This study was undertaken to investigate the relevance of using the pyrolysis-MS (Py-MS) technique to discriminate the production area of oysters harvested over two years and to assess from the data of the second year of harvest the potential of an alternative MS-based technique, the solid phase microextraction-MS (SPME-MS), to perform this discrimination. Oysters were harvested in various areas of France, and models of discrimination according to harvest season were built from Py-MS fingerprints and from virtual SPME-MS fingerprints obtained by summing the mass spectra generated by the SPME-GC-MS system. The treatment of the Py-MS data by a 21-12-3 artificial neural networks led to a correct classification of only 89.2% of the oyster samples according to shoreline. The misclassifications thus did not allow use of the Py-MS technique as a relevant tool for authentication of oyster origin. The assessment of the potential of the virtual SPME-MS fingerprints to discriminate the production area of oysters was undertaken on a part of the sample set. The virtual SPME-MS data were pretreated according to two methods, filtering of raw data (FRD) and comprehensive combinatory standard correction (CCSC), a recently developed chemometric method used for the correction of instrumental signal drifts in MS systems. The results obtained with the virtual SPME-MS fingerprints are promising because this technique, when the data were pretreated by the CCSC method, led to a successful discrimination of the oyster samples not only according to shoreline but also according to production region. This study confirms that an efficient correction method (CCSC) of instrumental drifts can considerably increase the discriminative information contained in the volatile fraction of food products.