Ricerc@Sapienza

An HS-SPME/GC–MS (Head Space-Solid Phase Micro Extraction/Gas Chromatography-Mass Spectrometry) method was developed and applied to 79 beers (craft and industrial products) selected among the most common brands of mass-produced beer to evaluate their flavour profile. 111 volatiles were identified in the samples. PLS-DA (Partial Least Squares-Discriminant Analysis) was used to classify beers according to their different production methods.

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.

Classification methods, i.e., the chemometric strategies for predicting a qualitative response, find many applications in the omic sciences, where often data are collected in order to categorize individuals (e.g. according to whether they were treated or administered a placebo or, for instance, depending on if they were healthy or ill). After a brief discussion of the differences between discriminant and modelling approaches, some of the techniques most commonly used in the omic fields are illustrated in greater detail.

Common hazelnuts are widely present in human diet all over the world, and their beneficial effects on the health have been extensively investigated and demonstrated. Different in-depth researches have highlighted that the harvesting area can define small variations in the chemical composition of the fruits, affecting their quality. As a consequence, it has become relevant to develop methodologies which would allow authenticating and tracing hazelnuts.

Pasta is a typical Italian food item obtained by durum wheat semolina/flour well-known and widely consumed all over the world. Since 2013, Gragnano Pasta, a typical aliment produced in a specific area in the South of Italy, has been awarded with the P.G.I. mark, remarking the high value of this product. Due to its peculiarity and its market value, it is important to characterize and authenticate the Gragnano Pasta. Considering this rationale, the present study aims at developing a non-destructive analytical methodology suitable for this goal.