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Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 32207

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Special Issue Editors

Dr. Arnaud Buhot

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Guest Editor

Grenoble Alpes University, CEA, CNRS, IRIG-SyMMES, 17 Rue des Martyrs, 38000 Grenoble, France
Interests: optoelectronic nose/tongue development; aptamer biosensors; surface plasmons resonance imaging; theory of microarrays (DNA or protein); biopolymer conformation; DNA based architectures; soft condensed matter
Special Issues, Collections and Topics in MDPI journals

Dr. Yanxia Hou

E-Mail Website
Guest Editor

IRIG-SYMMES, University Grenoble Alpes, CEA, CNRS, 38000 Grenoble, France
Interests: electronic noses; olfactory biosensors; electronic tongues; artificial olfaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is currently a rapidly growing demand for the sensitive and selective detection of volatile organic compounds (VOCs) in various domains, including environmental monitoring, air quality control, detection of pollution or gas leaks for personal/public safety, food safety and quality control, non-invasive medical diagnostics, etc. For these applications, portable devices such as miniaturized sensors are preferable for real-time and on-site analyses. Among them, biomimetic olfactory sensors and electronic noses are very promising. In particular, electronic noses, by mimicking human olfaction, consist of an array of sensors with cross-sensitivity to different samples and use advanced mathematical procedures for signal processing based on pattern recognition and/or multivariate statistics. In the last three decades, great efforts have been made for the development of sensitive and robust transduction systems of all types. Nevertheless, due to the limited number of sensors and especially the limited diversity of sensing materials (metal oxides, polymers, etc.), so far, the performance of most existing electronic noses is still far behind what we expect.

Therefore, this Special Issue of Sensors will highlight recent advances in the design and development of novel sensing materials to improve the performance of the olfactory sensors and electronic noses (in terms of sensitivity and selectivity). A particular emphasis will be put on the design of novel biomimetic sensing materials, i.e. the protein engineering of odorant binding proteins (OBPs) and olfactory receptors (ORs), the rational design of peptides by molecular modelling and/or computational screening, biopolymers such as DNA, synthetic receptors, molecular imprinted polymers (MIP), etc. Finally, there is also a particular interest in the applications of these devices in various domains, covering VOC and odour analysis, food and beverage analysis, environmental analysis and biomedical applications.

Submitted papers should present novel contributions and innovative applications. Relevant topical reviews are also welcome.

Dr. Arnaud Buhot
Dr. Yanxia Hou-Broutin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Olfactory sensors
Electronic noses
Odour sensors
Chemical sensors
Gas sensors
Sensor arrays
Biomimetic sensing materials
Odorant binding proteins (OBPs)
Olfactory receptors (ORs)
Volatile organic compounds (VOCs)
Odour analysis
Sensory analysis
Food and beverage analysis
Environmental analysis
Biomedical applications

Published Papers (6 papers)

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Research

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16 pages, 1990 KiB

Open AccessArticle

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

by Anup Vanarse, Adam Osseiran, Alexander Rassau and Peter van der Made

Sensors 2022, 22(2), 440; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020440 - 07 Jan 2022

Cited by 4 | Viewed by 3070

Abstract

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems. Full article

(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)

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17 pages, 2887 KiB

Open AccessArticle

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

by Anup Vanarse, Josafath Israel Espinosa-Ramos, Adam Osseiran, Alexander Rassau and Nikola Kasabov

Sensors 2020, 20(10), 2756; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102756 - 12 May 2020

Cited by 17 | Viewed by 6490

Abstract

Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications. Full article

(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)

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13 pages, 2798 KiB

Open AccessArticle

A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data

by Anup Vanarse, Adam Osseiran, Alexander Rassau and Peter van der Made

Sensors 2019, 19(22), 4831; https://0-doi-org.brum.beds.ac.uk/10.3390/s19224831 - 06 Nov 2019

Cited by 24 | Viewed by 6509

Abstract

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. Full article

(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)

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13 pages, 1397 KiB

Open AccessArticle

A Highly Selective Biosensor Based on Peptide Directly Derived from the HarmOBP7 Aldehyde Binding Site

by Tomasz Wasilewski, Bartosz Szulczyński, Marek Wojciechowski, Wojciech Kamysz and Jacek Gębicki

Sensors 2019, 19(19), 4284; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194284 - 03 Oct 2019

Cited by 32 | Viewed by 3512

Abstract

This paper presents the results of research on determining the optimal length of a peptide chain to effectively bind octanal molecules. Peptides that map the aldehyde binding site in HarmOBP7 were immobilized on piezoelectric transducers. Based on computational studies, four Odorant Binding Protein-derived Peptides (OBPPs) with different sequences were selected. Molecular modelling results of ligand docking with selected peptides were correlated with experimental results. The use of low-molecular synthetic peptides, instead of the whole protein, enabled the construction OBPPs-based biosensors. This work aims at developing a biomimetic piezoelectric OBPPs sensor for selective detection of octanal. Moreover, the research is concerned with the ligand binding affinity depending on different peptides’ chain lengths. The authors believe that the chain length can have a substantial influence on the type and effectiveness of peptide–ligand interaction. A confirmation of in silico investigation results is the correlation with the experimental results, which shows that the highest affinity to octanal is exhibited by the longest peptide (OBPP4 – KLLFDSLTDLKKKMSEC-NH₂). We hypothesized that the binding of long chain aldehydes to the peptide, mimicking the binding site of HarmOBP7, induced a conformational change in the peptide deposited on a selected transducer. The constructed OBPP4-based biosensors were able to selectively bind octanal in the gas phase. It was also shown that the sensors were characterized by high selectivity with respect to octanal, as well as to acetaldehyde and benzaldehyde. The results indicate that the OBPP4 peptide, mimicking the binding domain in the Odorant Binding Protein, can provide new opportunities for the development of biomimicking materials in the field of odor biosensors. Full article

(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)

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Review

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26 pages, 1468 KiB

Open AccessEditor’s ChoiceReview

Peptides, DNA and MIPs in Gas Sensing. From the Realization of the Sensors to Sample Analysis

by Sara Gaggiotti, Flavio Della Pelle, Marcello Mascini, Angelo Cichelli and Dario Compagnone

Sensors 2020, 20(16), 4433; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164433 - 08 Aug 2020

Cited by 20 | Viewed by 4722

Abstract

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in ‘point of care’ diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases. Full article

(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)

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28 pages, 4738 KiB

Open AccessReview

Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review

by Charlotte Hurot, Natale Scaramozzino, Arnaud Buhot and Yanxia Hou

Sensors 2020, 20(6), 1803; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061803 - 24 Mar 2020

Cited by 31 | Viewed by 6646

Abstract

Artificial noses are broad-spectrum multisensors dedicated to the detection of volatile organic compounds (VOCs). Despite great recent progress, they still suffer from a lack of sensitivity and selectivity. We will review, in a systemic way, the biomimetic strategies for improving these performance criteria, including the design of sensing materials, their immobilization on the sensing surface, the sampling of VOCs, the choice of a transduction method, and the data processing. This reflection could help address new applications in domains where high-performance artificial noses are required such as public security and safety, environment, industry, or healthcare. Full article

(This article belongs to the Special Issue Advances in Biomimetic Olfactory Sensors and Electronic Noses and Their Applications)

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