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Air pollution is a serious problem, and to decrease the emission of pollutants, governments have established environmental rules to limit the concentration of sulfur in diesel fuel. Control of sulfur content in diesel streams demands online measurement of this component, with low pure time delay and easy operation. In this regard, two-dimensional (2D) fluorescence spectroscopy becomes a promising choice for soft-sensor development. Despite fluorescence qualities, translation of fluorescence spectroscopic data into process knowledge is not a simple task, demanding multivariate analysis. This work aims to evaluate the applicability of 2D fluorescence spectroscopy for monitoring ultralow sulfur diesel streams (Diesel S10) and the capability of differentiation between ULSD and diesel streams with a higher sulfur concentration (Diesel S100). The evaluation of the different sample groups' fluorescence spectra made clear the ability of the technique to capture changes in the composition between Diesel S10 and Diesel S100. The results obtained using partial least squares discriminant analysis (PLS-DA) and random forest (RF) shows that fluorescence spectroscopy can be applied for the classification of Diesel S10 and Diesel S100 test samples. Models calibrated with both methodologies achieved 100% correct classification. The RF implementation was better in the selection of a few specific excitation/emission pairs (four) that could be used in the development of a customized sensor for diesel fuel classification.

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