Microplastic rapid detection method based on microscopic Raman spectrum

Abstract

The invention provides a method for rapidly detecting micro-plastics based on a micro-Raman spectrum, and belongs to the technical field of environmental pollution detection. The method comprises the following steps: dispersing a single layer of a particle sample on a filter membrane, dividing a to-be-detected filter membrane into Raman spectrum detection sub-regions, and performing full-automatic splicing by using a high-precision electric loading platform to generate a Mongolia microscopic image of the detection sub-regions; checking and calibrating the coordinate positioning error of the loading platform through a real-time video window; a Raman automatic particle analysis system is adopted to directionally obtain a target particle characteristic spectrum, and a standard spectrum database is combined to carry out qualitative and quantitative analysis on substances; and calculating the micro-plastic content distribution of the whole membrane through a regional proportion quantitative extrapolation method. According to the method, an extrapolation detection algorithm based on uniform distribution of the micro-plastics is established, efficient and rapid detection of the micro-plastics with small particle sizes in the environment can be realized, meanwhile, the method also has good applicability to the micro-plastics with large particle sizes, has relatively high detection precision, is accurate and reliable, is simple to operate, and improves the intelligent level of micro-plastic detection.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental pollution detection, and in particular to a method for rapidly detecting microplastics based on micro-Raman spectroscopy. Background Art

[0002] Microplastics generally refer to polymer particles with a particle size of less than 5mm, which are widely present in various environmental media, such as soil, water and air. Studies have shown that the abundance of microplastics in the environment is negatively correlated with their particle size. Among the microplastics detected so far, more than half are small-sized microplastics (<50μm). Among them, microplastics smaller than 30μm are the most common in agricultural soil systems. The demand for their detection has shifted from qualitative analysis to high-throughput quantitative monitoring. However, in current detection methods, the larger the particle size of microplastics, the easier it is to detect; the smaller the particle size of microplastics, the more susceptible it is to instrument precision or interference factors, and thus the more difficult it is to accurately qualitatively and quantitatively detect. Therefore, when qualitatively and quantitatively detecting small-sized microplastics, how to obtain microplastic information with a detection limit as low as possible for microplastic particle size is of great significance for accurately measuring the content of microplastics in environmental media.

[0003] Currently, the detection methods for microplastics mainly include destructive methods and non-destructive methods. Destructive methods mainly include pyrolysis gas chromatography-mass spectrometry (Py-GC-MS) and thermal extraction-desorption gas chromatography-mass spectrometry. Although these methods can accurately determine the chemical composition and content data of microplastics, they lack information on the morphology and abundance of microplastics, and the destruction of samples makes it impossible to retest. Fourier transform infrared (FTIR) and Raman spectroscopy are non-destructive technologies that can simultaneously provide information on the chemical composition, morphology, size and abundance of microplastics and are widely used. However, the detection limit of FTIR is 20μm. For example, Chinese invention patent CN 118443538 B discloses a method for detecting microplastics in farmland soil using Fourier transform infrared spectroscopy. By extracting features from spectral data and image data and integrating them, and further combining machine learning to extract high-level features in the image, the accuracy and efficiency of detection are improved. However, this solution can only identify particles ≥100μm and cannot meet the detection needs of microplastics with smaller particle sizes in soil. Although traditional Raman spectroscopy has the advantage of 1μm-level resolution, full-spectrum imaging detection is very time-consuming. The Raman spectrum identification of a single particle may take between 0.5s and 5min. For example, Chinese invention patent CN 117092088A discloses a Raman detection method for small-particle microplastics in water and soil environments. It uses graded filtration to concentrate the sample into small detection units, and uses a point-by-point surface scanning imaging detection method to achieve the detection of microplastics as low as 3μm. However, compared with single-point detection combined with particle analysis, the point-by-point surface scanning detection method will consume a lot of time on unnecessary background data collection, which consumes unnecessary time and cannot meet the needs of fast and accurate detection.

[0004] Therefore, there is an urgent need to provide a method for rapid detection of microplastics based on micro-Raman spectroscopy. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for rapid detection of microplastics based on micro-Raman spectroscopy. The method provided by the present invention can not only detect microplastic particles as low as 1μm, but also significantly reduce the detection time.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] The present invention provides a method for rapid detection of microplastics based on micro-Raman spectroscopy, comprising sample preparation, image acquisition, coordinate calibration, spectral detection and statistical calculation performed in sequence:

[0008] Sample preparation: extracting a particle sample from the sample to be tested, and then dispersing the particle sample in a single layer on a filter membrane to obtain a filter membrane to be tested;

[0009] Image acquisition: The filter membrane to be tested is fixed to the working surface of a glass slide, the center origin of the filter membrane to be tested is established in the coordinate system of the Raman microscope stage, and sub-regions are generated based on the sample distribution density on the filter membrane to be tested; the sub-regions include a central region, a transition region, and an edge region arranged in sequence from the center of the filter membrane to the edge;

[0010] Selecting a detection subregion within the subregion; setting grid coordinate parameters for automatically sampling the detection subregion; the number of the detection subregions is 5 to 11, and the distribution of the detection subregions covers the central region, the transition region, and the edge region; and the area of ​​the subregion accounts for 7.6 to 11.8% of the area of ​​the filter membrane to be detected;

[0011] The slide is driven by a motorized stage, and images of each detection sub-region are captured using a Raman microscope, which are then automatically stitched together to generate montage microscopic images of each detection sub-region. The montage microscopic images of each detection sub-region are automatically subjected to grayscale binarization processing by an image analysis module, so as to locate the spatial coordinates of the particle sample in the filter membrane to be detected based on a morphological algorithm.

[0012] Coordinate calibration: manually checking and determining the deviation between the positioning space coordinates of the particle sample in the electric loading platform and the actual coordinates of the particle sample, and correcting the position of the calibration center origin with the deviation to obtain the calibrated origin coordinates;

[0013] Spectrum detection: set the Raman collection parameters, and collect the point spectrum of the particle sample in each detection sub-region according to the calibrated origin coordinates of the particle sample by using the Raman automatic particle analysis function, to obtain the point spectrum data of each detection sub-region; and compare the point spectrum data of each detection sub-region with a standard polymer spectrum library based on a characteristic peak matching algorithm, to obtain the content of microplastics in each detection sub-region;

[0014] Statistical calculation: the total membrane microplastic content of the filter membrane to be detected is obtained by using a statistical calculation algorithm, and the calculation method of the total membrane microplastic content is shown in formula (1):

[0015]

[0016] In formula (1), N T is the total membrane microplastic content, and the unit is piece; N i is the content of microplastics in each detection sub-region, and the unit is piece; m is the number of detection sub-regions, and the unit is piece; S t is the area of the filter membrane to be detected; and S s is the area of each detection sub-region.

[0017] Preferably, the method for dispersing the particle sample on the filter membrane in a single layer comprises: mixing the particle sample and a surfactant solution to obtain a particle sample dispersion; and filtering the particle sample dispersion through a filter membrane to obtain the filter membrane to be detected.

[0018] Preferably, the concentration of the surfactant solution is 100-1000 mg / L; and the ratio of the mass of the particle sample to the volume of the surfactant solution is (10-30) mg:(30-100) mL.

[0019] Preferably, the sample to be detected comprises soil, water or air.

