CN109766909A - The micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion - Google Patents
The micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion Download PDFInfo
- Publication number
- CN109766909A CN109766909A CN201811440905.5A CN201811440905A CN109766909A CN 109766909 A CN109766909 A CN 109766909A CN 201811440905 A CN201811440905 A CN 201811440905A CN 109766909 A CN109766909 A CN 109766909A
- Authority
- CN
- China
- Prior art keywords
- sample
- micro
- fusion
- matrix
- plastics
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention provides a kind of micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion, comprising the following steps: 1. coastal environments collect micro- plastic sample and separate preparation.2. obtaining the microcell morphological image and infrared spectroscopy information of micro- plastic sample using infrared spectrometer.3. pair infrared spectroscopy information extraction correlated characteristic spectrum data matrix.4. pair microcell image information benefit texture feature extraction.5. pair microcell image information obtains surface yellowing chromaticity.6. establishing based on sample surface form-molecular spectrum Fusion Features prediction model.7. a pair prediction model is corrected, calibration model is obtained.8. extracting the Fusion Features matrix of sample to be tested, corresponding aging parsing result will be obtained in the Fusion Features Input matrix calibration model of sample to be tested.This programme realizes the micro- ageing of plastics degree fast resolving of complex environment by introducing spectrum carbonyl ratio, the parameters such as texture form yellowing coloration.
Description
Technical field
The present invention relates to the micro- plastic pollution analytic method of complicated coastal environment, especially a kind of seashore ring based on spectrogram fusion
The micro- ageing of plastics behavior analytic method in border.
Background technique
Micro- plastics have become a kind of emerging pollutant being concerned in global ocean and Coastal Zone Environment in recent years.Micro- modeling
Material aging rice seed is the Source Tracing in micro- plastic pollution source and the important parameter of micro- plastics dynamic growth and decline model in the environment.Micro- modeling
Expect that phenomena such as apparent coloration yellowing, face crack dusting occurs in sample surface in ageing process, and related to aging rice seed
The absorption intensity of functional group can aggravate that certain coupled relation is presented with degree of aging.But it is isolated in environmental sample
Micro- plastics surface irregularity or are attached to a large amount of ambient impurities due to by slacking, and will lead to can not in measurement process
Obtain high s/n ratio infrared spectroscopy.Concentration of the sample surface color also with sample interior Residual antioxidants in ageing process simultaneously
Related, face crack form is related with the degree of volatility of sample plasticizer.
Since the spectrum and configuration of surface of aging sample are by sample surface roughness and sample interior residual additives
It influences, if being difficult to meet micro- modeling under complex environment background using single infrared spectroscopy feature or the modeling of single morphological feature
Expect the accurate parsing of sample aging behavior.
Summary of the invention
In order to overcome the disadvantages mentioned above of the prior art and insufficient, micro- plastic sample aging row under a kind of coastal environment is provided
For accurate analytic method, use based on it is visible-infrared spectrum fusion method realize to environmental samples in typical seashore ring
The lossless parsing of aging rice seed under border, method is convenient and testing result high reliablity.
It is a kind of based on spectrogram fusion the micro- ageing of plastics behavior analytic method of coastal environment the following steps are included:
A. coastal environment is collected with different degree of agings and micro- plastic sample of partial size 1mm or more and carries out separation system
It is standby.
B. the microcell image information and infrared spectroscopy information of micro- plastic sample are acquired using Fourier transform infrared instrument,
Micro- plastic sample is randomly divided into calibration samples and forecast sample.
C. the infrared spectroscopy information is corrected using standard normal variable, then extracts optimal characteristics relevant to aging rice seed
Wavelength simultaneously establishes characteristic spectrum data matrix K.
D. utilize the common method of description texture to the microcell image information of acquisition: it is special that gray level co-occurrence matrixes extract texture
Sign calculates separately the texture of the energy C1, contrast C 2 of the spatial correlation characteristic co-occurrence matrix of gray scale as characterization textural characteristics
Quantizating index,
Wherein i, j are respectively pixel grey scale coordinate, and d is pixel standoff distance, and θ is error, and P (i, j, d, θ) is probability.
E. HSB system is utilized to the microcell image information of acquisition, i.e. color mode obtains form and aspect, brightness and saturation parameters
Characterize yellowing chromaticity.In HSB mode, H (hues) indicates form and aspect, and S (saturation) indicates saturation degree, B
(brightness) brightness is indicated.
