This degree project investigates how glass contamination in refuse-derived fuel for a fluidised bed boiler can be detected using near-infrared spectroscopy. It is motivated by the potential to reduce greenhouse gas emissions by replacing fossil fuels with refuse-derived fuel. The intent was to develop a multivariate predictive model of near-infrared spectral data to detect the presence of glass cullet against a background material that represents refuse-derived fuel. Existing literature was reviewed to confirm the usage of near-infrared spectroscopy as a sensing technology and determine the necessity of glass detection. Four unique background materials were chosen to represent the main components in municipal solid waste: wood shavings, shredded coconut, dry rice and whey powder. Samples of glass mixed with the background material were imaged using near-infrared spectroscopy, the resulting data was pre-processed and analysed using partial least squares regression. It was shown that a predictive model for quantifying coloured glass cullet content in one of several background materials were reasonably accurate with a validation coefficient of determination of 0.81 between the predicted and reference data. Models that used data from a single type of background material, wood shavings, were more accurate. Models for quantifying clear glass cullet content were significantly less accurate. These types of models could be applied to predict coloured glass content in different kinds of background materials. However, the presence of clear glass in municipal solid waste, and thus refuse-derived fuel, limit the opportunities to apply these methods to the detection of glass contamination in fuel.