Positive Matrix Factorization for dummies (better audio)

Dr. Lisa explains the theory behind Positive Matrix Factorization and give some advice on how to construct your input matrixes.

[wildvineyard]

HI all, I frequently get asked about how to construct your uncertainty matrix. Here is my advice: calculating uncertainty is tough. We use the RSD of surrogate recoveries, but if you don't have those, you can use the Relative Percent Difference of duplicates. Often the RPD ranges from tiny to >100%, so I usually scale it so that the uncertainties range from about 10% to about 35% but the ranking of the uncertainties among the analytes is the same. I have also used information on reproducibility that we got from the published method.

If all else fails, you can punt and use a sliding scale, starting with 10% for the things that you feel in your gut are certain (probably including things that are well above the LOD) and use 35% for the less certain things (including stuff near the LOD). For example, I would give a high uncertainty to things like nitrite since it is so reactive and hard to measure, and low uncertainty to things like TOC, which is pretty easy to measure. Keep in mind that PMF 5.0 takes the uncertainty in the same units as the input concentrations, so you have to multiply these percentages by the measured concentration. And you always give a much higher unc to NDs. I usually use three times the regular uncertainty for that analyte and apply it to NDs (multiplied by whatever value I'm using as a proxy for the ND, usually half the LOD).

You may have to try a few unc matrixes until you find one that works. And PMF 5.0 has that field under Base where you can add extra modeling uncertainty. Try it with 0% extra uncertainty and then keep increasing the extra uncertainty by 5% at a time until you get something that works.

[SaifiIzhar]

This is awesome. Looking for more in-depth discussion like rotation and fpeak values

[rexdalit]

Kindly drop the word "right" from your presentations. It adds nothing, and is distractive. (ps This version does have much better audio. Thx.)