E-Nose and e-Tongue combination for improved recognition of fruit juice samples

There are many important challenges related to food security analysis by application of chemical and electrochemical sensors. One critical parameter is the development of reliable tools, capable of performing an overall sensory analysis. In these systems, as much information as possible is required in relation to smell, taste and colour. Here, we investigated the possibility of using a multisensor data fusion approach, which combines an e-Nose and an e-Tongue, adept in generating combined aroma and taste profiles. In order to shed light on this concept, classification of various Tunisian fruit juices using a low-level of abstraction data fusion technique was attempted. Five tin oxide-based Taguchi Gas Sensors were applied in the e-Nose instrument and the e-Tongue was designed using six potentiometric sensors. Four different commercial brands along with eleven fruit juice varieties were characterised using the e-Nose and the e-Tongue as individual techniques, followed by a combination of the two together. Applying Principal Component Analysis (PCA) separately on the respective e-Nose and e-Tongue data, only few distinct groups were discriminated. However, by employing the low-level of abstraction data fusion technique, very impressive findings were achieved. The Fuzzy ARTMAP neural network reached a 100% success rate in the recognition of the eleven-fruit juices. Therefore, data fusion approach can successfully merge individual data from multiple origins to draw the right conclusions that are more fruitful when compared to the original single data. Hence, this work has demonstrated that data fusion strategy used to combine e-Nose and e-Tongue signals led to a system of complementary and comprehensive information of the fruit juices which outperformed the performance of each instrument when applied separately.