A method for realizing quantitative detection based on SERS signal repetition rate

Abstract

The application discloses a method for realizing quantitative detection based on SERS signal repetition rate, comprising: configuring sample detection objects with different concentrations; using a pre-prepared SERS substrate to perform SERS detection on the sample detection objects with different concentrations, and measuring the SERS signal repetition rate corresponding to each concentration; setting the range and total spectrum number of spectrum collection on the SERS substrate; obtaining the effective spectrum number of the characteristic spectrum of the sample detection object satisfying a preset condition according to the SERS detection result; taking the ratio of the effective spectrum number and the total spectrum number as the SERS signal repetition rate; counting the SERS signal repetition rates corresponding to all concentrations, and establishing the corresponding relationship between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object; when detecting a sample detection object with an unknown concentration, the SERS signal repetition rate of the sample detection object with the unknown concentration is measured by using the same SERS substrate, and the concentration of the sample detection object with the unknown concentration is obtained according to the corresponding relationship between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Raman spectrum detection, and particularly to a method for realizing quantitative detection based on SERS (Surface-Enhanced Raman Scattering) signal repetition rate. BACKGROUND

[0002] The ultra-sensitive spectrum detection technology based on SERS has important application value in the fields of environment, biology, medicine and the like. With the near-field enhancement characteristics of the surface plasmon of noble metals, the SERS spectrum detection sensitivity can be greatly improved. Among them, the three-dimensional (3D) nanoparticle aggregate structure with rich "hot spots" has become an excellent Raman enhancement detection substrate, in which the super-trace recognition detection of dye molecules is realized.

[0003] In the SERS detection, after the enhanced local electromagnetic field near the "hot spot" interacts with the signal molecules, the Raman signal of the signal molecules is enhanced. Among them, the SERS signal intensity is related to the substrate enhancement performance and the number of substrate surface molecules. Therefore, when the SERS substrate has uniform and stable enhancement capability, the SERS signal intensity has a certain change relationship with the molecular concentration. Based on this relationship, researchers have carried out a large number of quantitative SERS researches and methods for improving the quantitative accuracy. For example, the stability of quantitative detection can be improved by controllably forming "hot spots" through top-down and bottom-up preparation strategies; the accuracy of quantitative detection can be improved by using external standard method, internal standard method and standard addition method, etc.

[0004] However, at present, the SERS detection still has the problem that the Raman signal cannot be stably guaranteed, especially at low concentration or even single molecule level, it is more difficult to guarantee the stability of the SERS detection. Part of the reason is that the surface Raman enhancement as a kind of near-field enhancement detection means, the distance between the molecule and the substrate, the "hot spot" enhancement fluctuation of the substrate and the like will seriously affect the Raman signal, so that the SERS signal intensity fluctuates and the reproducibility decreases; on the other hand, the inherent flicker effect of the molecule, i.e. the molecule constantly enters and exits the "hot spot" region in space and time, the distance between the molecule and the "hot spot" cannot be controlled, causing signal fluctuation, even no signal condition, affecting the detection accuracy.

[0005] Therefore, especially at single molecule level, the change relationship between the SERS signal intensity and the concentration is invalid, the intensity fluctuates significantly, which seriously affects the accuracy of quantitative detection. At present, it is urgent to provide a SERS detection scheme with robustness at very low concentration. SUMMARY

[0006] The present application aims to provide a method for quantitative detection based on SERS signal repetition rate to overcome or at least alleviate at least one of the above-mentioned defects of the prior art.

[0007] To achieve the above-mentioned object, the present application provides a method for quantitative detection based on SERS signal repetition rate, characterized in that it comprises:

[0008] Step 1, configuring sample detection objects of different concentrations;

[0009] Step 2, using a pre-prepared SERS substrate to perform SERS detection on sample detection objects of different concentrations, and measuring the SERS signal repetition rate corresponding to each concentration; including:

[0010] Setting the range and total number of spectra for spectral collection on the SERS substrate;

[0011] According to the SERS detection results, obtaining the effective spectrum number of the sample detection object whose characteristic spectrum meets the preset condition;

[0012] Taking the ratio of the effective spectrum number and the total spectrum number as the SERS signal repetition rate;

[0013] Step 3, counting the SERS signal repetition rates corresponding to all concentrations respectively, and establishing the corresponding relationship between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object;

[0014] Step 4, when detecting a sample detection object of unknown concentration, measuring the SERS signal repetition rate of the sample detection object of unknown concentration using the same SERS substrate, and obtaining the concentration of the sample detection object of unknown concentration according to the corresponding relationship between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object.

