In henningte/ir: Functions to Handle and Preprocess Infrared Spectra

knitr::opts_chunk$set(
  collapse = TRUE,
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  fig.align = "center",
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library(kableExtra)

Introduction

Purpose

This vignette shows how to use main functions of the 'ir' package. This includes data import, data export, functions for spectral preprocessing, and functions for plotting. This vignette does not explain the data structure of ir objects (the objects 'ir' uses to store spectra) in detail and it does not describe general data manipulation functions (e.g. subsetting rows or columns, modifying variables) (for this, see vignette r rmarkdown::yaml_front_matter("ir-class.Rmd")$title). This vignette also does not explain the purpose of the spectral preprocessing functions, it just shows how to use them.

Structure

The vignette has three parts:

1. Data import and export
2. Plotting spectra
3. Spectral preprocessing

In part [Data import and export], I will show how to import spetra from csv files and how to import spetra from Thermo Galactic's spectral files (file extension .spc). I will also show how to export ir objects as csv files. To this end, I will use sample data which comes along with the 'ir' package. In part [Plotting spectra], I will show how to create simple plots of spectra and how these plots can be modified (for example to produce nice graphs for publications). In part [Spectral preprocessing] I will show the main preprocessing functions included in the 'ir' package and they can be combined to form preprocessing pipelines of increasing complexity.

Preparation

To follow this vignette, you have to install the 'ir' package as described in the Readme file and you have to load it:

library(ir)

Data import and export

Data import

To test importing spectra from files, I'll use sample data provided in the 'ir' package (in folder inst/extdata). First, I'll show how to import spectra from csv files and then how to import Thermo Galactic's spectral files (file extension .spc).

csv files

Spectra from csv files can be imported with ir_import_csv(). This function can import spectra from one or more csv files with the following format:

read.csv("../inst/extdata/klh_hodgkins_mir.csv") |>
  dplyr::select(1:5) |>
  dplyr::slice(1:6) |>
  kableExtra::kable()

This is a subset of the data we will import in a few moments. The first column must contain spectral channel values ("x axis values", e.g. wavenumbers for mid infrared spectra), and each additional column represents the intensity values ("y axis values", e.g. absorbances) of one spectrum. In the example above, there are four spectra in the csv file.

To import the data, you can simply pass the path to the file to ir_import_csv():

d_csv <- 
  ir_import_csv(
    "../inst/extdata/klh_hodgkins_mir.csv", 
    sample_id = "from_colnames"
  )

The argument sample_id = "from_colnames" tells ir_import_csv() to extract names for the spectra from the column names of the csv file.

If you have additional metadata available, you can bind these to the ir object in a second step (note: here, I use functions from the 'dplyr' package to reformat the metadata; you don't need to understand the details of this data cleanup to follow the rest of this vignette):

library(dplyr)
library(stringr)

# import the metadata
d_csv_metadata <- 
  read.csv(
    "./../inst/extdata/klh_hodgkins_reference.csv",
    header = TRUE,
    as.is = TRUE
  ) |>
  dplyr::rename(
    sample_id = "Sample.Name",
    sample_type = "Category",
    comment = "Description",
    holocellulose = "X..Cellulose...Hemicellulose..measured.",
    klason_lignin = "X..Klason.lignin..measured." 
  ) |>
  # make the sample_id values fit to those in `d_csv$sample_id` to make combining easier
  dplyr::mutate(
    sample_id =
      sample_id |>
      stringr::str_replace_all(pattern = "( |-)", replacement = "\\.")
  )

d_csv <- 
  d_csv |>
  dplyr::full_join(d_csv_metadata, by = "sample_id")

Now, d_csv has addition columns with the metadata contained in the separate file.

Thermo Galactic's spc files

Spectra from spc files can be imported with ir_import_spc(). This function can import spectra from one or more spc files:

d_spc <- ir_import_spc("../inst/extdata/1.spc", log.txt = FALSE)

In this case, names for the spectra and other metadata are extracted from the spc file(s) and added to the ir object. You can inspect d_spc to see these additional variables. The option log.txt = FALSE means that some of the metadata will not be imported. To import these additional metadata, you need to install version 0.200.0.9000 or higher of the 'hyperSpec' package, which is currently only available from GitHub (https://github.com/r-hyperspec/hyperSpec).

Data export

Data in ir objects can be exported in many ways. Here, I show how to export spectra to a csv file. The result has the same format as the sample data we imported in subsection [csv files].

To export the spectra, type:

# export only the spectra
ir_sample_data |>
  ir_export_prepare(what = "spectra") |>
  write.csv(tempfile("ir_sample_data_spectra", fileext = "csv"), row.names = FALSE)

To export the additional metadata contained in an ir object, type:

# export only the metadata
ir_sample_data |>
  ir_drop_spectra() |>
  write.csv(tempfile("ir_sample_data_metadata", fileext = "csv"), row.names = FALSE)

This exports the metadata to a separate csv file with the same row and column format as in ir_sample_data.

Plotting spectra

The 'ir' package provides a function to create simple plots of spectra:

plot(d_csv)

This will plot the intensity values ("y axis values", e.g. absorbances) of each spectrum versus the spectral channel values ("x axis values", e.g. wavenumbers), connected by a line. All spectra in an ir object are plotted in the same panel.

