Our Source Finder (SoFi) allows you to efficiently analyze your multivariate data with factor analytic tools and is not limited to a certain type of dataset, it is for any type of data: environmental, lab, reactor, economic, etc. as long as the data can be described using a simple factorization model.

SoFi applies a positive matrix factorization algorithm on your data for this deconvolution using the multilinear engine (ME-2), developped by Pentti Paatero.

Often SoFi is used for ambient air pollution measurements (indoor and outdoor), measured by a wide variety of instruments, either online of offline. However, SoFi is not limited to such kind of data, SoFi can be used on any kind of data. SoFi identifies (source fingerprint/profile) and quantifies (source concentration) sources.

It all sounds very complicated, but SoFi makes the entire analysis fast & simple. In short, our goal is to make your life easier by helping you with as many tools as possible with the analysis.

Discover our products

We offer a variety of different products. We are the market leader in source apportionement software. Furthermore, we are the sole official distributer of the multi-linear engine (ME-2), which was developped by Pentti Paatero. If you have any questions on our products, please don’t hesitate to contact us. We are happy to help you find the perfect software solution for your data.

SoFi Pro

Explore the world of PMF with SoFi Pro and have access to all features and our customer support for your data analyis journey.


Use our software to get source apportionment results from your instrument in real-time!

Data analyse service

Struggeling with your data? Not sufficient time for the data analysis? Not having enough expertise in your company?

No problem! In this case, we do the analysis for you.

Technical SoFi papers

European aerosol phenomenology 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets, Chen, G., Canonaco, F., Tobler, A., Aas, W., Alastuey, A., Allan, J., Atabakhsh, S., Aurela, M., Baltensperger, U., Bougiatioti, A., De Brito, J. F., Ceburnis, D., Chazeau, B., Chebaicheb, H., Daellenbach, K. R., Ehn, M., El Haddad, I., Eleftheriadis, K., Favez, O., Flentje, H., Font, A., Fossum, K., Freney, E., Gini, M., Green, D. C., Heikkinen, L., Herrmann, H., Kalogridis, A.-C., Keernik, H., Lhotka, R., Lin, C., Lunder, C., Maasikmets, M., Manousakas, M. I., Marchand, N., Marin, C., Marmureanu, L., Mihalopoulos, N., Mocnik, G., Necki, J., O’Dowd, C., Ovadnevaite, J., Peter, T., Petit, J.-E., Pikridas, M., Matthew Platt, S., Pokorná, P., Poulain, L., Priestman, M., Riffault, V., Rinaldi, M., Rózanski, K., Schwarz, J., Sciare, J., Simon, L., Skiba, A., Slowik, J. G., Sosedova, Y., Stavroulas, I., Styszko, K., Teinemaa, E., Timonen, H., Tremper, A., Vasilescu, J., Via, M., Vodicka, P., Wiedensohler, A., Zografou, O., Cruz Minguillón, M., and Prévôt, A. S. H. , Environ. Int., 166, 107325,, 2022.

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data, Canonaco, F., Tobler, A., Chen, G., Sosedova, Y., Slowik, J. G., Bozzetti, C., Daellenbach, K. R., El Haddad, I., Crippa, M., Huang, R.-J., Furger, M., Baltensperger, U., and Prévôt, A. S. H., Atmos. Meas. Tech., 14, 923-943,, 2021.

Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach, Crippa, M., Canonaco, F., Lanz, V. A., Aijala, M., Allan, J. D., Carbone, S., Capes, G., Ceburnis, D., Dall’Osto, M., Day, D. A., DeCarlo, P. F., Ehn, M., Eriksson, A., Freney, E., Hildebrandt Ruiz, L., Hillamo, R., Jimenez, J. L., Junninen, H., Kiendler-Scharr, A., Kortelainen, A. M., Kulmala, M., Laaksonen, A., Mensah, A., Mohr, C., Nemitz, E., O’Dowd, C., Ovadnevaite, J., Pandis, S. N., Petaja, T., Poulain, L., Saarikoski, S., Sellegri, K., Swietlicki, E., Tiitta, P., Worsnop, D. R., Baltensperger, U., and Prevot, A. S. H., Atmos. Chem. Phys., 14, 6159-6176,, 2014.

SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data, Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., and Prevot, A. S. H., Atmos. Meas. Tech., 6, 3649-3661,, 2013.


and more members of SoFi community (2022)


Citations of the SoFi paper since 2013 (2022)

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