[0015] Preferably, the SERS substrate is prepared by the following method:

[0016] Adding silver colloid solution to the toluene solution to generate a spherical two-phase interface, adding ethanol solution to the silver colloid solution, and self-assembling at the interface to form a silver nanoparticle array;

[0017] Adding a toluene solution of polymethyl methacrylate to the silver nanoparticle array for water absorption to obtain a silver nanoparticle aggregate;

[0018] Rinsing the surface of the aggregate with a large amount of toluene solution, and obtaining the silver nanoparticle aggregate used as the SERS substrate after drying.

[0019] Preferably, before setting the spectral collection range, collection step size and collection spectrum number on the SERS substrate in step 2, it includes:

[0020] The SERS substrate is immersed in a solution of the sample detection substance at different concentrations, and then taken out onto a clean silicon wafer surface and left to dry in air.

[0021] Preferably, step 1 comprises: using ultrapure water or ethanol to prepare the sample detection substance at different concentrations.

[0022] Preferably, the preset condition in step 2 comprises:

[0023] The signal intensity of the characteristic spectrum exceeds a threshold value, or the ratio of the signal intensity of the characteristic spectrum to the noise intensity exceeds a threshold value.

[0024] Preferably, step 3 comprises:

[0025] The corresponding relationship between the SERS signal repetition rate of the sample detection substance and the concentration of the sample detection substance is plotted with the logarithm of the concentration of the sample detection substance as the abscissa and the percentage value of the SERS signal repetition rate as the ordinate.

[0026] The corresponding relationship in the interval range that meets the preset condition is taken as the standard corresponding relationship.

[0027] Preferably, step 3 comprises:

[0028] The corresponding relationship curve in the interval range of 5%-90% of the SERS signal repetition rate is taken as the standard corresponding relationship.

[0029] The present application has the following advantages due to the above technical solutions:

[0030] By using the method provided by the present application, the SERS signal repetition rate has excellent robustness, the repetition rate fluctuation between different batches is small, the quantitative result is more accurate, and quantitative detection as low as single molecule order can be realized, and the quantitative result is more effective compared with the traditional quantitative method. Moreover, the quantitative detection method has universality and exhibits excellent quantitative detection results under different experimental conditions of biological molecules and SERS substrates, which provides a new way for realizing high-precision molecular quantitative detection in the field of SERS, and is expected to promote the further application and industrialization of SERS. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The flowchart of the method for realizing quantitative detection based on the SERS signal repetition rate provided by the embodiments of the present application is shown.

[0032] Figure 2 The flowchart of the method for realizing quantitative detection based on the SERS signal repetition rate provided by the embodiments of the present application is shown.

[0033] Figure 3 The schematic diagram showing the relationship between the SERS signal repetition rate of the crystal violet molecule and the concentration in Example 1 of the present application is shown.

[0034] Figure 4 A schematic diagram showing the quantitative prediction result in Embodiment 1 of the present application is shown.

[0035] Figure 5 A Raman spectrum diagram showing the SERS detection of a mixed solution of Rhodamine 6G and Nile Blue molecules in Embodiment 1 of the present application is shown.

[0036] Figure 6 A Raman spectrum diagram showing the SERS detection of a mixed solution of Rhodamine 6G and Nile Blue molecules in Embodiment 1 of the present application is shown. Figure 5 A bar chart corresponding to the Raman spectrum diagram showing the SERS detection of a mixed solution of Rhodamine 6G and Nile Blue molecules in Embodiment 1 of the present application is shown.

[0037] Figure 7 A schematic diagram showing the relationship between the SERS signal repetition rate and the concentration of thiophenol molecules in Embodiment 2 of the present application is shown.

[0038] Figure 8 A schematic diagram showing the quantitative prediction result in Embodiment 2 of the present application is shown. DETAILED DESCRIPTION

[0039] In the drawings, the same or similar notations represent the same or similar elements or elements having the same or similar functions. The embodiments of the present application will be described in detail below with reference to the drawings.

[0040] In the description of the present application, the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the scope of protection of the present application.

