A microplastic quantitative analysis method based on fluorescence detection model and three-dimensional reconstruction technology

Abstract

This invention discloses a quantitative analysis method for microplastics based on a fluorescence detection model and three-dimensional reconstruction technology, belonging to the field of microplastic detection, a typical emerging pollutant. The invention includes preprocessing the microplastic sample to impart intrinsic fluorescence properties, acquiring three-dimensional imaging data using a confocal fluorescence microscope, determining the material density based on the fluorescence fingerprint qualitative results of the microplastic type, and combining the volume data from the three-dimensional imaging reconstruction to achieve quantitative analysis of the microplastic's mass.

Description

Technical Field

[0001] This invention relates to a quantitative analysis method for microplastics, and more particularly to a quantitative analysis method for microplastics based on a fluorescence detection model and three-dimensional reconstruction technology. Background Technology

[0002] The large-scale production and widespread use of plastics globally generate massive amounts of plastic waste. After disposal, these plastics break down, become brittle, and degrade due to mechanical wear, thermal effects, solar radiation, and biological processes, ultimately forming microplastics (MPs, defined as particles smaller than 5 mm in diameter). Due to their tiny size, microplastics are easily mistaken for food by aquatic and terrestrial organisms and ingested, significantly inhibiting feeding activities, survival, and reproduction, and often accompanied by oxidative stress, cell damage, and inflammatory responses. Notably, microplastics smaller than 1 micrometer in diameter have been shown to penetrate cell membranes and the blood-brain barrier, accumulating in various tissues and organs of animals and plants, and eventually entering the human food chain. Microplastics have already been detected in various tissues, including human blood, placenta, liver, kidneys, and brain. Recent research has also revealed that microplastics in the blood can induce cerebral thrombosis by causing cell blockage and lead to neurobehavioral abnormalities. Therefore, reliable, repeatable, rapid, and economical methods for quantifying and monitoring microplastic pollution in the environment are crucial.

[0003] Existing methods for quantifying microplastics primarily focus on analyzing particle concentration, such as using transmission electron microscopy (TEM), atomic force microscopy (AFM), fluorescence microscopy, or micro-FTIR and micro-Raman spectroscopy for visual counting. However, in field investigations, the way microplastic concentration is expressed (particle concentration) differs significantly from mass flux (mass concentration). To address this, Chen et al. developed a microplastic mass conversion model to predict the mass of fibrous, fragmented, and particulate microplastics. However, this method is affected by the shape of the microplastics and cannot obtain accurate thickness information. Furthermore, while mass spectrometry can generate quantitative data on the mass of each polymer, it cannot perform chemical imaging and can damage the sample.

[0004] Most microplastics in the real environment are generated during various aging and transformation processes that occur after waste plastics enter the environment. During these processes, the morphology, surface characteristics, and microstructure of microplastics change, thus affecting their environmental behavior and interaction mechanisms with pollutants. Photochemical aging is one of the most important natural aging processes. Under sunlight, especially ultraviolet light, microplastics (MPs) undergo surface reactions such as bond breaking, oxygenation, dehydrogenation, or cross-linking. Furthermore, the bond breaking reaction of MPs is accompanied by the formation of new bonds. For example, plastics such as polyvinyl chloride (PVC) and polypropylene (PP) undergo bond breaking reactions under light to form conjugated olefin structures. The conjugated π bonds within these olefin structures are prone to π-π changes under light radiation. Electron transitions create a broad fluorescence band in the visible light region. Furthermore, the fluorescence in the visible light region effectively avoids interference from the autofluorescence of most organic substances (such as natural organic matter and proteins), providing theoretical support for the fluorescence recognition and analysis of aged microplastics in complex environmental media and biological matrices. However, due to the different molecular structures of microplastics, their photodegradation aging mechanisms inevitably differ, further affecting the trend of fluorescence changes. Consequently, different types of microplastics exhibit different fluorescence characteristics after aging. Fluorescence methods can sensitively distinguish various chemical impurities in complex media and accurately identify microplastics (MPs) while maintaining sample integrity. Moreover, this technology can be used in conjunction with microscopy for high-resolution imaging of MPs, enabling comprehensive analysis of plastic particles, including qualitative characteristics such as shape and surface properties, as well as quantitative information such as particle number, size distribution, and concentration. Existing technologies have developed a microplastic mass conversion model to predict the mass of fiber, fragment, and granular microplastics. However, this method is affected by the shape of the microplastics and cannot obtain accurate thickness information. In addition, mass spectrometry can generate quantitative data on the mass of each polymer, but it cannot perform chemical imaging and will destroy the sample. Summary of the Invention

