  One of the main difficulties to analyze modern spectroscopic datasets is due
to the large amount of data. For example, in atmospheric transmittance
spectroscopy, the solar occultation channel (SO) of the NOMAD instrument
onboard the ESA ExoMars2016 satellite called Trace Gas Orbiter (TGO) had
produced $\sim$10 millions of spectra in 20000 acquisition sequences since the
beginning of the mission in April 2018 until 15 January 2020. Other datasets
are even larger with $\sim$billions of spectra for OMEGA onboard Mars Express
or CRISM onboard Mars Reconnaissance Orbiter. Usually, new lines are discovered
after a long iterative process of model fitting and manual residual analysis.
Here we propose a new method based on unsupervised machine learning, to
automatically detect new minor species. Although precise quantification is out
of scope, this tool can also be used to quickly summarize the dataset, by
giving few endmembers ("source") and their abundances. We approximate the
dataset non-linearity by a linear mixture of abundance and source spectra
(endmembers). We used unsupervised source separation in form of non-negative
matrix factorization to estimate those quantities. Several methods are tested
on synthetic and simulation data. Our approach is dedicated to detect minor
species spectra rather than precisely quantifying them. On synthetic example,
this approach is able to detect chemical compounds present in form of 100
hidden spectra out of $10^4$, at 1.5 times the noise level. Results on
simulated spectra of NOMAD-SO targeting CH$_{4}$ show that detection limits
goes in the range of 100-500 ppt in favorable conditions. Results on real
martian data from NOMAD-SO show that CO$_{2}$ and H$_{2}$O are present, as
expected, but CH$_{4}$ is absent. Nevertheless, we confirm a set of new
unexpected lines in the database, attributed by ACS instrument Team to the
CO$_{2}$ magnetic dipole.
