Biofuel characterization constitutes a substantial improvement in the valorization of this resource and allows a rational and controlled use of its energy potential. Corrosion and slagging are primary concerns of solid biofuels; the occurrence and extent of these phenomena depend significantly on the concentration of chlorine in the solid biofuel and the presence of elements such as potassium, sulfur, sodium, phosphorus, calcium, magnesium, iron, or silicon. Solid biofuels quality parameters are being determined by official methods established by the European Standard Technology Committee. Nevertheless, their implementation in the bioenergetic industry is scarce because these methods are expensive, tedious, and time-consuming. Therefore, a faster, more reliable, and cheaper analytical technique is mandatory in order to detect high concentrations of these parameters and avoid subsequent damages in heat exchanging surfaces. Near-infrared (NIR) spectroscopy is an eligible technique due to its high response speed, low cost per sample, absence of sample preparation, and versatility for the analysis of many different products and parameters. In this work, 250 samples of olive stone, olive tree pruning and dry depleted olive pomace, known also as "orujillo", have been collected, and NIR prediction model for determination of parameters such as chlorine, sulfur, and potassium have been obtained and evaluated. Correlation between actual and predicted values (R-2) was used to test the performance of calibrations. Practical utility of the validation models were assessed using the ratio of standard error of prediction to standard deviation of the reference data (RPD). High accuracy in prediction for a test set has been achieved for chlorine, sulfur, and potassium content (R-2 > 0.9 and RPD > 3), and standard error of prediction (SEP) values obtained in external validation with 53 independent samples are 129 mg kg(-1) and 0.008% for chlorine and sulfur, respectively. This study illustrates the possibility of implementing the NIR technique in combination with multivariate data analysis to predict corrosive elements from olive byproducts in an economical and fast way.