F. characteristic spectrum data matrix K, textural characteristics C, yellowing chromaticity matrix P are acquired respectively using step c, d and e
It is warm to carry out characteristic layer, obtains fusion matrix M, M=[K C P].
G. by introducing carbonyl ratio, the parameters such as texture form and yellowing coloration, which are established, is based on configuration of surface-molecular spectrum
Fusion Features prediction model:
The spectrum picture der alterungs-kennwert of some sample is expressed as being shown as X1,X2To Xn；Assuming that possible spy after fusion
Sign matrix is M, by X1,X2... Xn composition carries out Model Fusion using weighted mean method, and weight can regard different characteristic vector tribute as
Offer the measurement of rate.
H. prediction model described in step f is corrected using calibration samples, obtains calibration model.
I. the image spectrum information to micrometer plastics is acquired using Fourier transform infrared instrument, is melted using what step f was obtained
It closes in the fusion forecasting model that Input matrix is obtained to step g, obtains corresponding aging parsing result.
To improve above scheme, the present invention is further arranged to: 1/3 micro- plastic sample is randomly selected in step b as pre-
Test sample sheet, 2/3 micro- plastic sample is as calibration samples.
To improve above scheme, the present invention is further arranged to: to the microcell aspect graph of micro- plastic sample in step c
The analysis method of picture and infrared spectroscopy information are as follows: handled using Wavelet Denoising Method, polynary scatter correction, standard normal variable correction side
Method pre-processes original spectral data, to obtain high-precision initial data.
To improve above scheme, the present invention is further arranged to: transparent polyethylene screening sample is directed in step c
1450cm-1And 1750cm-1The aging rice seed characteristic wavelength of neighbouring wave number.
To improve above scheme, the present invention is further arranged to: the calibration model precision root-mean-square error RMSE in step g
P < 0.15 corrects coefficient of determination R2>0.92。
The present invention is based on configuration of surface-Spectral Properties by introducing carbonyl ratio, the building of the parameters such as texture form yellowing coloration
The Fusion Features model of sign realizes the micro- ageing of plastics degree fast resolving of complex environment.It solves to be difficult to using single aging character
The problem of parsing sample aging behavior under actual complex system.The spectrum of used infrared microscopy skill non-destructive testing trace sample
Image information ensures the interim lossless repetition detection demand of sample aging, improves the utilization rate of sample information.This patent method
The lossless fast accurate parsing that complicated coastal environment acquires micro- plastic sample may be implemented, be the micro- plastic pollution of China's coastal environment
Supervision provide science support, have great importance to the improvement of the micro- plastic pollution in ocean.
The present invention is further described in detail below in conjunction with attached drawing.
Detailed description of the invention
Fig. 1 is detection method flow diagram；
Fig. 2 is the infrared spectroscopy carbonyl index figure of the aging sample of the embodiment of the present invention；
Fig. 3 is the chromaticity figure on the microcell surface of the aging sample of the embodiment of the present invention.
Specific embodiment
In the following, being specifically described by illustrative embodiment to the present invention.It should be appreciated, however, that not chatting further
In the case where stating, the feature in an embodiment can also be advantageously incorporated into other embodiments.
A kind of micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion, it is widest to be distributed in environment
Transparent polyethylene is that representative sample studies micro- plastic ageing behavior under complicated coastal environment.The following steps are included:
A. the micro- plastic sample of Zhejiang Province Long Wan tidal flat surface environment is acquired, along the deposition of newest high-water mark acquisition about 5cm thickness
Object is taken by steel sieve sieve and collects representative and sufficient amount of micro- plastic sample, transports laboratory back after being packed into hermetic bag.Benefit
Micro- plastic sample is rinsed with deionized water.After glass microfibre filter paper filtering screening, metal tray is put into 60
Degree Celsius oven drying, is finally packed into hermetic bag and is placed in cleaning and be protected from light place, obtains transparent polyethylene particle.
B. transparent polyethylene sample is divided into two classes.1/3 sample is randomly selected as forecast sample, 2/3 sample in total sample
This is as calibration samples.Specifically: 30 samples are picked out at random, totally 10 are used as forecast sample, remaining 20 as correction
Sample.
Using micro ft-ir spectroscopy instrument in experiment be HYPERION Fourier transform infrared instrument, equipped with infrared detector with
And infrared band 20X camera lens carries out information collection to different micro- plastic samples.Scanning times are 20, the wave-number range of record
4000cm-1–600cm-1, spectral resolution 4cm-1, the infrared spectroscopy and microcell image information of 30 samples is obtained.