[0041] The technical features in each embodiment and each implementation form of the present application can be combined with each other without conflict, and are not limited to the embodiment or implementation form in which the technical features are located.

[0042] The present application will be further described below with reference to the drawings and specific embodiments, and it should be pointed out that the technical solutions and design principles of the present application will be described in detail below only with one optimized technical solution, but the protection scope of the present application is not limited to this.

[0043] This paper relates to the following terms, in order to facilitate understanding, the meaning is explained as follows. Those skilled in the art should understand that the following terms can also have other names, but any other name should be considered consistent with the terms listed in this paper without deviating from its meaning.

[0044] The embodiment of the present application provides a method for realizing quantitative detection based on SERS signal repetition rate, as shown in the following formula (I): Figure 1 The embodiment of the present application provides a method for realizing quantitative detection based on SERS signal repetition rate, as shown in the following formula (I):

[0045] Step 1, configuring sample detection objects with different concentrations.

[0046] In this step, preferably, ultrapure water or ethanol is used to configure sample detection objects with different concentrations. The types of sample detection objects can be flexibly changed according to actual needs, including but not limited to dye molecules, such as rhodamine 6G, methylene blue, malachite green, crystal violet, p-methoxyphenylamine, and crystal violet.

[0047] Step 2, using a pre-prepared SERS substrate to perform SERS detection on sample detection objects with different concentrations, and measuring the SERS signal repetition rate corresponding to each concentration.

[0048] The embodiment of the present application provides a method for realizing quantitative detection based on SERS signal repetition rate, as shown in the following formula (I):

[0049] Setting the range and total number of spectra for spectrum collection on the SERS substrate;

[0050] According to the SERS detection result, the effective spectrum number of the characteristic spectrum of the sample detection object satisfying the preset condition is obtained;

[0051] Taking the ratio of the effective spectrum number and the total spectrum number as the SERS signal repetition rate.

[0052] The preset condition includes that the signal intensity of the characteristic spectrum exceeds a threshold value, or the ratio of the signal intensity of the characteristic spectrum and the noise intensity exceeds a threshold value.

[0053] For example, the preset condition is that the ratio of the signal intensity of the characteristic spectrum and the noise intensity is greater than or equal to 3.

[0054] Before setting the spectrum collection range, collection step and collection spectrum number on the SERS substrate, the SERS substrate can be soaked in sample detection object solutions with different concentrations, then taken out to the surface of a clean silicon wafer, and dried in air.

[0055] Step 3, counting the SERS signal repetition rates corresponding to all concentrations respectively, and establishing the corresponding relationship between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object.

[0056] The corresponding relationship between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object can be expressed in a graph.

[0057] In one example, the logarithm of the concentration of the sample detection object is taken as the abscissa, the percentage value of the SERS signal repetition rate is taken as the ordinate, and the corresponding relationship curve between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object is drawn.

[0058] Preferably, the corresponding relationship curve in the interval range satisfying the preset condition is taken as the calibration curve. For example, the corresponding relationship curve in the interval range of 5%-90% of the SERS signal repeatability is taken as the calibration curve.

[0059] Step 4, when detecting the sample detection object of unknown concentration, the SERS signal repeatability of the sample detection object of unknown concentration is measured by using the same SERS substrate, and the concentration of the sample detection object of unknown concentration is obtained according to the corresponding relationship between the SERS signal repeatability of the sample detection object and the concentration of the sample detection object.

[0060] Wherein, the SERS substrate can be a SERS substrate commonly used in the prior art, such as a noble metal SERS substrate. The SERS substrate can be obtained by using various preparation methods in the prior art, which will not be described herein.

[0061] In the present application, the SERS substrate can also be prepared by the following method:

[0062] A spherical two-phase interface is generated by adding a silver colloid solution to a toluene solution, and an ethanol solution is added to the silver colloid solution, and a silver nanoparticle array is self-assembled at the interface;

[0063] A silver nanoparticle aggregate is obtained by adding a toluene solution of polymethyl methacrylate to the silver nanoparticle array for water absorption;

[0064] The surface of the aggregate is rinsed with a large amount of toluene solution, and after drying, a silver nanoparticle aggregate used as a SERS substrate is obtained.

