Data fusion strategies in food analysis

With the growing availability of high throughput methodologies for food characterization and analysis, more and more data are being collected on food products that can be used for the authentication of their quality. In this context, the availability of different multi-block strategies, each with its own peculiarities and providing specific details on the investigated samples, allows to integrate the information from the different sources into a richer model with great flexibility. The aim of the present chapter is to present general perspectives on data-fusion, and to briefly discuss the potentialities of this strategy in the food analysis context. In order to provide an overview on such a wide topic as multi-block analysis, the chapter is conceptually divided into two parts. The first one, where the subject is approached from a theoretical standpoint (from Section 1 to Section 3), and a more practical part, in which selected applications of multi-block methods applied to authenticate or to check quality of foodstuff - such as, e.g., olive oil, wine, beer, vinegar, tea, and dairies - are described (Section 4). Throughout the text, general advantages and disadvantages of data-fusion strategies are depicted with a slight deeper attention into few specific methods.