About the software


FTIR is the most promising analytical technique for MP analysis (ID, quantification). However, analysing the large amount of data generated during scans is time-consuming and creates limitations.

A full automatization (or semi-automatization) of the data analysis is important to increase the accuracy and precision of the analysis.

”AWI automatic pipeline” was the first example of fully automatized MP data analysis. 

”AAU MPHunter” is another example of semi-automated software for MP analysis. It has a user friendly interface (FT-IR software-like), and it is customizable for the specific needs of the user.

MPHunter is written by Jes Vollertsen using RAD Studio (Embarcadero Delphi IDE), an object oriented programming environment allowing easy construction of user friendly interfaces preserving fast computational speed.

MPHunter requires DMD (Agilent) and .DX (Bruker) file conversion to MPHunter software file format (.spe)

It is able to manage of FPA data from different FPA detectors (e.g. 64x64 and 128x128)

Another advantage of the MPHunter, that it´s extremely ”light” application… around 5 MB.

siMPle combines the interface of MPHunter with AWI Automatic Pipeline for MP analysis, creating a freeware program for MP data analysis.

How does it work?

siMPle operates following these steps:

Manual analysis:

Multiple issues in data handling and managing

2-3 scans

per day

Most of FT-IR imaging softwares are not reliable for the MP analysis

Inaccurate analysis!


(10 mm diameter) scan velocity up to 4,5h

(128x128 FPA)


amount of data,

many GB to process

FPA-µFT-IR-Imaging spectroscopy for MP analysis:

First example of automatic pipeline (AWI)

FTIR Data processing

by Opus Macro

Based on a commercial

FTIR software

Image analysis

(Python Script)


identification, quantification and image analyis (particle size distribution)

Data aquisition


Pearson’s Correlation Coefficient analysis

Removing noise

using filters


particles as MP

For detecting the microplastics in the sample, an automated algorithm is applied where all reference spectra in the database are compared to all spectra in the map. The reference database contains both plastic polymers and natural materials, having spectra that show similarities to those of the plastics, have been used. The various materials in the spectra-database are assigned to different material groups such as PP, PE, PET, and so on. The algorithm used for detecting microplastic particles applies 2 thresholds of probability score. First the algorithm finds all pixels where the highest probability score  belongs to a plastic material and where that score is above the higher threshold. It analyses all the adjacent pixels and adds them to the plastic particle if they have a material belonging to the same material group and which has a probability score above the second threshold.



The so-identified plastic particles are then analyzed for the longest distance between pixels of the particle, yielding the major dimension of the particle. The minor dimension is found by assuming the particle shape is an ellipse and knowing the area of the particle in the scan. The third dimension, the thickness, is assumed as being 0.67 times the minor dimension. The volume is calculated assuming the particle is an ellipsoid. The mass is calculated from the volume and the density of the identified plastic material.




the found MP


The software appears in the following publications:

Liu, F., Olesen, K.B., Borregaard, A.R., Vollertsen, J., 2019. Microplastics in urban and highway stormwater retention ponds. Sci. Total Environ. 671.

Primpke, S., A. Dias, P., Gerdts, G., 2019. Automated identification and quantification of microfibres and microplastics. Anal. Methods 11, 2138–2147.

Primpke, S., Lorenz, C., Rascher-Friesenhausen, R., Gerdts, G., 2017. An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis. Anal. Methods 9, 1499–1511. 

Primpke, S., Wirth, M., Lorenz, C., Gerdts, G., 2018. Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy. Anal. Bioanal. Chem. 410, 5131–5141.

Cabernard, L.;  Roscher, L.;  Lorenz, C.;  Gerdts, G.; Primpke, S., Comparison of Raman and Fourier Transform Infrared Spectroscopy for the Quantification of Microplastics in the Aquatic Environment. Environmental Science & Technology 2018, 52 (22), 13279-13288. 

Primpke, S., Cross, R.K., Mintenig, S.M., Simon, M., Vianello, A., Gerdts, G., Vollertsen, J., 2020. Toward the Systematic Identification of Microplastics in the Environment: Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis. Appl. Spectrosc., 0003702820917760.

Meyns, M., Primpke, S., Gerdts, G., 2019. Library based identification and characterisation of polymers with nano-FTIR and IR-sSNOM imaging. Anal. Methods 11, 5195-5202.

Mintenig, S.M., Kooi, M., Erich, M.W., Primpke, S., Redondo- Hasselerharm, P.E., Dekker, S.C., Koelmans, A.A., van Wezel, A.P., 2020. A systems approach to understand microplastic occurrence and variability in Dutch riverine surface waters. Water Res., 115723.

The software was developed by Aalborg University, Denmark in collaboration with Alfred Wegener Institute, Germany.

About the developers

Contributions to the program have come from:

- Jes Vollertsen (Aalborg University)

- Sebastian Primpke (Alfred Wegener Institute)

- Gunnar Gerdts (Alfred Wegener Institute)

- Alvise Vianello (Aalborg University)

- Kristina Borg Olesen (Aalborg University)

- Marta Simon (Aalborg University)

- Fan Liu (Aalborg University)

- Nikki van Alst (Aalborg University)

License Agreement

License Agreement for the use of the software can be downloaded here:

Software for the automated detection of microplastic

Email: info@simple-plastics.eu

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