A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation

Marco, S.; Gutiérrez-Gálvez, A.; Lansner, Anders; Martínez, Dani; Rospars, Jean-Pierre; Beccherelli, Romeo; Perera, Alexandre; Pearce, Timothy Charles; Verschure, Paul F. M. J.; Persaud, Krishna

doi:10.1007/s00542-013-2020-8

Cited by 38 publications

(31 citation statements)

References 28 publications

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“…Applications of these arrays are frequently reported in the literature where they have been described as successfully applied to a wide variety of tasks (see, e.g., those reviewed in 103). However, it has been noted repeatedly that such devices do not seem to have made significant progress toward widespread commercial use (6,7). The reason for this is likely an overestimation of sensor array capabilities combined with an underestimation of sensing task complexity and a lack of sound validation and design principles (1-3).…”

Section: Sensor Arraysmentioning

confidence: 95%

“…Suslick and colleagues (56)(57)(58)(59)(60) have reported colorimetric sensor arrays applied to many complex chemical sensing problems. Promising work on surface-functionalized nanostructures has recently garnered much attention (61-64) and Marco et al (7), have developed a very large-scale array of conducting polymer chemical sensors for biomimetic olfaction.…”

Section: Sensor Arraysmentioning

confidence: 99%

“…The idea of employing such an array as a relatively general-purpose chemical detection system is inspired by analogy to the vertebrate olfactory system and garnered great enthusiasm starting in the mid-1980s (6). However, there have been difficulties since then in realizing widespread commercial appeal of such devices, at least as general-purpose solutions to varied chemical sensing applications (6,7). Nevertheless, the idea of sensor arrays as a potential alternative to more costly analytical instrumentation remains popular and continues to be an active area of research in the sensing community, where, owing to a persistent desire in most applications to utilize simpler, less costly hardware whenever possible, there remains significant motivation to develop and build sensor arrays for complex sensing tasks.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Array Design for Complex Sensing Tasks

Johnson

Rose-Pehrsson²

2015

Annual Rev. Anal. Chem.

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Chemical detection in complex environments presents numerous challenges for successful implementation. Arrays of sensors are often implemented for complex chemical sensing tasks, but systematic understanding of how individual sensor response characteristics contribute overall detection system performance remains elusive, with generalized strategies for design and optimization of these arrays rarely reported and even less commonly adopted by practitioners. This review focuses on the literature of nonspecific sensor array design and optimization strategies as well as related work that may inform future efforts in complex sensing with arrays.

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Section: Sensor Arraysmentioning

confidence: 95%

Section: Sensor Arraysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor Array Design for Complex Sensing Tasks

Johnson

Rose-Pehrsson²

2015

Annual Rev. Anal. Chem.

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“…The acquisition of sensor signals was performed by a PC104-standard embedded computer, that was designed for real-time acquisition, processing and visualization of sensory data for an autonomous mobile robot [28,29]. The voltage output lines of the circuit board were read by the 16-bit Analog-to-Digital Converter (ADC) board with 16 channels acquiring at a frequency of 25 Hz.…”

Section: Experimental Set Upmentioning

confidence: 99%

Bioinspired early detection through gas flow modulation in chemo-sensory systems

Ziyatdinov

Fonollosa

Fernández

et al. 2015

Sensors and Actuators B: Chemical

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The design of bioinspired systems for chemical sensing is an engaging line of research in machine olfaction. Developments in this line could increase the lifetime and sensitivity of artificial chemo-sensory systems. Such approach is based on the sensory systems known in live organisms, and the resulting developed artificial systems are targeted to reproduce the biological mechanisms to some extent. Sniffing behaviour, sampling odours actively, has been studied recently in neuroscience, and it has been suggested that the respiration frequency is an important parameter of the olfactory system, since the odour perception, especially in complex scenarios such as novel odourants exploration, depends on both the stimulus identity and the sampling method. In this work we propose a chemical sensing system based on an array of 16 metal-oxide gas sensors that we combined with an external mechanical ventilator to simulate the biological respiration cycle. The tested gas classes formed a relatively broad combination of two analytes, acetone and ethanol, in binary mixtures. Two sets of lowfrequency and high-frequency features were extracted from the acquired signals to show that the high-frequency features contain information related to the gas class. In addition, such information is available at early stages of the measurement, which could make the technique * Corresponding author.Email address: andrey.ziyatdinov@upc.edu (Andrey Ziyatdinov) suitable in early detection scenarios. The full data set is made publicly available to the community.

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“…Electronic nose (EN) has been rapidly developed as a novel gas detection technology in the past two decades. It is widely implicated in toxic/harmful gas detection [1], food safety [2], environmental monitoring [3], medical industry [4], and other fields [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Sun

Luo

et al. 2018

Journal of Electrical and Computer Engineering

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Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO 2 , CH 4 , NH 3 , and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.

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A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation

Cited by 38 publications

References 28 publications

Sensor Array Design for Complex Sensing Tasks

Sensor Array Design for Complex Sensing Tasks

Bioinspired early detection through gas flow modulation in chemo-sensory systems

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

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