For plotting, 'ir' uses the 'ggplot' package. This means that you can modify plots of spectra with all functions from 'ggplot2'. For example, we could color spectra according to the sample class:

library(ggplot2)

plot(d_csv) + 
  geom_path(aes(color = sample_type))

And of course, we can change axis labels, layout, etc, to create plots nice enough for publications:

plot(d_csv) + 
  geom_path(aes(color = sample_type)) +
  labs(x = expression("Wavenumber ["*cm^{-1}*"]"), y = "Absorbance") +
  guides(color = guide_legend(title = "Sample type")) +
  theme_classic() +
  theme(legend.position = "bottom")

Spectral preprocessing

'ir' provides many functions for spectral preprocessing and I'll show how to use the most important ones. All other preprocessing functions can be used in a similar way. To make it easier to compare the effect each function has, we'll have a look at the sample spectrum before any preprocessing:

plot(d_spc)

Baseline correction

Baseline correction with a rubberband algorithm (see the spc.rubberband function in the 'hyperspec' package):

d_spc |>
  ir_bc(method = "rubberband") |>
  plot()

Normalization

Normalization of intensity values by dividing each intensity value by the sum of all intensity values (note the different scale of the y axis in comparison to the spectrum before any preprocessing):

d_spc |>
  ir_normalize(method = "area") |>
  plot()

Normalization of intensity values by dividing each intensity value by the the intensity value at a specific wavenumber (the horizontal and vertical lines highlight that the intensity at the selected wavenumber is 1 after normalization):

d_spc |>
  ir_normalize(method = 1090) |>
  plot() +
  geom_hline(yintercept = 1, linetype = 2) +
  geom_vline(xintercept = 1090, linetype = 2)

The warning just says that the spectrum's wavenumber values did not exactly match the desired value and therefore the nearest value available was selected. To disable this warning, you can interpolate the spectrum to an appropriate resolution (see section [Interpolating] below).

Smoothing

Smoothing of spectra with the Savitzky-Golay algorithm (see the sgolayfilt() function from the 'signal' package for details):

d_spc |>
  ir_smooth(method = "sg", p = 3, n = 91, m = 0) |>
  plot()

Derivative spectra

Savitzky-Golay smoothing can also be used to compute derivative spectra (here the first derivative is computed by setting the argument m to 1. See ?ir_smooth() for more information):

d_spc |>
  ir_smooth(method = "sg", p = 3, n = 9, m = 1) |>
  plot()

Clipping

Spectra can be clipped to desired ranges for spectral channels ("x axis values", e.g. wavenumbers). Here, I clip the spectrum to the range [1000, 3000]:

d_spc |>
  ir_clip(range = data.frame(start = 1000, end = 3000)) |>
  plot()

Interpolating

Spectral interpolation (interpolating intensity values for new wavenumber values) can be performed. Here, intensity values are interpolated to integer wavenumbers increasing by 1 (by setting dw = 1) within the range of the data:

d_spc |>
  ir_interpolate(dw = 1) |>
  plot()

This is not easy to see from the plot, but the warning shown above (section [Normalization]) during normalization now does not appear:

d_spc %>%
  ir_interpolate(dw = 1) |>
  ir_normalize(method = 1090) |>
  plot() +
  geom_hline(yintercept = 1, linetype = 2) +
  geom_vline(xintercept = 1090, linetype = 2)

Interpolating regions

Sometimes, it is useful to replace parts of spectra by straight lines which connect the start and end points of a specified range. This can be done with ir_interpolate_region():

d_spc |>
  ir_interpolate_region(range = data.frame(start = 1000, end = 3000)) |>
  plot()

Binning

Spectral binning collects all intensity values in contiguous spectral ranges ("bins") with specified widths and averages these:

d_spc |>
  ir_bin(width = 30) |>
  plot()

Scaling

Scaling takes a set of spectra with the same x axis values and then applies base::scale() on the intensity values of all spectra for the same x axis value:

d_csv |>
  ir_scale(center = TRUE, scale = FALSE) |>
  plot()

Building preprocessing pipelines

With 'ir', it is very easy to build complex preprocessing workflows by "piping" together different preprocessing steps (using the pipe (%>%) operator in the 'magrittr' package):

d_spc |>
  ir_interpolate(dw = 1) |>
  ir_clip(range = data.frame(start = 700, end = 3900)) |>
  ir_bc(method = "rubberband") |>
  ir_normalise(method = "area") |>
  ir_bin(width = 10) |>
  plot()

Now, we have a baseline corrected spectrum, "area" normalized, clipped to [650, 3900], and binned to bin widths of 10 cm$^{-1}$.

Further information

Many more functions and options to handle and process spectra are available in the 'ir' package. These are described in the documentation. In the documentation, you can also read more details about the functions and options presented here.

To learn more about the structure and general functions to handle ir objects, see the vignette r rmarkdown::yaml_front_matter("ir-class.Rmd")$title.

Sources

The data contained in the csv file used in this vignette are derived from @Hodgkins.2018

Session info

sessionInfo()

References

henningte/ir documentation built on July 4, 2025, 5:12 p.m.