Because the methods specified by regulatory agencies for the determination of the physicochemical properties of biodiesel can be laborious and expensive, the development of alternative methodologies represents a major breakthrough. Thus, low-field nuclear magnetic resonance (NMR) is an advantageous option because it is nondestructive and reduces the cost and time consumption. In this study, the partial least squares (PLS) regression method was used to create models that correlated the decay curves of the Carr-Purcell-Meiboom-Gill (CPMG) signal, obtained from low-field NMR equipment (2.2 MHz for H-1), with the kinematic viscosity, specific mass and refractive index of biodiesel and their blends. Seventeen oilseeds diversified between edible and nonedible oils were utilized to synthesize the biodiesel and produce binary blends. Separately, multivariate calibration models were created only with biodiesel and blends with castor bean because these samples showed different tendencies from the others. The values of root mean square error of prediction (RMSEP) for the kinematic viscosity, specific mass and refractive index were equal to 0.1 mm(2).s( -1), 3.7 kg.m(-3) and 0.002, respectively, for samples of biodiesel and blends without castor bean and 0.6 mm(2).s(-1), 2.0 kg.in(-3) and 0.0005 for samples of biodiesel and blends with castor bean. The results reveal that the developed models are very satisfactory to predict the quality parameters of biodiesel and blends based on CPMG data with fairly good efficacy.