Last updated: 2019-08-13

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Knit directory: polymeRID/

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Rmd d525cc2 goergen95 2019-07-29 Start workflowr project.

Welcome to my website on polymeRID!

Here I present the results of my work for a master’s seminar at the University of Marburg concerned with microplastic in the environment.

Probe Seperators
Photo of two sediment separators taken by Sarah Brüning

Microplastic particles polluting the environment have been in the public focuse for some time now. The scientific efforts of analysing the occurences of particles in the envrionment and their effects on ecosystems and human health is manifold, yet there is a lack of consensus on methods for sampling, sample handling, analysis and identification, especially for samples from aquatic ecosystems. Some of the most urgent research questions concerned with microplastic in the envrionment are the analysis of effects on biological lifeforms (Zhang et al. 2019), their movement and distribution in the marine environment (Auta, Emenike, and Fauziah 2017) as well as in freshwater systems (Li, Liu, and Paul Chen 2018).

Different research questions demand for different methodologies for sampling, sample handling and labroratory analysis. However, the link between these knowledge gaps is that any analysis of microplastics in the environment needs a robust identification method to enable scientist to draw the right conclusions and to bring forward recommendations to the public and decision makers to act upon their research findings.

Evidently, there also exist a broad spectrum of different polymer identification strategies (Löder and Gerdts 2015; Rocha-Santos and Duarte 2015; Shim, Hong, and Eo 2017), ranging from traditional microscopy to spectroscopy as well as destructive methods of thermal analysis. A distinction has to be made concerning towards the extent of automation in identification processes. Lately, different approaches to automate in the identification process, either by individual elements or for whole samples on a focal plane, have reported to the scientific community (Masoumi, Safavi, and Khani 2012; Primpke et al. 2017; Lorenzo-Navarro et al. 2018; Zhang et al. 2018; Primpke, Dias, and Gerdts 2019)

This project sets out to contribute to the ease of the cumbersome process of classifying individual particles based on their spectral reflectance by hand. The idea is that up-to-date machine learning models applied to the high-dimensional spectral data of particles found in environmental samples can minimize the need for human intervention in the classification process and thus significantly speed up the process of categorizing found particles. Other studies have reported substantial accuracies by applying different sorts of machine learning algorithms such as hierarchical clustering (Primpke et al. 2017), support-vector-machines (V. Bianco P. Memmolo 2019), random forest (Hufnagl et al. 2019), as well as convolutional neural networks (Liu et al. 2017) to classify micro plastics and other materials spectra.

This project was grouped into different working steps, which also were designed to allow reproducibility as well individual alteration of the code and data base. These working steps are:

Averaged spectrum of all PE spectra within the data base.

Literature used on this page

Auta, H. S., C. U. Emenike, and S. H. Fauziah. 2017. “Distribution and importance of microplastics in the marine environment. A review of the sources, fate, effects, and potential solutions.” Pergamon. https://doi.org/10.1016/j.envint.2017.02.013.

Hufnagl, Benedikt, Dieter Steiner, Elisabeth Renner, Martin G.J. Löder, Christian Laforsch, and Hans Lohninger. 2019. “A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers.” Analytical Methods 11 (17): 2277–85. https://doi.org/10.1039/c9ay00252a.

Li, Jingyi, Huihui Liu, and J. Paul Chen. 2018. “Microplastics in freshwater systems: A review on occurrence, environmental effects, and methods for microplastics detection.” Water Research 137 (December 2017). Elsevier Ltd: 362–74. https://doi.org/10.1016/j.watres.2017.12.056.

Liu, Jinchao, Margarita Osadchy, Lorna Ashton, Michael Foster, Christopher J. Solomon, and Stuart J. Gibson. 2017. “Deep convolutional neural networks for Raman spectrum recognition: A unified solution.” Analyst 142 (21): 4067–74. https://doi.org/10.1039/c7an01371j.

Lorenzo-Navarro, Javier, Modesto Castrillón-Santana, May Gómez, Alicia Herrera, and Pedro A Marín-Reyes. 2018. “Automatic Counting and Classification of Microplastic Particles.” https://doi.org/10.5220/0006725006460652.

Löder, Martin G.J., and Gunnar Gerdts. 2015. “Methodology used for the detection and identification of microplastics—a critical appraisal.” In Marine Anthropogenic Litter, 201–27. Springer International Publishing. https://doi.org/10.1007/978-3-319-16510-3_8.

Masoumi, Hamed, SM Safavi, and Zahra Khani. 2012. “Identification and Classification of Plastic Resins using Near Infrared Reflectance.” Waset 6 (5): 213–20. http://www.waset.ac.nz/journals/waset/v65/v65-29.pdf.

Primpke, S., P. A. Dias, and G. Gerdts. 2019. “Automated identification and quantification of microfibres and microplastics.” Analytical Methods 11 (16): 2138–47. https://doi.org/10.1039/c9ay00126c.

Primpke, S., C. Lorenz, R. Rascher-Friesenhausen, and G. Gerdts. 2017. “An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis.” Analytical Methods 9 (9). Royal Society of Chemistry: 1499–1511. https://doi.org/10.1039/c6ay02476a.

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sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 19.1

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] plotly_4.9.0              tensorflow_1.14.0        
 [3] abind_1.4-5               e1071_1.7-2              
 [5] keras_2.2.4.1             workflowr_1.4.0.9001     
 [7] baseline_1.2-1            gridExtra_2.3            
 [9] stringr_1.4.0             prospectr_0.1.3          
[11] RcppArmadillo_0.9.600.4.0 magrittr_1.5             
[13] ggplot2_3.2.0             reshape2_1.4.3           
[15] dplyr_0.8.3              

loaded via a namespace (and not attached):
 [1] httr_1.4.1         tidyr_0.8.3        jsonlite_1.6      
 [4] viridisLite_0.3.0  foreach_1.4.7      shiny_1.3.2       
 [7] assertthat_0.2.1   yaml_2.2.0         pillar_1.4.2      
[10] backports_1.1.4    lattice_0.20-38    glue_1.3.1        
[13] reticulate_1.13    digest_0.6.20      promises_1.0.1    
[16] colorspace_1.4-1   htmltools_0.3.6    httpuv_1.5.1      
[19] Matrix_1.2-17      plyr_1.8.4         pkgconfig_2.0.2   
[22] SparseM_1.77       purrr_0.3.2        xtable_1.8-4      
[25] scales_1.0.0       whisker_0.3-2      later_0.8.0       
[28] git2r_0.26.1       tibble_2.1.3       generics_0.0.2    
[31] withr_2.1.2        lazyeval_0.2.2     crayon_1.3.4      
[34] mime_0.7           evaluate_0.14      fs_1.3.1          
[37] class_7.3-15       RcppZiggurat_0.1.5 tools_3.6.1       
[40] data.table_1.12.2  munsell_0.5.0      Rfast_1.9.5       
[43] compiler_3.6.1     rlang_0.4.0        grid_3.6.1        
[46] iterators_1.0.12   Rmisc_1.5          htmlwidgets_1.3   
[49] crosstalk_1.0.0    base64enc_0.1-3    labeling_0.3      
[52] rmarkdown_1.14     gtable_0.3.0       codetools_0.2-16  
[55] R6_2.4.0           tfruns_1.4         knitr_1.24        
[58] zeallot_0.1.0      rprojroot_1.3-2    stringi_1.4.3     
[61] parallel_3.6.1     Rcpp_1.0.2         tidyselect_0.2.5  
[64] xfun_0.8