title: Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.
abstract: The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.
authors.affiliation: CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
authors.forename: Sankho Turjo
authors.initials: ST
authors.lastname: Sarkar
authors.affiliation: CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India. Electronic address: amol.bhondekar@gmail.com.
authors.forename: Amol P
authors.initials: AP
authors.lastname: Bhondekar
authors.affiliation: Czech Technical University, Prague, Czech Republic.
authors.forename: Martin
authors.initials: M
authors.lastname: Macaš
authors.affiliation: CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
authors.forename: Ritesh
authors.initials: R
authors.lastname: Kumar
authors.affiliation: CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
authors.forename: Rishemjit
authors.initials: R
authors.lastname: Kaur
authors.affiliation: CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
authors.forename: Anupma
authors.initials: A
authors.lastname: Sharma
authors.affiliation: CSIR-Institute of Himalayan Bioresource Technology, HP, India.
authors.forename: Ashu
authors.initials: A
authors.lastname: Gulati
authors.affiliation: CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
authors.forename: Amod
authors.initials: A
authors.lastname: Kumar
mesh_terms: D000465:Algorithms; D032701:Biomimetics; D062609:Electronic Nose; D004867:Equipment Design; D016571:Neural Networks, Computer; D016011:Normal Distribution; D009666:Nose; D009812:Odorants; D055696:Olfactory Perception; D013662:Tea
chemical_list: D013662:Tea
keywords: Dynamically evolving spiking neural networks; Electronic nose; McNemar’s test; Spike latency coding; Spiking neural network; Tea