We introduce a fast computational method for feature selection that facilitates the accurate spectral analysis of a chemical species of interest in the presence of overlapping uncorrelated variance. Using a genetic algorithm in a data-driven approach, our method assigns predictors according to a template determined to minimize prediction variance in a calibration space. This template-oriented genetic algorithm (TOGA) efficiently establishes features of greatest significance and determines their optimal combination. We demonstrate the efficacy of TOGA using an elementary model system in which we seek to quantify a target monosaccharide in mixtures containing other sugars added in random amounts. The results establish TOGA as an effective and reliable technique for isolating signature spectra of targeted substances in complex mixtures.
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