Honey Floral Classification by Biomimetic Sensors

Several methods have been used for the determination of the floral and geographical origin of honey. They are mainly based on the analysis of its pollen content, sensory analysis, amino acids, volatile compounds, carbohydrates, phenolic compounds, organic acids, and marker presence. These methods are generally complex and time-consuming. This work proposes a fast analysis based on combination of array sensing that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours. The method is low reagent volumes consuming and it does not need previous samples processing. E-nose has been designed for automated detection and recognition of odours. It doesn’t decompose the volatile fraction of matrix in its constitutive components but supplies a global evaluation of aroma miming the human olfactory system. The sensor signals was convert to data that can be analysed by an appropriate statistical software to determine that one sample is similar or different from another. The electronic tongue behaves similarly to non-volatile compounds in a liquid. The present work is focused on classifying honey samples of four different botanical origins (Hedysarum coronarium L., Robinia pseudoacacia L., Citrus spp. and Castanea sativa Mill.), using an electronic nose (e-nose) and electronic tongue measurements. The data collected with the e-nose and the e-tongue were analyzed by statistical analysis (principal component analysis and discriminant function analysis) to find an analytical alternative for classification of honey samples with respect to pollen type, which requires long time and skilled workers. The initial data obtained shown that the e-nose and the e-tongue was able to separately discriminate monofloral honey samples, with a discrimination index of 100% and 86% respectively. It was found that the e-nose and the e-tongue have efficiently tools for botanical classification of honey samples