape of the Raman peak can provide information about the molecular structure of the nanomaterial, and the width is related to the dynamic characteristics of the nanomaterial molecules. For example, a wider Raman peak may indicate greater energy dissipation during the vibration of the nanomaterial molecules, which may be caused by intermolecular interactions or anharmonic vibrations within the molecules;|W i,l -W i,r | represents the difference in the symmetry degree of the Raman frequency shifts on the left and right sides of the i-th Raman peak with respect to the peak value of the Raman peak. When the difference in the Raman frequency shifts on both sides of the peak value of the Raman peak is more obvious, it indicates that the symmetry degree of the peaks on the left and right sides is lower, that is, when the shape of the Raman peak is more irregular, the greater the possibility that the current Raman peak is a redundant peak, and the greater the peak interference degree; On the other hand, It can reflect the waveform symmetry degree of the Raman peak, that is, when the waveform symmetry degree of the Raman peak is smaller, the greater the peak set characteristics of the Raman peak, and the more likely the currently analyzed Raman peak is a characteristic peak; When the width of the Raman peak is relatively narrow, this indicates that the actual vibration of the metal nanomaterial molecules coincides with the theoretical state to a higher degree, making the peak characteristics of the Raman peak more obvious, indicating that this Raman peak is more likely to be a characteristic peak.

[0070] Step S23: Define the peak factor as a particle, determine the peak factors of all Raman peaks in the spectral curve, and construct a particle set.

[0071] It can be explained that by analyzing the particle set, the spectral data can be analyzed, and then different material components of the nanomaterial can be identified and distinguished to eliminate redundant peaks.

[0072] It can be understood that in step S3, the fluctuation influence factor is calculated through the particle set, that is, in order to avoid misidentifying redundant peaks that are not characteristic of nanomaterial molecules as characteristic peaks when analyzing the spectral curve of metal nanomaterials, which affects the accurate calculation of the peak shift degree, it is necessary to evaluate the peak interference degree of multiple Raman peaks in each wavelength range of the spectral curve; Theoretically, if within the same wavelength band, the adsorption rate of nanomaterial molecules at different time points remains consistent and shows a certain similarity, so in the spectral curves collected at consecutive time points, the change in the particle amplitude within the same wavelength band should be relatively stable, but the presence of redundant peaks will cause a significant difference in the change rate. Therefore, it is necessary to extract the particle amplitude within a specific wavelength band from the spectral curves collected at consecutive moments, calculate its change rate, and then combine the peak factor of each particle to calculate the fluctuation influence factor of the particle.

[0073] Furthermore, in step S3, the fluctuation influence factor is calculated through the particle set, and the corresponding calculation formula is:

[0074]

[0075] where, ai represents the fluctuation influence factor of the \(i\)-th particle; \(t\) i,τ represents the peak factor corresponding to the \(i\)-th particle in the spectral curve at the \(\tau\)-th acquisition moment; \(t\) i+1,τ represents the peak factor corresponding to the \((i + 1)\)-th particle in the spectral curve at the \(\tau\)-th acquisition moment; \(l\) i,i+1 represents the distance between the \(i\)-th particle and the \((i + 1)\)-th particle; \(M\) represents the number of all spectral curves obtained based on the acquisition moments; norm represents the normalization function.

[0076] It should be noted that the fluctuation influence factor is used to describe the fluctuation characteristics of particles, \(l\) i,i+1 represents the distance between the \(i\)-th particle and the \((i + 1)\)-th particle, which is used to reveal the interaction between particles.

[0077] The overlapping parameters of the particles can be obtained according to the preprocessed spectral curves. It should be noted that due to the non-uniformity of the morphology, size, and surface structure of metal nanomaterials, there will be overlapping peaks in the detected spectral data, making data analysis complex; when multiple Raman peaks overlap and their signal intensities are close, it is difficult to accurately distinguish the redundant peaks contained in the overlapping peaks only using the particle filtering technique. Therefore, to solve this problem, it is necessary to obtain the overlapping parameters of the peaks in the spectral curve, that is, calculate the absolute value of the difference between the Raman frequency shifts corresponding to the peaks of adjacent Raman peaks within the same band, and record the difference between the total peak width of the Raman peak and the absolute value of the difference between the Raman frequency shifts corresponding to the peaks of its adjacent Raman peaks as the overlapping parameter to accurately quantify the overlapping degree between adjacent Raman peaks.