[0005] Purpose of the Invention: Addressing the issues of shape dependence in existing microplastic mass conversion models and the inability to perform chemical imaging and sample destruction in mass spectrometry analysis methods, this invention aims to provide a microplastic quantitative analysis method based on a fluorescence detection model and three-dimensional reconstruction technology. This method involves pre-treating the microplastic sample to impart intrinsic fluorescence properties, acquiring three-dimensional imaging data using a confocal fluorescence microscope, determining the material density based on the fluorescence fingerprint qualitative results of the microplastic type, and combining this with the volume data reconstructed from the three-dimensional imaging to achieve quantitative analysis of the microplastic's mass.

[0006] The present invention also provides an application of a quantitative analysis method for microplastics based on a fluorescence detection model and three-dimensional reconstruction technology.

[0007] Technical Solution: To achieve the above objectives, this invention provides a method for quantitative analysis of microplastics based on a fluorescence detection model and three-dimensional reconstruction technology, comprising:

[0008] (a) Obtaining microplastic samples to be tested after intrinsic fluorescence treatment;

[0009] (b) Scan the intrinsic fluorescence-treated microplastic sample obtained in step (a) to generate a three-dimensional fluorescence image;

[0010] (c) Reconstruct a three-dimensional model of the microplastic sample to be tested based on the three-dimensional fluorescence image and calculate its volume;

[0011] (d) Determine the density value based on the known types of the microplastic sample to be tested;

[0012] (e) Multiply the density value by the volume to obtain the mass of the microplastic sample.

[0013] Furthermore, the intrinsic fluorescence treatment in step (a) is a method to generate a characteristic fluorescent fingerprint in the microplastic sample to be tested without adding exogenous fluorescent substances.

[0014] Furthermore, the method for generating a three-dimensional fluorescence image in step (b) includes: performing Z-Stack scanning on the microplastic sample to be tested that has undergone intrinsic fluorescence treatment in step (a), acquiring the fluorescence signal of the sample layer by layer through optical tomography and Z-axis scanning, and reconstructing a three-dimensional fluorescence image.

[0015] Furthermore, the Z-Stack scanning and signal acquisition are performed using a confocal fluorescence microscope, which employs point scanning laser technology to improve resolution. The three-dimensional fluorescence image reconstruction is achieved using ZEN software (Zeiss, Germany).

[0016] Furthermore, the instrument parameters of the confocal fluorescence microscope are as follows: solid-state laser is a 405 nm, 488 nm, or 561 nm diode laser or dual excitation by two exciters; laser intensity: 0.5%-5%; scanning mode: Z-axis scanning; scanning detection band: 480-700 nm; imaging device: Airyscan; detector type: GaASP-PMT; detector gain: 600-750V; pinhole size: 0.5-1.0 AU; imaging pixel resolution: 128×128, 512×512, 1024×1024 or super-resolution imaging.

[0017] Furthermore, the specific method for step (c) of reconstructing the three-dimensional model of the microplastic sample to be tested based on the three-dimensional fluorescence image is as follows:

[0018] (1) Image segmentation: Based on the fluorescence intensity threshold, microplastic particles are separated from the background from the three-dimensional fluorescence image, and each individual microplastic particle is identified and extracted;

[0019] (2) Volume calculation: Calculate the precise volume based on the space volume occupied by each extracted microplastic particle;

[0020] (3) Output: The output is structured data containing the volume information of each particle.

