‘irpeat’ is an R package that contains simple functions to analyze infrared spectra of peat samples. Some functions may also work with organic matter samples in general.
Provided functions for analyzing infrared spectra of peat are:
- Computation of several humification indices.
- Klason lignin mass fraction (following Hodgkins et al. (2018) and Teickner and Knorr (2022)) (note that these models are not reliable for peat, see Teickner and Knorr (2022)).
- Holocellulose mass fraction (following Hodgkins et al. (2018) and Teickner and Knorr (2022)) (note that these models are not reliable for peat, see Teickner and Knorr (2022)).
- Peat electron accepting capacity (following Teickner, Gao, and Knorr (2022)).
- Peat electron donating capacity (following Teickner, Gao, and Knorr (2022)).
You can install ‘irpeat’ from GitHub using R via:
remotes::install_github(repo = "henningte/irpeat")‘irpeat’ relies on the R package ‘ir’ for handling infrared spectra.
You can load ‘irpeat’ in R with:
library(irpeat)
# load additional packages needed for this tutorial
library(ir)
library(ggplot2)You can test ‘irpeat’ with sample data from the R package ‘ir’:
ir::ir_sample_data
#> # A tibble: 58 × 7
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> <int> <chr> <chr> <chr> <units>
#> 1 1 GN 11-389 needles Abies Firma Momi fir 0.359944
#> 2 2 GN 11-400 needles Cupressocyparis leylandii… 0.339405
#> 3 3 GN 11-407 needles Juniperus chinensis Chine… 0.267552
#> 4 4 GN 11-411 needles Metasequoia glyptostroboi… 0.350016
#> 5 5 GN 11-416 needles Pinus strobus Torulosa 0.331100
#> 6 6 GN 11-419 needles Pseudolarix amabili Golde… 0.279360
#> 7 7 GN 11-422 needles Sequoia sempervirens Cali… 0.329672
#> 8 8 GN 11-423 needles Taxodium distichum Cascad… 0.356950
#> 9 9 GN 11-428 needles Thuja occidentalis Easter… 0.369360
#> 10 10 GN 11-434 needles Tsuga caroliniana Carolin… 0.289050
#> # … with 48 more rows, and 2 more variables: holocellulose <units>,
#> # spectra <named list>ir::ir_sample_data contains various ATR-MIR spectra of organic
reference material (e.g. newspaper, wood, grass).
A simple workflow could be, for example, to baseline correct the spectra
(using functions of the package ‘ir’) compute various humification
indices and Klason lignin and holocellulose mass fractions in the
samples. We use only the first few spectra from ir::ir_sample_data to
speed the computations a bit up.
x <-
ir::ir_sample_data[1:10, ] %>% # data
ir::ir_bc(method = "rubberband") %>% # baseline correction
irpeat::irp_hi() %>% # humification indices
irpeat::irp_klason_lignin_2(do_summary = TRUE) # Klason lignin contentx is identical to ir::ir_sample_data[1:10, ], but contains
additional columns for the computed humification indices (h1, h2,
h3, h4) and the computed Klason lignin content (klason_lignin_2)
x
#> # A tibble: 10 × 12
#> id_measurement id_sample sample_type sample_comment klason_lignin
#> * <int> <chr> <chr> <chr> [1]
#> 1 1 GN 11-389 needles Abies Firma Momi fir 0.360
#> 2 2 GN 11-400 needles Cupressocyparis leylandii… 0.339
#> 3 3 GN 11-407 needles Juniperus chinensis Chine… 0.268
#> 4 4 GN 11-411 needles Metasequoia glyptostroboi… 0.350
#> 5 5 GN 11-416 needles Pinus strobus Torulosa 0.331
#> 6 6 GN 11-419 needles Pseudolarix amabili Golde… 0.279
#> 7 7 GN 11-422 needles Sequoia sempervirens Cali… 0.330
#> 8 8 GN 11-423 needles Taxodium distichum Cascad… 0.357
#> 9 9 GN 11-428 needles Thuja occidentalis Easter… 0.369
#> 10 10 GN 11-434 needles Tsuga caroliniana Carolin… 0.289
#> # … with 7 more variables: holocellulose [1], spectra <list>, hi1 <dbl>,
#> # hi2 <dbl>, hi3 <dbl>, hi4 <dbl>, klason_lignin_2 (err) [g/g]Plot of the humification index (ratio of the intensities at 1420 and 1090 cm-1 (Broder et al. 2012)) versus the Klason lignin content:
ggplot2::ggplot(x, aes(x = quantities::drop_quantities(klason_lignin_2) * 100, y = hi1)) +
ggplot2::geom_point() +
ggplot2::labs(x = "Klason lignin content [mass-%]",
y = expression("Ratio of the intensities at"~1420~and~1090~cm^{-1}))
All computed quantities come with units and standard errors (thanks to the quantities package):
x$klason_lignin_2
#> Units: [g/g]
#> Errors: 0.05332995 0.03879912 0.03466596 0.03945795 0.03532837 ...
