Products

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. Our source apportionment software is designed to help you along all the

SoFi applies a positive matrix factorization algorithm on your data for this deconvolution, currently using the multilinear engine (ME-2), developped by Pentti Paatero. But we are also working on new PMF algorithms.

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) as well as 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

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We offer a variety of different products. We are the market leader in source apportionment 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.

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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, https://doi.org/10.1016/j.envint.2022.107325, 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, https://doi.org/10.5194/amt-14-923-2021, 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, https://doi.org/10.5194/acp-14-6159-2014, 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, https://doi.org/10.5194/amt-6-3649-2013, 2013.