The characterization of organic compounds in polluted matrices by eco-friendly 3D fluorescence spectroscopy coupled with chemometrics algorithms constitutes a powerful alternative to the separative techniques conventionally used1. However, the systematic presence of Rayleigh and/or Raman scattering signals in the Excitation Emission Matrices (EEMs) complicates the spectral decomposition via PARAllel FACtor analysis (PARAFAC) due to the non-trilinear structure of these signals. Likewise, specific problem of selectivity in spectroscopy for unexpected chemical components in a complex sample may render its chemical interpretation difficult at first glance. The chemical relevant information can then be complicated to extract, especially if the raw data is noisy. There are several strategies to overcome these drawbacks but weaknesses remain2. As a consequence, a new alternative method is proposed to handle these interferences, noise and rank deficiencies in the data and applied for the characterization of polycyclic aromatic compound (PAC) mixtures. It is based on an effective truncated singular value decomposition (MT-SVD) which does not require any prior knowledge on the raw data. The algorithm provides a valuable estimation of the optimal low-rank to choose on complex samples where selectivity problems are observed. It is a real alternative compared to other existing methods applied on 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. Finally, the MT-SVD is a flexible algorithm and it will be tested on other instrumental techniques (e.g. Raman imaging) and other types of samples.