The aim of the present study is to authenticate Grappa spirits and to develop a non-destructive methodology which would allow detecting possible adulteration (by less valuable spirits) on this product. Grappa is an Italian alcoholic drink obtained by distillation of grape marks which has recently received the Geographical Indication (GI) label. As a high added-value product, it is relevant to develop methodologies which allow its authentication and detecting possible frauds (e.g., adulterations); and, whether feasible, it would be suitable to achieve these goals through non-destructive approaches (in order to minimize the economic loss). Mid Infrared (MIR) and Near Infrared (NIR) spectroscopies have been used for the authentication and the characterization of the spirits under investigation. The present work is conceptually divided into two parts: the first one, centered on the authentication of grappa spirits, focused on distinguishing them from other Italian distillates, and a second one aimed at developing an analytical methodology suitable to discern between pure and adulterated grappas. Both classification problems have been investigated by PLS-DA and by three multi-block strategies, i.e., Multi-Block Partial Least Squares (MB-PLS), Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Covariance Selection (SO-CovSel) in order to test whether a data-fusion approach would lead to an improvements of the classification rates. The best results (in terms of predictions) were provided by multi-block strategies; in particular, they provided 100 % of correct classification when applied to discriminate pure and adulterated samples, suggesting these methodologies are definitely suitable for the proposed purpose.