[0065] By using the method provided by the present application, the SERS signal repeatability has excellent robustness, the repeatability fluctuation between different batches is small, the quantitative result is more accurate, and quantitative detection as low as single molecule order can be realized, and the quantitative result is more effective compared with the traditional quantitative method. Moreover, the quantitative detection method has universality, and excellent quantitative detection results are exhibited under different experimental conditions of biological molecules and SERS substrates, which provides a new way for realizing high-precision molecular quantitative detection in the field of SERS, and is expected to promote the further application and industrialization of SERS.

[0066] The method for realizing quantitative detection based on SERS signal repeatability provided by the present application is further introduced through specific examples.

[0067] Example 1

[0068] In this embodiment, a silver nanoparticle aggregate is used as a SERS detection substrate, crystal violet molecules are used as sample detection objects, and SERS signal repeatability is used as a feature for quantitative detection.

[0069] Figure 2 A flow chart of the method for realizing quantitative detection based on SERS signal repetition rate provided by the embodiment of the present application is shown, which comprises:

[0070] Step 21, preparing silver nanoparticle aggregates.

[0071] Spherical two-phase interface is generated by adding 50 μl silver colloid solution into 1 ml toluene solution, and then 20 μl ethanol solution is added into the silver colloid solution to initiate self-assembly of particles at the interface to form silver nanoparticle array. Toluene solution with 1% mass fraction of polymethyl methacrylate is configured to absorb water of the silver nanoparticle array to obtain silver nanoparticle aggregates. Finally, the surface of the silver nanoparticle aggregates is washed with a large amount of toluene solution, and the silver nanoparticle aggregates for SERS substrate are formed after drying.

[0072] Step 22, using the prepared silver nanoparticle aggregates to perform SERS detection on different concentrations of crystal violet.

[0073] The silver nanoparticle aggregates are soaked in crystal violet solutions with different concentrations prepared by ultrapure water (resistivity reaching 18 MΩ·cm) for 4 hours, for example. Then they are transferred to a clean silicon wafer, and SERS detection is performed after natural drying.

[0074] A Raman spectrometer with a laser wavelength of 532 nm and a laser power of 1 mW is used to place the silver nanoparticle aggregates soaked in the crystal violet solution on the sample table of the Raman spectrometer. Then a spectral collection range with a size of 30×30 μm 2 , for example, is selected on the surface of the silver nanoparticle aggregates by an electronic coupling component (Charged Coupled Device, CCD). The collection step is set to be 1 μm, for example, and the collection point number is 900, for example, that is, the total number of spectra for spectral collection is 900. The exposure time of the SERS spectrum is 1 s, for example, and the cumulative scanning number is two, for example.

[0075] Step 23, establishing the corresponding relationship between the SERS signal repetition rate of crystal violet and the concentration of crystal violet.

[0076] After the SERS detection is completed, the crystal violet spectrum meeting the preset condition is selected as the effective spectrum. The ratio of the number of effective spectra to the total number of spectra is defined as the SERS signal repetition rate. The SERS signal repetition rate of crystal violet molecules under different concentrations is counted to establish the corresponding relationship between the SERS signal repetition rate of crystal violet molecules and the concentration of crystal violet.

[0077] In this step, the spectrum of the crystal violet sample can be selected and processed by using Origin data processing software. The spectrum of the crystal violet at 910 cm -1The intensity of the Raman characteristic peak, the Raman peak intensity two-dimensional lattice diagram of 900 Raman spectra is drawn. The intensity of the Raman peak is set to be greater than three times the intensity of the spectral noise fluctuation. When the SERS intensity at 910 cm -1 -1 The ratio of the number of effective spectra to the total number of test spectra is defined as the repetition rate, and by processing and statistics at different concentrations, the SERS signal repetition rate of crystal violet molecules at different concentrations is finally calculated, and the relationship between the SERS signal repetition rate of crystal violet molecules and the concentration is successfully constructed, as shown in

[0078] The function relationship between adjacent repetition rates and concentrations is plotted with a linear relationship. Figure 3

[0079] After plotting the relationship between the SERS signal repetition rate of crystal violet molecules and the concentration, the corresponding relationship in a specific interval can be selected as an effective quantitative interval. For example, according to the change of the repetition rate with the concentration, the interval of 5%-90% of the change of the repetition rate with the concentration is selected, and in this embodiment, the concentration interval is: 1×10 -15 M-1×10 -13 M (Mol / L), and in this embodiment, the concentration interval is defined as the effective quantitative interval of the silver nanoparticle aggregate. The selection of this interval can be flexibly set according to the actual type of sample molecules and the type of SERS substrate, including but not limited to the specific interval range provided herein.