[0078] Furthermore, in step S3, the overlapping parameters of the particles are obtained according to the preprocessed spectral curves, and the corresponding calculation formula is as follows:

[0079]

[0080] where \(q\) i represents the overlapping parameter of the \(i\)-th particle; \(\lambda'\) i represents the total peak width of the \(i\)-th particle; \(l\) i,u represents the Raman frequency shift of the \(u\)-th Raman peak in the \(i\)-th particle; \(l\) i,u+1 represents the Raman frequency shift of the \((u + 1)\)-th Raman peak in the \(i\)-th particle; \(N\) represents the number of Raman peaks contained in the particle set.

[0081] It should be noted that It represents the proportion of the overlapping degree of particles in the total peak width value. The larger the proportion, the more serious the overlapping situation, which may be caused by factors such as uneven particle size distribution, complex surface morphology, or particle-particle interactions; the more the number of overlapping peaks, the more serious the overlapping situation, that is, in the Raman spectrum, multiple Raman peaks interfere with each other and are difficult to accurately distinguish, which not only increases the difficulty of data analysis but also may affect the accuracy of the final detection result.

[0082] The ultimate shape feature of the particle is determined by combining the fluctuation influence factor with the overlapping parameter of the corresponding particle, that is, the fluctuation influence factor of the particle is weighted by the overlapping parameter to obtain the ultimate shape feature of the particle.

[0083] Further, in step S3, the ultimate shape feature of the particle is determined by combining the fluctuation influence factor with the overlapping parameter of the corresponding particle, and the corresponding calculation formula is:

[0084] δ j =a i ×q i

[0085] where, δ j represents the ultimate shape feature of the i-th particle under current analysis; a i represents the fluctuation influence factor of the i-th particle; q i represents the overlapping parameter of the i-th particle.

[0086] It can be understood that by applying a clustering algorithm, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the ultimate shape feature data obtained by effectively concentrating and weighting the particles can be grouped, which is to identify and eliminate those particles that do not represent important feature processes, and can extract the main signal patterns, that is, through similarity-based classification, by analyzing the matching degree of the ultimate shape features of particles in the spectral curve at different acquisition times based on key information such as the signal intensity and frequency position carried by the particles, group the particles, and then remove those inconsistent data points, significantly reducing noise and data redundancy, and also ensuring that the particle set can more accurately reflect the true dynamic behavior of the system.

[0087] Further, in step S4, it includes:

[0088] Step S41: Conduct a clustering analysis on the ultimate shape features of the particles to obtain several clustering clusters, and calculate the degree of feature change of each clustering cluster.

[0089] It should be noted that based on the ultimate shape characteristics of the particles, recognition is carried out, and several clustering clusters with similar characteristics are divided. Then, the degree of characteristic change of each clustering cluster is calculated to distinguish the clustering cluster where the redundant peak is located.

[0090] Furthermore, in step S41, the degree of characteristic change of each clustering cluster is calculated, and the corresponding calculation formula is:

[0091]

[0092] where D j represents the degree of characteristic change of the j-th clustering cluster; δ j represents the ultimate shape characteristic of the i-th particle in the j-th clustering cluster; nj represents the number of clustering clusters; C j represents the geometric center of the j-th clustering cluster; p i,j represents the i-th particle in the j-th clustering cluster; ||p i,j -C j || 2 represents the distance from the i-th particle in the j-th clustering cluster to the geometric center of the j-th clustering cluster; m represents the number of particles included in the clustering cluster.

[0093] An explanation is made that it can illustrate the influence degree of time change on the clustering cluster, that is, combining the influence of time change and the characteristics of the clustering cluster, dynamically quantifying the influence range of time. The larger the value, the more obvious the influence of time on the clustering cluster, indicating that as time goes by, the characteristics of the clustering cluster may change greatly, and the quantified data can help the staff master the evolution process of the clustering cluster with time change; reflects the degree of dispersion of the particles in the clustering cluster. The greater the degree of dispersion, the less concentrated the distribution of the particles in the clustering cluster, and the more obvious the fluctuation degree of the clustering cluster, which can evaluate the stability of the clustering cluster and the distribution of the particles in the clustering cluster.

[0094] Step S42: Set a screening threshold. When the degree of characteristic change of the clustering cluster is greater than the screening threshold, the current clustering cluster contains redundant peaks and is removed.

[0095] As an optional implementation manner, in this embodiment, the screening threshold is T = 0.75.

