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Darius Goergen
Last updated: 2019-08-14
Checks: 6 1
Knit directory: polymeRID/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c52182b | goergen95 | 2019-08-13 | rebuid website |
| html | c52182b | goergen95 | 2019-08-13 | rebuid website |
| html | 6e92d01 | goergen95 | 2019-08-13 | Build site. |
| html | 6cfd689 | goergen95 | 2019-08-13 | Build site. |
| Rmd | 5774923 | goergen95 | 2019-08-13 | included preparation |
| html | cbbd5b4 | goergen95 | 2019-08-13 | Build site. |
| html | 471e893 | goergen95 | 2019-08-13 | Build site. |
| Rmd | b07b0a6 | goergen95 | 2019-08-13 | update of index file |
| html | e8c8be2 | goergen95 | 2019-08-13 | Build site. |
| html | 342cd44 | goergen95 | 2019-08-13 | Build site. |
| Rmd | 15bd467 | goergen95 | 2019-08-13 | update of index file |
| Rmd | 3b99a1b | goergen95 | 2019-08-08 | fixed typos in index |
| html | 32109b9 | goergen95 | 2019-08-07 | Build site. |
| Rmd | 7a32e1f | goergen95 | 2019-08-07 | included images in index |
| html | 99906c8 | goergen95 | 2019-08-07 | Build site. |
| Rmd | 7b2bbac | goergen95 | 2019-08-07 | changed theme |
| html | 5f2ca49 | goergen95 | 2019-08-07 | Build site. |
| Rmd | caf89e2 | goergen95 | 2019-08-07 | wflow_publish(c(“analysis/index.Rmd”)) |
| html | 348ad0a | goergen95 | 2019-08-05 | Build site. |
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| Rmd | 6c813f4 | goergen95 | 2019-07-29 | implemented workflowr |
| 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.
Photo of two sediment separators taken by Sarah Brüning
Micro-plastic particles polluting the environment have been in the public focus for some time now. The scientific efforts of analyzing the occurrences of particles in the environment 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 micro-plastic in the environment 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 laboratory analysis. However, the link between these knowledge gaps is that any analysis of micro-plastics 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 reproducible workflows as well individual alteration of the code and data base. These working steps are:
Preparation: At first the establishment of a comprehensive database of reference spectra was mandatory to allow the application of machine learning models. The preparation steps included spectral re-sampling and labeling of reference polymers and natural particles. Later, this database underwent some steps of baseline corrections as well as different level of pre-processing such as normalization and Savtizkiy-Golay filtering.
Exploration: At the different types of preprocessing techniques were assessed by a brute-force method in which all different levels of the data were presented to different machine learning models and their capability to correctly classify the data-set was captured. Additionally, different levels of noise was added to the data so that the models and preprocessing types which most robustly classify the spectra could be identified.
Calibration: After the exploration stage, a applicable algorithm which can be calibrated to a potentially changing database needed to be established. That was important, so that the code can be used in the future as well e.g. when the reference database should be extended or the wave-numbers of interest might change.
Classification: This last stage is the core part of the project in the sense that at this stage real environmental samples are to be classified in a user-friendly way to ease the categorization process. That means that some accuracy values of the classification need to be easily accessible as well as some possibilities for a human agent to assess the accuracy
Fig.1: Example of spectra characteristics for all PE spectra within the data base.
Citations 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.
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.
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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 openxlsx_4.1.0.1
[13] magrittr_1.5 ggplot2_3.2.0
[15] reshape2_1.4.3 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 xtable_1.8-4 purrr_0.3.2
[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 mime_0.7
[34] crayon_1.3.4 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 zip_2.0.3
[43] Rfast_1.9.5 compiler_3.6.1 rlang_0.4.0
[46] grid_3.6.1 iterators_1.0.12 Rmisc_1.5
[49] htmlwidgets_1.3 crosstalk_1.0.0 base64enc_0.1-3
[52] labeling_0.3 rmarkdown_1.14 gtable_0.3.0
[55] codetools_0.2-16 R6_2.4.0 tfruns_1.4
[58] knitr_1.24 zeallot_0.1.0 rprojroot_1.3-2
[61] stringi_1.4.3 parallel_3.6.1 Rcpp_1.0.2
[64] tidyselect_0.2.5 xfun_0.8