Author: Philipp Baumann and Thomas Knecht
Date: 2026-01-29

# Reading OPUS binary files from Bruker® spectrometers in R

#### Philipp Baumann and Thomas Knecht

#### 2026-01-29

# Motivation

Spectrometers from Bruker® Optics GmbH & Co. save spectra in a special binary file format called OPUS. The OPUS suite and file specification are proprietary, therefore we have reverse-engineered our reader. With {opusreader2}, you can extract your spectroscopic data from binary files for most Fourier-Transform Infrared (FT-IR) spectrometers from Bruker® Optics GmbH directly in R.

Each OPUS file is linked to one aggregated sample measurement, which is usually obtained using the OPUS spectroscopy software. These files have numbers as file extensions, and the numbers typically increase sequentially for each sample name, based on the settings chosen in the OPUS software.

# OPUS data extraction

Each OPUS file is processed by first extracting metadata information from a dedicated part of the file header. As the picture below shows, the content of the files changes based on the instrument type, the data (spectrum) blocks that the user chooses to save, and the specific measurement settings.

OPUS measurement settings for Bruker INVENIO® Fourier-transform (FT) mid-infrared spectrometer

In addition to result spectra, the binary files include:

- a comprehensive set of measurement parameters (configurations, conditions)
- single-channel data from background measurements performed before the sample
- other types of intermediate spectra

Currently, we support the “data blocks to be saved” for Fourier-transform infrared spectrometers.

## Reading and parsing OPUS files in R

The main function, `read_opus()`, reads one or more OPUS files and returns a nested list of class `list_opusreader2`. Each list contains both the spectral data and metadata for each file.

Let’s see how this works using an example file from a Bruker ALPHA® spectrometer. This instrument measures mid-infrared spectra, here using a diffuse-reflectance accessory. The soil sample measurement shown is included with the package and is also part of the data set in Baumann et al. (2020).

Data parameters for absorbance (AB) spectrum

We can extract all these blocks from the example OPUS file. In fact, we can access even more blocks than are displayed in the official OPUS software.

    library("opusreader2")
    file_1 <- opus_test_file()
    spectrum_1 <- read_opus(dsn = file_1)

The `dsn` argument is the data source name. It can be a character vector of folder paths (to read files recursively) or specific OPUS file paths.

`read_opus()` returns a nested list of class `"list_opusreader2"`.

    print(spectrum_1)
    #> ╔==============================================================================╗
    #>                      OPUS collection with [1m1 spectrum[22m
    #> ╚==============================================================================╝
    #> [1mMeasurement metadata:[22m
    #> * date-time range (UTC): [2020-05-10 21:10:26 , 2020-05-10 21:10:26]
    #> [1m
    #> * data blocks (#spectra) -------------------------------------------------------
    #> [22m   refl_no_atm_comp (1);   sc_ref (1);   sc_sample (1);
    #> [1m
    #> * parameter blocks (#spectra) --------------------------------------------------
    #> [22m   acquisition (1);   acquisition_ref (1);   fourier_transformation (1);
    #>   fourier_transformation_ref (1);   info_block (1);   instrument (1);
    #>   instrument_ref (1);   lab_and_process_param_processed (1);
    #>   lab_and_process_param_raw (1);   optics (1);   optics_ref (1);
    #>   quant_report_refl (1);   refl_no_atm_comp_data_param (1);   sample (1);
    #>   sc_ref_data_param (1);   sc_sample_data_param (1);
    class(spectrum_1)
    #> [1] "list_opusreader2" "list"
    names(spectrum_1)
    #> [1] "test_spectra.0"

At the top level, the list structure matches how data is organized in the Bruker OPUS viewer.

    meas_1 <- spectrum_1[["BF_lo_01_soil_cal.1"]]
    names(meas_1)
    #> NULL

To understand block names and their contents, see the help page `?read_opus`. Block names use `camel_case` at the second level of the `"opusreader2_list"` output, making them easy to access programmatically.

Printing the entire `"opusreader2_list"` can flood the console. For easier exploration, use RStudio’s list preview with `View(spectrum_1)` or examine specific elements with `names()` and `str()`.

Some list elements may not be visible in the Bruker® viewer pane, but we compile them because they are either useful or actually part of the files:

1. `basic_metadata`: Minimal metadata to identify measurements, including file name, sample name, and timestamps. Useful for organizing spectral libraries and prediction workflows.
2. `ab_no_atm_comp_data_param`: Parameters for the absorbance (AB) block before atmospheric compensation.
3. `ab_no_atm_comp`: Absorbance data before atmospheric compensation.

    str(meas_1$basic_metadata)
    #>  NULL

All types of data and parameters within OPUS files are encoded with three capital letters each.

For example, to check the frequency of the first point (FXV), use:

    meas_1$ab_data_param$parameters$FXV$parameter_value
    #> NULL

Besides the data or parameter values, the output of each parsed OPUS block contains the block type, channel type, text type, additional type, the offset in bytes, next offset in bytes, and the chunk size in bytes for particular data blocks. This is decoded from the file header and allows for traceability in the parsing process.

    class(meas_1$ab_data_param)
    #> [1] "NULL"
    str(meas_1$ab_data_param)
    #>  NULL

    str(meas_1$instrument)
    #>  NULL

This example spectrum was measured with atmospheric compensation (removing masking information from carbon dioxide and water vapor bands), as set in the OPUS software. OPUS files track all processing steps and macros, so you can access both raw and processed data. This enables thorough quality control before modeling or making predictions on new samples.

## Reading OPUS files recursively from a folder

You can also provide a folder as the data source name (`dsn`). This makes it easy to read all OPUS files found within a folder and its subfolders. Here, we demonstrate this using the test files that come with the {opusreader2} package, which are also used for unit testing.

    test_dsn <- system.file("extdata", "test_data", package = "opusreader2")
    data_test <- read_opus(dsn = test_dsn)
    names(data_test)
    #> [1] "617262_1TP_C-1_A5.0" "629266_1TP_A-1_C1.0" "BF_lo_01_soil_cal.1"
    #> [4] "MMP_2107_Test1.001"  "test_spectra.0"

To get the instrument name from each test file, you can use:

    get_instrument_name <- function(data) {
      return(data$instrument$parameters$INS$parameter_value)
    }

    lapply(data_test, get_instrument_name)
    #> $`617262_1TP_C-1_A5.0`
    #> [1] "INVENIO-R"
    #>
    #> $`629266_1TP_A-1_C1.0`
    #> [1] "VERTEX 70"
    #>
    #> $BF_lo_01_soil_cal.1
    #> [1] "Alpha"
    #>
    #> $MMP_2107_Test1.001
    #> [1] "Tango"
    #>
    #> $test_spectra.0
    #> [1] "TENSOR II"

## Reading OPUS files in parallel

We implemented a parallel interface in `read_opus()` to efficiently read large collections of spectra from multiple OPUS files. The default and recommended backend, mirai, orchestrates reading files concurrently using asynchronous parallel mapping over individual OPUS files. The main advantage is faster reads, better and also fault-tolerant error handling (i.e. custom) actions when a file cannot be read.

Behind the scene, mirai uses nanonext and NNG (Nanomsg Next Gen) messaging, which allows high throughput and low latency between individual R processes.

All you have to do is launching daemons.

    if (!require("mirai")) {
      library("mirai")
      daemons(n = 2L, dispatcher = TRUE)

      (data <- read_opus(dsn = files_1000, parallel = TRUE))
    }

The task of reading files can be done locally or through distributed systems over a network. For further details, check out the mirai, and its vignettes.