{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T16:04:44Z","timestamp":1785254684609,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.<\/jats:p>","DOI":"10.3390\/s19224831","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T06:52:36Z","timestamp":1573109556000},"page":"4831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-4283","authenticated-orcid":false,"given":"Anup","family":"Vanarse","sequence":"first","affiliation":[{"name":"School of Engineering, Edith Cowan University, 6027 Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9611-9345","authenticated-orcid":false,"given":"Adam","family":"Osseiran","sequence":"additional","affiliation":[{"name":"School of Engineering, Edith Cowan University, 6027 Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-5681","authenticated-orcid":false,"given":"Alexander","family":"Rassau","sequence":"additional","affiliation":[{"name":"School of Engineering, Edith Cowan University, 6027 Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"van der Made","sequence":"additional","affiliation":[{"name":"Brainchip Inc., Aliso Viejo, CA 92656, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1109\/JSEN.2012.2207887","article-title":"Large-scale chemical sensor array testing biological olfaction concepts","volume":"12","author":"Bernabei","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sawan, M. (2015). Artificial olfactory systems. Handbook of Biochips: Integrated Circuits and Systems for Biology and Medicine, Springer.","DOI":"10.1007\/978-1-4614-6623-9"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0303-2647(96)01660-7","article-title":"Computational parallels between the biological olfactory pathway and its analogue \u2018the electronic nose\u2019: Part ii. Sensor-based machine olfaction","volume":"41","author":"Pearce","year":"1997","journal-title":"Biosystems"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1038\/299352a0","article-title":"Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose","volume":"299","author":"Persaud","year":"1982","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1016\/j.bios.2011.04.015","article-title":"A bio-inspired two-layer multiple-walled carbon nanotube-polymer composite sensor array and a bio-inspired fast-adaptive readout circuit for a portable electronic nose","volume":"26","author":"Wang","year":"2011","journal-title":"Biosens Bioelectron"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.foodchem.2017.11.013","article-title":"Electronic noses in classification and quality control of edible oils: A review","volume":"246","author":"Majchrzak","year":"2018","journal-title":"Food Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jfoodeng.2014.07.019","article-title":"Electronic noses for food quality: A review","volume":"144","author":"Loutfi","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.juro.2014.01.113","article-title":"Detection of prostate cancer by an electronic nose: A proof of principle study","volume":"192","author":"Roine","year":"2014","journal-title":"J. Urol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"197","DOI":"10.2174\/0929867324666171004164636","article-title":"Electronic noses in medical diagnostics","volume":"26","author":"Wojnowski","year":"2019","journal-title":"Curr. Med. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.3390\/s90705099","article-title":"Applications and advances in electronic-nose technologies","volume":"9","author":"Wilson","year":"2009","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., and Rassau, A. (2017). An investigation into spike-based neuromorphic approaches for artificial olfactory systems. Sensors, 17.","DOI":"10.3390\/s17112591"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8364","DOI":"10.1021\/ac8007048","article-title":"Bioinspired methodology for artificial olfaction","volume":"80","author":"Raman","year":"2008","journal-title":"Anal. Chem."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jaeger, D., and Jung, R. (2013). Neuromorphic sensors, olfaction. Encyclopedia of Computational Neuroscience, Springer.","DOI":"10.1007\/978-1-4614-7320-6"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TCSI.2006.888677","article-title":"Analog vlsi circuit implementation of an adaptive neuromorphic olfaction chip","volume":"54","author":"Koickal","year":"2007","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3389\/fnins.2012.00083","article-title":"Implementation of olfactory bulb glomerular-layer computations in a digital neurosynaptic core","volume":"6","author":"Imam","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1109\/TCSI.2011.2143090","article-title":"A cmos single-chip gas recognition circuit for metal oxide gas sensor arrays","volume":"58","author":"Ng","year":"2011","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3389\/fnins.2016.00115","article-title":"A review of current neuromorphic approaches for vision, auditory, and olfactory sensors","volume":"10","author":"Vanarse","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_18","unstructured":"Brainchip Holding Ltd. (2019, August 28). Akida Development Environment. Available online: https:\/\/www.brainchipinc.com\/products\/akida-development-environment."},{"key":"ref_19","unstructured":"Brainchip Holding Ltd. (2019, August 28). Akida Neuromorphic System-On-Chip. Available online: https:\/\/www.brainchipinc.com\/products\/akida-neuromorphic-system-on-chip."},{"key":"ref_20","unstructured":"CSIRO, Amalia, B., and Stephen, T. (2015). Electronic nose (fox) recording of 20 chemicals. CSIRO Data Collect."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"026002","DOI":"10.1088\/1748-3190\/11\/2\/026002","article-title":"Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system","volume":"11","author":"Diamond","year":"2016","journal-title":"Bioinspir. Biomim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/JSEN.2002.800688","article-title":"Pattern analysis for machine olfaction: A review","volume":"2","year":"2002","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3189","DOI":"10.1109\/JSEN.2012.2192920","article-title":"Signal and data processing for machine olfaction and chemical sensing: A review","volume":"12","author":"Marco","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.snb.2013.01.088","article-title":"Optimal feature selection for classifying a large set of chemicals using metal oxide sensors","volume":"187","author":"Nowotny","year":"2013","journal-title":"Sens. Actuators B Chem."},{"key":"ref_25","unstructured":"Buckman, J., Roy, A., Raffel, C., and Goodfellow, I. (May, January 30). Thermometer encoding: One hot way to resist adversarial examples. Proceedings of the ICLR 2018 Conference Blind Submission location, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"665","DOI":"10.3389\/fnins.2018.00665","article-title":"On practical issues for stochastic stdp hardware with 1-bit synaptic weights","volume":"12","author":"Yousefzadeh","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3389\/fncom.2018.00024","article-title":"Unsupervised feature learning with winner-takes-all based stdp","volume":"12","author":"Mamalet","year":"2018","journal-title":"Front. Comput. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1109\/JPROC.2014.2346153","article-title":"Retinomorphic event-based vision sensors: Bioinspired cameras with spiking output","volume":"102","author":"Posch","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/82.842110","article-title":"Point-to-point connectivity between neuromorphic chips using address events","volume":"47","author":"Boahen","year":"2000","journal-title":"IEEE Trans. Circuits Syst. II Analog Digit. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3389\/fnins.2013.00119","article-title":"Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex","volume":"7","author":"Pearce","year":"2013","journal-title":"Front. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., and Rassau, A. (2019). Real-time classification of multivariate olfaction data using spiking neural networks. Sensors, 19.","DOI":"10.3390\/s19081841"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"774","DOI":"10.3389\/fnins.2018.00774","article-title":"Deep learning with spiking neurons: Opportunities and challenges","volume":"12","author":"Pfeiffer","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00422-019-00797-7","article-title":"An unsupervised neuromorphic clustering algorithm","volume":"113","author":"Diamond","year":"2019","journal-title":"Biol. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3389\/fnins.2015.00491","article-title":"Comparing neuromorphic solutions in action: Implementing a bio-inspired solution to a benchmark classification task on three parallel-computing platforms","volume":"9","author":"Diamond","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.neunet.2015.07.014","article-title":"Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification","volume":"71","author":"Sarkar","year":"2015","journal-title":"Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1109\/TCSI.2017.2697945","article-title":"A bio-inspired analog gas sensing front end","volume":"64","author":"Huang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4831\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:18Z","timestamp":1760189538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,6]]},"references-count":36,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224831"],"URL":"https:\/\/doi.org\/10.3390\/s19224831","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,6]]}}}