Bioenergy is one of the most important renewable energy sources for electricity, heat and biofuels. Solid biomass in form of woodchip is widespread but woodchip quality variability is a concern. The quality monitoring is difficult due to the complexity of standard analysis being slow, destructive and expensive and requiring skilled operators. Moreover, some information, like the origin and source of the biofuels, is difficult to be measured. The aim of this study is the development of innovative models based on NIRS and multivariate data analysis starting from 150 samples collected from an important biomass power plant for the prediction of ash and moisture contents. Different preprocessing techniques were employed to remove physical phenomena before performing multivariate data analysis. The results highlight the possibility to exploit the moisture content model for screening application in quality control process of the solid biofuels, while the ash content model can be used for the discrimination of the material between high or low-quality classes, so for rough applications. In conclusion, NIR spectroscopy can be implemented directly in the production line, allowing the improvement of the solid biofuels quality control and overcoming the problems of sampling and sample representativeness.