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Increased use of biomass can diversify the fuel-supply and lead to a more secure energy supply. Increasing the use of renewable, carbon neutral forms of energy, such as phototrophic crops, will allow for decreased use of fossil fuels which is essential to decrease the levels of greenhouse gasses in our atmosphere. Pelletizing of dried biomass has several advantages such as increasing energy density, improving storability and reducing handling and transport costs. Variability within the properties of pellets produced from energy crops is influenced by a plethora of factors including plant genetics, growing environment, harvesting method, storage, climatic conditions and seasonal variation. There is a need to develop new technologies and techniques for real time determination of pellet calorific value. Hyperspectral images can be obtained by recorded multiple line-scans over a moving sample and combining them to create an image. The potential of hyperspectral imaging in conjunction with chemometrics to predict calorific values of pellets produced from varying blends of energy grasses and Pine wood (Pinus) was assessed. Energy grasses assessed in this study were Tall Fescue (Festuca arundinacea) and Reed Canary Grass (Phalaris arundinacea). Spectral data within the near infrared spectral region of 900 to 1,700 nm was assessed. Partial least squares regression model for calorific value prediction, over a range of 16.4 to 18.9 MJ kg-1, gave a correlation coefficient (R) of 0.90 with a root mean square error of cross validation of 0.32 MJ kg-1 (number of samples = 9). Results demonstrated that hyperspectral imaging in combination with chemometrics has the potential to be employed in a real time pellet grading system. This would allow for increased efficiency in energy conversion systems.