This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types

International audienceIn the present work, the potential of an electronic nose to differentiate the geographical origin of the Moroccan virgin olive oils based on their volatile profile was investigated. An electronic gas sensor array system composed of 6 metal oxide semiconductor sensors was used to generate a chemical fingerprint (pattern) of the volatile compounds present in olive oils. Multivariate statistical approach showed good discrimination between the classes of the 27-sample of the dataset population. The results of this study provide promising perspectives for the use of a low-cost and rapid system for the verification of geographical origin of the olive oils based on their volatile profile