In preclinical applications, the high specificity of quantitative 19F MRI may be compromised by non-negligible signal contributions from fluorinated anesthetics (e.g. isoflurane). Here, we demonstrate the feasibility of chemical shift encoding (CSE) with multi-resonance fluorine signal modeling and least-squares estimation image reconstruction for 19F MRI. We optimize noise performance (NSA) and use a 3D spoiled gradient-echo acquisition to separate signal contributions from perfluoro-15-crown-5-ether (PFCE) and isoflurane. The method is tested in mixed PFCE/isoflurane phantoms showing effective signal separation. The CSE reconstruction removes isoflurane signal contributions in 19F MR images of PFCE in vivo, potentially reducing errors in 19F concentration quantification.