[0020] Preferably, when the sample to be detected is soil, the method for extracting the particle sample from the sample to be detected comprises:

[0021] (1) mixing the soil with anhydrous ethanol and then performing soaking treatment to obtain a soil suspension;

[0022] (2) washing the soil suspension obtained in step (1) through a screen to obtain solid on the screen and a washing liquid;

[0023] (3) filtering and concentrating the washing liquid obtained in step (2) through a small-pore-size filter membrane to obtain a concentrated liquid and solid on the small-pore-size filter membrane; the pore size of the small-pore-size filter membrane is ≤1 μm;

[0024] (4) mixing the solid on the screen obtained in step (2), the concentrated liquid obtained in step (3), the solid on the small-pore-size filter membrane and a flotation liquid, and then performing density flotation to obtain flotation particles.

[0025] (5) The flotation particles obtained in step (4) are mixed with the digestion solution, chemically digested, and then graded filtered using a large-pore filter membrane and a small-pore filter membrane to obtain a particle sample.

[0026] Preferably, the number of the detection sub-regions is 6; the distribution of the detection sub-regions is: 1 central region, 3 transition regions and 2 edge regions; the sampling method is: S-type sampling.

[0027] Preferably, the Raman acquisition parameters include: laser wavelength of 532-785 nm; grating density of 300-1800 lines / mm; lens magnification of 5-100 times; Raman shift range of 100-4000 cm -1 ; Scanning single point exposure time is 0.1~5s; Laser power is 0.05~10%; Single accumulation.

[0028] Preferably, the method for determining the content of microplastics in each detection sub-region includes: comparing the point spectrum data of each detection sub-region with the standard polymer spectrum library using an algorithm based on characteristic peak matching to obtain first qualitative microplastic particles and suspected microplastic particles; performing a secondary detection on the suspected microplastic particles under optimized Raman acquisition parameters to obtain point spectrum data of suspected microplastic particles; comparing the point spectrum data of the suspected microplastic particles with the standard polymer spectrum library using an algorithm based on characteristic peak matching to obtain second qualitative microplastic particles; the sum of the number of the first qualitative microplastic particles and the number of the second qualitative microplastic particles is the content of microplastics in each detection sub-region;

[0029] The first qualitative judgment standard for microplastic particles is: the matching degree between the point spectrum data of each detection sub-area and the standard polymer spectrum library is ≥75%; the judgment standard for suspected microplastics is: the matching degree between the point spectrum data of each detection sub-area and the standard polymer spectrum library is 45-75%; the second qualitative judgment standard for microplastic particles is: the matching degree between the point spectrum data of suspected microplastic particles and the standard polymer spectrum library is ≥75%.

[0030] Preferably, the optimized Raman acquisition parameters include: laser wavelength of 532-785 nm; grating density of 300-1800 lines / mm; lens magnification of 5-100 times; Raman shift range of 100-4000 cm -1 The exposure time of a single scanning point is 0.1 to 5 seconds, the laser power is 0.05 to 10%, and the cumulative number of times is 2 to 5 times.

[0031] Preferably, the area of ​​the detection sub-region accounts for 9.2% of the area of ​​the filter membrane to be detected.

[0032] The present invention provides a method for rapid detection of microplastics based on micro-Raman spectroscopy. The present invention disperses a single layer of particle samples on a filter membrane to prevent the particle samples from stacking, thereby being able to accurately detect the number of particle samples. The present invention generates detection sub-areas based on the sample distribution density on the filter membrane to be detected, which diverge from the central origin to the edge of the filter membrane to be detected and include a central area, a transition area, and an edge area in sequence; the number of the detection sub-areas is 5 to 11, and the distribution of the detection sub-areas covers the central area, transition area, and edge area; the area of ​​the detection sub-areas accounts for 7.6 to 11.8% of the area of ​​the filter membrane to be detected; the distribution of the detection sub-areas selected by the present invention covers the central area, transition area, and edge area of ​​the filter membrane to be detected. This selection can cover particle samples of concentric circles of different radii of the filter membrane to be detected. The selected detection sub-areas are representative, and the detection sub-areas The area only accounts for 7.6 to 11.8% of the area of ​​the filter membrane to be detected, which can significantly reduce the area of ​​the detection sub-region; the present invention uses fully automatic splicing to generate montage microscopic images of each detection sub-region; manually checks and calibrates the coordinate positioning error of the loading platform; uses a Raman automatic particle analysis system to directionally acquire the characteristic spectrum of the target particles, obtains the point spectrum data of each detection sub-region, and combines it with the standard polymer spectrum library to perform qualitative and quantitative analysis of the substance; uses the regional ratio quantitative extrapolation method (statistical extrapolation method) to infer the distribution of microplastic content in the entire membrane without the need for full membrane detection, greatly shortening the detection time and enabling rapid detection of microplastics on the filter membrane to be detected. The method provided by the present invention establishes an extrapolation detection algorithm (statistical extrapolation algorithm) based on the uniform distribution of microplastics, which can achieve rapid detection of microplastics in the environment. The results of the examples show that the method provided by the present invention can detect microplastic particles as low as 1 μm, and is also well applicable to large-particle microplastics; moreover, the accuracy of the method provided by the present invention when estimating the microplastic content of the entire film using the 6-point S-shaped detection sub-area extrapolation method is greater than 91%; compared with full-film detection, the time is shortened by more than 90%, and it also has high detection accuracy, is accurate and reliable, and is simple to operate, thereby improving the speed and intelligence level of soil microplastic detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flowchart of the method for rapid detection of microplastics based on micro-Raman spectroscopy provided by the present invention;

[0034] Figure 2 This is a point distribution diagram of six different detection sub-areas on the filter membrane to be detected in Example 1 of the present invention;

[0035] Figure 3 The microscopic morphology and Raman spectrum of microplastics in soil in Example 1 of the present invention are shown;

[0036] Figure 4 The microscopic morphology and Raman spectrum of microplastics as small as 1 μm in the soil in Example 1 of the present invention are shown;

[0037] Figure 5 This is a visualization interface of the method for rapid detection of microplastics based on micro-Raman spectroscopy in Example 1 of the present invention;

[0038] Figure 6 This is a distribution diagram of 11 different detection sub-areas on the filter membrane to be detected in Example 2 of the present invention;

[0039] Figure 7 This is a distribution diagram of the standard microplastic content in 11 different detection sub-areas on the filter membrane to be detected in Example 2 of the present invention;

[0040] Figure 8 This is a distribution variation coefficient diagram for different sub-regions of the standard microplastic filter membrane in Example 2 of the present invention;

[0041] Figure 9 The microscopic morphology and Raman spectrum of microplastics on the filter membrane to be detected in Example 2 of the present invention are shown;

[0042] Figure 10 This is a point distribution diagram of the 6-point S-shaped sampling detection sub-areas on the filter membrane to be detected in Example 3 of the present invention;

[0043] Figure 11 The extrapolation accuracy and time consumption of the 6-point S-shaped sampling strategy in Example 3 of the present invention. DETAILED DESCRIPTION

[0044] The present invention provides a method for rapidly detecting microplastics based on micro-Raman spectroscopy, which comprises sample preparation, image acquisition, coordinate calibration, spectrum detection and statistical calculation performed in sequence.

[0045] The method for rapid detection of microplastics based on micro-Raman spectroscopy provided by the present invention includes sample preparation: extracting a particle sample from a sample to be tested, and then dispersing the particle sample in a single layer on a filter membrane to obtain a filter membrane to be tested.

