CN109766909B - Analysis method for aging behavior of shore environment microplastic based on spectrogram fusion - Google Patents
Analysis method for aging behavior of shore environment microplastic based on spectrogram fusion Download PDFInfo
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Abstract
The invention provides a spectrogram fusion-based coast environment microplastic aging behavior analysis method, which comprises the following steps of: 1. the micro plastic samples are collected and prepared separately in a coastal environment. 2. And obtaining the micro-region morphological image and infrared spectrum information of the micro-plastic sample by using an infrared spectrometer. 3. And extracting a relevant characteristic spectrum data matrix from the infrared spectrum information. 4. Texture features are advantageously extracted for the microcell image information. 5. And obtaining the surface yellowing color characteristics of the micro-region image information. 6. And establishing a characteristic fusion prediction model based on sample surface morphology-molecular spectrum. 7. And correcting the prediction model to obtain a correction model. 8. Extracting a feature fusion matrix of the sample to be detected, and inputting the feature fusion matrix of the sample to be detected into a correction model to obtain a corresponding aging analysis result. According to the scheme, parameters such as spectral carbonyl ratio, yellowing degree of texture morphology and the like are introduced, so that quick analysis of the aging degree of the microplastic in a complex environment is realized.
Description
Technical Field
The invention relates to a method for analyzing pollution of a complex coast environment microplastic, in particular to a method for analyzing aging behavior of the coast environment microplastic based on spectrogram fusion.
Background
In recent years, microplastic has become a new contaminant of interest in the global marine and coastal zone environment. The aging behavior of the microplastic is an important parameter of the traceability analysis of the microplastic pollution source and the dynamic degradation model of the microplastic in the environment. Obvious phenomena of color yellowing, surface cracking and pulverization occur in the surface of the micro plastic sample in the aging process, and the absorption strength of functional groups related to aging behaviors can be aggravated along with the aging degree to present a certain coupling relation. However, the microplastic separated from the environmental sample is subjected to weathering, so that the surface of the microplastic is rugged or a large amount of environmental impurities are attached, and a high signal-to-noise ratio infrared spectrum cannot be obtained in the measuring process. Meanwhile, the color of the surface of the sample in the aging process is related to the concentration of residual antioxidant in the sample, and the morphology of the surface cracks is related to the volatilization degree of the plasticizer of the sample.
Because the spectrum and the surface morphology of the aged sample are influenced by the surface roughness of the sample and the residual additives in the sample, if single infrared spectrum characteristics or single morphological characteristic modeling is adopted, accurate analysis of the aging behavior of the micro plastic sample under the background of a complex environment is difficult to meet.
Disclosure of Invention
In order to overcome the defects and shortcomings of the prior art, the accurate analysis method for the aging behavior of the micro plastic sample in the coastal environment is provided, the nondestructive analysis of the aging behavior of the environmental sample in the typical coastal environment is realized by adopting a method based on visible-infrared spectrum fusion, the method is convenient and fast, and the reliability of the detection result is high.
A spectrogram fusion-based coast environment microplastic aging behavior analysis method comprises the following steps:
a. microplastic samples with different ageing degrees and particle sizes of more than 1mm are collected in a coastal environment and separated for preparation.
b. And collecting micro-region image information and infrared spectrum information of the micro-plastic sample by using a Fourier transform infrared instrument, wherein the micro-plastic sample is randomly divided into a correction sample and a prediction sample.
c. And correcting the infrared spectrum information by adopting a standard normal variable, extracting the optimal characteristic wavelength related to the aging behavior, and establishing a characteristic spectrum data matrix K.
d. The general method for describing textures is utilized for the acquired micro-region image information: extracting texture features from the gray level co-occurrence matrix, respectively calculating energy C1 and contrast C2 of the gray level spatial correlation characteristic co-occurrence matrix as texture quantization indexes for representing the texture features,
wherein i and j are pixel gray coordinates, d is a pixel separation distance, θ is an error, and P (i, j, d, θ) is a probability.
e. And obtaining hue, brightness and saturation parameters to represent yellowing and chromaticity characteristics of the obtained micro-region image information by using an HSB system, namely a color mode. In the HSB mode, H (hues) represents hue, S (saturation) represents saturation, and B (brightness) represents brightness.
f. C, d and e are used for respectively obtaining a characteristic spectrum data matrix K and a texture characteristic C, and a yellowing chromaticity characteristic matrix P is subjected to characteristic layer fusion to obtain a fusion matrix M, M= [ KC P ].
g. By introducing carbonyl ratio, texture morphology, yellowing degree and other parameters, a characteristic fusion prediction model based on surface morphology-molecular spectrum is established:
the spectral image aging characteristic value for a sample is denoted as X 1 ,X 2 To Xn; assuming that the possible feature matrix after fusion is M, and the X is used for 1 ,X 2 … Xn, model fusion is performed by using a weighted average method, and the weight can be regarded as a measure of the contribution rate of different feature vectors.
h. And f, correcting the prediction model in the step f by using a correction sample to obtain a correction model.
i. And (c) acquiring image spectrum information of the micro plastic to be detected by using a Fourier transform infrared instrument, and inputting the fusion matrix obtained in the step (f) into the fusion prediction model obtained in the step (g) to obtain a corresponding aging analysis result.
