Nanometer material detection method based on surface enhanced Raman spectroscopy

Abstract

The invention relates to the technical field of optical detection, in particular to a nano material detection method based on surface enhanced Raman spectroscopy, which comprises the following steps: acquiring spectral data at different acquisition moments based on a nano material, generating a spectral curve, and preprocessing; analyzing the preprocessed spectrum curve to obtain an initial shape feature as a crest factor, and constructing a particle set; calculating a fluctuation influence factor through the particle set, obtaining an overlapping parameter of the particle according to the preprocessed spectrum curve, and combining the fluctuation influence factor with the overlapping parameter to determine a final shape feature of the particle; carrying out clustering analysis on the ultimate shape features of the particles to obtain a plurality of clusters, calculating the feature change degree of each cluster, carrying out screening and redundant peak elimination according to the feature change degree, and correcting the spectrum curve after elimination; quantitative analysis is carried out based on the corrected spectrum curve, so that the method has higher adaptability to data noise, redundant information and external environment change, and the result has higher robustness.

Description

Technical Field

[0001] The invention relates to the technical field of optical detection, and in particular to a nano material detection method based on surface enhanced Raman spectroscopy. Background Art

[0002] Surface-Enhanced Raman Spectroscopy (SERS) is a high-sensitivity, high-resolution and non-destructive spectral detection technology based on the surface enhancement effect. It can directly detect the molecules themselves and provide detailed molecular information without destroying the samples. Therefore, it has been widely used in the field of metal nanomaterials detection.

[0003] The characteristic peaks of Raman spectroscopy are closely related to the chemical structure of molecules. The spectrum obtained through SERS can provide rich qualitative and quantitative information for molecular structure analysis and component identification. Nanomaterials of specific size and shape, such as metal nanoparticles, nanotubes, nanorods, etc., can enhance the local electromagnetic field through the surface plasmon resonance (SPR) phenomenon, thereby enhancing the Raman scattering signal; the use of SERS technology can be used to conduct in-depth analysis and research on 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 nanotechnology.

[0004] Due to the heterogeneity of metal nanomaterials in morphology, size and surface structure, mixed molecular adsorption may occur during surface plasmon resonance (SPR), which will lead to the enhancement of multiple Raman signals, and these signals may originate from different surface or interface states, so multiple overlapping peaks will appear in the spectrum; for redundant peaks appearing in the spectral curve, existing methods usually use particle filtering (PF) and independent component analysis (ICA) to eliminate them, among which particle filtering is a nonlinear, non-Gaussian system estimation technology based on the Monte Carlo method. However, when multiple peaks overlap and the signal intensities are similar, the redundant peaks formed cannot be effectively separated by particle filtering alone due to the overlap of characteristic signals of multiple molecules or molecular groups, resulting in difficulty in distinguishing effective information from noise interference; in addition, if the signal overlap is too complex, or the independence of the signal is weak, such as when the peak width or intensity is 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] In order 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 nanomaterial detection method based on surface enhanced Raman spectroscopy. The technical solution adopted is as follows: 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 was performed based on the corrected spectral curves.

[0006] Preferably, obtaining spectral data at different acquisition times based on nanomaterials, generating spectral curves, and performing preprocessing include: 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.

[0007] Preferably, the initial shape feature obtained by analyzing the preprocessed spectral curve is a crest factor, and the crest 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 spectral curve, and construct a particle set.

[0008] Preferably, the initial shape characteristics of the Raman peak are calculated, and the corresponding calculation formula is: in, Indicates the first The crest factor of the Raman peak; Indicates The half-width of a Raman peak; Indicates The Raman frequency shift corresponding to the left half of the spectral data in the Raman peak; Indicates The Raman frequency shift corresponding to the right half of the spectrum data in the Raman peak; Expressed as a natural constant The exponential function of base .

[0009] Preferably, the fluctuation impact factor is calculated by particle set, and the corresponding calculation formula is: in, Indicates The fluctuation influence factor of each particle; Indicates The particle in The corresponding crest factor in the spectrum curve at each acquisition moment; Indicates The particle in The corresponding crest factor in the spectrum curve at each acquisition moment; Indicates Particles and The distance between particles; Indicates the number of all spectral curves acquired at the acquisition time; Represents the normalization function.

