Increasing consumer awareness of food safety issues requires the development of highly sophisticated techniques for the authentication of food commodities. The food products targeted for falsification are either products of high commercial value or those produced in large quantities. For this reason, the present investigation is directed towards the characterization of coffee samples according to the geographical origin. The conducted research involves the development of a rapid headspace solid-phase microextraction (HS-SPME)-gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) method that is utilized for the verification of geographical origin traceability of coffee samples. As opposed to the utilization of traditional univariate optimization methods, the current study employs the application of multivariate experimental designs to the optimization of extraction-influencing parameters. Hence, the two-level full factorial first-order design aided in the identification of two influential variables: extraction time and sample temperature. The optimum set of conditions for the two variables was 12 min and 55 degrees C, respectively, as directed by utilization of Doehlert matrix and response surface methodology. The high-throughput automated SPME procedure was completed by implementing a single divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 microm metal fiber with excellent durability properties ensuring the completion of overall sequence of coffee samples. The utilization of high-speed TOFMS instrument ensured the completion of one GC-MS run of a complex coffee sample in 7.9 min and the complete list of benefits provided by ChromaTOF software including fully automated background subtraction, baseline correction, peak find and mass spectral deconvolution algorithms was exploited during the data evaluation procedure. The combination of the retention index (RI) system using C(8)-C(40) alkanes and the mass spectral library search was utilized for the confirmation of analyte identity in the reference authentic Brazilian coffee sample. The semi-quantitative results were then submitted to statistical evaluation, namely principal component analysis (PCA) for the establishment of geographical origin discriminations.