Big bluestem is a dominant warm-season perennial native grass that has underutilized potential as a bioenergy crop. The objective of this study was to leverage a high-throughput, cost-effective phenotype of cellulose and hemicellulose contents in big bluestem biomass using near-infrared (NIR) spectroscopy to facilitate plant breeding and genetics studies. In order to develop NIR prediction models, a set of 56 big bluestem samples with seven genotypes from four planting locations in 2010 and 2011 were analyzed according to traditional wet chemical methods. Advanced multivariate analysis techniques and NIR spectroscopy improved the prediction models based on value of the coefficient of determination (R-2). Partial least squares proved to be a better quantitative method than principal component regression based on larger R-2, ratio of standard error of prediction set to sample standard deviation (RPD), and root mean square error of prediction (RMSEP) when developing NIR prediction models. The spectral range from 4000 to 7500 cm I with the first derivative treatment yielded a better prediction model than full range, with R-2 of 0.92, RMSEP of 0.67%, and RPD of 4.52 in the validation sample set for cellulose and R-2 of 0.91, RMSEP of 0.72%, and RPD of 3.12 for hemicellulose. These models provide good insight into the relationship between chemical bonds and structure sugars of big bluestem, allowing a rapid and accurate determination of cellulose and hemicellulose contents at low cost. (C) 2017 Elsevier Ltd. All rights reserved.