[0010] Preferably, the overlap parameter of the particles is obtained according to the preprocessed spectral curve, and the corresponding calculation formula is: in, Indicates The overlap parameter of the particles; Indicates The total peak width of the particles; Indicates Particle The Raman frequency shift of the Raman peak; Indicates Particle The Raman frequency shift of the Raman peak; Indicates the number of Raman peaks contained in the particle set.

[0011] Preferably, the fluctuation influence factor is combined with the overlap parameter of the corresponding particle to determine the final shape characteristics of the particle, and the corresponding calculation formula is: in, Indicates the current analysis The final shape characteristics of each particle; Indicates The fluctuation influence factor of each particle; Indicates Overlap parameter of each particle.

[0012] Preferably, cluster analysis is performed on the final shape characteristics of the particles to obtain a number of clusters, the characteristic variation degree of each cluster is calculated, screening is performed according to the characteristic variation degree, redundant peaks are eliminated, and the spectral curve after elimination is corrected, 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.

[0013] Preferably, the characteristic variation degree of each cluster is calculated, and the corresponding calculation formula is: in, Indicates The degree of change in the characteristics of the clusters; Indicates The first The final shape characteristics of each particle; Indicates the number of clusters; Indicates The geometric center of the clusters; Indicates The first Particles; Indicates The first Particles to The distance between the geometric centers of the clusters; Indicates the number of particles contained in the cluster.

[0014] Preferably, the 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.

[0015] The present invention has the following beneficial effects: By optimizing the existing particle filtering technology, the random sampling and weighting mechanism of the particle filter enables the present application to extract meaningful signals from complex dynamic processes, and combine it with the clustering algorithm to process the spectral data, identify redundant peaks and eliminate them, so as to effectively deal with various complex situations that may arise in the experiment, so that the existing detection means have stronger adaptability to data noise, redundant information and changes in the external environment, and the obtained data analysis results are more robust. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of the steps of a nanomaterial detection method based on surface enhanced Raman spectroscopy provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, characteristics and effects of a nanomaterial detection method based on surface enhanced Raman spectroscopy proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] A specific scheme of a nanomaterial detection method based on surface enhanced Raman spectroscopy provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the steps of a nanomaterial detection method based on surface enhanced Raman spectroscopy provided by an embodiment of the present invention, the method comprising: Step S1: acquiring spectral data at different acquisition times based on nanomaterials, generating spectral curves, and performing preprocessing; Step S2: Analyze the preprocessed spectral curve to obtain the initial shape feature as the crest factor, define the crest factor as a particle, and construct a particle set; Step S3: Calculate the fluctuation influence factor through the particle set, obtain the particle overlap parameter according to the preprocessed spectral curve, and combine the fluctuation influence factor with the overlap parameter of the corresponding particle to determine the final shape characteristics of the particle; Step S4: cluster analysis is performed on the final shape characteristics of the particles to obtain a number of clusters, the characteristic variation degree of each cluster is calculated, screening is performed according to the characteristic variation degree, redundant peaks are eliminated, and the spectral curve after elimination is corrected; Step S5: Performing quantitative analysis based on the corrected spectral curve.

[0022] To better explain, surface enhanced Raman spectroscopy (SERS) is a spectral analysis technology based on the Raman scattering effect. It uses rough metal surfaces or nanostructures to enhance Raman signals to achieve high-sensitivity detection of the molecular structure of substances. Its principle involves surface plasmon resonance. When laser irradiates the surface of metal nanostructures, it will produce a local electromagnetic field enhancement effect on the surface, thereby greatly amplifying the Raman scattering signal of the molecule. It is suitable for trace analysis and can enhance the Raman signal to arrive times, making the weak signals that were originally difficult to detect become clearly discernible.

[0023] In surface enhanced Raman scattering (SERS) experiments, the acquired spectral signals are often easily interfered by various factors, including but not limited to the noise generated by the equipment itself, the constant changes in environmental conditions, and the randomness of the adsorption process. 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 adsorption site selection or inappropriate desorption process. 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 nanomaterial detection results.

[0024] Raman peak refers to the characteristic peak on the spectral curve, which indicates the specific energy state of molecular vibration or rotation. The position, shape and intensity of the Raman peak can provide important information about molecular structure, chemical bond type and intermolecular interaction.

[0025] As an optional implementation, in this embodiment, the nanomaterial refers to a metal nanostructure, that is, a metal material with a size at the nanometer level, which can significantly enhance the Raman scattering signal, making the detection process more sensitive and efficient.

