SoFi standard

The Source Finder software


When using SoFi

SoFi can be used on every type of multivariate data (environmental, lab, reactors, economic, etc.), whenever preservation of mass plays a central role and the data can be described using a simple factorization model, i.e. a multilinear regression on multivariate data.

SoFi performs this deconvolution on your data by applying the positive matrix factorization algorithm (PMF, Paatero 1994) governed through the multilinear engine (ME-2, Paatero 1999).

It all sounds very complicated, SoFi makes the entire analysis fast & simple.

What is SoFi

SoFi is a software package written in the powerful and extremely dynamic software environment IGOR (WaveMetrics Inc., Portland, OR, USA).

SoFi allows to easily explore and pretreat the data prior to the PMF algorithm. Points and variables can be blacklisted, downweighted..

Constraints representing a priori information can be simply added.

The user can explore PMF results in every detail to make sure the results are reasonable and satisfactory.

Learn more from the SoFi manual (button below).

Are you new to the SoFi community?

Please contact datalystica to receive the credentials for the download area and do not forget to subscribe to the mailing list to make sure you don't miss any updates on SoFi, PMF and ME-2.

What do you need to get started

SoFi runs in the software environment IGOR, hence you need to be in possess of a valid IGOR license. Contact WaveMetrics Inc. for more details on IGOR.

Currently, SoFi calls the ME-2 executable for solving the positive matrix factorization algorithm (PMF). You need windows or a windows emulator. The ME-2 solver is property of Dr. Pentti Paatero. Download the ME-2 solver (button below) and either contact Dr. Paatero or go to SoFi Pro to purchase a license key for ME-2 through Datalystica. Note that the solver alone will not work without the ME-2 key.

Download SoFi or ME-2 solver (restricted area, requires authentication credentials)

Do you want to have access to all functionalities for a more detailed data analysis? Read more about SoFi Pro.

Technical SoFi papers

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., Prévôt A.S.H., ATMOSPHERIC MEASUREMENT TECHNIQUES 6, 3649 (2013). DOI: 10.5194/amt-6-3649-2013

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., Äijälä M., Allan J. D., Carbone S., Capes G., Ceburnis D., M., D. O., 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. A., Mohr C., Nemitz E., O'Dowd C., Ovadnevaite J., Pandis S. N., Petäjä T., Poulain L., Saarikoski S., Sellegri K., Swietlicki E., Tiitta P., Worsnop D. R., Baltensperger U., and Prevot A. S. H. ATMOSPHERIC CHEMISTRY AND PHYSICS 14, 6159 (2014). DOI: 10.5194/acp-14-6159-2014

Seasonal differences in oxygenated organic aerosol composition: implications for emissions sources and factor analysis Canonaco F., Slowik J.G., Baltensperger U., Prévôt A.S.H., ATMOSPHERIC CHEMISTRY AND PHYSICS 15, 6993 (2015). DOI: 10.5194/acp-15-6993-2015

Anew 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., Sosedova Y., Slowik J.G., Bozzetti C., Dällenbach K.R., Crippa M., Huang R.-J., Furger M., Baltensperger U., Prévôt A. S. H., ATMOSPHERIC MEASUREMENT TECHNIQUES, DOI: 10.5194/amt-14-923-2021


and more members of Sofi community (2020)


Citations of the Sofi paper since 2013 (2020)