[0021] Furthermore, before the image segmentation step, the xyz axis pixel dimensions of the three-dimensional image are extracted; the image segmentation step specifically includes: setting a global threshold and binarizing the image to separate microplastic particles from the background; performing morphological operations on the binarized data to fill holes and remove noise; and performing 3D connected component labeling analysis to identify and extract the voxel spatial distribution information of each individual microplastic particle.

[0022] Furthermore, the volume calculation step includes: calculating the three-dimensional physical dimensions and volume of each microplastic particle, wherein the volume is obtained by multiplying the total number of voxels occupied by the particle by the physical volume of a single voxel; the result output is to generate a structured data table containing a unique particle identifier (ID), three-dimensional dimensions and volume values.

[0023] Furthermore, the type of microplastic sample to be tested in step (d) is determined by analyzing the fluorescence fingerprint spectrum of the microplastic and classifying it using a machine learning model.

[0024] Furthermore, the method for obtaining the mass of the microplastic sample to be tested in step (e) is as follows: according to claim 9, the type of microplastic sample obtained by machine learning is used to find its density from a table, and the mass of the microplastic is calculated using the formula m=ρV (m is mass, ρ is density, and V is volume).

[0025] This invention utilizes intrinsic fluorescence 3D reconstruction technology to obtain the volume of individual microplastic particles. Compared to methods that predict 3D volume based on 2D images, this method improves upon traditional methods by providing a significant dimension, particularly for predicting the volume of irregularly shaped microplastic particles. Furthermore, compared to mass spectrometry (which can generate quantitative data on the mass of each polymer but severely damages the sample and cannot obtain single-particle imaging or individual microplastic volume), this method does not damage the sample, allows for chemical imaging, and can obtain the volume of individual particles.

[0026] This invention is the first to obtain a three-dimensional image based on the intrinsic fluorescence of microplastics, calculate the volume of microplastics based on the three-dimensional image, and predict the mass of microplastics by combining the density of the qualitative results.

[0027] Compared to methods that predict three-dimensional volume from two-dimensional images, the method of this invention improves upon traditional approaches by adding a dimension, particularly for predicting the volume of irregular microplastic particles. Furthermore, compared to mass spectrometry (which can generate quantitative data on the mass of each polymer but severely damages the sample and cannot obtain single-particle imaging or individual microplastic volume), the method of this invention does not damage the sample, allows for chemical imaging, and can obtain the volume of individual particles.

[0028] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] (1) The present invention provides a method for quantitative analysis of microplastics based on a fluorescence detection model and three-dimensional reconstruction technology, which simultaneously meets the requirements for qualitative and quantitative detection of microplastics;

[0030] (2) The present invention provides a quantitative analysis method for microplastics based on fluorescence detection model and three-dimensional reconstruction technology. The method obtains the mass information of microplastics through three-dimensional reconstruction and voxel volume calculation, without relying on the shape model of microplastics.

[0031] (3) The present invention provides a method for quantitative analysis of microplastics based on a fluorescence detection model and three-dimensional reconstruction technology, which can perform high-resolution three-dimensional imaging without destroying the sample;

[0032] (4) The present invention provides a method for quantitative analysis of microplastics based on fluorescence detection model and three-dimensional reconstruction technology. It relies on super-resolution imaging of confocal fluorescence microscopy. The detection limit in the XY direction can reach 120 nm and the detection limit in the Z direction can reach 350 nm, which breaks through the detection size of infrared and Raman. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the preprocessing method used in this invention.

[0034] Figure 2 This is a confocal fluorescence image of the eight microplastics in this invention.

[0035] Figure 3 This is a schematic diagram of the three-dimensional fluorescent structure of 50 μm PVC in this invention.

