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Darius Goergen
Last updated: 2019-08-19
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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
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 research fields 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 towards the extent of automatisation in the identification process. Recently, different approaches to automate the task of polymer classification, either by for individual particles or for a whole collection of samples, e.g. on a focal plane, 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 of particles. 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 microplastics and other materials found in environmental samples.
This project was grouped into different working steps, which were designed to allow the reproducibility of the workflows to the greatest extent possible as well as to allow alterations of the code and extensions to the 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. We used a OpenSource data base published by Primpke et al. (2018). For potential extensions of the data base, we created a workflow of spectral resampling and baseline correction of reference polymers and other particles in accordance with the original data base.
Exploration: Different types of preprocessing techniques were assessed by a cross-validation approach in which all different levels of the data were presented to a selection of machine learning models and their capability to correctly classify the data set was captured. Additionally, different levels of noise were added to the data so that the models and preprocessing types 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 data base when needed. That step is crucial, so that the work presented here can be used in the future, e.g. when the reference data base will be extended or in the case of a change in the spectral resolution of the samples.
Classification: At this last 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.
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.
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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