The increasing use of conventional biofuels, e.g. fatty acid methyl esters (FAME), the so-called biodiesel, and paraffinic advanced biofuels, e.g. hydroprocessed esters and fatty acids (HEFA), well-known as renewable diesel, in mixtures with petroleum diesel is a feasible alternative in order to increase the use of renewable energy sources in the transport sector. However, paraffinic advanced biofuels are a mixture of paraffins naturally found also in petroleum-derived fuels. Thus, the distinction and quantification of such advanced biofuels in mixtures with biodiesel and petroleum diesel in an efficient way is a challenge for the quality control of this type of fuel blend. In this work, we present an innovative analytical method to distinguish and quantify renewable diesel and biodiesel contents in diesel fuel blends with adequate accuracy in a fast, simple and cost-effective manner through the employment of nearinfrared (NIR) spectroscopy and chemometrics. Multivariate calibration models using partial least squares (PLS) or support vector machines (SVM) provided prediction results of HEFA renewable diesel and biodiesel contents with values of root mean square error of prediction (RMSEP) up to 0.5% (v/ v) and relative error of prediction (REP) up to 2.7%, being adequate for real-world applications. (C) 2015 Elsevier Ltd. All rights reserved.