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Overview

‘ir’ is an R package to import, handle and preprocess infrared spectra. Infrared spectra are stored as list columns in data frames which enables efficient storage of metadata along with the spectra and using ‘tidyverse’ functions for data operations.

Spectra in the following formats can be imported:

  1. csv files with individual spectra.
  2. Thermo Galactic’s spc files with individual spectra.

Functions for spectral preprocessing and data handling:

  1. baseline correction with:
    • a polynomial baseline
    • a convex hull baseline
    • a Savitzky-Golay baseline (Lasch 2012)
  2. binning
  3. clipping
  4. interpolating (resampling, linearly)
  5. replacing selected parts of a spectrum by a straight line
  6. averaging spectra within specified groups
  7. normalizing spectra:
    • to the maximum intensity
    • to the intensity at a specific x value
    • so that all intensity values sum to 1
    • vector normalization
    • Standard Normal Variate correction
  8. smoothing:
    • Savitzky-Golay smoothing
    • Fourier smoothing
  9. computing derivatives of spectra using Savitzky-Golay smoothing
  10. spectral arithmetic (addition, subtraction, multiplication, division)
  11. computing the variance of intensity values (optionally after subtracting reference spectra)
  12. computing maxima, minima, median, and ranges of intensity values of spectra
  13. Atmospheric background correction (Perez-Guaita et al. 2013)
  14. Scaling intensity values in spectra
  15. Wrapper to sampling algorithms implemented in the ‘prospectr’ package.
  16. plotting
  17. tidyverse methods

How to install

You can install ‘ir’ from CRAN:

You can install the development version of ‘ir’ from GitHub:

remotes::install_github(repo = "henningte/ir", ref = "dev")

How to use

Load ‘ir’:

# load ir package
library(ir)

# load additional packages needed for this tutorial
library(ggplot2)

For brief introductions, see below and the two vignettes:

  1. Introduction to the ‘ir’ package
  2. Introduction to the irclass

Sample workflow

Here is an example preprocessing pipeline for the sample data in the package (ir_sample_data) which does baseline correction, binning (bin width of 10), normalization (so that the maximum intensity value is 1 and the minimum intensity value is 0), and finally plots the preprocessed spectra for each sample and sample type:

ir_sample_data |>                                      # data
  ir::ir_bc(method = "rubberband") |>                  # baseline correction
  ir::ir_bin(width = 10) |>                            # binning
  ir::ir_normalize(method = "zeroone") |>              # normalization
  plot() + ggplot2::facet_wrap(~ sample_type)          # plot

Data structure

You can load the sample data with:

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     
#> # ℹ 48 more rows
#> # ℹ 2 more variables: holocellulose <units>, spectra <named list>

ir_sample_data is an object of class ir. An Object of class ir is a data frame where each row represents one infrared measurement and column spectra contains the infrared spectra (one per row) as list column. This allows to store metadata along each spectrum (for example the N content for each sample) and to manipulate ir objects with ‘tidyverse’ functions.

The column spectra is a list column of data frames, meaning that each cell of spectra contains a data frame which contains the data for one spectrum. For example, here are the first rows of the first spectrum in ir_smaple_data:

# View the first ten rows of the first spectrum in ir_sample_data
head(ir_sample_data$spectra[[1]])
#> # A tibble: 6 × 2
#>       x        y
#>   <int>    <dbl>
#> 1  4000 0.000361
#> 2  3999 0.000431
#> 3  3998 0.000501
#> 4  3997 0.000571
#> 5  3996 0.000667
#> 6  3995 0.000704

Column x contains values for the spectral channel (in this case wavenumbers [cm-1]) and column y the corresponding intensity values.

How to cite

Please cite this R package as:

Henning Teickner (2025). ir: Functions to Handle and Preprocess Infrared Spectra. DOI: 10.5281/zenodo.5747169. Accessed 06 Apr 2025. Online at https://zenodo.org/record/5747169.

Companion packages

The irpeat package provides functions to analyze infrared spectra of peat (humification indices, prediction models) and uses the ‘ir’ package to handle spectral data.

Licenses

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.

Contributions

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.

Sources

ir_sample_data is derived from Hodgkins et al. (2018) and was reformatted 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 are originally from De la Cruz, Osborne, and Barlaz (2016).

References

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.
Lasch, Peter. 2012. “Spectral Pre-Processing for Biomedical Vibrational Spectroscopy and Microspectroscopic Imaging.” Chemometrics and Intelligent Laboratory Systems 117 (August): 100–114. https://doi.org/10.1016/j.chemolab.2012.03.011.
Perez-Guaita, David, Julia Kuligowski, Guillermo Quintás, Salvador Garrigues, and Miguel de la Guardia. 2013. “Atmospheric Compensation in Fourier Transform Infrared (FT-IR) Spectra of Clinical Samples.” Applied Spectroscopy 67 (11): 1339–42. https://doi.org/10.1366/13-07159.