The eemUtils package is a collection of useful functions that act as an expansion of the existing existing R fluorescence analysis framework provided by the eemR, staRdom and EEM packages. Many of the functions within this package are alterations or wrappers for existing functions from eemR, staRdom or EEM - thus, if you use the functions from this package, please allocate proper credit to those packages and their authors.
This package is a work in progress, and is subject to change. Always back up your data!
If you have any questions or comments, I can be reached at m.r.p.harris@keele.ac.uk.
Below are some examples of functions within this package.
An updated EEM plotter, directly adapted from staRdom::ggeem(). It
retains much of the same functionality, with tweaked graphical elements.
Designed for use in my thesis and future pubs. It has integrated options
for binning, and the default colour scheme takes after MATLAB’s ‘jet’
scheme. Below are two examples, using some Black Tea OM SRM.
pacman::p_load(eemUtils,staRdom,eemR,ggplot2,cowplot,pracma,magrittr,magick,tidyverse)
ILSMBT_36_eem_average <- (function(...)get(data(...,envir = new.env())))("ILSMBT_36_eem_average")
intp_eem <- eemUtils::interpolate_eem(ILSMBT_36_eem_average, n_pp = 2, verbose = FALSE)
p1 <- eemUtils::ggeem2(ILSMBT_36_eem_average, contour = TRUE, legend = FALSE, title_text = "Black tea 36-EEM avg") + theme(aspect.ratio = 1/1)
p2 <- eemUtils::ggeem2(intp_eem, bin_vals = "colpal", contour = FALSE, legend = FALSE, title_text = "Black tea 36-EEM avg interp + bin") + theme(aspect.ratio = 1/1)
cowplot::plot_grid(p1,p2,
ncol = 2, nrow = 1)
A simple conversion of staRdom::eempf_comps3D(), but for use with
sample EEM data, rather than outputs from a PARAFAC model. Extremely
useful when checking individual samples for the presence of scatter
lines or artefacts, or for quickly identifying the point of maximum
fluorescence within part of an EEM. Option for the darker, MATLAB
‘jet’-esque colourscheme, or for a more vibrant rainbow as is used in
staRdom.
To view a 3D, interactive render of the black tea from the two plots above, download the BlackTea_plot_eem_3D_widget html file in the ‘extras’ folder and open it with a suitable program (e.g. Chrome).
Generate_CORCONDIA() is a simple function wrapper for staRdom’s
existing core consistency diagnostic function
staRdom::eempf_corcondia(). It produces a more legible output.
Use extrpf_loadings() to get the modeled per-sample fluorescence
intensity loadings for each component within a set of PARAFAC models.
This is a simple way to get quick series data from any number of PARAFAC
models generated by staRdom::eem_parafac().
Outputs from extrpf_loadings() can be immediately passed to
get_pfload_percent(), which will calculate the percentage contribution
of each modeled component to a given sample’s total fluorescence
intensity.
This function incorporates two methods in order to find the area under the Raman peak of water, for EEM data Raman normalisation purposes. The first method utilises a port of the MATLAB package drEEM’s RamanIntegrationRange function, which uses adjustable gradient detection to identify the start and end of the Raman peak. The method is presented and discussed at length in Murphy, 2011. The second method is a straightforward, fixed-range integration used by the Aqualog fluorometer, which assumes the Raman peak extends from 380nm to 410nm at 350nm excitation.
Some sample Raman curve spectra are included in the package as below.
library(pacman)
#> Warning: package 'pacman' was built under R version 4.0.5
pacman::p_load(eemUtils,ggplot2,cowplot,pracma,magrittr,magick)
data(SampleRamanCurves)
head(SampleRamanCurves)
#> Wavelength Spectra.1 Spectra.2 Spectra.3 Spectra.4 Spectra.5
#> 1 246.355 0.19417 1.20432 -2.23798 -6.22403 -0.47937
#> 2 247.477 -5.24265 -0.40144 -1.22071 -1.19693 0.23968
#> 3 248.599 -1.94172 -1.00360 3.86560 -0.95754 0.95873
#> 4 249.721 5.24265 1.00360 -0.61036 4.30894 4.07461
#> 5 250.844 3.29229 -4.31084 -1.83981 -7.30603 2.16744
#> 6 251.966 3.02511 8.04115 -1.35844 -10.92216 -4.00087extract_ramanpeak_areas() can then be used to get the areas under the
raman peaks, and exported as image and/or .gif files for visualisation.
The below .gif shows a use of the drEEM RamanIntegrationRange method,
using gradient detection.
eemUtils::extract_ramanpeak_areas(RAMdat = SampleRamanCurves, range_upper = 500, method = "RIR", output_dir = NULL, gif = FALSE)
#> Legacy peak max at em397, lying between ex370:428
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> [01] IR = 381:409 | Peak Area = 2000.84287218425
#> [02] IR = 381:409 | Peak Area = 2005.96666376586
#> [03] IR = 381:408 | Peak Area = 1995.67397722841
#> [04] IR = 380:410 | Peak Area = 2041.30312091936
#> [05] IR = 378:408 | Peak Area = 2041.94451089284This function can also optionally produce images or a .gif of the detected peak bounds and areas.
To get access to the functions in eemUtils, simply use the devtools package to install the package from github.
devtools::install_github("MRPHarris/eemUtils")23/09/21 | An updated ggplot2-compatible EEM plotter, ggeem2(), has
been added. This function updates the plotting provided by staRdom’s
ggeem() function, tweaking the graphical parameters and adding an option
for intensity value binning via eem_bin(). The check_eem() function
has been removed, as it is no longer useful.
01/10/21 | ggeem2() now has multi-plot support, and inherits
staRdom::ggeem()’s class handling.
17/01/22 | Various bug fixes. Gradually removing
extrpf_spectra_or_eems(), as that workflow and associated functions
have already been superseded by other functions in this package.
18/01/22 | Removed extrpf_spectra_or_eems(). Functionality provided
by the functions extrpf_peak_spectra() and extrpf_eems(), which are
easier to use and have more efficient syntax.
Massicotte, P. (2019). eemR: Tools for Pre-Processing Emission-Excitation-Matrix (EEM) Fluorescence Data. R package version 1.0.1. https://CRAN.R-project.org/package=eemR
Murphy, K. R. (2011). A Note on Determining the Extent of the Water Raman Peak in Fluorescence Spectroscopy. Applied Spectroscopy, 65(2), 233–236. https://doi.org/10.1366/10-06136
Murphy, K. R., Stedmon, C. A., Graeber, D., & Bro, R. (2013). Fluorescence spectroscopy and multi-way techniques. PARAFAC. Analytical Methods, 5, 6557–6566. doi: 10.1039/C3AY41160E
Pucher, M., Wünsch, U., Weigelhofer, G., Murphy, K., Hein, T., & Graeber, D. (2019). staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R. Water, 11, 2366. doi: 10.3390/w11112366
Trivittayasil, V. (2016). EEM: Read and Preprocess Fluorescence Excitation-Emission Matrix (EEM) Data. R package version 1.1.1. https://CRAN.R-project.org/package=EEM