[0046] In the present invention, the sample to be tested preferably includes soil, water or air, more preferably soil. The method provided by the present invention can quickly detect microplastics and can be detected by separating the microplastics in the sample to be tested. Therefore, it can be applied to detect the presence of microplastics in the sample to be tested. Microplastics are present in soil, water or air. The method provided by the present invention is applicable to the detection of microplastics in soil, water or air.

[0047] In the present invention, when the sample to be tested is soil, the method for extracting a particle sample from the sample to be tested preferably includes:

[0048] (1) mixing soil with anhydrous ethanol and then soaking the mixture to obtain a soil suspension;

[0049] (2) washing the soil suspension obtained in step (1) through a sieve to obtain a solid on the sieve and a washing liquid;

[0050] (3) filtering and concentrating the cleaning solution obtained in step (2) through a small-pore filter membrane to obtain a concentrated solution and a solid on the small-pore filter membrane; the pore size of the small-pore filter membrane is ≤1 μm;

[0051] (4) mixing the solid on the net obtained in step (2), the concentrated solution obtained in step (3), the solid on the small-pore filter membrane and the flotation liquid, and performing density flotation to obtain flotation particles;

[0052] (5) The flotation particles obtained in step (4) are mixed with the digestion solution, chemically digested, and then graded filtered using a large-pore filter membrane and a small-pore filter membrane to obtain a particle sample.

[0053] In the present invention, soil is preferably mixed with anhydrous ethanol and then soaked to obtain a soil suspension.

[0054] The present invention has no particular limitation on the source of the soil, and any soil that can be obtained by those skilled in the art can be used.

[0055] The present invention has no particular limitation on the ratio of the mass of the soil to the volume of the anhydrous ethanol, as long as the soil can be completely immersed in the anhydrous ethanol. In an embodiment of the present invention, the ratio of the mass of the soil to the volume of the anhydrous ethanol can be 5g:50mL.

[0056] The present invention has no particular limitation on the method of mixing the soil and anhydrous ethanol, as long as the soil and anhydrous ethanol can be fully and uniformly mixed.

[0057] In the present invention, the soaking treatment is preferably carried out at room temperature; the soaking treatment time is preferably 10 to 30 minutes. As one embodiment of the present invention, the soaking treatment time can be 10 minutes, 15 minutes, 20 minutes, 25 minutes, or 30 minutes. The soaking treatment of the present invention can effectively remove dirt and biofilm adhered to the surface of microplastics, thereby improving the efficiency of microplastic extraction.

[0058] After obtaining the soil suspension, the present invention preferably washes the soil suspension through a screen to obtain solids on the screen and a washing liquid.

[0059] In the present invention, the mesh aperture of the screen cleaning is preferably 300 to 1000 mesh. As an embodiment of the present invention, the mesh aperture of the screen cleaning can be 300 mesh, 400 mesh, 500 mesh, 600 mesh, 700 mesh, 800 mesh, 900 mesh or 1000 mesh. The present invention can separate the soil matrix from the microplastics in the soil suspension through screen cleaning. The present invention has no special restrictions on the operation method of the screen cleaning, and can be carried out by a conventional screen cleaning method. In an embodiment of the present invention, the screen cleaning is preferably carried out by fully stirring and cleaning the screen with a glass rod. The present invention uses a glass rod to fully stir and clean the screen to destroy soil aggregates and release the "hidden" microplastics therein, and the obtained filtrate and the filter residue on the screen are combined to obtain pre-treated microplastic particles with a full particle size range (1 to 5000 μm).

[0060] In the present invention, the cleaning liquid obtained after the screen is cleaned is preferably allowed to stand for 1 to 8 hours, preferably 2 to 5 hours.

[0061] After obtaining the solid on the network and the cleaning liquid, the present invention preferably filters and concentrates the cleaning liquid using a small-pore filter membrane to obtain a concentrated liquid and solid on the small-pore filter membrane.

[0062] In the present invention, the pore size of the small-pore filter membrane is preferably ≤1 μm. The present invention does not particularly limit the material of the small-pore filter membrane, and any conventional filter membrane can be used. In an embodiment of the present invention, the material of the small-pore filter membrane can be polytetrafluoroethylene (PTFE). The present invention can enrich particles larger than 1 μm in the cleaning liquid by filtering and concentrating the cleaning liquid through the small-pore filter membrane, forming them in the concentrated liquid and the solid on the small-pore filter membrane.

[0063] The present invention does not specifically limit the volume multiple of the filtration and concentration method; it can be adjusted according to operational needs. In the present invention, the cleaning liquid is subsequently subjected to density flotation. The cleaning liquid contains a large amount of water, and direct density flotation requires a large amount of flotation liquid. The present invention can enrich particles >1 μm in the cleaning liquid through filtration and concentration, reducing the volume of the concentrated liquid and reducing the amount of flotation liquid required for subsequent flotation. Therefore, the present invention does not require a specific volume multiple of the filtration and concentration method; adjustment can be made by sufficiently reducing the volume of the concentrated liquid.

[0064] After obtaining the concentrated liquid and the solid on the small-pore filter membrane, the present invention preferably mixes the solid on the net, the concentrated liquid, the solid on the small-pore filter membrane and the flotation liquid and then performs density flotation to obtain flotation particles.

[0065] In the present application, the density of the floatation liquid is adjusted according to the type of microplastics. In the embodiments of the present application, the microplastics in the soil generally enter the soil environment through the ways of agricultural mulch residues, sludge and organic fertilizer application, surface water irrigation and atmospheric deposition, and therefore the density of the microplastics in the soil is small. In the embodiments of the present application, the density of the floatation liquid is preferably 1.4-1.8 g / cm 3 , and more preferably 1.5-1.6 g / cm 3 . In the present application, the density of the floatation liquid is controlled in the above range, and the microplastics can be subjected to density floatation by using the density difference between the microplastics and the floatation liquid.

[0066] In the present application, the floatation liquid preferably comprises a NaI solution, a NaBr solution, a CaCl2 solution or a ZnCl2 solution, and more preferably a ZnCl2 solution. In the present application, when the floatation liquid is preferably a ZnCl2 solution, the cost is low, the recovery rate of the microplastics is high, and the subsequent detection interference is low. The concentration of the floatation liquid is not particularly limited in the present application, and the density of the above floatation liquid can be controlled to be 1.4-1.8 g / cm 3 .

[0067] In the present application, the ratio of the mass of the solid on the net, the volume of the concentrated liquid and the volume of the floatation liquid is preferably (1-20) g:(5-50) mL:(150-500) mL, and more preferably (4-10) g:(10-20) mL:(300-400) mL.

[0068] The operation method of the density floatation is not particularly limited in the present application, and a conventional operation method of density floatation can be used. The soil matrix interference is removed by density floatation in the present application.

[0069] After obtaining the floatation particles, the floatation particles are mixed with a digestion liquid in the present application, subjected to chemical digestion, and then subjected to fractional filtration by using a large-pore filter and a small-pore filter, respectively, to obtain a particle sample.

[0070] In the present application, the digestion liquid is preferably an H2O2 solution. In the embodiments of the present application, the concentration of the H2O2 solution can be 30 wt%.

[0071] In the present application, the method for mixing the floatation particles with the digestion liquid is preferably oscillation, and the oscillation rate of the oscillation is preferably 90-120 rpm, and more preferably 100-110 rpm. In the present application, the mixing of the floatation particles with the digestion liquid starts the chemical digestion.