In order to perfect the scheme, the invention is further provided with: in the step b, 1/3 micro plastic sample is randomly selected as a prediction sample, and 2/3 micro plastic sample is used as a correction sample.
In order to perfect the scheme, the invention is further provided with: in the step c, the analysis method for the micro-region morphological image and the infrared spectrum information of the micro-plastic sample comprises the following steps: and preprocessing the original spectrum data by adopting wavelet denoising treatment, multi-element scattering correction and a standard normal variable correction method, so as to obtain high-precision original data.
In order to perfect the scheme, the invention is further provided with: 1450cm for clear polyethylene sample screening in step c -1 And 1750cm -1 The aging behavior characteristic wavelength of nearby wavenumbers.
In order to perfect the scheme, the invention is further provided with: correcting the model accuracy root mean square error RMSE P in step g<0.15, correction of the determination coefficient R 2 >0.92。
According to the invention, by introducing parameters such as carbonyl ratio, yellowing degree of texture morphology and the like, a characteristic fusion model based on surface morphology-spectrum characteristics is constructed, so that quick analysis of the aging degree of the microplastic in a complex environment is realized. The problem that sample aging behaviors under an actual complex system are difficult to analyze by adopting a single aging characteristic is solved. The adopted microscopic infrared technology can be used for nondestructive testing of spectrum image information of a trace sample, so that nondestructive repeated testing requirements of sample aging stages are met, and the utilization rate of sample information is improved. The method can realize nondestructive rapid and accurate analysis of the micro-plastic samples collected in the complex coastal environment, provides scientific support for supervision of micro-plastic pollution in the coastal environment in China, and has important significance for treatment of the marine micro-plastic pollution.
The invention is described in more detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic flow chart of the detection method of the present invention;
FIG. 2 is an infrared spectrum carbonyl index plot of an aged sample according to an embodiment of the present invention;
FIG. 3 is a graph of chromaticity characteristics of a micro-domain surface of an aged sample according to an embodiment of the present invention.
Detailed Description
The present invention will be specifically described below by way of exemplary embodiments. It is to be understood, however, that features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
A spectrogram fusion-based analysis method for ageing behavior of microplastic in coast environment uses transparent polyethylene with the most widely distributed environment as a representative sample to study the ageing behavior of microplastic in complex coast environment. The method comprises the following steps:
a. and collecting microplastic samples of the surface environment of the tidal flat of the Longwan of Zhejiang province, collecting sediment with the thickness of about 5cm along the latest climax line, screening and collecting representative and sufficient microplastic samples by a steel sieve, filling the microplastic samples into a sealing bag, and then transporting the microplastic samples back to a laboratory. The micro plastic sample is rinsed with deionized water. And (3) filtering and screening by using glass microfiber filter paper, putting the glass microfiber filter paper into a metal tray, drying the glass microfiber filter paper in a baking oven at 60 ℃, and finally putting the glass microfiber filter paper into a sealing bag and placing the glass microfiber filter paper in a clean and light-proof place to obtain transparent polyethylene particles.
b. Transparent polyethylene samples are divided into two classes. 1/3 sample is randomly selected as a prediction sample from the total samples, and 2/3 sample is used as a correction sample. The method comprises the following steps: 30 samples were randomly selected, 10 as predicted samples, and the remaining 20 as corrected samples.
The microscopic infrared spectrum instrument in the experiment is a high Fourier transform infrared instrument, and is provided with an infrared detector and an infrared band 20X lens to collect information of different micro plastic samples. The number of scans was 20, and the recorded wave number range was 4000cm -1 –600cm -1 Spectral resolution of 4cm -1 An infrared spectrum and micro-region image information of 30 samples were obtained in total.
c. And preprocessing the corrected sample spectrum data by adopting standard normal variable correction to obtain high-precision spectrum data.