[0036] Figure 4 This is a schematic diagram of image segmentation and individual microplastic extraction.

[0037] Figure 5 This is a schematic diagram of the three-dimensional fluorescent structure of 20 μm PVC in this invention.

[0038] Figure 6 This is a schematic diagram of the three-dimensional fluorescent structure of 100 μm PVC in this invention.

[0039] Figure 7A correlation diagram between the predicted quality and the actual quality of the 3D model.

[0040] Specific implementation methods

[0041] The present invention will be further described below with reference to specific embodiments.

[0042] Example 1

[0043] Microplastics PE, PP, PS, PVC, PU, ​​PET, PMMA and PA are commercially available conventional plastics, manufactured by GuanBu-Tech (Shanghai, China).

[0044] like Figure 1 As shown, aged microplastics of polyethylene (PE), PP, polystyrene (PS), PVC, polyurethane (PU), polyethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyamide (PA) were prepared to induce autofluorescence in eight microplastics. The microplastics were placed in a confocal microplate, exposed to ozone first, and then photo-aged. Ozone was generated using an ozone generator, with an exposure time of 24 h and an ozone concentration of 30 g / m³. 3 (NTP), ozone flow rate of 0.5 L / min, temperature controlled at 25℃; air gas flow rate during photoaging process of 0.3 L / min; reaction time of photoirradiation reaction of 12 h (500 W mercury lamp), reaction temperature of 25℃, and light irradiation intensity of 100 mW / cm². 2 Using confocal fluorescence microscopy to image aged microplastics, such as... Figure 2 As shown, all eight microplastics exhibited autofluorescence.

[0045] Aging standard spherical PVC microplastics (50 μm in diameter) were used to collect three-dimensional images using confocal fluorescence microscopy. The confocal plane (i.e., Z-axis depth) was adjusted, with the first layer being the section where fluorescence began to appear, gradually focusing downwards until the last layer was the section where fluorescence disappeared. The Z-axis step size was automatically set. The solid-state laser used in the confocal fluorescence microscope was a dual-excitation system of 405 nm and 488 nm diode lasers; laser intensity: 2%; scanning mode: Z-axis scan; scanning detection band: 480-700 nm; imaging device: Airyscan; detector type: GaASP-PMT; detector gain: 720 V; pinhole size: 1.0 AU; imaging pixel resolution: 512×512. The three-dimensional fluorescence structure was reconstructed using software (ZEN software (Zeiss, Germany)). Figure 3As shown. The following operations were performed on the three-dimensional images of the collected PVC fluorescent balls: (1) Pixel size extraction analysis: Extract the pixel size along the xyz axis, in μm. (2) Image segmentation: Set a global threshold to separate the microplastic particles from the background area and binarize the fluorescence data; perform morphological operations on the binarized data, including closing operations (filling small holes) and removing small area noise; perform 3D connected component labeling analysis to identify and extract the voxel spatial distribution information of each independent microplastic particle. The image segmentation results of a certain layer are shown in the figure. Figure 4 As shown. (3) Calculate the volume: Perform the following measurements on each segmented microplastic particle: three-dimensional physical dimensions (maximum projected length along the X, Y, and Z axes); particle volume: calculated based on the total number of voxels occupied by the particle multiplied by the physical volume of a single voxel. (4) Determine the density of the microplastic by looking up the table according to the type of microplastic, and calculate the mass of the microplastic using the formula m=ρV (m is the mass, ρ is the density, and V is the volume).