[0072] In the present invention, the chemical digestion temperature is preferably 40-60°C. As one embodiment of the present invention, the chemical digestion temperature can be 40°C, 45°C, 50°C, 55°C, or 60°C. In the present invention, the chemical digestion time is preferably 12-48 hours, preferably 24-36 hours. The present invention can eliminate and remove interfering organic matter with a density similar to that of microplastics through chemical digestion.

[0073] In the present invention, the pore size of the small-pore filter membrane is preferably 1 μm; the pore size of the large-pore filter membrane is preferably 50 μm. The present invention does not specifically limit the material of the large-pore filter membrane, and any conventional filter membrane can be used. In an embodiment of the present invention, the material of the small-pore filter membrane and the large-pore filter membrane can be nylon (PA). The present invention performs graded filtration on the suspension obtained after chemical digestion using a small-pore filter membrane and a large-pore filter membrane to achieve particle size classification of the particles in the suspension obtained after chemical digestion, thereby obtaining particle samples of >50 μm and 1-50 μm.

[0074] The method for rapidly detecting microplastics based on micro-Raman spectroscopy provided by the present invention includes sample preparation: dispersing a single layer of the particle sample on a filter membrane to obtain a filter membrane to be detected.

[0075] In the present invention, the method of dispersing the particle sample monolayer on the filter membrane to obtain the filter membrane to be tested preferably includes: mixing the particle sample and a surfactant solution to obtain a particle sample dispersion; filtering the particle sample dispersion through the filter membrane to obtain the filter membrane to be tested.

[0076] The present invention has no particular limitation on the particle size of the particle sample, and any conventional particle size can be used. In the present invention, the particle size of the particle sample is preferably 1 to 50 μm.

[0077] In the present invention, the surfactant in the surfactant solution preferably includes one or more of anionic surfactants, cationic surfactants and non-ionic surfactants. In the present invention, the anionic surfactant is preferably sodium dodecylbenzenesulfonate and / or sodium dodecyl sulfate. In the present invention, the cationic surfactant is preferably hexadecyltrimethylol bromide. In the present invention, the non-ionic surfactant is preferably Tween 80. The above-mentioned surfactants used in the present invention are amphiphilic compounds containing a hydrophobic end and a hydrophilic end, which improve the suspension performance of the particle sample through steric hindrance and surface coverage, and promote the uniform dispersion of the particle sample in a single layer on the filter membrane.

[0078] In the present invention, the concentration of the surfactant solution is preferably 100 to 1000 mg / L. As an embodiment of the present invention, the concentration of the surfactant solution may be 100 mg / L, 200 mg / L, 300 mg / L, 400 mg / L, 500 mg / L, 600 mg / L, 700 mg / L, 800 mg / L, 900 mg / L or 1000 mg / L. In the present invention, the ratio of the mass of the particle sample to the volume of the surfactant solution is preferably (10 to 30) mg: (30 to 100) mL, more preferably (20 to 30) mg: (50 to 100) mL. The present invention controls the concentration of the surfactant solution within the above range, which is more conducive to improving the dispersibility of the particle sample.

[0079] In the present invention, the method for mixing the particle sample and the surfactant solution is preferably ultrasound. The present invention has no particular restrictions on the power and duration of the ultrasound, as long as the particle sample is fully dispersed in the surfactant solution. In an embodiment of the present invention, the ultrasound duration can be 5 minutes.

[0080] The present invention has no particular limitation on the size of the filter membrane, and any conventional filter membrane can be used. In the present invention, the diameter of the filter membrane is preferably 13 to 47 mm, more preferably 13 to 20 mm; and the pore size of the filter membrane is preferably 0.8 μm.

[0081] In the present invention, the filter membrane is preferably made of glass cellulose membrane, anodized aluminum membrane, silver-plated membrane, nylon membrane, or polyester gold-coated membrane, more preferably polyester gold-coated membrane. Different filter membranes can be selected to concentrate particle samples based on analytical and identification requirements. Compared to other filter membranes, polyester gold-coated membranes have a smooth surface, a simple structure, are inherently Raman-inactive, and exhibit a high optical contrast with target particles.

[0082] The present invention has no particular limitation on the filtering method. Conventional filtering methods can be used to pass the liquid in the particle sample dispersion through the filter membrane, and the particle sample can be retained on the filter membrane. In an embodiment of the present invention, the filtering method can be vacuum filtration.

[0083] The method provided by the present invention for rapid detection of microplastics based on micro-Raman spectroscopy includes image acquisition: fixing the filter membrane to be detected on the working surface of the slide, establishing the center origin of the filter membrane to be detected in the coordinate system of the Raman microscope loading platform, and generating a detection sub-area based on the sample distribution density on the filter membrane to be detected.

[0084] In the present application, the method for fixing the filter membrane to be detected on the working surface of the slide preferably comprises: cleaning and coating the surface of the slide with 75% ethanol solution, and fixing the filter membrane to be detected on the working surface of the slide in a bubble-free manner. As an embodiment of the present application, the filter membrane to be detected is fixed on the slide with water-soluble glue, or the surface of the slide is cleaned and moistened with a 75% alcohol wipe, and then the filter membrane to be detected is tightly attached to the surface of the slide with a small amount of non-volatile moisture.

[0085] The present application does not have special limitations on the method for establishing the center origin of the filter membrane to be detected in the coordinate system of the Raman microscope stage, and a conventional method for determining the center origin can be used.

[0086] The present application does not have special limitations on the method for generating sub-regions based on the sample distribution density on the filter membrane to be detected, and a conventional method for determining the sub-regions can be used. In the present application, the sub-regions include a center region, a transition region, and an edge region arranged in sequence from the center to the edge of the filter membrane to be detected. In the present application, the areas of the center region, the transition region, and the edge region are preferably divided according to the particle distribution coefficient of variation (CV), wherein the region with CV≤4% is determined as the center region with the most stable particle distribution, the region with CV>5% is determined as the edge region with unstable particle distribution throughout the membrane, and the region with CV between 4% and 5% is determined as the transition region with relatively stable particle distribution.

[0087] After generating the sub-regions, the present application selects detection sub-regions in the sub-regions and sets the grid coordinate parameters for automatic sampling of the detection sub-regions. In the present application, the number of detection sub-regions is 5-11, preferably 6. In the present application, the distribution of the detection sub-regions covers the center region, the transition region, and the edge region; the area of the detection sub-regions accounts for 7.6-11.8% of the area of the filter membrane to be detected, preferably 9.2%. In the present application, the diameter of the filter membrane to be detected is 13-47 mm, and when the number of detection sub-regions is 6, the area of each detection sub-region can be 1×1.2 mm 2 ~4×4 mm 2 .

[0088] In the present application, the number of detection sub-regions is preferably 6, the distribution of the detection sub-regions is preferably 1 center region, 3 transition regions, and 2 edge regions, and the sampling method for selecting the detection sub-regions in the sub-regions is preferably S-shaped sampling. The detection sub-regions selected by the above strategy have good representativeness, and can improve the accuracy of the detection results with as little detection area as possible.

[0089] In the embodiment of the present application, the grid coordinate parameters of the automatic sampling detection sub-area are used to determine the coordinate origin of the detection sub-area, which can be (0, 0), (2000, 2000), (-2000, 2000), (-2000, -2000), (3300, -2800) and (0, 4200).