[0026] Furthermore, step S1 includes: 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.

[0027] Optionally, in this embodiment, the spectrometer is a confocal micro-Raman spectrometer, which is a high-precision spectral analysis device that places the excitation light source, sample and detector on the same focal plane, greatly improving the collection efficiency and resolution of spectral signals. It collects only scattered light from a tiny area on the surface of the nanomaterial through a confocal optical system, effectively reducing the interference of background noise and enhancing the signal-to-noise ratio of the signal. It can also quickly respond and accurately record the intensity of Raman scattered light, providing a reliable basis for subsequent data analysis.

[0028] Specifically, a confocal micro-Raman spectrometer is used to collect spectral data of the metal nanomaterial to be detected within a preset time interval, and spectral data at different collection times are obtained. The Raman frequency shift is used as the horizontal axis and the Raman scattering intensity is used as the vertical axis to draw a spectral curve image, which is recorded as the Raman spectral curve of the nanomaterial; and a change step is set based on the preset time interval to define different collection moments, one collection moment corresponds to a Raman spectral curve of the nanomaterial, and then several Raman spectral curves of the nanomaterial are obtained; then a sliding average filter is used to perform smooth signal processing on the Raman spectral curve of the nanomaterial, that is, the signal is smoothed by calculating the average value of continuous points in the spectral curve to obtain a denoised spectral curve, so as to avoid the existence of noise covering up or distorting important spectral features.

[0029] Understandably, 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 SERS substrates to achieve significant signal enhancement. Since the adsorption sites of molecules on the surface of metal nanomaterials are randomly distributed, the adsorption and desorption processes of molecules change dynamically at different time points, that is, the spectral data obtained from the SERS experiment may contain fluctuations and changes from various sources, such as changes in the adsorption state of molecules, noise during the experiment, etc., but 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. The present application optimizes particle filtering and combines it with a clustering algorithm. By analyzing a series of spectral data in a time series and using the particle set after particle filtering, the fluctuations caused by the adsorption and desorption processes can be effectively eliminated to accurately remove redundant data, allowing staff to perform more accurate material detection and analysis based on the corrected spectral curve.

[0030] Furthermore, step S2 includes: Step S21: performing peak detection on the preprocessed spectrum curve to extract the signal intensity and frequency position of the Raman peak.

[0031] It is explained that in the spectral curve of metal nanomaterials, the difference in the adsorption of molecules at different times is reflected in the different peak characteristics of the Raman peak. The corresponding signal intensity, frequency position, shape characteristics and other information are obtained by analyzing the peaks in the spectral data; then the geometric shape characteristics of each peak at different times, that is, the geometric characteristics of the peaks in the same Raman frequency shift are used as particles; it can be explained that the peaks in the Raman spectrum correspond to the vibration modes of specific chemical bonds inside the nanomaterial. Under ideal conditions, 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, and then a particle set is constructed, that is, the peak characteristics at different collection times are reflected by the particle set, providing a data basis for subsequent analysis or optimization.

[0032] Step S22: Calculate the initial shape feature of the Raman peak, recorded as the peak factor.

[0033] As an optional implementation manner, the initial shape feature refers to the symmetry degree of the Raman peak.

[0034] Specifically, a sliding window is used to perform peak detection on all spectral curves after smooth filtering by judging the local maximum value in the window, and the signal intensity, frequency position and other characteristics of the peak are extracted; in the spectral curve, the adsorption difference of the nanomaterial is reflected in the shape characteristics of the Raman peak at different times, among which the half-peak width of the Raman peak reflects the vibration mode of the nanomaterial and is used to grasp the characteristics of the nanomaterial; the symmetry of the peak is used to describe the waveform concentration of the Raman peak, reflecting the structural characteristics of the metal nanomaterial molecules at the current acquisition moment, and then the half-peak width of the Raman peak is combined with the spectral data to calculate the initial shape characteristics of the Raman peak, that is, the peak factor.

[0035] Furthermore, in step S22, the initial shape characteristics of the Raman peak are calculated, and the corresponding calculation formula is: in, Indicates the first The crest factor of the Raman peak; Indicates The half-width of a Raman peak; Indicates The Raman frequency shift corresponding to the left half of the spectral data in the Raman peak; Indicates The Raman frequency shift corresponding to the right half of the spectrum data in the Raman peak; Expressed as a natural constant The exponential function of base .

