Last updated: 2019-09-05
Checks: 7 0
Knit directory: polymeRID/
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Untracked files:
Untracked: analysis/elsevier-harvard.csl
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a848def | goergen95 | 2019-09-05 | changed citation style |
| html | 070e93f | goergen95 | 2019-08-22 | Build site. |
| Rmd | 1f53022 | goergen95 | 2019-08-21 | updated preparation.html |
| html | 1f53022 | goergen95 | 2019-08-21 | updated preparation.html |
| html | f2ee83c | goergen95 | 2019-08-19 | Build site. |
| html | d960dc2 | goergen95 | 2019-08-19 | included calibration |
| html | b846f0b | goergen95 | 2019-08-19 | Build site. |
| Rmd | de84a71 | goergen95 | 2019-08-19 | large update for website |
| html | de84a71 | goergen95 | 2019-08-19 | large update for website |
| Rmd | 66caad0 | goergen95 | 2019-08-19 | new shiny link |
| html | 66caad0 | goergen95 | 2019-08-19 | new shiny link |
| Rmd | f43f417 | goergen95 | 2019-08-19 | added plotly shiny app |
| html | f43f417 | goergen95 | 2019-08-19 | added plotly shiny app |
| Rmd | adc9d8e | goergen95 | 2019-08-19 | prepraratio plotly |
| html | adc9d8e | goergen95 | 2019-08-19 | prepraratio plotly |
| Rmd | 6a86688 | goergen95 | 2019-08-19 | prepraration without messages and warnings |
| html | 6a86688 | goergen95 | 2019-08-19 | prepraration without messages and warnings |
| Rmd | a90881b | goergen95 | 2019-08-19 | prepraration without shiny servers II |
| html | a90881b | goergen95 | 2019-08-19 | prepraration without shiny servers II |
| Rmd | fee623f | goergen95 | 2019-08-19 | prepraration without shiny servers |
| html | fee623f | goergen95 | 2019-08-19 | prepraration without shiny servers |
| Rmd | 807b758 | goergen95 | 2019-08-19 | test of rendering shiny app in preparation.html |
| html | 807b758 | goergen95 | 2019-08-19 | test of rendering shiny app in preparation.html |
| html | b125bc5 | goergen95 | 2019-08-16 | fixed error with pca in classification - now based of training data pca |
| html | 2385fbc | goergen95 | 2019-08-14 | republish for layout change |
| Rmd | 5d28ce0 | goergen95 | 2019-08-14 | changed citation note |
| html | 5d28ce0 | goergen95 | 2019-08-14 | changed citation note |
| Rmd | afd89c2 | goergen95 | 2019-08-14 | fixed error in preparation concering FUR class |
| html | afd89c2 | goergen95 | 2019-08-14 | fixed error in preparation concering FUR class |
| Rmd | c3f088e | goergen95 | 2019-08-13 | started exploration tab |
| html | c3f088e | goergen95 | 2019-08-13 | started exploration tab |
| html | c52182b | goergen95 | 2019-08-13 | rebuid website |
| html | 6e92d01 | goergen95 | 2019-08-13 | Build site. |
| Rmd | 9ca3d89 | goergen95 | 2019-08-13 | added website directory mirror |
| html | 9ca3d89 | goergen95 | 2019-08-13 | added website directory mirror |
| html | 6cfd689 | goergen95 | 2019-08-13 | Build site. |
| Rmd | 5774923 | goergen95 | 2019-08-13 | included preparation |
For this project we used a data base published by Primpke et al. (2018) online. The data base can be downloaded here. The authors state that the samples were collected based on the FTIR-spectrometer Bruker Tensor 27 System for the spectral range 4000 to 40 1/cm. Additionally, some data of polymer-based fibers and spectra of biological origins were received from the Bremer Faserinstitut. During pre-processing, they applied a concave rubberband correction based on ten iterations and 64 baseline points. They also excluded the C02 band between 2200 to 2420 1/cm by setting the data points to 0. This should be kept in mind, since the inclusion of additional reference samples requires the same procedure for the database to stay in a consistent state. The data shows a spectral resolution of 2.1 1/cm. Additional reference samples need to be resampled to the same spectral resolution and the same baseline correction should be applied.
To ensure consistency, the data base was read into R and the wavenumbers were saved in a separate file for the future use of resampling additional reference spectra.
library(openxlsx)
url = "https://static-content.springer.com/esm/art%3A10.1007%2Fs00216-018-1156-x/MediaObjects/216_2018_1156_MOESM2_ESM.xlsx"
data = openxlsx::read.xlsx(url)
# extract wavenumbers from first row
wavenumbers = as.numeric(names(data)[2:1864])
# saving wavenumbers to reference sample directory
saveRDS(wavenumbers, paste0(ref, "wavenumbers.rds"))An important feature of any data base is the distribution of different classes. Here, we only print the 20 most common classes, because there are a lot of reference samples only found once or twice within the database.
data$Abbreviation = as.factor(data$Abbreviation)
summary(data$Abbreviation)[1:10] PES PP LDPE HDPE PET PE Nylon PA PS PUR
15 12 11 10 9 8 7 7 7 7 We are interested in assigning the correct class to potential plastic particles. The most important classes to us found in the database are the ones of artificial polymer origin. However, sometimes particles of biological origins will also be subject to a spectral analysis, because they resemble the appearance of microplastic in environmental samples. Any machine-learning algorithm trained only with reference samples from plastics would eventually assign one of these classes to the particles of biological origins. It will only assign the class with the greatest similarity to the classes it has learned. This can lead to so-called false positive errors. To reduce the occurence of false positives we include some of the samples of biological origin as well. We summarize these samples to broader classes.
