Abstract : The high cost and limited availability of emerging alternatives fuels is often a major impediment to certification of these fuels as Fit-For-Purpose (FFP) for the U.S. Navy. A method whereby a candidate fuel could be rapidly screened for many FFP properties, using a minimal volume ( 1 mL), would overcome this limitation. In order to meet this challenge, algorithmic modeling strategies were derived that established the statistical relationships between composition and critical FFP fuel properties. This has allowed us to develop partial least squares (PLS) models based on gas chromatography-mass spectrometry (GC-MS) data that predict fuel properties. The Fuel Composition and Screening Tool (FCAST) was developed as a general fuel screening tool that combines GC-MS based property predictions with a compositional profiler to provide a variety of useful information about a fuel sample.