## Iterative Non-Negative Matrix Factorization Filter for Blind Deconvolution in Photon/Ion Counting

journal contribution

posted on 11.03.2019 by Scott
R. Griffin, John A. Biechele-Speziale, Casey J. Smith, Ximeng You-Dow, Julia K. White, Si-Wei Zhang, Julie Novak, Zhen Liu, Garth J. Simpson#### journal contribution

Any type of content formally published in an academic journal, usually following a peer-review process.

A digital
filter based on non-negative matrix factorization (NMF)
enables blind deconvolution of temporal information from large data
sets, simultaneously recovering both photon arrival times and the
instrument impulse response function (IRF). In general, the measured
digital signals produced by modern analytical instrumentation are
convolved by the corresponding IRFs, which can complicate quantitative
analyses. Common examples include photon counting (PC), chromatography,
super resolution imaging, fluorescence imaging, and mass spectrometry.
Scintillation counting, in particular, provides a signal-to-noise
advantage in measurements of low intensity signals, but has a limited
dynamic range due to pulse overlap. This limitation can complicate
the interpretation of data by masking temporal and amplitude information
on the underlying detected signal. Typical methods for deconvolution
of the photon events require advanced knowledge of the IRF, which
is not generally trivial to obtain. In this work, a sliding window
approach was developed to perform NMF one pixel at a time on short
segments of large (e.g., 25 million point) data sets. Using random
initial guesses for the IRF, the NMF filter simultaneously recovered
both the deconvolved photon arrival times and the IRF. Applying the
NMF filter to the analysis of triboluminescence (TL) data traces of
active pharmaceutical ingredients enabled discrimination between different
hypothesized physical origins of the signal.