The U.S. Naval Research Laboratory has been engaged in a research program to develop sensor-based technologies to perform rapid automated fuel-quality surveillance. This approach is based on the development of quantitative models from the partial least-squares (PLS) regression of near-infrared (NIR) spectroscopic measurements of a representative calibration set of petroleum-derived fuels. As fuels from nonpetroleum sources become available it will be necessary to extend these chemometric models to accommodate Fischer-Tropsch (FT) synthetic fuels and biofuels. This extension is complicated by the fact that these new fuels will be initially introduced as blending components with petroleum-derived fuels. Chemometric modeling methodologies have been developed to identify and estimate the content of FT and biofuel present; then this information is used to estimate the bulk properties of the blends. With this approach, biodiesel content can be predicted, with respect to absolute error, to within 1.7% of its true value 95% of the time with a lower limit of detection of 1.5% using a single PLS model. The diesel fuel PLS property prediction models are applicable to diesel fuels blended with biodiesel fuel once that particular biodiesel fuel is incorporated in said models. The FT content in blends with petroleum fuels can be predicted, with respect to absolute error, to within 6.9% of its true value 95% of the time with a lower limit of detection of 15% using a series of paired PLS models for identification and quantification. In the presence of FT fuel, the PLS property models can be used after applying a correction factor that is derived from the identity and concentration of the FT fuel present.