Design of a very large chemical sensor system for mimicking biological olfaction

Beccherelli, Romeo; Zampetti, Emiliano; Pantalei, S.; Bernabei, M.; Persaud, Krishna

doi:10.1016/j.snb.2009.11.031

Cited by 74 publications

(43 citation statements)

References 12 publications

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“…Thus, a large number of identical receptors allow a consistent sense of smell even when the individual sensor elements are continuously replaced. This approach, i.e., building large arrays with redundant sensors, has only recently been studied in the EU project NEUROCHEM primarily for better understanding of the sense of smell [36]. Due to the increased complexity and cost of the sensor array and electronic read-out this approach is not used on practical applications.…”

Section: Multisensor Arrays and Electronic Nosesmentioning

confidence: 99%

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Reimann

Schütze

2013

Springer Series on Chemical Sensors and Biosensors

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Selectivity will remain one of the main challenges for gas sensors. The majority of single gas sensors have a broad response spectrum and additional effort is required to provide specific information from the recorded raw data. Sensor arrays comprising a number of different sensors, either of the same basic type or combining different sensor principles, are often employed to improve the selectivity. Another approach is the dynamic operation of a single sensor element leading to a virtual multisensor. Depending on the application, both classification, i.e., recognition of a certain gas or gas mixture, and quantification, i.e., determining the concentration of a target gas, are required. For evaluation of the higher-dimensional data vectors obtained from the sensor system, different methods are commonly used, e.g., unsupervised and supervised multivariate statistics or artificial neural networks. For comprehensive classification, a multistep process is employed, typically comprising data preprocessing, feature extraction, dimensionality reduction, and finally classification. This chapter addresses typical methods used for gas sensor data evaluation and also briefly addresses challenges relating to sensor calibration and drift compensation. Four application examples are shown in detail demonstrating the use of different sensor configurations and data processing approaches: robust ozone quantification, sweat odor assessment trained by correlation with a human sensory panel, fire detection in buildings, and finally underground fire detection in coal mines requiring very high selectivity.

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Section: Multisensor Arrays and Electronic Nosesmentioning

confidence: 99%

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Reimann

Schütze

2013

Springer Series on Chemical Sensors and Biosensors

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“…We also explored the translation of key features of the olfactory system architecture as receptor redundancy and massive convergence to biomimetic artefacts. In this sense a large scale chemical sensor array consisting of 64K sensors of tens of different types has been built within this project, mimicking the olfactory epithelium 9,10 .…”

Section: Ill Olfactory Cortexmentioning

confidence: 99%

Biologically inspired large scale chemical sensor arrays and embedded data processing

et al. 2013

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Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. EU Funded Project NEUROCHEM (Bio-ICT-FET-216916) has developed novel computing paradigms and biologically motivated artefacts for chemical sensing taking inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built featuring a large scale sensor array (65K elements) in conducting polymer technology mimicking the olfactory receptor neuron layer, and abstracted biomimetic algorithms have been implemented in an embedded system that interfaces the chemical sensors. The embedded system integrates computational models of the main anatomic building blocks in the olfactory pathway: the olfactory bulb, and olfactory cortex in vertebrates (alternatively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions. Finally, the algorithmic models are tested with an odour robot with navigation capabilities in mixed chemical plumes .

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“…In order to reproduce the outstanding performance of the biological olfactory system, artificial systems including algorithms and large sensor arrays, have been developed by several authors [13][14][15]. It has also been shown that the approach based on bio systems can overcome typical sensor failures [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Odorant recognition using biological responses recorded in olfactory bulb of rats

Vizcay

Duarte‐Mermoud

Aylwin

2015

Computers in Biology and Medicine

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Design of a very large chemical sensor system for mimicking biological olfaction

Cited by 74 publications

References 12 publications

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Biologically inspired large scale chemical sensor arrays and embedded data processing

Odorant recognition using biological responses recorded in olfactory bulb of rats

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