## 2 Analytical Strategies for Authentication and Quality Control
### Multi-Platform Techniques  
Alcoholic beverages and food in general have a complex composition, and data from single analytical techniques may not be sufficient to fully characterize such complex products. Analytical characterization and authentication of foodstuffs can benefit from combining data from different instrumental sources. Multi-platform analysis, in fact, presents several advantages over single techniques, such as increased information, reduced impact of interferents and lower prediction errors [68]. A study based on a multi-platform approach for differentiating Gr from other distillates was carried out with a set of 75 representative samples of traditional Gr and other Italian fruit and cereal distillates produced in the Italian regions of Trentino Alto Adige and Veneto [69]. The spirits were analyzed with GC-MS, MIR and NIR techniques and classified with SO-PLS-LDA [28; 70; 71] and SO-CovSel-LDA [27] to combine multi-platform data into single classification models [72; 73]. The SO-PLS-LDA approach provided the best results, with a total classification rate of 100%, compared to the SO-CovSel-LDA model with a total classification rate of 76.5%. The difference between the two results was attributed to the fact that the NIR data were excluded in the creation of the SO-CovSel-LDA model because the cross-validation procedure for this model indicated the NIR block as irrelevant. On the contrary, it probably provided relevant information to discriminate the two categories under consideration. Another multiple analytical approach was used to differentiate Gr distillates matured in different types of wood casks [74]. In this study, 15 Gr samples obtained from virgin Moscato pomace and aged using oak or poplar wood fragments of different sizes and toasting levels were analyzed by GC-MS, NIR, electronic nose (E-nose) and 1H NMR. GC-MS analyses were performed to obtain an aromatic characterization of the distillates, while the other techniques were carried out for classification purposes. The E-nose employed was a portable commercial model with a sensor array consisting of 10 different metal oxide semiconductors. Eachsensor generated a specific response to volatiles, simulating the human nose. There are many advantages of 1H NMR, such as its high reliability and reproducibility, which make it suitable for statistical studies on geographical origin and adulteration recognition, where minute variations of many components must be detected simultaneously [75]. Unlike the previous study, the multi-platform data were processed individually without combining them into a single classification model. The GC-MS data were statistically processed by ANOVA and PCA and the NIR, NMR and E-nose data by PCA. The spectral region between 11 and 5.5 ppm was selected for the NMR statistical analysis because it was linked to relevant aromatic compounds such as phenols, aldehydes and aromatic groups. Good separation was shown by 1H NMR-PCA analysis according to the toasting level but with only partial separation among the groups with respect to the different wood formats. The spectral ranges 6900-6800 cm\({}^{-1}\) and 5500-4000 cm\({}^{-1}\) were selected for NIR characterization. The NIR statistical analysis highlighted discriminating power between the wood format groups, but further studies with larger datasets are needed to confirm its potential. The E-nose showed good capability to classify the samples, mainly according to the assortment of wood. This capability, combined with the ease of use and portability of the device, makes the technique particularly promising. The results of these studies highlight the effectiveness of multi-block strategies for authenticating and protecting the geographical indications under study also in relation to the aging technologies used.