Five different softwoods were used to investigate fast methods for predicting quantitative chemical information via near-infrared (NIR) spectroscopy. In biomass-related industries, fast collection of chemical information from a feedstock is needed. Prior to predicting quantitative information, a principal component analysis (PCA) using NIR spectra was conducted to evaluate the possibility of discriminating the softwoods. As a result of PCA, the five species were divided into three groups. This result indicated that the extractive compounds were key factors because the powder samples were separated by species having a similar extractive content. The partial least square (PLS) method was applied to develop a calibration model for predicting chemical composition. This model showed good performance in predicting the extractive and lignin content of all species. The calibration results of the extractive and lignin content for all species were indicated as R2 = 0.99. The cross-validation of the components for all species also showed an excellent value of R2 = 0.98 and 0.97, respectively. Based on our results, it was possible to suggest a useful tool for providing rapid information about wood used in the bioenergy and pulp production fields.