Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

The contents of saturates, aromatics, and polars in crude oil were determined using carbon-13 nuclear magnetic resonance spectroscopy ( 13 C NMR) associated with support vector regression (SVR) and a genetic algorithm (GA) for the simultaneous selection of spectral variables and SVR model parameters. The developed models presented prediction sample errors of 4.4% (w/w) for saturates, 4.3% (w/w) for aromatics (w/w), and 3.7% (w/w) for polars. These results are acceptable for the petroleum industry, considering that the error obtained by the standard methodology is 5% (w/w), which is the maximum value of variation allowed in SARA analysis. The proposed methodology made these determinations using small amounts of samples (approximately 2 mL) in a relatively short time (approximately 2 h).

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The total acid number (TAN) was determined in the atmospheric residue (AR) and vacuum residue (VR) of the petroleum distillation process using mid-infrared spectroscopy with attenuated total reflection in association with chemometric methods. Calibration and prediction sets consisted of 44 and 13 samples, respectively (16 AR samples and 41 VR samples). Calibration models were developed using three variableselection models: interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and backward interval partial least squares (biPLS). Different treatments and preprocessing steps were also evaluated for development of the models. The treatment based on the first derivative with a SavitzkyGolay filter and the mean centered was selected for model construction. A root-mean-square error of prediction of 0.164 mg g -1 of KOH was achieved using the biPLS algorithm with spectra divided into 20 intervals and combinations of