Last updated: 2019-08-13
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Knit directory: polymeRID/
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Modified: ref/wavenumbers.rds
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 the current project we used a data base published by Primpke et al. (2018) online. The data base can be downloaded here. The authors state 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 preprocessing, they applied a concave rubberband correction based on 10 iterations and 64 baseline points. They also excluded the C02 band between 2420 to 2200 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 data base to stay in a consistent state. The data provided by Primpke et al. (2018) shows a spectral resolution of 2.1 1/cm. Additional reference samples need be resampled to the same spectral resolution.
To ensure this, the data base was read into R and the wave number index was saved in a separate file for future use.
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"))We can take a look at the distribution of the different classes found in the data base. Here, I only print the 20 most common classes since there are a lot of reference samples only found once.
data$Abbreviation = as.factor(data$Abbreviation)
summary(data$Abbreviation)[1:20] PES PP LDPE
15 12 11
HDPE PET PE
10 9 8
Nylon PA PS
7 7 7
PUR C PC
7 6 6
alkyd_varnish PVC ethylene_vinyl_acetate
4 4 3
ethylene_vinyl_alcohol PESTUR ABS
3 3 2
Aramid cellulose
2 2 Because we are interested in assigning the right class to potential plastic particles, the most important classes found in the data base to us are the ones of artificial origin. However, sometimes also particles of biological origins are subject to spectral analysis, because they resemble the appearance of micro plastics. Any machine learning algorithm trained only with reference samples from plastics would eventually assign a plastic class also to the particles of biological origin. To reduce the error of false positives, we include some of the samples of biological origin based on broad classes.
# furs and wools
indexFur = grep("fur", data$Abbreviation)
indexWool = grep("wool", data)
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$Abbreviation)
polyNames = names(counts)[1:11] # only major polymers
syntPolymer = syntPolymer[which(syntPolymer$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"
syntPolymer$class[grep("HDPE",syntPolymer$class)] = "PE"
syntPolymer$class[grep("LDPE",syntPolymer$class)] = "PE"We can now bind the reference samples together and take a look at the distribution of classes in the resulting data frame.
data = rbind(furs,wood,fibre,syntPolymer)
data$class = as.factor(data$class)
summary(data$class)FIBRE FUR PA PC PE PES PET PP PS PUR WOOD
27 13 14 6 29 15 9 12 7 7 4 We see that we have in total 99 reference samples of plastic polymers and 44 of biological origin. Within the plastic samples, we find that the data is slightly unbalanced towards PE which dominates the distribution of plastic samples. This could prove as an disadvantage if machine learning algorithms pick up this unbalance by minimizing their error rate simply with more frequently predicting PE. However, at this point, we will leave the resulting data base as it is and save it to disk. We save the data in individual files as well as in a comprehensive data base. This way later extensions to the data base will be easier.
write.csv(data, file = paste0(ref, "reference_database.csv"), row.names=FALSE)
# writing class control file
classIndex = as.character(unique(data$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"))Primpke, Sebastian, Marisa Wirth, Claudia Lorenz, and Gunnar Gerdts. 2018. “Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy.” Analytical and Bioanalytical Chemistry 410 (21). Analytical; Bioanalytical Chemistry: 5131–41. 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] yaml_2.2.0 base64enc_0.1-3 rlang_0.4.0 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 foreach_1.4.7 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.3.0 zip_2.0.3 codetools_0.2-16
[21] evaluate_0.14 knitr_1.24 SparseM_1.77 tfruns_1.4
[25] class_7.3-15 Rcpp_1.0.2 scales_1.0.0 backports_1.1.4
[29] jsonlite_1.6 fs_1.3.1 digest_0.6.20 stringi_1.4.3
[33] grid_3.6.1 rprojroot_1.3-2 tools_3.6.1 lazyeval_0.2.2
[37] tibble_2.1.3 crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[41] zeallot_0.1.0 Matrix_1.2-17 assertthat_0.2.1 rmarkdown_1.14
[45] iterators_1.0.12 R6_2.4.0 git2r_0.26.1 compiler_3.6.1