[0036] 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 properties of the nanomaterial molecules. For example, a wider Raman peak may indicate that the nanomaterial molecules have a larger energy dissipation during the vibration process, which may be caused by the interaction between molecules or the anharmonic vibration within the molecule. Indicates The difference in the symmetry degree of the left and right Raman frequency shifts in a Raman peak about 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 means that the symmetry degree of the left and right peaks is lower, that is, when the shape of the Raman peak is more irregular, the possibility that the current Raman peak is a redundant peak is greater, and its peak interference is greater; on the other hand, It can reflect the waveform symmetry of the Raman peak, that is, the smaller the waveform symmetry of the Raman peak, the greater the peak aggregation characteristics of the Raman peak, and the more likely the Raman peak currently analyzed is to be a characteristic peak; when the width of the Raman peak is narrow, this indicates that the actual vibration of the metal nanomaterial molecules has a high degree of overlap with the theoretical state, making the peak characteristics of the Raman peak more obvious, indicating that the Raman peak is more likely to be a characteristic peak.

[0037] Step S23: defining the peak factor as a particle, determining the peak factors of all Raman peaks in the spectrum curve, and constructing a particle set.

[0038] It can be explained that by analyzing the particle set, the spectral data can be analyzed to identify and distinguish the different material components of the nanomaterials to eliminate redundant peaks.

[0039] It can be understood that in step S3, the fluctuation influence factor is calculated by the particle set, that is, in order to avoid erroneously identifying the redundant peaks of non-nanomaterial molecular characteristics as characteristic peaks when analyzing the spectral curve of metal nanomaterials, which affects the accurate calculation of the peak deviation, it is necessary to evaluate the peak interference of multiple Raman peaks in each wavelength range in the spectral curve; theoretically, if the adsorption rate of nanomaterial molecules at different time points in the same band remains consistent and shows certain similarities, then in the spectral curves collected at consecutive time points, the particle amplitude change in the same band should be relatively stable, but the existence of redundant peaks will lead to significant differences in the rate of change, so it is necessary to extract the particle amplitude in a specific band from the spectral curves collected at consecutive moments, and calculate its rate of change, and then combine the peak factor of each particle to calculate the particle fluctuation influence factor.

[0040] Furthermore, in step S3, the fluctuation impact factor is calculated by the particle set, and the corresponding calculation formula is: in, Indicates The fluctuation influence factor of each particle; Indicates The particle in The corresponding crest factor in the spectrum curve at each acquisition moment; Indicates The particle in The corresponding crest factor in the spectrum curve at each acquisition moment; Indicates Particles and The distance between particles; Indicates the number of all spectral curves acquired at the acquisition time; Represents the normalization function.

[0041] It is explained that the wave influence factor is used to describe the wave characteristics of particles. Indicates Particles and The distance between particles is used to reveal the interactions between particles.

[0042] Obtaining the overlapping parameters of the particles based on the preprocessed spectral curve shows that, due to the heterogeneity of the morphology, size and surface structure of metal nanomaterials, there will be overlapping peaks in the spectral data obtained by detection, which makes data analysis complicated; when multiple Raman peaks overlap and the signal intensities are close, it is difficult to accurately distinguish the redundant peaks contained in the overlapping peaks using only particle filtering technology. Therefore, in order to solve this problem, it is necessary to obtain the overlapping parameters of the peaks in the spectral curve, that is, to calculate the absolute value of the difference in Raman frequency shifts corresponding to the peaks of adjacent Raman peaks in the same band, and record the difference between the total peak width of the Raman peak and the absolute value of the difference in Raman frequency shifts corresponding to the peaks of its adjacent Raman peaks as the overlapping parameter, so as to accurately quantify the degree of overlap between adjacent Raman peaks.

[0043] Furthermore, in step S3, the overlap parameter of the particles is obtained according to the preprocessed spectral curve, and the corresponding calculation formula is: in, Indicates The overlap parameter of the particles; Indicates The total peak width of the particles; Indicates Particle The Raman frequency shift of the Raman peak; Indicates Particle The Raman frequency shift of the Raman peak; Indicates the number of Raman peaks contained in the particle set.