[0096] Specifically, based on the screening threshold, redundant peaks in the Raman spectrum are removed, that is, when the degree of characteristic change of the clustering cluster is greater than the screening threshold, when D j > T, it indicates that the possibility of the clustering cluster containing redundant peak particles is greater, and it needs to be removed to reduce unnecessary duplicate information and improve the quality of spectral data and the accuracy of analysis; conversely, when D jWhen <T>, it indicates that the Raman spectrum is normal and the possibility of redundant peaks appearing is small.

[0097] Step S43: Use Gaussian fitting to reconstruct the peak shape of the spectral curve after removing redundant peaks, obtain the correction parameters of each Raman peak after reconstruction, and correct the spectral curve through the correction parameters.

[0098] Specifically, use Gaussian fitting to reconstruct the peak shape of the Raman peaks in the spectral curve after removing redundant peaks, obtain the correction parameters of each Raman peak after reconstruction, so as to effectively improve the resolution of the spectral curve and make the analysis results of the Raman spectrum more accurate and reliable; among them, Gaussian fitting first determines the position, full width at half maximum, and peak height of each Raman peak, which together constitute the correction parameters. By adjusting these parameters, the spectral curve can be finely corrected, making the reconstructed spectral curve closer to the true Raman spectral characteristics, obtaining more real spectral data, not only enhancing the signal-to-noise ratio of the spectral data, but also improving the resolution and reliability of the data, laying a solid foundation for subsequent data analysis; in addition, Gaussian fitting can not only identify the main peaks in the spectral data, but also reveal the weak peak shape characteristics that may be masked by noise or background signals.

[0099] Furthermore, in step S5, it includes:

[0100] Perform signal processing on the corrected spectral curve to generate a three-dimensional spectrogram, display the position and intensity changes of the characteristic peaks, and perform quantitative analysis to generate quantifiable data, and obtain the analysis results in any form of table and picture.

[0101] Specifically, by applying Origin (OriginLab Corporation) data analysis software, perform a series of signal processing operations on the spectral curves obtained at different acquisition times and corrected, generate a three-dimensional spectrogram to clearly display the position and intensity changes of the characteristic peaks, and the Origin software also allows users to present the results of quantitative analysis in various ways, including any form of performance such as tables and graphs. Based on these corrected surface-enhanced Raman spectral curves, precise quantitative analysis of metal nanomaterials can be carried out, providing highly sensitive and highly selective analysis means for various chemical and biological detections.

[0102] For better illustration, Origin data analysis software is a powerful data processing and analysis tool, widely used in fields such as scientific research, engineering design, and statistical analysis. It provides rich data analysis functions, including data import and export, data preprocessing, statistical analysis, signal processing, image processing, etc. It supports multiple data formats and can easily handle data from different sources. In addition, the user interface of Origin software is intuitive and easy to use, enabling efficient data analysis work. It also provides a powerful graph drawing function, which can create high-quality two-dimensional and three-dimensional charts to visually display the data analysis results.

[0103] Understandably, by optimizing the existing particle filtering technology, the random sampling and weighting mechanism of particle filtering enables this application to extract meaningful signals from complex dynamic processes. That is, the initial shape feature of the spectral curve is calculated as the peak factor, a particle set is constructed, and through the particle set, the fluctuation influence factor and the overlap parameter are respectively obtained. Combining the two determines the ultimate shape feature of the particle, and in combination with the clustering algorithm, the spectral data is processed to identify and remove redundant peaks, so as to effectively handle various complex situations that may occur in the experiment, making the existing detection means more adaptable to data noise, redundant information, and changes in the external environment, and the obtained data analysis results are more robust.

