With the advent of the -omics era, classical technology platforms, such as hyphenated mass spectrometry, are currently undergoing a transformation toward high-throughput application. These novel platforms yield highly detailed metabolite profiles in large numbers of samples. Such profiles can be used as fingerprints for the accurate identification and classification of samples as well as for the study of effects of experimental conditions on the concentrations of specific metabolites. Challenges for the application of these methods lie in the acquisition of high-quality data, data normalization, and data mining. Here, a high-throughput fingerprinting approach based on analysis of headspace volatiles using ultrafast gas chromatography coupled to time of flight mass spectrometry (ultrafast GC/TOF-MS) was developed and evaluated for classification and screening purposes in food fermentation. GC-MS mass spectra of headspace samples of milk fermented by different mixed cultures of lactic acid bacteria (LAB) were collected and preprocessed in MetAlign, a dedicated software package for the preprocessing and comparison of liquid chromatography (LC)-MS and GC-MS data. The Random Forest algorithm was used to detect mass peaks that discriminated combinations of species or strains used in fermentations. Many of these mass peaks originated from key flavor compounds, indicating that the presence or absence of individual strains or combinations of strains significantly influenced the concentrations of these components. We demonstrate that the approach can be used for purposes like the selection of strains from collections based on flavor characteristics and the screening of (mixed) cultures for the presence or absence of strains. In addition, we show that strain-specific flavor characteristics can be traced back to genetic markers when comparative genome hybridization (CGH) data are available.