Bioethanol can be obtained from wood by simultaneous enzymatic saccharification and fermentation step (SSF). However, for enzymatic process to be effective, a pretreatment is needed to break the wood structure and to remove lignin to expose the carbohydrates components. Evaluation of these processes requires characterization of the materials generated in the different stages. The traditional analytical methods of wood, pretreated materials (pulps), monosaccharides in the hydrolyzated pulps, and ethanol involve laborious and destructive methodologies. This, together with the high cost of enzymes and the possibility to obtain low ethanol yields from some pulps, makes it suitable to have rapid, nondestructive, less expensive, and quantitative methods to monitoring the processes to obtain ethanol from wood. In this work, infrared spectroscopy (IR) accompanied with multivariate analysis is used to characterize chemically organosolv pretreated Eucalyptus globulus pulps (glucans, lignin, and hemicellulosic sugars), as well as to predict the ethanol yield after a SSF process. Mid (4,000-400 cm(-1)) and near-infrared (12,500-4,000 cm(-1)) spectra of pulps were used in order to obtain calibration models through of partial least squares regression (PLS). The obtained multivariate models were validated by cross validation and by external validation. Mid-infrared (mid-IR)/NIR PLS models to quantify ethanol concentration were also compared with a mathematical approach to predict ethanol yield estimated from the chemical composition of the pulps determined by wet chemical methods (discrete chemical data). Results show the high ability of the infrared spectra in both regions, mid-IR and NIR, to calibrate and predict the ethanol yield and the chemical components of pulps, with low values of standard calibration and validation errors (root mean square error of calibration, root mean square error of validation (RMSEV), and root mean square error of prediction), high correlation between predicted and measured by the reference methods values (R (2) between 0.789 and 0.997), and adequate values of the ratio between the standard deviation of the reference methods and the standard errors of infrared PLS models relative performance determinant (RPD) (greater than 3 for majority of the models). Use of IR for ethanol quantification showed similar and even better results to the obtained with the discrete chemical data, especially in the case of mid-IR models, where ethanol concentration can be estimated with a RMSEV equal to 1.9 g L-1. These results could facilitate the analysis of high number of samples required in the evaluation and optimization of the processes.