[0104] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A nanomaterial detection method based on surface enhanced Raman spectroscopy, characterized in that: The method comprises: Based on nanomaterials, spectral data at different collection times are obtained, spectral curves are generated, and preprocessing is performed; The initial shape feature is obtained by analyzing the preprocessed spectral curve as the crest factor, and the crest factor is defined as a particle to construct a particle set; The fluctuation influence factor is calculated through the particle set, the overlap parameter of the particle is obtained according to the preprocessed spectral curve, and the final shape characteristics of the particle are determined by combining the fluctuation influence factor with the overlap parameter of the corresponding particle; Perform cluster analysis on the final shape characteristics of the particles to obtain several clusters, calculate the degree of characteristic change of each cluster, screen according to the degree of characteristic change, remove redundant peaks, and correct the spectral curve after removal; Quantitative analysis is performed based on the corrected spectral curve; The initial shape feature is obtained by analyzing the preprocessed spectral curve as the peak factor, and the peak factor is defined as a particle to construct a particle set, including: Perform peak detection on the preprocessed spectral curve to extract the signal intensity and frequency position of the Raman peak; Calculate the initial shape characteristics of the Raman peak, recorded as the crest factor; Define the peak factor as a particle, determine the peak factors of all Raman peaks in the spectrum curve, and construct a particle set; Calculate the initial shape characteristics of the Raman peak, and the corresponding calculation formula is: Among them, t i represents the crest factor of the ith Raman peak on the preprocessed spectral curve; W i represents the half-peak width of the i-th Raman peak; W i,l represents the Raman frequency shift corresponding to the left half of the spectral data in the i-th Raman peak; W i,r represents the Raman frequency shift corresponding to the right half of the spectrum data in the i-th Raman peak; exp() represents an exponential function with the natural constant e as the base; The fluctuation impact factor is calculated by particle set, and the corresponding calculation formula is: Among them, a i represents the fluctuation influence factor of the ith particle; t i,τ represents the peak factor corresponding to the spectrum curve of the i-th particle at the τ-th acquisition time; t i+1,τ represents the peak factor corresponding to the spectrum curve of the i+1th particle at the τth acquisition time; l i,i+1 represents the distance between the i-th particle and the i+1-th particle; M represents the number of all spectral curves acquired at the acquisition time; norm represents the normalization function; The particle overlap parameters are obtained according to the preprocessed spectral curve, and the corresponding calculation formula is: Among them, q i represents the overlap parameter of the i-th particle; λ′ i represents the total peak width of the ith particle; l i,u represents the Raman frequency shift of the u-th Raman peak in the i-th particle; l i,u+1 represents the Raman frequency shift of the u+1th Raman peak in the ith particle; N represents the number of Raman peaks contained in the particle set; The final shape characteristics of the particles are determined by combining the fluctuation influence factor with the overlap parameter of the corresponding particles. The corresponding calculation formula is: d j =a i ×q i Among them, δ j represents the final shape characteristics of the i-th particle currently analyzed; a i represents the fluctuation influence factor of the i-th particle; q i represents the overlap parameter of the i-th particle; Perform cluster analysis on the final shape characteristics of the particles to obtain several clusters, calculate the degree of characteristic change of each cluster, screen according to the degree of characteristic change, remove redundant peaks, and correct the spectral curve after removal, including: Perform cluster analysis on the final shape characteristics of the particles to obtain several clusters, and calculate the degree of characteristic change of each cluster; Set the screening threshold. When the characteristic change degree of the cluster is greater than the screening threshold, the current cluster contains redundant peaks and is removed. The peak shape of the spectral curve after removing redundant peaks is reconstructed using Gaussian fitting, and the correction parameters of each Raman peak after reconstruction are obtained, and the spectral curve is corrected by the correction parameters; Calculate the degree of feature change of each cluster, and the corresponding calculation formula is: Among them, D j Indicates the degree of change of the characteristics of the jth cluster; δ j represents the final shape feature of the i-th particle in the j-th cluster; n j Indicates the number of clusters; C j represents the geometric center of the jth cluster; p i,j represents the i-th particle in the j-th cluster; ||p i,j -C j || 2 represents the distance from the ith particle in the jth cluster to the geometric center of the jth cluster; m represents the number of particles contained in the cluster.

2. A nanomaterial detection method based on surface enhanced Raman spectroscopy as claimed in claim 1, characterized in that: Based on nanomaterials, spectral data at different acquisition times are obtained, spectral curves are generated, and preprocessing is performed, including: The nanomaterial to be tested is obtained, and the spectral data at different acquisition times are obtained using a spectrometer. The Raman frequency shift is defined as the horizontal axis and the Raman scattering intensity is defined as the vertical axis to generate a spectral curve. The spectral curve is preprocessed by sliding average filtering to obtain a denoised spectral curve.

3. The method for detecting nanomaterials based on surface enhanced Raman spectroscopy according to claim 1, characterized in that: Quantitative analysis based on the corrected spectral curve includes: The corrected spectral curve is subjected to signal processing to generate a three-dimensional spectrum diagram to display the position and intensity changes of the characteristic peaks, and quantitative analysis is performed to generate quantifiable data, and the analysis results are obtained in the form of either tables or pictures.

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