C. calibration samples spectroscopic data is pre-processed using standard normal variable correction, obtains high-precision spectrum number
According to.
To the polyethylene sample infrared spectroscopy of acquisition, extracts optimal characteristics wavelength relevant to aging rice seed and establish spectrum
Data matrix.Method particularly includes: it is directed to all band spectral information, screens 1450cm-1、1750cm-1The aging row of neighbouring wave number
It is characterized wavelength.Linear coupling relationship is presented in the absorption intensity and degree of aging of characteristic carbonyl functional group near above-mentioned.
D. to the microcell image information of acquisition, texture morphological feature is extracted using gray level co-occurrence matrixes, calculates separately correspondence
The energy C1 of co-occurrence matrix, contrast C 2, the degree of correlation characterize textural characteristics as texture quantizating index, wherein wherein i,
J is respectively pixel grey scale coordinate, and d is pixel standoff distance, and θ is error, and P (i, j, d, θ) is probability:
E. the microcell image information obtained obtains H (hues) form and aspect, S (saturation) saturation degree, B using HSB system
(brightness) color characteristics such as brightness characterize yellowing colorimetric properties
V=max
Since saturation degree and the correlation of brightness are significant, form and aspect H and brightness B two are only considered when extracting color characteristics
A characteristic parameter.Data show that the form and aspect of yellow sample surface are 55-75, and brightness is greater than 40.Isabelline sample form and aspect and yellow
It is close, but brightness only has 10-20.Black sample surface extraction form and aspect highest, but brightness value is minimum.
F. characteristic spectrum data matrix K, textural characteristics C, yellowing chromaticity matrix P are acquired respectively using step c, d and e
Progress characteristic layer is warm to obtain fusion matrix M, M=[K, C, P].
G. by introducing carbonyl ratio, the parameters such as texture form and yellowing coloration, which are established, is based on configuration of surface-molecular spectrum
Fusion Features prediction model:
X1, X2 to Xn are expressed as the spectrum picture der alterungs-kennwert of some sample；Assuming that possible feature after fusion
Value is M, is made of X1, X2 ... Xn, carries out Model Fusion using weighted mean method, weight can regard different characteristic vector accuracy as
Measurement.
In the precision of forecasting model established in the present embodiment, predicted root mean square error RMSEP=0.122.
H. Fusion Features prediction model is corrected using calibration samples, obtains calibration model.
H. the image spectrum information to micrometer plastics is acquired using Fourier transform infrared instrument, is melted using what step f was obtained
It closes in the fusion forecasting model that Input matrix is obtained to step g, obtains corresponding aging parsing result.This specific embodiment is only pair
Explanation of the invention, is not limitation of the present invention, and those skilled in the art can basis after reading this specification
Need to make the present embodiment the modification of not creative contribution, but as long as all by special in scope of the presently claimed invention
The protection of sharp method.
Claims (1)
1. a kind of micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion, which is characterized in that including following step
It is rapid:
A. coastal environment is collected with different degree of agings and micro- plastic sample of partial size 1mm or more and carries out separation preparation；
B. the microcell image information and infrared spectroscopy information of micro- plastic sample are acquired using Fourier transform infrared instrument, it is described
Micro- plastic sample is randomly divided into calibration samples and forecast sample；
C. the infrared spectroscopy information extraction of acquisition is corrected using canonical variable method, then extracted and sample aging behavior phase
The optimal characteristics wavelength of pass simultaneously establishes characteristic spectrum data matrix K；
D. the method for description texture: gray level co-occurrence matrixes texture feature extraction is utilized to the microcell image information of acquisition, point
Not Ji Suan gray scale space co-occurrence matrix energy C1, contrast C2As characterization textural characteristics texture quantizating index,
Wherein i, j are respectively pixel grey scale coordinate, and d is pixel standoff distance, and θ is error, and P (i, j, d, θ) is probability；
E. form and aspect, brightness and saturation parameters are obtained using RGB conversion HSB system to the microcell image information of acquisition and mentioned