[0090] After setting the grid coordinate parameters of the automatic sampling detection sub-area, the present application drives the slide glass by using the motorized stage, takes images of each detection sub-area by using the Raman microscope, and automatically splices to generate a montage microscopic image of each detection sub-area; the image analysis module is used to automatically perform gray value binary processing on the montage microscopic image of each detection sub-area, and the spatial coordinates of the particle sample in the detection filter are located based on the morphological algorithm.

[0091] The present application does not have special limitations on the method of automatically splicing to generate a montage microscopic image of each detection sub-area by using the motorized stage, and the motorized stage and the objective lens provided by the Raman spectrum can be used.

[0092] In the present application, the image analysis module is preferably a Partical analyse image analysis module.

[0093] The present application does not have special limitations on the method of locating the spatial coordinates of the particle sample in the detection filter based on the morphological algorithm, and the function provided by the Raman spectrum can be used. In the present application, the image analysis module can also be used to determine the particle size distribution and the geometric morphology at the same time, so that a particle characteristic database containing the particle size distribution, the geometric morphology and the coordinates can be obtained.

[0094] The method for rapidly detecting microplastics based on microscopic Raman spectrum provided by the present application includes coordinate calibration: manually checking and determining the deviation between the spatial coordinates of the particle sample in the motorized stage and the actual coordinates of the particle sample, correcting the center origin position by using the deviation to obtain the calibrated origin coordinates.

[0095] In the present application, the method of manual checking is preferably video window checking and calibration of the stage coordinate positioning error. The present application can realize real-time calibration of the stage coordinate positioning error by manual checking, and improve the stability of the detection result.

[0096] In the present application, the method for correcting the center origin position of the calibration preferably comprises: determining the deviation (Δx, Δy) between the positioning space coordinates of the particle sample in the object platform and the actual coordinates of the particle sample; and adjusting the center origin of the filter membrane to be detected from the (0, 0) point to the (0±Δx, 0±Δy) point. The present application can avoid the coordinate deviation caused by the image splicing error and the inherent error of the object table movement precision, so that the smaller particle size microplastics cannot be accurately detected.

[0097] In some embodiments of the present application, the present application determines the minimum particle size of the Raman limited detection under 50 times objective lens as 1 μm, so as to avoid the inaccuracy of the detection result of the smaller particle size particles caused by the inconsistency between the coordinates obtained by the montage microscopic image acquisition and the actual particle coordinate distribution. The inconsistency between the coordinates is caused by the image splicing error or the movement precision of the object table, or the inertial motion of the particles on the filter membrane. The present application avoids the difference between the positioning space coordinates and the actual coordinates by checking the deviation between the positioning space coordinates of the platform and the actual coordinates of the particles through the real-time video window after the montage microscopic image is spliced, and correcting the center origin position of the calibration, so as to realize the effective detection of the microplastics as low as 1 μm.

[0098] The method for rapidly detecting microplastics based on microscopic Raman spectrum provided by the present application comprises spectral detection: setting Raman collection parameters, collecting the point spectrum of the particle sample in each detection sub-region according to the calibrated origin coordinates of the particle sample by using the Raman automatic particle analysis function, and obtaining the point spectrum data of each detection sub-region; comparing the point spectrum data of each detection sub-region with the standard polymer spectrum library by using the algorithm based on characteristic peak matching, and obtaining the content of microplastics in each detection sub-region.

[0099] In the present application, the method for determining the content of microplastics in each detection sub-region preferably comprises: comparing the point spectrum data of each detection sub-region with the standard polymer spectrum library by using the algorithm based on characteristic peak matching, and obtaining the first qualitative microplastic particles and the suspected microplastic particles; performing secondary detection (i.e. re-inspection) on the suspected microplastic particles under the optimized Raman collection parameters, and obtaining the point spectrum data of the suspected microplastic particles; comparing the point spectrum data of the suspected microplastic particles with the standard polymer spectrum library by using the algorithm based on characteristic peak matching, and obtaining the second qualitative microplastic particles; and the sum of the number of the first qualitative microplastic particles and the number of the second qualitative microplastic particles is the content of microplastics in each detection sub-region.

[0100] In the present application, the Raman acquisition parameters preferably include: the laser wavelength is preferably 532-785 nm, more preferably 532-633 mm; the grating density is preferably 300-1800 lines / mm, more preferably 1500-1800 lines / mm; the magnification of the lens is preferably 5-100 times, more preferably 10-50 times; the Raman shift range is preferably 100-4000 cm -1 , more preferably 200-3000 cm -1 ; the single-point exposure time is preferably 0.1-5 s, more preferably 0.1-4 s; the laser power is preferably 0.05-10%, more preferably 0.1-5%; single accumulation. The present application uses the above Raman acquisition parameters to improve the accuracy of the detection results.

[0101] The present application uses the Raman automatic particle analysis function to collect the target particle spectrum in a targeted manner, avoiding the time consumption and redundant data generation in the point-by-point surface scanning of useless background information.

[0102] The present application preferably pre-processes the point spectrum data of each detection sub-region first, and then compares the point spectrum data of each detection sub-region with the standard polymer spectrum library using an algorithm based on characteristic peak matching. In the present application, the pre-processing method is preferably cosmic ray removal and baseline correction preprocessing.

[0103] The present application does not have special limitations on the operation method of comparing the point spectrum data of each detection sub-region with the standard polymer spectrum library using an algorithm based on characteristic peak matching, and automatic comparison using the characteristic peak matching algorithm and the standard polymer spectrum library provided with the Raman spectrum can be performed.

[0104] In the present application, the determination standard of the first qualitative microplastic particle is that the matching degree of the point spectrum data of each detection sub-region with the standard polymer spectrum library is ≥75%. The present application can qualitatively analyze the microplastic corresponding to the point spectrum data of the detection sub-region according to the above determination standard.

[0105] In the present application, the determination standard of the suspected microplastic is that the matching degree of the point spectrum data of each detection sub-region with the standard polymer spectrum library is 45-75%. The present application can qualitatively analyze the suspected microplastic corresponding to the point spectrum data of each detection sub-region using the above determination standard.

[0106] The first qualitative microplastic particle and the suspected microplastic particle are obtained; the suspected microplastic particle is detected again under optimized Raman acquisition parameters to obtain the point spectrum data of the suspected microplastic particle, and the point spectrum data of the suspected microplastic particle is compared with the standard polymer spectrum library using an algorithm based on characteristic peak matching to obtain the second qualitative microplastic particle.

[0107] In the present application, the optimized Raman acquisition parameters preferably include: the laser wavelength is preferably 532-785 nm, more preferably 532-633 mm; the grating density is preferably 300-1800 lines / mm, more preferably 1500-1800 lines / mm; the magnification of the lens is preferably 5-100 times, more preferably 10-50 times; the Raman shift range is preferably 100-4000 cm -1 , more preferably 200-3000 cm -1 ; the single-point scanning exposure time is preferably 0.1-5 s, more preferably 0.1-4 s; the laser power is preferably 0.05-10%, more preferably 0.1-5%; the cumulative number is preferably 2-5 times, more preferably 2-3 times. The present application uses the above-mentioned optimized Raman acquisition parameters, which can improve the accuracy of the detection results.

[0108] In the present application, the determination standard of the second qualitative microplastic particles is that the matching degree of the point spectrum data of the suspected microplastic particles with the standard polymer spectrum library is ≥75%. The present application can qualitatively determine the second qualitative microplastic particles corresponding to the point spectrum data of the suspected microplastic particles according to the above-mentioned determination standard.