[0046] Example 2

[0047] The content of this embodiment is the same as that of Embodiment 1, except that: A 20 μm diameter standard spherical PVC microplastic was aged, and its three-dimensional image was collected using a confocal fluorescence microscope. The confocal plane (i.e., Z-axis depth) was adjusted, with the section where fluorescence first appeared as the first layer, gradually focusing downwards until the section where fluorescence disappeared became the last layer, and the Z-axis step size was automatically set. The solid-state laser used in the confocal fluorescence microscope was a dual-excitation system of 405 nm and 488 nm diode lasers; laser intensity: 2%; scanning mode: Z-axis scanning; scanning detection band: 480-700 nm; imaging device: Airyscan; detector type: GaASP-PMT; detector gain: 720 V; pinhole size: 1.0 AU; imaging pixel resolution: 512*512. The three-dimensional fluorescence structure was reconstructed using software (ZEN software (Zeiss, Germany)). Figure 5As shown. The following operations were performed on the three-dimensional images of the collected PVC fluorescent balls: (1) Extraction of pixel size analysis: Extract the pixel size of the xyz axis, in μm. (2) Image segmentation: Set a global threshold to separate the microplastic particles from the background area and binarize the fluorescence data; perform morphological operations on the binarized data, including closing operation (filling small holes) and removing small area noise; perform 3D connected region labeling analysis to identify and extract the voxel spatial distribution information of each independent microplastic particle. (3) Calculation of volume: The following measurements were performed on each segmented microplastic particle: three-dimensional physical size (maximum projected length along the X, Y, Z axes); particle volume: calculated based on the total number of voxels occupied by the particle multiplied by the physical volume of a single voxel. (4) According to the type of microplastic, the density was found in the table, and the mass of the microplastic was calculated using the formula m=ρV (m is mass, ρ is density, V is volume).

[0048] Example 3

[0049] The content of this embodiment is the same as that of Embodiment 1, except that: A 100 μm diameter standard spherical PVC microplastic was aged, and its three-dimensional image was collected using a confocal fluorescence microscope. The confocal plane (i.e., Z-axis depth) was adjusted, with the section where fluorescence first appeared as the first layer, gradually focusing downwards until the section where fluorescence disappeared became the last layer, and the Z-axis step size was automatically set. The solid-state laser used in the confocal fluorescence microscope was a dual-excitation system of 405 nm and 488 nm diode lasers; laser intensity: 2%; scanning mode: Z-axis scanning; scanning detection band: 480-700 nm; imaging device: Airyscan; detector type: GaASP-PMT; detector gain: 720 V; pinhole size: 1.0 AU; imaging pixel resolution: 512×512. The three-dimensional fluorescence structure was reconstructed using software (ZEN software (Zeiss, Germany)). Figure 6 As shown. The following operations were performed on the three-dimensional images of the collected PVC fluorescent balls: (1) Extraction of pixel size analysis: Extract the pixel size of the xyz axis, in μm. (2) Image segmentation: Set a global threshold to separate the microplastic particles from the background area and binarize the fluorescence data; perform morphological operations on the binarized data, including closing operation (filling small holes) and removing small area noise; perform 3D connected region labeling analysis to identify and extract the voxel spatial distribution information of each independent microplastic particle. (3) Calculation of volume: The following measurements were performed on each segmented microplastic particle: three-dimensional physical size (maximum projected length along the X, Y, Z axes); particle volume: calculated based on the total number of voxels occupied by the particle multiplied by the physical volume of a single voxel. (4) According to the type of microplastic, the density was found in the table, and the mass of the microplastic was calculated using the formula m=ρV (m is mass, ρ is density, V is volume).

[0050] Example 4

[0051] The method of Example 1 was used to predict the mass of microplastics, and the result was compared with the actual mass of microplastics (the volume was calculated based on the diameter of a standard sphere and then multiplied by the density). The correlation between the actual mass and the predicted mass of microplastics is shown in the figure below. Figure 7 The predicted quality for 20 μm, 50 μm, and 100 μm is on the same order of magnitude as the actual quality, indicating that the prediction results are accurate.

[0052] The above description of the present invention and its embodiments is illustrative and not restrictive. The data used is only one embodiment of the present invention, and the actual data combination is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar embodiments and examples without departing from the spirit of the invention, they shall fall within the protection scope of the present invention.