#> V1 V2 V3 V4 V5 V6 V7 V8
#> 0.3763523 0.3422666 0.2545192 0.3087898 0.2965548 0.2768367 0.3138414 0.3517650
#> V9 V10
#> 0.3388824 0.2918090Henning Teickner plans, as part of his PhD project, to extensively extent ‘irpeat’ by developing a set of calibration models that can predict various peat physicochemical properties from mid infrared spectra. These models should be finished by September 2024. Currently, a data compendium (pmird) is developed to collect the data required for this task.
Please cite this R package as:
Henning Teickner, Suzanne B. Hodgkins (2022). irpeat: Functions to Analyze Mid Infrared Spectra of Peat Samples. Accessed 2022-07-29. Online at https://github.com/henningte/irpeat.
Text and figures : CC-BY-4.0
Code : See the DESCRIPTION file
Data : CC BY 4.0 attribution requested in reuse. See the sources section for data sources and how to give credit to the original author(s) and the source.
We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
The data and prediction models for holocellulose and Klason lignin
(irp_content_h_hodgkins_model, irp_content_kl_hodgkins_model) are
derived from Hodgkins et al. (2018) and were restructured to match the
requirements of ir. The original article containing the data can be
downloaded from https://www.nature.com/articles/s41467-018-06050-2 and
is distributed under the Creative Commons Attribution 4.0 International
License (https://creativecommons.org/licenses/by/4.0/). The data on
Klason lignin and holocellulose content was originally derived from De
La Cruz, Florentino B., Osborne, and Barlaz (2016).
Modified prediction models for holocellulose and Klason lignin
(model_holocellulose_2, model_klason_lignin_2) are derived from
Teickner and Knorr (2022).
Data and models for the electrochemical accepting and donating capacities (EAC, EDC) of peat were derived from Teickner, Gao, and Knorr (2022) and Teickner, Gao, and Knorr (2021)
This packages was developed in R (R version 4.2.0 (2022-04-22 ucrt)) (R Core Team 2019) using functions from devtools (Wickham, Hester, and Chang 2019), usethis (Wickham and Bryan 2019), rrtools (Marwick 2019) and roxygen2 (Wickham et al. 2019).
Broder, T., C. Blodau, H. Biester, and K. H. Knorr. 2012. “Peat decomposition records in three pristine ombrotrophic bogs in southern Patagonia.” Biogeosciences 9 (4): 1479–91. https://doi.org/10.5194/bg-9-1479-2012.
De La Cruz, Florentino B., Jason Osborne, and Morton A. Barlaz. 2016. “Determination of Sources of Organic Matter in Solid Waste by Analysis of Phenolic Copper Oxide Oxidation Products of Lignin.” Journal of Environmental Engineering 142 (2): 04015076. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001038.
Hodgkins, Suzanne B., Curtis J. Richardson, René Dommain, Hongjun Wang, Paul H. Glaser, Brittany Verbeke, B. Rose Winkler, et al. 2018. “Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance.” Nature Communications 9 (1): 3640. https://doi.org/10.1038/s41467-018-06050-2.
Marwick, Ben. 2019. “rrtools: Creates a Reproducible Research Compendium.” https://github.com/benmarwick/rrtools.
R Core Team. 2019. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Teickner, Henning, Chuanyu Gao, and Klaus-Holger Knorr. 2021. “Reproducible Research Compendium with R Code and Data for: ’Electrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition’.” Zenodo. https://doi.org/10.5281/zenodo.5792970.
———. 2022. “Electrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition.” Global Biogeochemical Cycles, February. https://doi.org/10.1029/2021GB007160.
Teickner, Henning, and Klaus-Holger Knorr. 2022. “Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid Infrared Spectra.” Preprint. Soil and methods. https://doi.org/10.5194/soil-2022-27.
Wickham, Hadley, and Jennifer Bryan. 2019. “usethis: Automate Package and Project Setup.” https://CRAN.R-project.org/package=usethis.
Wickham, Hadley, Peter Danenberg, Gábor Csárdi, and Manuel Eugster. 2019. “roxygen2: In-Line Documentation for R.” https://CRAN.R-project.org/package=roxygen2.
Wickham, Hadley, Jim Hester, and Winston Chang. 2019. “devtools: Tools to Make Developing R Packages Easier.” https://CRAN.R-project.org/package=devtools.