Using Spike-Based Bio-Inspired Olfactory Model for Data Processing in Electronic Noses

Conventional electronic noses need complicated data preprocessing and tedious feature reduction steps for different sensors and applications. To overcome the drawbacks, a bio-inspired data processing method using a spike-based olfactory model is proposed in this paper, which consists of spike encoding by virtual olfactory receptor neurons (VORNs) and subsequent processing in a bionic olfactory bulb (BOB) model. Each VORN transduces the continuous sensor responses into spike time points, which are relayed to the BOB to enhance the operation efficiency. It is easy to extract useful features from BOB's outputs due to their specific oscillation patterns, which simplifies the subsequent steps of feature generation. Three classification methods are used to identify seven Chinese liquors. The experimental results show that the proposed method achieves a better classification performance than the conventional methods.