The use of woodchip, and biofuels in general, is a fundamental step towards the transition from fossil fuel to renewable energy. The growth in the demand for wood fuels and the inherent variability in the properties of woody material lead to the need to verify its quality. EN ISO 17225-4 divides woodchip in different quality classes according to chemical-physical parameters and quality attributes. In this study, we have coupled near infrared spectroscopy with Partial Least Square regression to model gross calorific value and ash content of woodchip samples. Moreover, variables selection methods were tested in order to improve the models and get better prediction. Gross calorific value and ash content were predicted with a standard error of 234 J/g and 0.44%, respectively. The results indicate that the models could be used in screening applications and near infrared spectroscopy is a promising tool in the evaluation of biomass quality.