The eemR package implements various functions used calculate metrics from excitation-emission matrix (EEM) as well as to preform pre-processing corrections before PARAFAC analysis (Bro 1997; C. A. Stedmon and Markager 2005; Murphy et al. 2013). All functions from this package start with the eem_ prefix.

library(eemR)
ls("package:eemR")
#>  [1] "absorbance"              "eem_biological_index"   
#>  [3] "eem_coble_peaks"         "eem_cut"                
#>  [5] "eem_export_matlab"       "eem_extract"            
#>  [7] "eem_fluorescence_index"  "eem_humification_index" 
#>  [9] "eem_inner_filter_effect" "eem_raman_normalisation"
#> [11] "eem_read"                "eem_remove_blank"       
#> [13] "eem_remove_scattering"   "eem_set_wavelengths"

The package can be installed using the following command.

devtools::install_github("PMassicotte/eemR")

Please note this is a very alpha version of the package for testing purpose only.

At the moment, the following EEM types are supported:

• Cary Eclipse .csv files

• Aqualog .dat files

• Shimadzu .TXT files

EEM can be read using the eem_read() function. Please fill an issue if you have other file formats you would like to add to the package.

At the moment I need files from:

• FluoromaxIII

• Perkin elmer

• Hitachi

library(eemR)

## Reading a single eem
file <- system.file("extdata/cary/eem", "sample1.csv", package = "eemR")
eem <- eem_read(file)

plot(eem)

## Reading a folder
folder <- system.file("extdata/cary/eem", package = "eemR")
eem <- eem_read(folder)

plot(eem) ## Plot the first eem

plot(eem, which = 2) ## Plot the second eem

## Aqualog EEM
folder <- system.file("extdata/aqualog", package = "eemR")
eem <- eem_read(folder)

plot(eem) ## Plot the first eem

Some spectrophotometers (such as Shimadzu) do not include excitation wavelengths in the fluorescence files. In these cases, we can use the eem_set_wavelengths() function to manually provide vectors of emission and/or excitation wavelengths.

folder <- system.file("extdata/shimadzu", package = "eemR")
eems <- eem_read(folder)
#> Shimadzu files do not contain excitation wavelengths.
#> Please provide them using the eem_set_wavelengths() function.

eems <- eem_set_wavelengths(eems, ex = seq(230, 450, by = 5))

Extracting of removing EEMs can be useful when reading a bunch of files containing both measurements and blank fluorescence. This can be done easily using eem_extract(). For example, lets read a whole folder and then remove the blank water.

folder <- system.file("extdata/cary", package = "eemR")

eems <- eem_read(folder, recursive = TRUE)

blank <- eem_extract(eems, "nano", remove = FALSE)
#> Extracted nano

eems <- eem_extract(eems, "nano", remove = TRUE)
#> Removed nano


## Remove sample 1 to 3
res <- eem_extract(eems, 1:3, remove = TRUE)
#> Removed sample1 sample2 sample3

The current implemented metrics are:

1. The fluorescence index (FI) developed by McKnight et al. (2001).

2. The fluorescence peaks proposed by Coble (1996).

3. The fluorescence humification index (HIX) by Ohno (2002).

4. The biological fluorescence index (BIX) by Huguet et al. (2009).

library(eemR)

folder <- system.file("extdata/cary/eem", package = "eemR")
eem <- eem_read(folder)

eem_fluorescence_index(eem, verbose = FALSE)
#> Source: local data frame [3 x 2]
#> 
#>    sample       fi
#>     (chr)    (dbl)
#> 1 sample1 1.264782
#> 2 sample2 1.455333
#> 3 sample3 1.329413

eem_coble_peaks(eem, verbose = FALSE)
#> Source: local data frame [3 x 6]
#> 
#>    sample        b         t        a        m         c
#>     (chr)    (dbl)     (dbl)    (dbl)    (dbl)     (dbl)
#> 1 sample1 1.545298 1.0603312 3.731836 2.424096 1.8149415
#> 2 sample2 1.262997 0.6647042 1.583489 1.023593 0.7709074
#> 3 sample3 1.474086 1.3162812 8.416034 6.063355 6.3179129

eem_humification_index(eem, verbose = FALSE)
#> Source: local data frame [3 x 2]
#> 
#>    sample       hix
#>     (chr)     (dbl)
#> 1 sample1  6.379562
#> 2 sample2  4.254848
#> 3 sample3 13.024623

eem_humification_index(eem, verbose = FALSE, scale = TRUE)
#> Source: local data frame [3 x 2]
#> 
#>    sample       hix
#>     (chr)     (dbl)
#> 1 sample1 0.8644906
#> 2 sample2 0.8096995
#> 3 sample3 0.9286968

eem_biological_index(eem, verbose = FALSE)
#> Source: local data frame [3 x 2]
#> 
#>    sample       bix
#>     (chr)     (dbl)
#> 1 sample1 0.7062640
#> 2 sample2 0.8535423
#> 3 sample3 0.4867927

Three types of correction are currently supported:

1. eem_remove_blank() which subtract a water blank from the eem.

2. eem_remove_scattering() which remove both Raman and Rayleigh scattering.

3. eem_raman_normalisation() which normalize EEM fluorescence intensities (Lawaetz and Stedmon 2009).

4. eem_inner_filter() which correct for both primary and secondary inner-filter effect.