Chromaticity matrix P is taken, in HSB system, H indicates form and aspect, and S indicates saturation degree, and B indicates brightness；
F. characteristic spectrum data matrix K, textural characteristics Matrix C are acquired respectively using step c, d and e, chromaticity matrix P will
Three matrixes progress characteristic layers are warm to obtain fusion matrix: M=[K C P]；
G. by introducing textural characteristics, the parameters such as yellowing coloration and characteristic spectrum are established more based on configuration of surface-molecular spectrum
Source Fusion Features prediction model:
X is expressed as the spectrum picture der alterungs-kennwert of some sample1,X2To Xn；Assuming that possible eigenmatrix is after fusion
M, by X1,X2... Xn composition carries out Model Fusion, weight β using weighted mean methodiFor the degree of different characteristic vector contribution rate
Amount；
H. prediction model obtained in step g is corrected using calibration samples, obtains calibration model；
I. the image spectrum information to micrometer plastics, the fusion matrix that step f is obtained are acquired using Fourier transform infrared instrument
It is input in the evaluation prediction model that step g is obtained, obtains corresponding aging parsing result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811440905.5A CN109766909B (en) | 2018-11-29 | 2018-11-29 | Analysis method for aging behavior of shore environment microplastic based on spectrogram fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811440905.5A CN109766909B (en) | 2018-11-29 | 2018-11-29 | Analysis method for aging behavior of shore environment microplastic based on spectrogram fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109766909A true CN109766909A (en) | 2019-05-17 |
| CN109766909B CN109766909B (en) | 2023-10-17 |
Family
ID=66450186
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811440905.5A Active CN109766909B (en) | 2018-11-29 | 2018-11-29 | Analysis method for aging behavior of shore environment microplastic based on spectrogram fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109766909B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111007033A (en) * | 2019-12-09 | 2020-04-14 | 温州大学 | Trace acetylene gas concentration detection method based on spectrum and power spectrum feature fusion |
| CN111563622A (en) * | 2020-04-30 | 2020-08-21 | 西安交通大学 | Stator bar insulation aging degree prediction method based on gray level co-occurrence matrix and deep learning |
| CN112577885A (en) * | 2020-11-27 | 2021-03-30 | 南京大学 | Humidity control in-situ microscopic infrared characterization method for micro plastic |
| WO2021205545A1 (en) * | 2020-04-07 | 2021-10-14 | 株式会社ピリカ | Estimation device, estimation method, and estimation program |
| CN114563370A (en) * | 2022-03-11 | 2022-05-31 | 昆明理工大学 | Method for evaluating aging behavior of micro-plastic |
| CN117554319A (en) * | 2023-10-20 | 2024-02-13 | 广东省水利水电科学研究院 | Method, system, device and storage medium for detecting abundance of microplastic |
| CN118392815A (en) * | 2024-06-26 | 2024-07-26 | 四川新康意众申新材料有限公司 | Microplastic identification method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6438261B1 (en) * | 1998-09-03 | 2002-08-20 | Green Vision Systems Ltd. | Method of in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples |
| CN105486655A (en) * | 2015-11-23 | 2016-04-13 | 中国科学院南京土壤研究所 | Rapid detection method for organic matters in soil based on infrared spectroscopic intelligent identification model |
| CN105699239A (en) * | 2016-02-23 | 2016-06-22 | 江苏中烟工业有限责任公司 | Method for analyzing moisture retention ability of tobaccos and tobacco products by aid of near-infrared spectral models |
| CN106203510A (en) * | 2016-07-11 | 2016-12-07 | 南京大学 | A kind of based on morphological feature with the hyperspectral image classification method of dictionary learning |
| CN106645049A (en) * | 2016-09-30 | 2017-05-10 | 大连海洋大学 | Method for detecting plastic content of marine organism |
| CN108663339A (en) * | 2018-05-15 | 2018-10-16 | 南京财经大学 | Corn online test method of going mouldy based on spectrum and image information fusion |
-
2018
- 2018-11-29 CN CN201811440905.5A patent/CN109766909B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6438261B1 (en) * | 1998-09-03 | 2002-08-20 | Green Vision Systems Ltd. | Method of in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples |
| CN105486655A (en) * | 2015-11-23 | 2016-04-13 | 中国科学院南京土壤研究所 | Rapid detection method for organic matters in soil based on infrared spectroscopic intelligent identification model |
| CN105699239A (en) * | 2016-02-23 | 2016-06-22 | 江苏中烟工业有限责任公司 | Method for analyzing moisture retention ability of tobaccos and tobacco products by aid of near-infrared spectral models |
| CN106203510A (en) * | 2016-07-11 | 2016-12-07 | 南京大学 | A kind of based on morphological feature with the hyperspectral image classification method of dictionary learning |
