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 use of portable micro-spectrometers such as a micro near infrared region (microNIR) spectrometer is a promising technique for solving analytical problems in several areas of science. This work evaluated the potential of microNIR in quality control of Arabica coffee. Arabica coffee has a high commercial value product, motivating the development of analytical methods with high sensitivity and accuracy for detection of its adulteration. Herein, microNIR was successfully used to determine the quality of Arabica coffee by identification and quantification of adulterations such as Robusta coffee (in different roasting levels), as well as corn, peels, and sticks. MicroNIR was combined with multivariate calibration by partial least squares (PLS) and principal component analysis (PCA). A total of 125 blends were produced, containing thirteen different concentrations of the adulterants (corn and peels/sticks, and the Robusta coffee) ranging from 1 to 100wt%. Developed PCA and PLS models were also applied to monitor the quality of sixteen commercial coffee samples. The results obtained using microNIR proved the ability of the method to be efficient and capable in the prediction of adulterations with minimum quantification levels (LOQs of 5-8wt%), being able to be applied to quality control of commercial coffee samples. Therefore, microNIR can reduce and simplify the time of analysis and sample preparation step, as well as to guarantee the efficiency of real-time data acquisition owing to its portability.

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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).