# furs and wools
indexFur = grep("fur", data$Abbreviation)
indexWool = grep("wool", data$Abbreviation)
furs = data[c(indexFur, indexWool), ]
furs = furs[ , c(2:1864)] # leave out index column
names(furs) = paste("wvn", wavenumbers, sep="")
furs$class = "FUR"
# fibres
indexFibre = grep("fibre", data$Abbreviation)
fibre = data[indexFibre, ]
fibre = fibre[ , c(2:1864)] # leave out index column
names(fibre) = paste("wvn", wavenumbers, sep="")
fibre$class = "FIBRE"
# wood
indexWood = grep("wood", data$Abbreviation)
wood = data[indexWood, ]
wood = wood[ , c(2:1864)] # leave out index colums
names(wood) = paste("wvn", wavenumbers, sep="")
wood$class = "WOOD"
# synthetic polymers
polyIndex = which(data$`Natural./Synthetic` =="synthetic polymer")
syntPolymer = data[polyIndex,]
counts = summary(syntPolymer$Abbreviation)
polyNames = names(counts)[1:10] # only major polymers
syntPolymer = syntPolymer[which(syntPolymer$Abbreviation %in% polyNames) , ]
classes = droplevels(syntPolymer$Abbreviation)
syntPolymer = syntPolymer[ , c(2:1864)] # leave out index column
names(syntPolymer) = paste("wvn",wavenumbers,sep="")
syntPolymer$class = as.character(classes)
# lets group together some synthetic polymer classes
syntPolymer$class[grep("Nylon",syntPolymer$class)] = "PA"We now bind the reference samples together and take a look at the distribution of classes in the resulting data frame, which is the concrete database used for the following calculations.
data = rbind(furs,wood,fibre,syntPolymer)
data$class = as.factor(data$class)
summary(data$class)FIBRE FUR HDPE LDPE PA PE PES PET PP PS PUR WOOD
27 23 10 11 14 8 15 9 12 7 7 4 In total, 93 (53%) reference samples of plastic polymers are present in the database and 44 (47%) of biological origin. Within the plastic samples, we found that the data is very balanced with no single class showing less than seven samples. For the samples of biological origin, however, the class FIBRE dominates the distribution. This could prove a disadvantage if a machine-learning algorithm picks up this unbalance by minimizing its error-rate simply by more frequently predicting the FIBRE class. At this point, we will leave the resulting data-base as it is and save it to disk. We save the data in individual files as well as, in a comprehensive file in .csv format. This way we ensure that later extensions to the database are more easily manageable.
write.csv(data, file = paste0(ref, "reference_database.csv"), row.names=FALSE)
# writing class control file
classIndex = as.character(unique(data$class))
for (class in classIndex){
tmp = data[data$class==class , ]
write.csv(tmp, file = paste0(ref, "reference_", class, ".csv"), row.names=FALSE)
}
write(classIndex, paste0(ref, "classes.txt"))One can find below the spectra found within the database. By selecting an entry in the drop-down menu the plot of the respective class will be shown. The solid grey line in the center of the plot indicates the mean value for all samples of the respective wavenumbers. The grey ribbon indicates the standard deviation from that mean value, while the dashed lines show the minimum and the maximum values, respectively.
Primpke, S., Wirth, M., Lorenz, C., Gerdts, G., 2018. Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy. Analytical and Bioanalytical Chemistry 410, 5131–5141. https://doi.org/10.1007/s00216-018-1156-x
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] tensorflow_1.14.0 abind_1.4-5
[3] e1071_1.7-2 keras_2.2.4.1
[5] workflowr_1.4.0.9001 baseline_1.2-1
[7] gridExtra_2.3 stringr_1.4.0
[9] prospectr_0.1.3 RcppArmadillo_0.9.600.4.0
[11] openxlsx_4.1.0.1 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] reticulate_1.13 tidyselect_0.2.5 xfun_0.8 purrr_0.3.2
[5] lattice_0.20-38 colorspace_1.4-1 generics_0.0.2 htmltools_0.3.6
[9] base64enc_0.1-3 yaml_2.2.0 rlang_0.4.0 later_0.8.0
[13] pillar_1.4.2 glue_1.3.1 withr_2.1.2 foreach_1.4.7
[17] plyr_1.8.4 munsell_0.5.0 gtable_0.3.0 zip_2.0.3
[21] codetools_0.2-16 evaluate_0.14 knitr_1.24 SparseM_1.77
[25] tfruns_1.4 httpuv_1.5.1 class_7.3-15 highr_0.8
[29] Rcpp_1.0.2 xtable_1.8-4 promises_1.0.1 scales_1.0.0
[33] backports_1.1.4 jsonlite_1.6 mime_0.7 fs_1.3.1
[37] digest_0.6.20 stringi_1.4.3 shiny_1.3.2 grid_3.6.1
[41] rprojroot_1.3-2 tools_3.6.1 lazyeval_0.2.2 tibble_2.1.3
[45] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 zeallot_0.1.0
[49] Matrix_1.2-17 assertthat_0.2.1 rmarkdown_1.14 iterators_1.0.12
[53] R6_2.4.0 git2r_0.26.1 compiler_3.6.1