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Darius Goergen
Last updated: 2019-08-22
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Knit directory: polymeRID/
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| Rmd | 6c813f4 | goergen95 | 2019-07-29 | implemented workflowr |
| Rmd | d525cc2 | goergen95 | 2019-07-29 | Start workflowr project. |
Introduction
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
Microplastic particles polluting the environment has 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 microplastic are the effects on biological lifeforms (Zhang et al. 2019), their movement through and distribution in marine environments (Auta, Emenike, and Fauziah 2017) as well as in freshwater ecosystems (Li, Liu, and Paul Chen 2018).
Different research questions demand different methodologies for sampling, sample handling and laboratory analysis. However, the link between different research domains is that any analysis of microplastic in the environment needs a robust identification method to enable scientists to bring forward meaningful recommendations to the public and decision makers.
A broad spectrum of different polymer identification strategies evidently exists (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 towards the extent of automatization in the identification process. Recently, different approaches to automate the task of polymer classification, either by individual particles or for a whole collection of samples simultaneously have been 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 classification. Other studies have reported substantial accuracies by applying different kinds 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 the spectra of microplastic and other materials found in environmental samples.
This project was grouped into different working steps, which were designed to allow to reproduce the workflows to the greatest extent possible as well as to allow alterations of the code and extensions to the database. 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. We used an OpenSource database published by Primpke et al. (2018). For potential future extensions, we created a workflow of spectral resampling and baseline correction for reference polymers and other particles to ensure the consistency of the database.
Exploration: Different types of pre-processing techniques were assessed by a cross-validation approach in which different representations of data were presented to a selection of machine-learning models. Their capability to correctly classify the database was captured. Additionally, increasing levels of noise were added to the data so that the models and pre-processing techniques which most robustly classify polymer spectra could be identified.
Calibration: After the exploration stage, the best performing models were chosen to create a decision fusion model. A workflow was created to calibrate these models to a potentially changing database when needed. This step is crucial so that the work presented here can be used in the future, i.e. in the case of a extension of the reference database or a change in the spectral resolution.
Classification: At the final stage of the project, a workflow was created to classify real environmental samples in a user-friendly way to ease the classification process. Here, some accuracy values of the classification are extracted and comprehensively handed to the user, as well as some plots for a visual confirmation of the classification results. This way, it is ensured that the results are easily accessible and the possibility for a human agent to assess the quality of the classification is implemented.
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.
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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] reticulate_1.13 tidyselect_0.2.5 xfun_0.8
[4] purrr_0.3.2 lattice_0.20-38 colorspace_1.4-1
[7] generics_0.0.2 viridisLite_0.3.0 htmltools_0.3.6
[10] yaml_2.2.0 base64enc_0.1-3 rlang_0.4.0
[13] pillar_1.4.2 glue_1.3.1 withr_2.1.2
[16] foreach_1.4.7 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.3.0 zip_2.0.3 htmlwidgets_1.3
[22] codetools_0.2-16 evaluate_0.14 knitr_1.24
[25] SparseM_1.77 tfruns_1.4 class_7.3-15
[28] Rcpp_1.0.2 scales_1.0.0 backports_1.1.4
[31] jsonlite_1.6 fs_1.3.1 digest_0.6.20
[34] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2
[37] tools_3.6.1 lazyeval_0.2.2 tibble_2.1.3
[40] tidyr_0.8.3 crayon_1.3.4 whisker_0.3-2
[43] pkgconfig_2.0.2 zeallot_0.1.0 Matrix_1.2-17
[46] data.table_1.12.2 httr_1.4.1 assertthat_0.2.1
[49] rmarkdown_1.14 iterators_1.0.12 R6_2.4.0
[52] git2r_0.26.1 compiler_3.6.1