A novel strategy for determining the enantiomeric composition of phenylalanine samples that combines ordinary fluorescence spectroscopy, guest-host cyclodextrin chemistry, and multivariate regression modeling is investigated. Partial-least-squares regression (PLS-1) models were developed from fluorescence spectral data obtained with a series of samples containing cyclodextrin guest-host complexes of phenylalanine with different known enantiomeric compositions. The regression models were subsequently validated by determining the enantiomeric composition of a set of independently prepared phenylalanine samples. The ability of the models to correctly predict the enantiomeric compositions of future samples was evaluated in terms of the root-mean-square percent relative error (RMS%RE). The RMS%RE in the mol fraction of D-phenylalanine ranged from 1.3% to 3.0% when beta-cyclodextrin was used as the host molecule for different guest-host concentrations. The RMS%RE in the mol fraction of D-phenylalanine obtained in a similar validation study conducted with gamma-cyclodextrin ranged between 1.8% and 4.0% for different guest-host concentrations. Compared with previous studies done in absorption, fluorescence data were found to be more sensitive and the spectral differences observed as a function of enantiomeric composition were more uniformly spaced, making regression modeling more reliable. As a result, good regression models could be made at lower concentrations than were possible previously when absorption measurements were used.