[0109] In the present application, the sum of the number of the first qualitative microplastic particles and the number of the second qualitative microplastic particles is the content of microplastics in each detection sub-region.

[0110] The method for rapidly detecting microplastics based on microscopic Raman spectrum provided by the present application includes statistical calculation: using a statistical estimation method to obtain the full-membrane microplastic content of the filter membrane to be detected, and the calculation formula of the full-membrane microplastic content is shown as formula (1):

[0111]

[0112] In the formula (1), N T is the full-membrane microplastic content, unit: pieces; N i is the content of microplastics in each detection sub-region, unit: pieces; m is the number of detection sub-regions, unit: pieces; S t is the area of the filter membrane to be detected; S s is the area of each detection sub-region.

[0113] The present application uses the above-mentioned method to use the regional proportion quantitative extrapolation method to estimate the microplastic content distribution of the full-membrane to be detected.

[0114] The flowchart of the method provided by the present application is preferably as shown in Figure 1The present application utilizes ethanol to clean the soil sample with a screen, filters the obtained filtrate (i.e. cleaning solution), and then performs flotation on the filter residue on the screen, and then digestion to obtain the digested microplastics (i.e. particle sample); the digested microplastics are transferred using an SDS solution and filtered onto a detection filter membrane to obtain a to-be-detected filter membrane; then a sub-region distribution of the to-be-detected filter membrane is selected for extrapolation, and a montage microscopic image of the distribution of microplastics on the filter membrane is further obtained; then the coordinate origin is adjusted and corrected according to the difference between the navigation position of the sample loading platform and the actual position of the particles; the Raman spectrum of the particles is collected, and the full-membrane microplastics are qualitatively and quantitatively detected by using the extrapolation method. The present application is based on the extrapolation detection algorithm of uniform distribution of microplastics, and can realize efficient and rapid detection of small-particle microplastics in the environment, and has good applicability to large-particle microplastics, has high detection precision, is accurate and reliable, and is simple to operate, and improves the intelligent level of microplastic detection.

[0115] The technical solutions in the present application will be clearly and completely described below in combination with the embodiments in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0116] Embodiment 1

[0117] A method for rapidly detecting microplastics based on microscopic Raman spectrum, comprising the following steps of sample extraction, sample preparation, image acquisition, coordinate calibration, spectrum detection and statistical calculation in sequence:

[0118] Sample extraction: 5g of long-term mulched cotton field soil sample is taken into a conical flask, 50mL of anhydrous ethanol solution is added for full immersion for 15min to obtain a soil suspension; the obtained soil suspension is cleaned using a 600-mesh stainless steel screen under a pressurized liquid flow, the cleaning solution is left to stand for 1h, and after being concentrated with a polytetrafluoroethylene filter membrane with a pore size of 1μm, a concentrated solution and a solid on a small-pore-size filter membrane are obtained; the concentrated solution and the solid on the small-pore-size filter membrane are transferred into a new conical flask with 200mL of ZnCl2 solution with a density of 1.5g / cm 3 After 12h of flotation, the upper layer of particles and organic matter mixture is collected by filtering with a polytetrafluoroethylene filter membrane with a pore size of 1μm, and then the interference organic matter in the particles is removed by further digestion with 30% H2O2 at 60℃ and 115rpm for 24h, and finally the mixed solution after reaction is classified and filtered through 50μm and 1μm filter membranes to obtain a particle sample with a particle size of 1-50μm;

[0119] Sample preparation: Elute the particle sample from a 1 μm filter membrane with 50 mL of 300 mg / L SDS solution into a beaker and mix with ultrasound for 5 minutes to fully disperse the particle sample in the solution. Then, vacuum filter the particle sample onto a polyester gold-coated membrane to obtain the filter membrane to be tested.

[0120] Image acquisition: Use 75% ethanol solution to clean and coat the surface of the slide, and fix the filter membrane to be tested on the working surface of the slide in a bubble-free manner; establish the center origin (0, 0) of the filter membrane in the Raman microscope stage coordinate system, and generate sub-regions based on the sample distribution density on the filter membrane to be tested; select 6 detection sub-regions in the sub-region according to the 6-point S-type sampling strategy, and the center coordinates of each detection sub-region are (0, 0), (2000, 2000), (-2000, 2000), (-2000, -2000), (3300, -2800), and (0, 4200). The point distribution diagram of the 6 different detection sub-regions on the filter membrane to be tested is shown in Figure 2. Figure 2 As shown; and using a high-precision motorized stage to obtain montage microscopic images of each detection sub-area under a 50× objective lens;

[0121] The Partical Analyze image analysis module automatically performs grayscale binarization on the montage microscopic images of each detection sub-area, and locates the spatial coordinates of the particle samples in the filter membrane to be detected based on the morphological algorithm, constructing a particle feature database including particle size distribution, geometric morphology and coordinates;

[0122] Coordinate calibration: Use the particle analysis function to drive the stage to move to the specified coordinate point obtained from the montage microscope image. Manually check and determine the deviation (Δx, Δy) between the positioning space coordinates of the particle sample on the stage and the actual coordinates of the particle sample. Adjust the center origin of the filter to be tested from (0, 0) to (0±Δx, 0±Δy), and use the deviation to correct the calibration center origin position.

[0123] Spectral detection: Set Raman acquisition parameters: laser wavelength 532nm; grating density 1800 lines / mm; lens magnification 50x; Raman shift range 200-3200cm -1; the single-point exposure time for scanning was 1 s; the laser power was 10%; and the single accumulation. After calibrating the wave number with a silicon wafer, the Raman spectrum of each particle point was collected to obtain the category, particle size, circumference, area, and microscopic morphology information of the particle sample. After the spectrum data were obtained, cosmic ray removal and baseline correction preprocessing were performed to obtain the point spectrum data of each detection sub-region; then, the point spectrum data of each detection sub-region were compared with the standard polymer spectrum library using an algorithm based on feature peak matching, the matching degree of the point spectrum data of each detection sub-region to the standard polymer spectrum library was ≥75%, and the first qualitative microplastic particles were obtained; the matching degree of the point spectrum data of each detection sub-region to the standard polymer spectrum library was 45-75%, and the suspected microplastic particles were determined; the suspected microplastic particles were detected under the optimized Raman collection parameters: the laser wavelength was 532 nm; the grating density was 1800 lines / mm; the magnification of the lens was 50 times; the Raman shift range was 200-3200 cm -1 ; the single-point exposure time for scanning was 2 s; the laser power was 10%; and the accumulation number was 2; the matching degree of the point spectrum data of the suspected microplastic particles to the standard polymer spectrum library was ≥75%, and the second qualitative microplastic particles were determined. The sum of the number of the first qualitative microplastic particles and the number of the second qualitative microplastic particles was the content of microplastics in each detection sub-region; and the results are shown in Table 1:

[0124] Statistical calculation: the total membrane microplastic content of the filter membrane to be detected was obtained by statistical calculation, and the calculation method of the total membrane microplastic content is shown in formula (1):

[0125]

[0126] In formula (1), N T is the total membrane microplastic content, and the unit is piece; N i is the content of microplastics in each detection sub-region, and the unit is piece; m is the number of detection sub-regions, and the unit is piece; S t is the area of the filter membrane to be detected π×5 2 mm 2 ; and S s is the area of each detection sub-region 1.2 mm 2 , and the results are shown in Table 1:

[0127] Table 1: Microplastic detection results in different detection sub-regions in soil and extrapolated total membrane microplastic content

[0128]

[0129] From the above results, it can be seen that the detection test is carried out according to the above steps, and Table 1 is the microplastic detection results in different detection sub-regions in soil and the extrapolated total membrane microplastic content. It is found that the second scanning with optimized parameters improves the overall detection reliability by more than 24.2%.

