Fourier transforming near infrared (FT-NIR) and mid infrared (FT-MIR) spectroscopies combined with multivariate curve resolution alternating least squares (MCR-ALS) and partial least squares (PLS) regression were utilized to identify and quantify residual vegetable oil adulteration in commercial diesel. In addition, physiochemical methods following ASTM standards were performed for comparison in identifying diesel adulteration. In total, 16 commercial diesel samples containing 8% biodiesel and 10 mg sulfur/kg (named S10B8) were prepared with the addition of residual vegetable oil (1-60%) and were analyzed by FT-NIR and FT-MIR spectroscopies. The physical-chemical properties analyzed were atmospheric distillation, flash point, kinematic viscosity and specific mass tests. Only the distillation curve was able to identify the adulteration. A paired t-test and an F-test were performed for the MCR-NIR, MCR-MIR, PLS-NIR and PLS-MIR models. Through the results, it can be stated that all the models used in this work are valid for a 95% confidence level. Furthermore, no statistical difference was observed between the estimated concentrations of adulterant and the reference values. In this work, MCR-ALS was applied as a spectral decomposition tool as a new approach to identify the adulterant profile on biodiesel/diesel blends, followed by its quantification. MCR-ALS was able to recover the related pure spectral profile of the fuels and adulterant, and to accurately predict the concentration of frying oil in biodiesel/diesel samples.