And extracting optimal characteristic wavelength related to aging behaviors from the obtained polyethylene sample infrared spectrum and establishing a spectrum data matrix. The specific method comprises the following steps: screening 1450cm for full-band spectral information -1 、1750cm -1 The aging behavior characteristic wavelength of nearby wavenumbers. The absorption intensity of the characteristic carbonyl functional group in the vicinity shows a linear coupling relation with the aging degree.
d. Extracting texture morphological characteristics from the acquired micro-region image information by using a gray level co-occurrence matrix, and respectively calculating energy C1, contrast C2 and relativity of the corresponding co-occurrence matrix as texture quantization indexes to represent the texture characteristics, wherein i and j are pixel gray level coordinates respectively, d is a pixel separation distance, θ is an error, and P (i, j, d, θ) is probability:
e. the obtained micro-region image information utilizes an HSB system to obtain H (hues) hue, S (saturation) saturation, B (brightness) brightness and other color characteristics to represent yellowing chromaticity characteristics
v＝max。
Since the correlation between saturation and luminance is remarkable, only two characteristic parameters of hue H and luminance B are considered in extracting color characteristics. The data shows that the yellow sample surface has a hue of 55-75 and a brightness of greater than 40. The brown-yellow sample had a hue similar to yellow, but a brightness of only 10-20. The black sample surface extraction hue is highest but the brightness value is lowest.
f. C, d and e are used for respectively obtaining a characteristic spectrum data matrix K and a texture characteristic C, and the yellowing chromaticity characteristic matrix P is subjected to characteristic layer fusion to obtain a fusion matrix M, M= [ K, C, P ].
g. By introducing carbonyl ratio, texture morphology, yellowing degree and other parameters, a characteristic fusion prediction model based on surface morphology-molecular spectrum is established:
the spectral image aging characteristic value for a certain sample is represented as X1, X2 to Xn; assuming that the possible eigenvalue after fusion is M, which consists of X1, X2 … Xn, model fusion is carried out by using a weighted average method, and the weight can be regarded as the measurement of the accuracy of different eigenvectors.
In the accuracy of the prediction model established in the present embodiment, the prediction root mean square error rmsep=0.122.
h. And correcting the feature fusion prediction model by using the correction sample to obtain a correction model.
i. And (c) acquiring image spectrum information of the micro plastic to be detected by using a Fourier transform infrared instrument, and inputting the fusion matrix obtained in the step (f) into the fusion prediction model obtained in the step (g) to obtain a corresponding aging analysis result.
The present embodiment is only for explanation of the present invention and is not to be construed as limiting the present invention, and modifications to the present embodiment, which may not creatively contribute to the present invention as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present invention.
Claims (1)
1. A spectrogram fusion-based coast environment microplastic aging behavior analysis method is characterized by comprising the following steps of:
a. collecting micro plastic samples with different ageing degrees and particle sizes of more than 1mm in a coastal environment, and separating and preparing the micro plastic samples;
b. collecting micro-region image information and infrared spectrum information of the micro-plastic sample by using a Fourier transform infrared instrument, wherein the micro-plastic sample is randomly divided into a correction sample and a prediction sample;
c. the method comprises the steps of extracting the obtained infrared spectrum information, correcting by adopting a standard variable method, extracting the optimal characteristic wavelength related to sample aging behaviors, establishing a characteristic spectrum data matrix K, and obtaining carbonyl ratio through the characteristic spectrum data matrix K;
d. extracting texture features from the obtained micro-region image information by using a gray level co-occurrence matrix describing a common method of textures, respectively calculating energy C1 and contrast C2 of the gray level space co-occurrence matrix as texture quantization indexes representing the texture features,
wherein i and j are pixel gray coordinates, d is a pixel separation distance, θ is an error, and P (i, j, d, θ) is probability;
e. obtaining hue, brightness and saturation parameters of the obtained micro-region image information by utilizing an RGB conversion HSB system, and extracting a yellowing chromaticity characteristic matrix P, wherein in the HSB system, H represents hue, S represents saturation, and B represents brightness;
f. c, d and e are utilized to respectively obtain a characteristic spectrum data matrix K, a texture characteristic matrix C and a yellowing chromaticity characteristic matrix P, and the three matrices are subjected to characteristic layer fusion to obtain a fusion matrix: m= [ kcp ];
g. by introducing carbonyl ratio, texture features and yellowing color features, a multisource feature fusion prediction model based on surface morphology-molecular spectrum is established:
the spectral image aging characteristic value for a certain sample is denoted as X 1 ,X 2 To Xn; assuming that the possible feature matrix after fusion is M, and the X is used for 1 ,X 2 … Xn, model fusion is carried out by using a weighted average method, and the weight is beta i A measure of the contribution rate for the different feature vectors;
h. correcting the prediction model obtained in the step g by using a correction sample to obtain a correction model;
i. and (c) acquiring image spectrum information of the micro plastic to be detected by using a Fourier transform infrared instrument, and inputting the fusion matrix obtained in the step (f) into the fusion prediction model obtained in the step (g) to obtain a corresponding aging analysis result.
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| CN111563622B (en) * | 2020-04-30 | 2022-04-22 | 西安交通大学 | Method for predicting insulation aging degree of stator bar |
| CN112577885B (en) * | 2020-11-27 | 2021-12-17 | 南京大学 | Humidity control in-situ microscopic infrared characterization method for micro plastic |
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