Claims

1. A quantitative analysis method for microplastics based on a fluorescence detection model and three-dimensional reconstruction technology, characterized in that... include: (a) Obtaining microplastic samples to be tested after intrinsic fluorescence treatment; (b) Scan the intrinsic fluorescence-treated microplastic sample obtained in step (a) to generate a three-dimensional fluorescence image; (c) Reconstruct a three-dimensional model of the microplastic sample to be tested based on the three-dimensional fluorescence image and calculate its volume; (d) Determine the density value based on the known types of the microplastic sample to be tested; (e) Multiply the density value by the volume to obtain the mass of the microplastic sample.

2. The method for quantitative analysis of microplastics according to claim 1, characterized in that, In step (a), intrinsic fluorescence treatment is a method to generate a characteristic fluorescent fingerprint in the microplastic sample to be tested without adding exogenous fluorescent substances.

3. The method for quantitative analysis of microplastics according to claim 1, characterized in that, The method for generating a three-dimensional fluorescence image in step (b) includes: performing Z-Stack scanning on the microplastic sample to be tested that has undergone intrinsic fluorescence treatment in step (a), acquiring the fluorescence signal of the sample layer by layer through optical tomography and Z-axis scanning, and reconstructing a three-dimensional fluorescence image.

4. The method for quantitative analysis of microplastics according to claim 3, characterized in that, The Z-Stack scanning and signal acquisition are performed using a confocal fluorescence microscope, which employs point scanning laser technology to improve resolution.

5. The method for quantitative analysis of microplastics according to claim 4, characterized in that, The instrument parameters of the confocal fluorescence microscope are as follows: solid-state laser is a 405 nm, 488 nm, or 561 nm diode laser or dual excitation of two exciters; laser intensity: 0.5%-5%; scanning mode: Z-axis scanning; scanning detection band: 480-700 nm; imaging device: Airyscan; detector type: GaASP-PMT; detector gain: 600-750 V; pinhole size: 0.5-1.0 AU; imaging pixel resolution: 128×128, 512×512, 1024×1024 or super-resolution imaging.

6. The method for quantitative analysis of microplastics according to claim 1, characterized in that, The specific method for reconstructing the three-dimensional model of the microplastic sample to be tested based on the three-dimensional fluorescence image in step (c) is as follows: (1) Image segmentation: Based on the fluorescence intensity threshold, microplastic particles are separated from the background from the three-dimensional fluorescence image, and each individual microplastic particle is identified and extracted; (2) Volume calculation: Calculate the precise volume based on the space volume occupied by each extracted microplastic particle; (3) Output: The output is structured data containing the volume information of each particle.

7. The method for quantitative analysis of microplastics according to claim 6 or 7, characterized in that, Before the image segmentation step, the xyz axis pixel dimensions of the three-dimensional image are extracted. The image segmentation step specifically includes: setting a global threshold and binarizing the image to separate microplastic particles from the background; performing morphological operations on the binarized data to fill holes and remove noise; and performing 3D connected component labeling analysis to identify and extract the voxel spatial distribution information of each individual microplastic particle.

8. The method for quantitative analysis of microplastics according to claim 6, characterized in that, The volume calculation step includes: calculating the three-dimensional physical dimensions and volume of each microplastic particle, wherein the volume is obtained by multiplying the total number of voxels occupied by the particle by the physical volume of a single voxel; the result output is a structured data table containing a unique particle identifier (ID), three-dimensional dimensions and volume values.

9. The method for quantitative analysis of microplastics according to claim 1, characterized in that, The type of microplastic sample to be tested in step (d) is determined by analyzing the fluorescence fingerprint spectrum of the microplastic and classifying it using a machine learning model.

10. The method for quantitative analysis of microplastics according to claim 1, characterized in that, The method for obtaining the mass of the microplastic sample to be tested in step (e) is as follows: according to claim 9, the type of microplastic sample obtained by machine learning is used to find its density from a table, and the mass of the microplastic is calculated using the formula m=ρV (m is mass, ρ is density, and V is volume).

Images