by Dirk Mittelstraß | Last update: Jul 20, 2023

About this blog

Luminescence dating might be the best tool to determine the age of Pleistocene landscapes and archaeological artefacts. Over the last years, I have been researching on new data analysis methods for some specific luminescence dating applications as private side projects in my free time. This blog is intended to give you access and further information about those projects

Luminescence dating and spectroscopy as a service

You can hire me on a fee basis to perform detailed data analyses with R of your OSL, TL or IR-RF measurements. I provide the calculation of equivalent dose statistics and paleodoses including the evaluation of test steps and rejection criteria. You will also get a detailed PDF report and the source code of the data analysis. Write me a mail if you are interested:

If you like to outsource sample preparation and/or the luminescence measurements itself, I recommend to use the services of the LUNA luminescence laboratory at the Helmholtz Institute Freiberg for Resource Technology (HZDR) in Germany. The LUNA lab is lead by Dr. Margret Fuchs () and is specialized on spectroscopic investigations for mineral exploration and characterization. It is also well-equipped for geochronological investigations including OSL and TL dating and spectrometry. One special capability of the LUNA lab is the high-quality mineral separation in sediments through froth flotation.

My research projects

Dating application Data analysis software
CW-OSL component separation at quartz Introduction & Tutorial
Examples for quartz CW-OSL component identification and separation
Spatially resolved IR-RF at K-feldspars Research article published in the scientific journal Geochronology.
Download and installation
Example data and sequences
SR-RF macro Handbook and GitHub page


2023-07-20 OSLdecomposition package is actually used!

Last month I visited the Luminescence and ESR dating conference in Copenhagen and was surprised and happy to find out that my R package OSLdecomposition is already used (or at least tested) by some work groups while others are interested to use it in the future. I was not aware of that because I got no feedback so far. However, that gave me renewed motivation to finish the scientific paper about the OSLdecomposition R package.

If you like to cite the package before the paper is available please use the reference you get if you type this in R:


By the way, I outlined the future update to 1.1.0:

  • Add a Method.control argument and K.min to fit_OSLcurve() to allow more tweaking of the genetic algorithm.
  • Possibility to set one or more fixed decay constant to fit_OSLcurve() to enable interesting new fitting approaches.
  • Revise plot_OSLcurve(), especially add the option to draw the signal components as stacked areas.
  • Add HMTL report output to RLum.OSL_correction().
  • Simplify the component analysis for IR-RF measurements by adding the new wrapper functions RLum.RF_correction() and RLum.RF_fitting().
  • Add a function get_RLum.sequence() to return the measurement sequences used in a data set as knitr-compatible table and include it in RLum.OSL_correction(). and RLum.RF_correction().
  • Revise sum_OSLcurves(), especially speed up the far too slow plot output.
  • Ensure the package is ready for the XLUM data format.

Much of the above points were already coded and tested for other projects and just need to be merged into the main branch of OSLdecomposition. However, as this update has not the highest priority for me right now and the whole development is a hobbiest project anyway, please do not expect that the update will be released within the next months. Nonetheless, I will keep you updated and invite everyone to write their comments and wishes in the associated GitHub thread.

2023-06-25 IR-RF component fitting

In my latest side project, I analysed the shapes of IR-RF signal curves of a variety of K-feldspar samples with methods from OSLdecomposition. Apparently, IR-RF curves can be sufficiently described by multi-exponential fittings, see the example below. Although further investigations are necessary, I summarized my findings in my poster for the LED conference 2023.

Figure: Multi-exponential curve fitting results for a RF70 measurement of an aliquot of sample Gi149
Figure: Multi-exponential curve fitting results for a RF70 measurement of an aliquot of sample Gi149

2022-11-16 Sontag-Gonzales et al. (2022) published in Radiation Measurements

A new scientific paper I co-authored is published: Wavelength calibration and spectral sensitivity correction of luminescence measurements for dosimetry applications: Method comparison tested on the IR-RF of K-feldspar written by Mariana Sontag-Gonzales who is part of Markus Fuchs’s work group in Giessen. Mariana did an excellent job in describing multiple approaches for wavelength and sensitivity calibration of for luminescence spectroscopy and comparing them. As case study, the paper includes also IR-RF peak analyses of two K-feldspar samples. doi: 10.1016/j.radmeas.2022.106876.

2022-08-14 Package OSLdecomposition is released on CRAN

Finally, my OSLdecomposition function library for R is released on CRAN. The package can now be installed using the RStudio package manager or by simply typing this into your R environment:


If you are not familiar with what it is all about or how to use the package, check out the Introduction & Tutorial page.

2022-07-31 CRAN release of package OSLdecomposition soon

My biggest scientific child, the OSLdecomposition package, is about to be released at CRAN soon. I already submitted the package to CRAN in May this year. The review process revealed a few minor issues and one big one: The necessary computing time to perform the fitting of the global OSL curve with fit_OSLcurve() exceeded the allowed limits for the code examples. Even with minimum examples, I could not get the computing time below 10 seconds while only 5 seconds are allowed for CRAN releases. As it turned out, my approach to calculate the residual sum of squares (RSS) did a lot of memory allocations and these are highly inefficient in R. By incorporating the RSS calculation directly into to decompose_OSLcurve() and taking care to create only as many vectors and data.frame columns as truly necessary, I could speed up the curve fitting between 3 and 5 times.

2021-06-19 Spatially resolved IR-RF paper published / vDEUQUA 2021

The research article presenting the method of grain-wise IR-RF dating of K-feldspars developed by Sebastian Kreutzer and myself has passed the review process in Geochronology and is now officially available at

If you would like to discuss the future of IR-RF dating with me or Sebastian, why not register for the vDEUQUA 2021 online conference? The vDEUQUA is about quaternary climate and environmental changes, the methods to investigate them and how quarternary processes shaped our todays world. The conference is virtual, free of charge and happens from 2021-09-30 to 2021-10-01. See you there!

2021-02-24 ‘OSLdecomposition’ update

The package OSLdecomposition got an update, see the Changelog for details. The function RLum.OSL_correction() includes now an algorithm that checks if the records contain not stimulated periods at beginning aor end of the measurement and removes them. This is for example useful to enable component separation in single-grain measurements.

2021-01-28 Preprint of spatially resolved IR-RF paper available

I’m happy to anounce that our manuscript about spatially resolved IR-RF dating is accepted for discussion in the journal Geochronology. The preprint is available at Short comments are allowed until March 11.

2020-11-26 ‘OSLdecomposition’ release

Just in time for the DLED2020, we released our R package OSLdecomposition for signal component identification and separation in multi-exponentially decaying measurements. Our focus laid on continous-wave optically stimulated luminescence (CW-OSL) measurements of quartz samples for dosimetry purposes. But you might find the package helpful for other applications too. Check out our DLED poster for a quick introduction into the Why and How.

2020-11-26 goes online

Welcome to my site about new data analysis methods for luminescence dating measurements. Since about 2013, I worked at number of methods on this issue. This blog is meant to give you access to some of my findings and the software I developed. As this blog will slowly get more content over the next months, I hope to see you around sometime!

© Dirk Mittelstraß, 2020 - 2023 | This website was created with Rmarkdown