[0130] Figure 3 The microscopic morphology and Raman spectrum of each type of microplastic in the soil in Example 1 (<50 μm) are shown in FIG. 1. Figure 3 In FIG. 1, a, b, c, and d are the microscopic morphologies of PA, PE, PET, and PS microplastics detected in the environment, respectively, and the corresponding Raman spectra are also shown. As can be seen from the results, Raman can effectively detect small particle size microplastics in the environment.

[0131] Figure 4 The microscopic morphology and Raman spectrum of 1 μm PA microplastics in the soil in Example 1 are shown in FIG. 2. Figure 5 The visualization interface of the method for rapid detection of microplastics based on microscopic Raman spectroscopy in Example 1 is shown in FIG. 3. Figure 4 and 5 As can be seen, when microplastics are detected using the rapid detection method of Raman, the distribution of different microplastics on the filter membrane is clear, and the particle size, morphology, and other key parameters of the microplastics can be obtained while qualitatively identifying the microplastics.

[0132] Example 2

[0133] In this example, 300-mesh PE, PP, and PET particles and 1500-mesh and 750-mesh PS microspherical microplastics were selected as research objects to represent microplastic samples of different particle sizes and particle morphologies for spiking verification experiments. The distribution of microplastics in 11 different detection sub-regions on the filter membrane was evaluated, and the particle distribution stability in different filter membrane regions was also evaluated.

[0134] The detection method comprises the following steps:

[0135] S1, mixing PE, PP, PET, and PS microplastics with a surfactant solution, and obtaining a uniformly dispersed microplastic suspension by sufficient stirring and ultrasonic treatment, specifically comprising:

[0136] S11, taking 300 mg of SDS powder and adding it to 1 L of RO water, stirring thoroughly and ultrasonic treatment for 5 min to form a 300 mg / L SDS solution;

[0137] S12, respectively weighing 300 mg of PE, PP, PET, and PS and adding them to 1 L of the previously prepared SDS solution, stirring for 5 min, and then ultrasonic treatment for 2 min to ensure uniform suspension, to prepare respective microplastic stock solutions.

[0138] S2, respectively taking 200 μL of each type of microplastic stock solution and mixing them thoroughly in a clean beaker for 5 min to form different microplastic suspension samples. The microplastic suspension samples were vacuum filtered onto a polyester gold-coated membrane to obtain the filter membrane to be detected;

[0139] The steps of sample preparation, image acquisition, coordinate calibration, spectrum detection and statistical calculation are the same as those in Example 1.

[0140] Figure 6 This is a distribution diagram of the points of 11 different detection sub-areas on the filter membrane to be detected in Example 2. Figure 7 The distribution content of standard microplastics in 11 different detection sub-areas on the filter membrane to be detected in Example 2. Figure 6 and 7 It can be seen that the sample preparation method provided by the present invention makes the content of various types of microplastics between 11 different detection sub-areas of the filter membrane (such as Figure 6 ) There was no statistical difference (eg Figure 7 ), and particle size and particle morphology have no effect on their distribution, indicating that this method can effectively promote the uniform distribution of microplastics on the filter membrane, and is therefore suitable for calculating the microplastic content of the entire membrane by extrapolating the detection results of local detection sub-areas.

[0141] To further systematically evaluate the subtle differences in particle distribution between different regions of the filter membrane, the coefficient of variation (CV) of the particle distribution corresponding to different regions was calculated (standard deviation / mean × 100%). The results are shown in Table 2:

[0142] Table 2 Statistics of coefficient of variation (CV) in different regions of the filter membrane

[0143]

[0144]

[0145] From the above results, it can be seen that although the particles are generally uniformly distributed across the entire membrane, a closer examination of the distribution variability in different membrane regions revealed subtle heterogeneity, with good stability in the central region (CV = 3.44 ± 1.50%), moderate uniformity in the transition region (CV = 4.5 ± 1.31%), and high variability in the edge region (CV = 6.90 ± 5.13%). The high variability at the edge can be attributed to the interaction between the filter membrane and the vacuum filtration device, resulting in local turbulence, particle redistribution, and inevitable microplastic loss due to wall adhesion. Therefore, when determining the extrapolated subregion, the spatial variability of different regions must be considered to improve the accuracy of extrapolated detection. Figure 8 It also shows a trend of decreasing distribution stability from the center to the edge of the filter membrane.

[0146] Figure 9 The microscopic morphology and Raman spectrum of the microplastics on the filter membrane to be tested in this embodiment are shown in FIG. Figure 9In the figure, a is the standard PP microplastic microscopic morphology and Raman spectrum; b is the standard PE microplastic microscopic morphology and Raman spectrum; c is the standard PET microplastic microscopic morphology and Raman spectrum; d is the standard PS microplastic microscopic morphology and Raman spectrum.

[0147] Example 3

[0148] In this embodiment, 1500 mesh, 750 mesh and 300 mesh single particle size PS microspheres with fixed shape are selected for spiking verification experiments to represent different particle size microplastics for spiking verification experiments. The fixed shape microplastics are selected to automatically count the PS particles with fixed shape by Python without collecting the Raman spectrum of each particle point, so as to realize the accuracy analysis of the extrapolation results of different detection sub-regions, and further determine the optimal sampling strategy by comprehensively considering the time cost and detection accuracy.

[0149] S1, 3 mg of 1500 mesh and 750 mesh microspherical PS microplastics were weighed respectively, 50 mg of 300 mesh microspherical PS microplastics was mixed with 50 mL of 300 mg / L SDS solution, and then the mixture was stirred and ultrasonicated to obtain particle sample dispersions with different particle sizes;

[0150] S2, 1 mL of the particle sample dispersion with different particle sizes was taken respectively and vacuum filtered onto a polyester gold coating film to obtain a filter membrane to be detected;

[0151] S3, the surface of the glass slide was cleaned and coated with 75% ethanol solution, and the filter membrane carrying the microplastic sample was fixed on the working surface of the glass slide in a bubble-free manner. The center origin (0, 0) of the filter membrane was established in the Raman microscope stage coordinate system, the center coordinates of each detection sub-region were set according to the 6-point S-type sampling strategy (0, 0), (2000, 2000), (-2000, 2000), (-2000, -2000), (3300, -2800), (0, 4200), and the montage microscopic images of each detection sub-region were obtained under a 50x objective lens using an electric stage platform;

[0152] S4, the fixed shape PS particles in each montage microscopic image were automatically segmented and counted by image analysis, and the total membrane microplastic content = Σ (detection sub-region microplastic content) / (6 x 1.2 mm 2 / π×5 2 mm 2 ).

[0153] In the figure, a is the standard PP microplastic microscopic morphology and Raman spectrum; b is the standard PE microplastic microscopic morphology and Raman spectrum; c is the standard PET microplastic microscopic morphology and Raman spectrum; d is the standard PS microplastic microscopic morphology and Raman spectrum. Figure 10 According to the above steps, five parallel verification experiments were carried out, and it was found that when the Figure 10The detection accuracy of the 6-point S-shaped sampling strategy shown in the 6 detection sub-regions extrapolated to the whole film is as shown in the table Figure 11 Figure 11 In the table, a is the accuracy of extrapolating the whole film when the particle size is 10 mu m, 20 mu m and 50 mu m, and b is the time comparison of detecting 6 detection sub-regions and extrapolating the whole film and whole film detection.