[0044] To explain, It indicates the proportion of particle overlap to the total peak width. The larger the proportion, the more serious the overlap, which may be caused by factors such as uneven particle size distribution, complex surface morphology or interaction between particles. The more overlapping peaks there are, the more serious the overlap, which means that in the Raman spectrum, multiple Raman peaks interfere with each other and are difficult to distinguish accurately, which not only increases the difficulty of data analysis, but also may affect the accuracy of the final test results.

[0045] The final shape characteristics of the particle are determined by combining the fluctuation influence factor with the overlap parameter of the corresponding particle, that is, the fluctuation influence factor of the particle is weighted by the overlap parameter to obtain the final shape characteristics of the particle.

[0046] Furthermore, in step S3, the fluctuation influence factor is combined with the overlap parameter of the corresponding particle to determine the final shape characteristics of the particle, and the corresponding calculation formula is: in, Indicates the current analysis The final shape characteristics of each particle; Indicates The fluctuation influence factor of each particle; Indicates Overlap parameter of each particle.

[0047] It can be understood that by applying a clustering algorithm, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the final shape feature data obtained after weighting the particles can be effectively grouped and processed. The purpose is to identify and eliminate particles that do not represent important characteristic processes, and to extract the main signal patterns, that is, through similarity-based classification, through the key information such as signal intensity and frequency position carried by the particles, the matching degree of the final shape features of the particles in the spectral curves at different acquisition times is analyzed, and the particles are grouped, thereby removing those inconsistent data points, significantly reducing noise and data redundancy, and ensuring that the particle set can more accurately reflect the true dynamic behavior of the system.

[0048] Furthermore, step S4 includes: Step S41: cluster analysis is performed on the final shape features of the particles to obtain a number of clusters, and the degree of feature change of each cluster is calculated.

[0049] It can be explained that the identification is performed based on the final shape features of the particles, and several clusters with similar features are divided, and then the degree of feature change of each cluster is calculated to distinguish the clusters where the redundant peaks are located.

[0050] Furthermore, in step S41, the characteristic variation degree of each cluster is calculated, and the corresponding calculation formula is: in, Indicates The degree of change in the characteristics of the clusters; Indicates The first The final shape characteristics of each particle; Indicates the number of clusters; Indicates The geometric center of the clusters; Indicates The first Particles; Indicates The first Particles to The distance between the geometric centers of the clusters; Indicates the number of particles contained in the cluster.

[0051] To explain, It can explain the influence of time changes on clusters, that is, it combines the influence of time changes and cluster characteristics to dynamically quantify the influence range of time. The larger the value, the more obvious the influence of time on clusters, indicating that as time goes by, the characteristics of clusters may change greatly. The quantified data can help staff understand the evolution of clusters over time. It reflects the degree of discreteness of particles in the cluster. The greater the degree of discreteness, the less concentrated the distribution of particles in the cluster, and the more obvious the fluctuation of the cluster. It can evaluate the stability of the cluster and the distribution of particles in the cluster.

[0052] Step S42: Setting a screening threshold. When the characteristic variation degree of a cluster is greater than the screening threshold, the current cluster contains redundant peaks, which are removed.

[0053] As an optional implementation, in this embodiment, the screening threshold is .

[0054] Specifically, redundant peaks in the Raman spectrum are removed based on the screening threshold, that is, when the characteristic change degree of the cluster is greater than the screening threshold, When , it means that the cluster contains more likely redundant peak particles, which need to be eliminated to reduce unnecessary repeated information and improve the quality of spectral data and the accuracy of analysis; on the contrary, when When , it indicates that the Raman spectrum is normal and the possibility of redundant peaks is small.

[0055] Step S43: using Gaussian fitting to reconstruct the peak shape of the spectrum curve after removing redundant peaks, obtaining correction parameters of each Raman peak after reconstruction, and correcting the spectrum curve by using the correction parameters.

[0056] Specifically, Gaussian fitting is used to reconstruct the Raman peak shape in the spectral curve after removing redundant peaks, and the correction parameters of each Raman peak after reconstruction are obtained to effectively improve the resolution of the spectral curve, making the analysis results of the Raman spectrum more accurate and reliable; wherein, Gaussian fitting first determines the position, half-width and peak height of each Raman peak, which together constitute the correction parameters. By adjusting these parameters, the spectral curve can be finely corrected, so that the reconstructed spectral curve is closer to the real Raman spectral characteristics, and more realistic spectral data is obtained, which not only enhances the signal-to-noise ratio of the spectral data, but also improves 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 those weak peak shape features that may be masked by noise or background signals.

