The characterization
of organic compounds in polluted matrices
by eco-friendly three-dimensional (3D) fluorescence spectroscopy coupled
with chemometric algorithms constitutes a powerful alternative to
the separation techniques conventionally used. However, the systematic
presence of Rayleigh and Raman scattering signals in the excitation–emission
matrices (EEMs) complicates the spectral decomposition via PARAllel
FACtor analysis (PARAFAC) due to the nontrilinear structure of these
signals. Likewise, the specific problem of selectivity in spectroscopy
for unexpected chemical components in a complex sample may render
its chemical interpretation difficult at first glance. The relevant
chemical information can then be complicated to extract, especially
if the raw data is noisy. There are several strategies to overcome
these drawbacks, but weaknesses remain. As a consequence, a new alternative
method is proposed to handle these interferences, the noise, and the
rank deficiencies in the data and applied for the characterization
of polycyclic aromatic compound (PAC) mixtures. It is based on effective
truncated singular-value decomposition (MT-SVD) that does not require
any prior knowledge of the raw data. The algorithm provides a valuable
estimation of the global rank to choose on complex samples where selectivity
problems are observed. It is a real alternative compared to other
existing methods applied to the fluorescence matrix to filter the
signal from noise or light scattering effects. The first exploratory
results of the proposed algorithm are promising to handle matrix rank
deficiencies as well as the effects of noise and light scattering
on complex PAC mixtures.
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