Lignocellulosic biomass from sugarcane (Saccharum spp. hybrids) could potentially be a major feedstock for second-generation biofuel production. Consequently, selecting sugarcane varieties with favorable biomass characteristics, typically less enzymatic recalcitrance and better saccharification yield without sugaryield penalty, will be important in sugarcane breeding. Economical and high-throughput techniques for profiling the major biomass components of this complex system will facilitate selection of clones with ideal lignocellulosic composition from large numbers of genotypes in breeding programs. We used a combined high-throughput profiling approach to evaluate the biomass composition of samples from a sugarcane germplasm collection. This employed near-infrared (NIR) spectroscopy for fiber characterization and high-performance liquid chromatography (HPLC) for determining the sugar content in juice. The results for 331 samples, from a diverse sugarcane population of 186 genotypes, derived from 143 parents of different genetic backgrounds, showed that high-quality NIR spectroscopic predictions were feasible for cellulose, hemicellulose, lignin, and extractives values in fiber, and sugars in juice were suitably analyzed by HPLC. The analysis of total biomass indicated that this NIR-and HPLC-based highthroughput method allowed a robust phenotypic assessment of a large number of samples for the key biomass traits in the sugarcane system, including total dry biomass, fiber, sugar content, and theoretical ethanol yields, and could potentially become the method of choice for sugarcane germplasm screening in breeding programs targeting the support of biofuel production.

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