The use of mid-infrared spectrometry with horizontal attenuated total reflectance and Fourier transform, along with methods of variable selection by intervals, that is, interval partial least-squares (iPLS), backward interval partial least-squares (biPLS), and synergy interval partial least-squares (siPLS), were evaluated in order to quantify the Jatropha methyl biodiesel content in mixtures with diesel in the range from 0.25 to 30.00% (v/v). The spectral data were obtained in quintuplicate and corrected using the baseline technique. The constructed variable selection models were compared with the global partial least-squares (PLS) model by applying the F test to the root mean square error of prediction (RMSEP) value. The model with the best predictive capacity was iPLS₂₄ (i.e., dividing the full spectrum into 24 equidistant intervals) constructed in the region from 650 to 750 cm⁻¹ with 101 variables. The parameters obtained were the root mean square error of calibration (RMSEC) of 0.22%, the root mean square error of cross-validation (RMSECV) of 0.25%, RMSEP of 0.24%, limit of detection (LD) of 0.40% (v/v) and limit of quantification (LQ) of 0.13% (v/v). Therefore, the interval variable selection method was efficient in obtaining a spectral region that provided better predictive capacity than the global PLS model. In addition, with the selection of a smaller number of variables, it is possible to build portable equipment at a lower cost for use by fuel quality control agencies.