A novel fuzzy based signal analysis technique in electronic nose and electronic tongue for black tea quality analysis

An advanced fuzzy approach of recognition of signal with time for tea classification with responses from electronic nose, electronic tongue and the combined sensor response of electronic nose and electronic tongue is attempted in this paper. In our model, neither priori choice of the suitable features (like peak, mean value, etc.) is considered, nor syntactic primitives as elementary components of signals are selected. Rather a new linguistic classification method named Fuzzy based Response of Signal with Time (FRST) is proposed. The novelty of the work is that instead of considering the complete signal or performing any statistical analysis on the complete signal, the sensor response is processed at the real time and the fuzzy partition is done to the sensor response spaces. At the same time, fuzzy partition is also applied on the time axis. Thus, we can assign an important role to each point of the signal depending on its position.