The objective of the work was to assess the relevance for the authentication of food of a novel chemometric method developed to correct mass spectrometry (MS) data from instrumental drifts, namely, the comprehensive combinatory standard correction (CCSC). Applied to gas chromatography (GC)-MS data, the method consists in analyzing a liquid sample with a mixture of n internal standards and in using the best combination of standards to correct the MS signal provided by each compound. The paper focuses on the authentication of the type of feeding in farm animals based on the composition in volatile constituents of their adipose tissues. The first step of the work enabled on one hand to ensure the feasibility of the conversion of the adipose tissue sample into a liquid phase required for the use of the CCSC method and on the other hand, to determine the key parameters of the extraction of the volatile fraction from this liquid phase by dynamic headspace. The second step showed the relevance of the CCSC pre-processing of the MS fingerprints generated by dynamic headspace-MS analysis of lamb tissues, for the discrimination of animals fed exclusively with pasture (n=8) or concentrate (n=8). When compared with filtering of raw data, internal normalization and correction by a single standard, the CCSC method increased by 17.1-, 3.3- and 1.3-fold, respectively, the number of mass fragments which discriminated the type of feeding. The final step confirmed the advantage of the CCSC pre-processing of dynamic headspace-gas chromatography-MS data for revealing molecular tracers of the type of feeding those number (n=72) was greater when compared to the number of tracers obtained with raw data (n=42), internal normalization (n=63) and correction by a single standard (n=57). The relevance of the information gained by using the CCSC method is discussed.