The industrial process of soybean oil hydrogenation usually has its quality control performed through time-consuming methodologies that generate chemical residues. Thus, it is necessary to develop faster low cost waste-free instrumentation methodologies. The aim of this work was to evaluate an ultra-compact near-infrared spectrometer in tandem with the partial least squares regression (PLSR) or support vector regression (SVR) in the control of the hydrogenation process. Models were adjusted to predict the amount of saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), trans fatty acids (TFA), polyunsaturated fatty acids (PUFA), and the iodine value (IV). The values predicted by the PLSR and SVR models were compared to the experimental values obtained by gas chromatography (GC-FID). As NIRS spectra present a large number of variables, a methodology for feature selection was also assessed. Good multivariate models were obtained for IV, MUFA, PUFA, and TFA for PLSR, and good models for IV and UFA when using SVR. The feature selection using the correlation vector was also efficient, maintaining the performance of the models and reducing by up to 78% the amount of variables used for the PLSR and 85% for the SVR. The values obtained for RMSEC, RMSECV, and square correlation coefficient (r2) remained very close for both PLSR and SVR. The residual prediction deviation (RPD) was adequate for the quality control of the hydrogenation process for IV and PUFA in the PLSR and for PUFA in the SVR. It is worth noting that the spectrometer used has being low cost, effortless-assembly and easy handling, which allows its use in any environment. Thus, through the results obtained, it was demonstrated that the NIRS methodology in tandem with PLSR or SVR could be used to monitor the industrial hydrogenation process of soybean oil.