[0057] Furthermore, step S5 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.

[0058] Specifically, by applying the Origin (OriginLab Corporation) data analysis software, a series of signal processing operations are performed on the spectral curves acquired and corrected at different collection times to generate a three-dimensional spectrum diagram to clearly show the position of the characteristic peaks and their intensity changes. The Origin software also allows users to present the results of quantitative analysis in a variety of ways, including tables and graphs. Based on these corrected surface-enhanced Raman spectral curves, accurate quantitative analysis of metal nanomaterials can be performed, providing highly sensitive and highly selective analysis methods for various chemical and biological detection.

[0059] To better explain, Origin data analysis software is a powerful data processing and analysis tool, widely used in scientific research, engineering design, statistical analysis and other fields. 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 process data from different sources; in addition, the user interface of Origin software is intuitive and easy to use, which can perform efficient data analysis work, and provides powerful graphics drawing functions, which can create high-quality two-dimensional and three-dimensional charts to intuitively display data analysis results.

[0060] It can be understood that by optimizing the existing particle filtering technology, the random sampling and weighting mechanism of the particle filter enables the present application to extract meaningful signals from complex dynamic processes, that is, to calculate the initial shape characteristics of the spectral curve as the peak factor, construct a particle set, and analyze the particle set to obtain the fluctuation influence factor and the overlap parameter respectively, and combine the two to determine the final shape characteristics of the particle, and combine it with the clustering algorithm to process the spectral data, identify redundant peaks and eliminate them, so as to effectively deal with various complex situations that may arise in the experiment, so that the existing detection means have stronger adaptability to data noise, redundant information and changes in the external environment, and the obtained data analysis results are more robust.

[0061] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on 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 was performed based on the corrected spectral curves.

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: 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 spectral curve, and construct a particle set.

4. A nanomaterial detection method based on surface enhanced Raman spectroscopy as claimed in claim 3, characterized in that: Calculate the initial shape characteristics of the Raman peak, and the corresponding calculation formula is: in, Indicates the first The crest factor of the Raman peak; Indicates The half-width of a Raman peak; Indicates The Raman frequency shift corresponding to the left half of the spectral data in the Raman peak; Indicates The Raman frequency shift corresponding to the right half of the spectrum data in the Raman peak; Indicated by natural constant The exponential function of base .

5. The method for detecting nanomaterials based on surface enhanced Raman spectroscopy according to claim 3, characterized in that: The fluctuation impact factor is calculated by particle set, and the corresponding calculation formula is: in, Indicates The fluctuation influence factor of each particle; Indicates The particle in The corresponding crest factor in the spectrum curve at each acquisition moment; Indicates The particle in The corresponding crest factor in the spectrum curve at each acquisition moment; Indicates Particles and The distance between particles; Indicates the number of all spectral curves acquired at the acquisition time; Represents the normalization function.

6. A nanomaterial detection method based on surface enhanced Raman spectroscopy as claimed in claim 5, characterized in that: The particle overlap parameters are obtained according to the preprocessed spectral curve, and the corresponding calculation formula is: in, Indicates The overlap parameter of the particles; Indicates The total peak width of the particles; Indicates Particle The Raman frequency shift of the Raman peak; Indicates Particle The Raman frequency shift of the Raman peak; Indicates the number of Raman peaks contained in the particle set.

7. A nanomaterial detection method based on surface enhanced Raman spectroscopy as claimed in claim 6, characterized in that: 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: in, Indicates the current analysis The final shape characteristics of each particle; Indicates The fluctuation influence factor of each particle; Indicates Overlap parameter of each particle.

8. The method for detecting nanomaterials based on surface enhanced Raman spectroscopy according to claim 7, characterized in that: 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.

9. A nanomaterial detection method based on surface enhanced Raman spectroscopy as claimed in claim 8, characterized in that: Calculate the degree of feature change of each cluster, and the corresponding calculation formula is: in, Indicates The degree of change in the characteristics of the clusters; Indicates The first The final shape characteristics of each particle; Indicates the number of clusters; Indicates The geometric center of the clusters; Indicates The first Particles; Indicates The first Particles to The distance between the geometric centers of the clusters; Indicates the number of particles contained in the cluster.

10. The nanomaterial detection method 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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