Neuronose: An empirical study of neuromorphic approaches for the development of an artificial olfactory system

Electronic nose systems, popularly known as e-noses, are one of the classic examples of analytical devices that have been researched extensively, but have had limited commercial success for applications outside of a laboratory environment. Based on the idea of emulating the biological olfactory pathway, e-nose systems generally consist of a chemo-resistive array as a sensing front-end that transduces the interaction with aromatic compounds into electrical signals. In the next stage, a signal conditioning unit performs pre-processing and feature extraction, and modulates the sensor responses into unique “odour-prints” to represent a chemical compound. Finally, a pattern-recognition engine is implemented that provides odour identification results. While this three-stage architecture seems simplistic, the realisation of each stage is significantly complex, starting from the selection of appropriate sensing materials for the front-end array to the handling of the highly multi-dimensional data generated, and the implementation of effective pattern-recognition algorithms for this data. Although advances in computing techniques have enabled a variety of algorithms for preprocessing, feature extraction, and pattern-recognition, their short-comings in terms of computational resource requirement, processing latency, and classification accuracy have largely limited the application of e-nose systems to laboratory environments. Moving away from statistical pattern-matching techniques, e-nose systems greatly benefited from application of conventional machine learning approaches for generation of meaningful features, application of dimensionality reduction, and more advanced pattern-recognition techniques. However, these improvements were insufficient to overcome the effects of their data-intensive structure and implementation complexity that hindered their performance in real-world applications. The emergence of neuromorphic engineering, a bio-inspired method that mimics the neur