CN109766909A - Analysis method of microplastic aging behavior in coastal environment based on spectral fusion - Google Patents

Analysis method of microplastic aging behavior in coastal environment based on spectral fusion Download PDF

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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
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micro
fusion
matrix
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CN109766909B (en
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陈熙
陈孝敬
袁雷鸣
施一剑
朱德华
户新宇
杨硕
李理敏
黄建林
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Wenzhou University
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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

The micro- ageing of plastics behavior analytic method of coastal environment based on spectrogram fusion
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)

1.一种基于谱图融合的海岸环境微塑料老化行为解析方法,其特征在于,包括以下步骤:1. a coastal environment microplastic aging behavior analysis method based on spectrum fusion, is characterized in that, comprises the following steps: a.海岸环境收集具有不同老化程度且粒径1mm以上的微塑料样本并进行分离制备;a. The coastal environment collects microplastic samples with different aging degrees and a particle size of more than 1 mm and separates them for preparation; b.利用傅里叶变换红外仪采集所述微塑料样本的微区图像信息和红外光谱信息,所述微塑料样本随机分为校正样本和预测样本;b. Using a Fourier transform infrared instrument to collect the micro-region image information and infrared spectrum information of the micro-plastic sample, and the micro-plastic sample is randomly divided into a correction sample and a prediction sample; c.对获取的红外光谱信息提取采用标准变量方法进行校正,再提取与样本老化行为相关的最优特征波长并建立特征光谱数据矩阵K;c. Use the standard variable method to correct the obtained infrared spectral information extraction, and then extract the optimal characteristic wavelength related to the aging behavior of the sample and establish the characteristic spectral data matrix K; d.对获取的所述微区图像信息利用描述纹理的方法:灰度共生矩阵提取纹理特征,分别计算灰度的空间共生矩阵的能量C1、对比度C2作为表征纹理特征的纹理量化指标, d. Using the method of describing texture for the acquired micro-area image information: extracting texture features by grayscale co-occurrence matrix, respectively calculating the energy C 1 and contrast C 2 of the gray-scale spatial co-occurrence matrix as texture quantification indicators characterizing texture features, 其中i、j分别为像素灰度坐标,d为像素相隔距离,θ为误差,P(i,j,d,θ)为概率;where i and j are the pixel grayscale coordinates, d is the distance between pixels, θ is the error, and P(i, j, d, θ) is the probability; e.对获取的所述微区图像信息利用RGB转化HSB体系获得色相、亮度和饱和度参数并提取色度特征矩阵P,在HSB体系中,H表示色相,S表示饱和度,B表示亮度;e. Utilize the RGB conversion HSB system to obtain the hue, brightness and saturation parameters for the acquired micro-region image information and extract the chromaticity feature matrix P, in the HSB system, H represents the hue, S represents the saturation, and B represents the brightness; f.利用步骤c、d和e分别求得特征光谱数据矩阵K、纹理特征矩阵C,色度特征矩阵P,将三个矩阵进行特征层融和得到融合矩阵:M=[K C P];f. Use steps c, d and e to obtain the characteristic spectral data matrix K, the texture characteristic matrix C, and the chromaticity characteristic matrix P, respectively, and perform the characteristic layer fusion of the three matrices to obtain a fusion matrix: M=[K C P]; g.通过引入纹理特征,泛黄色度和特征光谱等参数建立基于表面形态-分子光谱的多源特征融合预测模型:g. Establish a multi-source feature fusion prediction model based on surface morphology-molecular spectrum by introducing parameters such as texture features, yellowing degree and characteristic spectrum: 对于某个样本的光谱图像老化特征值表示为X1,X2至Xn;假设融合后可能的特征矩阵为M,由X1,X2…Xn组成,利用加权平均法进行模型融合,权重为βi为不同特征向量贡献率的度量;The aging characteristic values of the spectral image of a certain sample are expressed as X 1 , X 2 to Xn; assuming that the possible characteristic matrix after fusion is M, which is composed of X 1 , X 2 ... Xn, the weighted average method is used for model fusion, and the weight is β i is a measure of the contribution rate of different eigenvectors; h.利用校正样本对步骤g中得到的预测模型进行校正,得到校正模型;h. Correct the prediction model obtained in step g by using the correction sample to obtain a correction model; i.利用傅里叶变换红外仪采集待测微塑料的图像光谱信息,将步骤f得到的融合矩阵输入到步骤g得到的评价预估模型中,获得相应老化解析结果。i. Use the Fourier transform infrared instrument to collect the image spectral information of the microplastics to be tested, input the fusion matrix obtained in step f into the evaluation prediction model obtained in step g, and obtain the corresponding aging analysis results.
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