An HS-SPME/GC-MS procedure was optimised in order to characterize the aromatic fingerprint of 82 spirit drinks, belonging to Grappa GI samples and other distillates. “Grappa” is a geographical indication (GI) allowed by EC Regulation No 110/2008 only for Italian-made grape marc spirit. Multivariate chemometric techniques were applied to the collected chromatographic profiles in order to classify the samples on the basis of chemical information provided by their volatile composition data. Partial Least Squares-Discriminant Analysis (PLS-DA) showed natural grouping of samples highlighting good classification results, providing a good separation between the two distillate categories with high prediction accuracy (the total classification rate on the external test set was 95%). In addition, VIP scores of PLS-DA models were calculated, allowing to identify a reduced number of volatile compounds which are relevant for discrimination of the different categories. Also Soft Independent Modelling by Class Analogies (SIMCA) was tested to build class models for predictive classification purposes. The proposed approach may represent a powerful tool suitable to assess the authenticity of Grappa GI samples and, hence, it may help to protect this product from possible frauds by verifying whether samples comply with the product specification.

Flavour fingerprint for the differentiation of Grappa from other Italian distillates by GC-MS and chemometrics

Alessandra Biancolillo
2019-01-01

Abstract

An HS-SPME/GC-MS procedure was optimised in order to characterize the aromatic fingerprint of 82 spirit drinks, belonging to Grappa GI samples and other distillates. “Grappa” is a geographical indication (GI) allowed by EC Regulation No 110/2008 only for Italian-made grape marc spirit. Multivariate chemometric techniques were applied to the collected chromatographic profiles in order to classify the samples on the basis of chemical information provided by their volatile composition data. Partial Least Squares-Discriminant Analysis (PLS-DA) showed natural grouping of samples highlighting good classification results, providing a good separation between the two distillate categories with high prediction accuracy (the total classification rate on the external test set was 95%). In addition, VIP scores of PLS-DA models were calculated, allowing to identify a reduced number of volatile compounds which are relevant for discrimination of the different categories. Also Soft Independent Modelling by Class Analogies (SIMCA) was tested to build class models for predictive classification purposes. The proposed approach may represent a powerful tool suitable to assess the authenticity of Grappa GI samples and, hence, it may help to protect this product from possible frauds by verifying whether samples comply with the product specification.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/139275
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