The eem_remove_blank() function subtract blank (miliq) water from eem. Scatter bands can often be reduced by subtracting water blank (Murphy et al. 2013).

file <- system.file("extdata/cary/eem", "sample1.csv", package = "eemR")
eem <- eem_read(file)

file <- system.file("extdata/cary", "nano.csv", package = "eemR")
blank <- eem_read(file)

res <- eem_remove_blank(eem, blank)

plot(eem)
plot(res)

The eem_remove_scattering() function removes both Raman and Rayleigh scattering from EEMs.

res <- eem_remove_scattering(eem = eem, type = "raman", order = 1, width = 10)
res <- eem_remove_scattering(eem = res, type = "rayleigh", order = 1, width = 10)

plot(res)

The eem_raman_normalisation() function implement a simple calibration method for fluorescence intensity using only the integrated area of a water Raman peak. More details can be found in Lawaetz and Stedmon (2009).

res <- eem_raman_normalisation(res, blank)
#> Raman area: 9.501974

plot(res)

To account for reabsorption of the light emitted by fluorophores in the water, absorbance spectra are used for correction of both primary and secondary inner filtering effects in the EEMs (Ohno 2002; Parker and Barnes 1957; Kothawala et al. 2013).

data("absorbance")

res <- eem_inner_filter_effect(eem = res,
                               absorbance = absorbance,
                               pathlength = 1) ## 1 cm fluo pathlenght
#> Range of IFE correction factors: 0.6432503 0.9889723

plot(res)

The names of absorbance variables are expected to match those of the eems. If the appropriate absorbance spectrum is not found, an uncorrected eem will be returned and a warning message will be printed.

Kothawala et al. (2013) have shown that a 2-fold dilution was required for samples presenting total absorbance > 1.5. Accordingly, a message will warn the user if total absorbance is greater than this threshold.

PARAFAC analysis was made easy with the fantastic Matlab drEEM toolbox (Murphy et al. 2013). The function eem_export_matlab() can be used to export the EEMs into a m-file directly usable in Matlab by the drEEM toolbox.

folder <- system.file("extdata/cary/eem", package = "eemR")
eem <- eem_read(folder)

filename <- paste(tempfile(), ".mat", sep = "")

eem_export_matlab(filename, eem)
#> Successfully exported 3 EEMs to /tmp/RtmpB2AhLz/file5fd376f26587.mat.

## It is also possible to export more than one object at time
eem_export_matlab(filename, eem, eem)
#> Successfully exported 6 EEMs to /tmp/RtmpB2AhLz/file5fd376f26587.mat.

Note that the name of the structure generated by the function will be OriginalData to complement with PARAFAC standard. Then, the importation into Matlab is made easy using the load() function. Please note that there is a bug preventing to keep matrix dimension. Simply use the reshape() function after you exported data.

load('FileName.mat');

OriginalData.X = reshape(OriginalData.X, ...
    OriginalData.nSample, ...
    OriginalData.nEm, ...
    OriginalData.nEx);

% Start PARAFAC analysis here...

Bro, Rasmus. 1997. “PARAFAC. Tutorial and applications.” Chemometrics and Intelligent Laboratory Systems 38 (2): 149–71. doi:10.1016/S0169-7439(97)00032-4.

Coble, Paula G. 1996. “Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy.” Marine Chemistry 51 (4): 325–46. doi:10.1016/0304-4203(95)00062-3.

Huguet, A., L. Vacher, S. Relexans, S. Saubusse, J.M. Froidefond, and E. Parlanti. 2009. “Properties of fluorescent dissolved organic matter in the Gironde Estuary.” Organic Geochemistry 40 (6). Elsevier Ltd: 706–19. doi:10.1016/j.orggeochem.2009.03.002.

Kothawala, Dolly N., Kathleen R. Murphy, Colin A. Stedmon, Gesa A. Weyhenmeyer, and Lars J. Tranvik. 2013. “Inner filter correction of dissolved organic matter fluorescence.” Limnology and Oceanography: Methods 11 (12): 616–30. doi:10.4319/lom.2013.11.616.

Lawaetz, A J, and C A Stedmon. 2009. “Fluorescence Intensity Calibration Using the Raman Scatter Peak of Water.” Applied Spectroscopy 63 (8): 936–40. doi:10.1366/000370209788964548.

McKnight, Diane M., Elizabeth W. Boyer, Paul K. Westerhoff, Peter T. Doran, Thomas Kulbe, and Dale T. Andersen. 2001. “Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity.” Limnology and Oceanography 46 (1). American Society of Limnology; Oceanography: 38–48. doi:10.4319/lo.2001.46.1.0038.

Murphy, Kathleen R., Colin a. Stedmon, Daniel Graeber, and Rasmus Bro. 2013. “Fluorescence spectroscopy and multi-way techniques. PARAFAC.” Analytical Methods 5 (23): 6557. doi:10.1039/c3ay41160e.

Ohno, Tsutomu. 2002. “Fluorescence Inner-Filtering Correction for Determining the Humification Index of Dissolved Organic Matter.” Environmental Science & Technology 36 (4): 742–46. doi:10.1021/es0155276.

Parker, C. a., and W. J. Barnes. 1957. “Some experiments with spectrofluorimeters and filter fluorimeters.” The Analyst 82 (978): 606. doi:10.1039/an9578200606.

Stedmon, Colin A, and Stiig Markager. 2005. “Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis.” Limnology and Oceanography 50 (2): 686–97. doi:10.4319/lo.2005.50.2.0686.