[0154] In combination Figure 11 It can be seen that the extrapolation accuracy of small (10 mu m), medium (20 mu m) and large (50 mu m) microplastics is 94.18+6.90%, 91.14+1.82% and 95.67+2.46% respectively; at the same time, compared with the whole film detection, the detection time can be shortened by more than 90% when the whole film is estimated by the 6-point detection sub-region extrapolation method. The results show that the 6-point detection sub-region extrapolation method for estimating the whole film microplastic content has good feasibility.

[0155] In summary, the detection method provided by the present application can quickly detect small particle size microplastics as low as 1 mu m, and also has good applicability to large particle size microplastics, and can realize rapid and accurate detection of small particle size microplastics in soil or other samples containing microplastic particles.

[0156] The above only describes the preferred embodiments of the present application, and it should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.​

Claims

1. A method for rapid detection of microplastics based on micro-Raman spectroscopy, characterized in that: It includes sample preparation, image acquisition, coordinate calibration, spectrum detection and statistical calculation in sequence; Sample preparation: extracting a particle sample from the sample to be tested, and then dispersing the particle sample in a single layer on a filter membrane to obtain a filter membrane to be tested; Image acquisition: The filter membrane to be tested is fixed to the working surface of a glass slide, the center origin of the filter membrane to be tested is established in the coordinate system of the Raman microscope stage, and sub-regions are generated based on the sample distribution density on the filter membrane to be tested; the sub-regions include a central region, a transition region, and an edge region arranged in sequence from the center of the filter membrane to the edge; Selecting a detection subregion within the subregion; setting grid coordinate parameters for automatically sampling the detection subregion; the number of the detection subregions is 5 to 11, and the distribution of the detection subregions covers the central region, the transition region, and the edge region; and the area of ​​the detection subregion accounts for 7.6 to 11.8% of the area of ​​the filter membrane to be detected; A motorized stage is used to drive the slide, and a Raman microscope is used to capture multiple images of each detection sub-region, which are then automatically stitched together to generate a montage microscopic image of each detection sub-region. An image analysis module automatically performs grayscale binarization on the montage microscopic image of each detection sub-region, and a morphological algorithm is used to locate the spatial coordinates of the particle sample in the filter membrane to be detected. Coordinate calibration: manually checking and determining the deviation between the positioning space coordinates of the particle sample in the electric loading platform and the actual coordinates of the particle sample, and correcting the position of the calibration center origin with the deviation to obtain the calibrated origin coordinates; Spectral detection: Set Raman acquisition parameters and use the Raman automatic particle analysis function to collect point spectra of the particle samples in each detection sub-area according to the calibrated origin coordinates of the particle samples to obtain point spectrum data for each detection sub-area. Use an algorithm based on characteristic peak matching to compare the point spectrum data of each detection sub-area with the standard polymer spectrum library to obtain the content of microplastics in each detection sub-area. Statistical calculation: The whole membrane microplastic content of the filter membrane to be tested is obtained using a statistical inference method. The calculation formula for the whole membrane microplastic content is shown in formula (1): In the formula (1), N T is the microplastic content of the whole film, in pieces; N i is the content of microplastics in each detection sub-area, in pieces; m is the number of detection sub-areas, in pieces; S t is the area of ​​the filter membrane to be tested; S s is the area of ​​each detection subregion.

2. The method according to claim 1, characterized in that The particle sample is dispersed in a single layer on the filter membrane. The method for obtaining the filter membrane to be tested includes: mixing the particle sample and a surfactant solution to obtain a particle sample dispersion; filtering the particle sample dispersion through the filter membrane to obtain the filter membrane to be tested.

3. The method according to claim 2, characterized in that The concentration of the surfactant solution is 100-1000 mg / L; the ratio of the mass of the particle sample to the volume of the surfactant solution is (10-30) mg: (30-100) mL.

4. The method according to claim 1, wherein The sample to be tested includes soil, water or air.

5. The method according to claim 4, characterized in that When the sample to be tested is soil, the method for extracting a particle sample from the sample to be tested includes: (1) mixing soil with anhydrous ethanol and then soaking the mixture to obtain a soil suspension; (2) washing the soil suspension obtained in step (1) through a sieve to obtain a solid on the sieve and a washing liquid; (3) filtering and concentrating the cleaning solution obtained in step (2) through a small-pore filter membrane to obtain a concentrated solution and a solid on the small-pore filter membrane; the pore size of the small-pore filter membrane is ≤1 μm; (4) mixing the solid on the net obtained in step (2), the concentrated solution obtained in step (3), the solid on the small-pore filter membrane and the flotation liquid, and performing density flotation to obtain flotation particles; (5) The flotation particles obtained in step (4) are mixed with the digestion solution, chemically digested, and then graded filtered using a large-pore filter membrane and a small-pore filter membrane to obtain a particle sample.

6. The method according to claim 1, characterized in that The number of the detection sub-regions is 6; the distribution of the detection sub-regions is: 1 central region, 3 transition regions and 2 edge regions; the method of selecting the detection sub-regions within the sub-regions is: using S-type sampling to select the detection sub-regions within the sub-regions.

7. The method according to claim 1, characterized in that The Raman acquisition parameters include: laser wavelength of 532-785 nm; grating density of 300-1800 lines / mm; lens magnification of 5-100 times; Raman shift range of 100-4000 cm -1 ; Scanning single point exposure time is 0.1~5s; Laser power is 0.05~10%; Single accumulation.

8. The method according to claim 1, characterized in that The method for determining the content of microplastics in each detection sub-region includes: using an algorithm based on characteristic peak matching to compare the point spectrum data of each detection sub-region with a standard polymer spectrum library to obtain first qualitative microplastic particles and suspected microplastic particles; performing a secondary detection on the suspected microplastic particles under optimized Raman acquisition parameters to obtain point spectrum data of the suspected microplastic particles; using an algorithm based on characteristic peak matching to compare the point spectrum data of the suspected microplastic particles with a standard polymer spectrum library to obtain second qualitative microplastic particles; the sum of the number of the first qualitative microplastic particles and the number of the second qualitative microplastic particles is the content of microplastics in each detection sub-region; The first qualitative judgment standard for microplastic particles is: the matching degree between the point spectrum data of each detection sub-area and the standard polymer spectrum library is ≥75%; the judgment standard for suspected microplastics is: the matching degree between the point spectrum data of each detection sub-area and the standard polymer spectrum library is 45-75%; the second qualitative judgment standard for microplastic particles is: the matching degree between the point spectrum data of suspected microplastic particles and the standard polymer spectrum library is ≥75%.

9. The method according to claim 8, characterized in that The optimized Raman acquisition parameters include: laser wavelength of 532-785 nm; grating density of 300-1800 lines / mm; lens magnification of 5-100 times; Raman shift range of 100-4000 cm -1 ; The exposure time of a single scanning point is 2 to 5 seconds, the laser power is 0.05 to 10%; the cumulative number of times is 2 to 5 times.

10. The method according to claim 1, characterized in that The area of ​​the detection sub-region accounts for 9.2% of the area of ​​the filter membrane to be detected.

Images