[0080] Step 24, test the SERS signal repetition rate of the unknown concentration of crystal violet molecule solution in the effective quantitative interval, and determine the concentration of the unknown concentration of crystal violet molecule solution according to the relationship between the SERS signal repetition rate of crystal violet molecules and the concentration.

[0081] In the above effective quantitative interval, when the SERS signal repetition rate of the unknown concentration of crystal violet molecule solution is tested, the silver nanoparticle aggregate prepared in the same way is used as the SERS substrate, and the experimental detection process, instrument parameters and spectrum processing standard are consistent with the acquisition process.

[0082] In this embodiment, before step 24, the concentration can also be quantitatively predicted by comparing the repetition rate calibration quantitative curve and the corresponding relationship between the repetition rate and the concentration, and the accuracy is judged. The quantitative prediction result is as shown in Figure 4The error bars (the range between the upper and lower lines of each point) in the figure represent the error interval of the quantitative concentration, which is calculated by repeating the experiment four times. For a more intuitive observation, the function y = x curve is drawn. When the quantitative concentration is closer to the curve, the quantitative result is more accurate. The experimental results show that the repeatability quantitative method has excellent quantitative detection results at the single molecule level.

[0083] In this embodiment, to prove that the quantitative method has successfully realized single molecule level quantitative detection, a double analyte detection method is used to determine the single molecule detection level of silver nanoparticle aggregates. The specific process includes:

[0084] An ultrapure water solution with a concentration of 5 x 10 -14 M of rhodamine 6G and Nile blue molecules with a molar ratio of 1:1. The silver nanoparticle aggregates are soaked in the mixed solution for 4 hours (h), then transferred to a clean silicon wafer, naturally dried, and placed on the sample table of the Raman spectrometer. A 20 x 20 μm 2 spectrum acquisition range, the acquisition step is 1 μm, the laser wavelength is 532 nm, and the power is 1 mW. After the collection is completed, 400 Raman spectra can be obtained. The following four spectral events exist in the spectrum, such as Figure 5 shown, respectively, are only rhodamine 6G Raman peak positions, denoted as R6G events; only Nile blue molecule Raman peak positions, denoted as NB events; both molecules have Raman peaks, denoted as Both events; and no spectral events, denoted as Null events. Further, the number of R6G event, NB event and Both event spectra is counted in the obtained spectrum, and a bar chart is drawn, as shown in Figure 6 It can be seen that the number of R6G event and NB event spectra is obviously higher than that of Mixed event spectra, and the sum of R6G event and NB event accounts for 80.9% of the total of the above three events, which indicates that the detection has entered the single molecule level.

[0085] Example 2

[0086] In this embodiment, the thiol phenol molecule is used as the sample detection, and the steps are the same as in Example 1, which will not be repeated here. The difference is that the thiol phenol analyte solution is configured using an ethanol solution, and the characteristic peak of thiol phenol is 1073 cm -1 , which is different from the characteristic peak of crystal violet at 910 cm -1 in Example 1.

[0087] Figure 7 The SERS signal repeatability of the thiol phenol molecule as a function of concentration is shown.

[0088] According to the change of the repetition rate with the concentration, the interval of the change of the repetition rate with the concentration is selected to be 3%-80%, and in this embodiment, the concentration interval is 5x10 -15 M-5x10 -12 M, and in this embodiment, the concentration interval is defined as the effective quantitative interval of the silver nanoparticle aggregate.

[0089] In the above effective quantitative interval, the same type of silver nanoparticle aggregate is used as the SERS substrate to detect the repetition rate of the p-thiol phenol molecule at multiple concentrations in the effective quantitative interval. The experimental detection process, instrument parameters and spectral processing standards need to be consistent with those when collecting the calibration curve.

[0090] The concentration is quantitatively predicted by comparing the repetition rate calibration quantitative curve and the corresponding relationship between the repetition rate and the concentration. The quantitative detection results of two different batches are shown in Figure 8 , where the abscissa is the logarithm of the standard concentration, and the ordinate is the logarithm of the predicted concentration. For a more intuitive observation, the function y=x curve is plotted. Experiments show that within the quantitative concentration interval, the concentration predicted by the repetition rate is in good agreement with the actual concentration.

[0091] It is easy to understand that the experimental detection process, instrument parameters and spectral processing standards in the above embodiments of the present application can be flexibly adjusted according to actual needs, which are not limited herein.

[0092] By using the method provided by the present application, the SERS signal repetition rate has excellent robustness, the repetition rate fluctuation between different batches is small, the quantitative result is more accurate, and the quantitative detection as low as single molecule order can be realized. Compared with the traditional quantitative method, the quantitative result is more effective. Moreover, the quantitative detection method has universality, and exhibits excellent quantitative detection results under different experimental conditions of biomolecules and SERS substrates, which provides a new way for high-precision molecular quantitative detection in the field of SERS, and is expected to promote the further application and industrialization of SERS.

[0093] Finally, it should be pointed out that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them. Those skilled in the art should understand that the technical solutions described in the above embodiments can be modified, or some technical features can be replaced by equivalents; these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for realizing quantitative detection based on SERS signal repetition rate, characterized in that, Correspondence between the concentration of the sample detection object and the SERS signal repetition rate is detected, and quantitative detection of the sample detection object is realized according to the correspondence, comprising: Step 1, configuring sample detection objects with different concentrations; Step 2, using a pre-prepared SERS substrate to perform SERS detection on sample detection objects with different concentrations, and measuring the SERS signal repetition rate corresponding to each concentration; including: Setting the range and total number of spectra for spectral collection on the SERS substrate; including: selecting a spectral collection range on the SERS substrate, setting the collection step to obtain the number of collection points, and obtaining the total number of spectra for spectral collection; According to the SERS detection result, the effective spectrum number of the sample detection object whose characteristic spectrum meets the preset condition is obtained; including: when the intensity of the Raman peak of a spectrum is greater than three times the intensity of the spectral noise fluctuation, the spectrum is counted as an effective spectrum; The ratio of the effective spectrum number to the total spectrum number is taken as the SERS signal repetition rate, and the SERS signal repetition rate corresponding to each concentration is obtained; Step 3, by processing and statistics under different concentrations, the SERS signal repetition rate of crystal violet molecules under different concentrations is finally calculated, and the relationship between the SERS signal repetition rate of crystal violet molecules and the concentration is successfully constructed, the function relationship between the adjacent repetition rate and the concentration is drawn with a linear relationship, the SERS signal repetition rate corresponding to all concentrations is counted, and the correspondence between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object is established; including: taking the logarithm of the concentration of the sample detection object as the abscissa, and taking the percentage value of the SERS signal repetition rate as the ordinate, a correspondence curve between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object is drawn, and the correspondence curve of the SERS signal repetition rate in the preset interval range is taken as the calibration curve; Step 4, when detecting a sample detection object with an unknown concentration, the SERS signal repetition rate of the sample detection object with the unknown concentration is measured using the same SERS substrate, and the concentration of the sample detection object with the unknown concentration is obtained according to the correspondence between the SERS signal repetition rate of the sample detection object and the concentration of the sample detection object; including: the concentration value is determined based on the calibration curve by measuring the signal repetition rate of the sample detection object with the unknown concentration; Step 3 includes: The corresponding curve with the SERS signal repeatability in the range of 5% to 90% is selected as the standard correspondence, and the concentration range is: 1x10 -15 Mol / L-1x10 -13 Mol / L.

2. The method of claim 1, wherein the SERS signal intensity is measured at a specific wavelength. The SERS substrate is prepared by the following method: Add silver colloid solution to the toluene solution to generate a spherical two-phase interface, add ethanol solution to the silver colloid solution, and self-assemble to form a silver nanoparticle array at the interface; Add a toluene solution of polymethyl methacrylate to the silver nanoparticle array for water absorption, and obtain a silver nanoparticle aggregate; Rinse the surface of the aggregate with a large amount of toluene solution, and obtain the silver nanoparticle aggregate used as the SERS substrate after drying.

3. The method of claim 1, wherein the SERS signal intensity is measured at a specific wavelength. Before setting the range and total number of spectra for spectral collection on the SERS substrate in step 2, the SERS substrate is immersed in a sample detection object solution with different concentrations, then taken out onto a clean silicon wafer surface, and dried in air. Step 1 includes: using ultrapure water or ethanol to configure sample detection objects with different concentrations.

4. The method of claim 1, wherein the SERS signal intensity is measured at a specific wavelength. ​

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