| CN106645049A (en) * | 2016-09-30 | 2017-05-10 | 大连海洋大学 | Method for detecting plastic content of marine organism |
| CN108663339A (en) * | 2018-05-15 | 2018-10-16 | 南京财经大学 | Corn online test method of going mouldy based on spectrum and image information fusion |
Non-Patent Citations (2)
| Title |
|---|
| ZHOU LONGYIN等: "Distribution of microplastics and its source in the sediments of the Le’an River in Poyang Lake" * |
| 周倩;章海波;李远;骆永明;: "海岸环境中微塑料污染及其生态效应研究进展" * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111007033A (en) * | 2019-12-09 | 2020-04-14 | 温州大学 | Trace acetylene gas concentration detection method based on spectrum and power spectrum feature fusion |
| WO2021205545A1 (en) * | 2020-04-07 | 2021-10-14 | 株式会社ピリカ | Estimation device, estimation method, and estimation program |
| CN111563622A (en) * | 2020-04-30 | 2020-08-21 | 西安交通大学 | Stator bar insulation aging degree prediction method based on gray level co-occurrence matrix and deep learning |
| CN111563622B (en) * | 2020-04-30 | 2022-04-22 | 西安交通大学 | Method for predicting insulation aging degree of stator bar |
| CN112577885A (en) * | 2020-11-27 | 2021-03-30 | 南京大学 | Humidity control in-situ microscopic infrared characterization method for micro plastic |
| CN114563370A (en) * | 2022-03-11 | 2022-05-31 | 昆明理工大学 | Method for evaluating aging behavior of micro-plastic |
| CN117554319A (en) * | 2023-10-20 | 2024-02-13 | 广东省水利水电科学研究院 | Method, system, device and storage medium for detecting abundance of microplastic |
| CN118392815A (en) * | 2024-06-26 | 2024-07-26 | 四川新康意众申新材料有限公司 | Microplastic identification method |
| CN118392815B (en) * | 2024-06-26 | 2024-09-03 | 四川新康意众申新材料有限公司 | Microplastic identification method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109766909B (en) | 2023-10-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109766909A (en) | The micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion | |
| Wang et al. | A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery | |
| Pitarch et al. | Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations | |
| Dierssen et al. | Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high‐resolution airborne imagery | |
| CN107014763B (en) | Chlorophyll remote-sensing inversion device and method | |
| CN105631904B (en) | A kind of eutrophic lake algae total inventory remote sensing estimation method | |
| CN102997856B (en) | A kind of marine oil spill oil film thickness high-spectrum remote-sensing evaluation method based on Parameter lookup step | |
| CN107845090B (en) | Silicon wafer detection method and silicon wafer detection device | |
| Garcia et al. | Quantification of floating macroalgae blooms using the scaled algae index | |
| CN110865040A (en) | Sky-ground integrated hyperspectral water quality monitoring and analyzing method | |
| CN105203466B (en) | Algae total inventory remote sensing estimation method under the conditions of a kind of non-algal tufa of eutrophic lake | |
| CN110261405B (en) | Insulator pollution component identification method based on microscopic hyperspectral technology | |
| Li et al. | Estimation of chlorophyll-a concentrations in a highly turbid eutrophic lake using a classification-based MODIS land-band algorithm | |
| CN106315856B (en) | The MODIS satellite synchronization monitoring method of eutrophic lake cyanobacterial bloom and aquatic vegetation | |
| Shanmugam | CAAS: An atmospheric correction algorithm for the remote sensing of complex waters | |
| CN105303030B (en) | A kind of MODIS remote sensing estimation method of eutrophic lake algae Gauss vertical characteristics structural parameters | |
| CN109001218A (en) | Apple surface defect quick nondestructive recognition methods based on high light spectrum image-forming technology | |
| CN103389255A (en) | Method for predicting water content of tea on basis of near-infrared hyperspectral textural feature modeling | |
| CN109300133B (en) | Urban river network area water body extraction method | |
| CN106290389B (en) | The algal tufa and non-algal tufa condition classification method of a kind of eutrophic lake MODIS images | |
| KR20100011549A (en) | Atmospheric environment monitoring system and analysis method of duststorm and associated dustfall and anthropogenic air pollution using the same | |
| CN111750994B (en) | Spectral measurement method based on digital camera imaging model | |
| Dierssen | Overview of hyperspectral remote sensing for mapping marine benthic habitats from airborne and underwater sensors | |
| CN115908864A (en) | MODIS image-based seawater color angle extraction method and water color condition remote sensing classification method | |
| Finn et al. | Hα-derived Star Formation Rates for the z= 0.84 Galaxy Cluster Cl J0023+ 0423B |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |