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Advanced Electronic Noses and Chemical Detection Systems

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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Electrochemical Devices and Sensors".

Deadline for manuscript submissions: closed (21 December 2021) | Viewed by 26284

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Special Issue Editor

Prof. Dr. James Covington

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Guest Editor

School of Engineering, University of Warwick, Coventry CV4 7AL, UK
Interests: electronic noses; machine olfaction; chemical sensors; MEMS; smart sensor systems; data analysis; deep learning; neural networks; industrial applications and medical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The electronic nose has been in continual development since its conception in the 1980’s. Traditional electronic noses, based on a small array of chemical sensors, have previously shown utility in a spectrum of applications ranging from medical and environmental monitoring to food and beverages, agriculture, security, process control, and mobile sensing. These systems have found favour due to their ability to simplify and classify complex chemical environments into a single output, making them almost unique within analytical instruments. This is achieved by mimicking biological olfaction, where chemicals are considered as a whole, instead of individually—as humans do.

In recent years, there has been development of ever more sophisticated sensors, sensor systems, and measurement methods, based on both chemical and physical principles. These include the use of multi-measurement methods of the same sensing layer, nanomaterials, thermal modulation of sensors, UV activation, pre-concentrators, sensor arrays, micro-systems, optical systems and micro-GC (gas chromatography), and ion mobility approaches. These have brought other challenges in not only constructing and using these more sophisticated instruments, but also in analysing the high-dimensional data sets. This has led to the use of modern machine learning and deep learning approaches to extract key information from these advanced systems.

This Special Issue of Chemosensors focusses on the design and development of chemical sensors, electronic noses and chemical detection systems. These systems can use arrays of chemical sensors or take physical measurements, such as Ion Mobility and Optical detection. This special issue also covers the data processing aspects of such systems, particularly the use of machine learning. Finally, it covers the application of these systems, particularly in the food, medical, environmental monitoring and agricultural sectors. We look forward to receiving papers on the latest developments in this field.

Prof. Dr. James Covington
Guest Editor

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Keywords

Electronic nose
Artificial olfaction
Machine olfaction
Chemical sensors
Sensing systems
Sensing materials
Chemical and physical sensors
Machine learning and deep learning
Environmental monitoring
Medical applications
Agricultural applications

Published Papers (6 papers)

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Research

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10 pages, 2480 KiB

Open AccessArticle

k-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances Affected by Drift Caused by Early Life Aging

by Marco Abbatangelo, Estefanía Núñez-Carmona, Veronica Sberveglieri, Elisabetta Comini and Giorgio Sberveglieri

Chemosensors 2020, 8(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors8010006 - 03 Jan 2020

Cited by 8 | Viewed by 3022

Abstract

The drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important topics in this field. The work aims to test the performance of MOX gas sensors over the aging process. Firstly, sensors were tested with ethanol to understand their behavior and response changes. In parallel, beers with different alcoholic content were analyzed to assess what happened in a real application scenario. With ethanol analysis, it was possible to quantify drift of the baseline of the sensors and changes that could affect their responses over time (from day 1 to day 51). Conversely, the beer dataset has been exploited to evaluate how two different classifiers perform the classification task based on the alcohol content of the samples. A hybrid k-nearest neighbors artificial neural network (k-NN-ANN) approach and “standard” k-NN were used to evaluate to distinguish among the samples when the measures were affected by drift. To achieve this goal, data acquired from day one to day six were used as training to predict data collected up to day 51. Overall, performances of the two methods were similar, even if the best result in terms of accuracy is reached by k-NN-ANN (96.51%). Full article

(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)

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11 pages, 2171 KiB

Open AccessArticle

3D-Printed Graphene Electrodes Applied in an Impedimetric Electronic Tongue for Soil Analysis

by Tatiana Americo da Silva, Maria Luisa Braunger, Marcos Antonio Neris Coutinho, Lucas Rios do Amaral, Varlei Rodrigues and Antonio Riul, Jr.

Chemosensors 2019, 7(4), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors7040050 - 24 Oct 2019

Cited by 20 | Viewed by 3331

Abstract

The increasing world population leads to the growing demand for food production without expanding cultivation areas. In this sense, precision agriculture optimizes the production and input usage by employing sensors to locally monitor plant nutrient within agricultural fields. Here, we have used an electronic tongue sensing device based on impedance spectroscopy to recognize distinct soil samples (sandy and clayey) enriched with macronutrients. The e-tongue setup consisted of an array of four sensing units formed by layer-by-layer (LbL) films deposited onto 3D-printed graphene-based interdigitated electrodes (IDEs). The IDEs were fabricated in 20 min using the fused deposition modeling process and commercial polylactic acid-based graphene filaments. The e-tongue comprised one bare and three IDEs functionalized with poly(diallyldimethylammonium chloride) solution/copper phthalocyanine-3,4′,4″,4‴-tetrasulfonic acid tetrasodium salt (PDDA/CuTsPc), PDDA/montmorillonite clay (MMt-K), and PDDA/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) LbL films. Control samples of sandy and clayey soils were enriched with different concentrations of nitrogen (N), phosphorus (P), and potassium (K) macronutrients. Sixteen soil samples were simply diluted in water and measured using electrical impedance spectroscopy, with data analyzed by principal component analysis. All soil samples were easily distinguished without pre-treatment, indicating the suitability of 3D-printed electrodes in e-tongue analysis to distinguish the chemical fertility of soil samples. Our results encourage further investigations into the development of new tools to support precision agriculture. Full article

(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)

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16 pages, 1289 KiB

Open AccessArticle

Sugars’ Quantifications Using a Potentiometric Electronic Tongue with Cross-Selective Sensors: Influence of an Ionic Background

by Vinicius da Costa Arca, António M. Peres, Adélio A. S. C. Machado, Evandro Bona and Luís G. Dias

Chemosensors 2019, 7(3), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors7030043 - 04 Sep 2019

Cited by 14 | Viewed by 3028

Abstract

Glucose, fructose and sucrose are sugars with known physiological effects, and their consumption has impact on the human health, also having an important effect on food sensory attributes. The analytical methods routinely used for identification and quantification of sugars in foods, like liquid chromatography and visible spectrophotometry have several disadvantages, like longer analysis times, high consumption of chemicals and the need for pretreatments of samples. To overcome these drawbacks, in this work, a potentiometric electronic tongue built with two identical multi-sensor systems of 20 cross-selectivity polymeric sensors, coupled with multivariate calibration with feature selection (a simulated annealing algorithm) was applied to quantify glucose, fructose and sucrose, and the total content of sugars as well. Standard solutions of ternary mixtures of the three sugars were used for multivariate calibration purposes, according to an orthogonal experimental design (multilevel fractional factorial design) with or without ionic background (KCl solution). The quantitative models’ predictive performance was evaluated by cross-validation with K-folds (internal validation) using selected data for training (selected with the K-means algorithm) and by external validation using test data. Overall, satisfactory predictive quantifications were achieved for all sugars and total sugar content based on subsets comprising 16 or 17 sensors. The test data allowed us to compare models’ predictions values and the respective sugar experimental values, showing slopes varying between 0.95 and 1.03, intercept values statistically equal to zero (p-value ≥ 0.05) and determination coefficients equal to or greater than 0.986. No significant differences were found between the predictive performances for the quantification of sugars using synthetic solutions with or without KCl (1 mol L⁻¹), although the adjustment of the ionic background allowed a better homogenization of the solution’s matrix and probably contributed to an enhanced confidence in the analytical work across all of the calibration working range. Full article

(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)

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11 pages, 4648 KiB

Open AccessArticle

Resistance-Capacitance Gas Sensor Based on Fractal Geometry

by Taicong Yang, Fengchun Tian, James A. Covington, Feng Xu, Yi Xu, Anyan Jiang, Junhui Qian, Ran Liu, Zichen Wang and Yangfan Huang

Chemosensors 2019, 7(3), 31; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors7030031 - 15 Jul 2019

Cited by 11 | Viewed by 4257

Abstract

An important component of any chemiresistive gas sensor is the way in which the resistance of the sensing film is interrogated. The geometrical structure of an electrode can enhance the performance of a gas-sensing device and in particular the performance of sensing films with large surface areas, such as carbon nanotubes. In this study, we investigated the influence of geometrical structure on the performance of gas sensors, combining the characteristics of carbon nanotubes with a novel gas sensor electrode structure based on fractal geometry. The fabricated sensors were tested with exposure to nitric oxide, measuring both the sensor resistance and capacitance (RC) of the sensor responses. Experimental results showed that the sensors with fractal electrode structures had a superior performance over sensors with traditional geometrical structures. Moreover, the RC characteristics of these fractal sensors could be further improved by using different test frequencies that could aid in the identification and quantification of a target gas. Full article

(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)

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14 pages, 16466 KiB

Open AccessArticle

The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

by Shidiq Nur Hidayat, Kuwat Triyana, Inggrit Fauzan, Trisna Julian, Danang Lelono, Yusril Yusuf, N. Ngadiman, Ana C.A. Veloso and António M. Peres

Chemosensors 2019, 7(3), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors7030029 - 09 Jul 2019

Cited by 39 | Viewed by 6547

Abstract

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry. Full article

(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)

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Review

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28 pages, 1450 KiB

Open AccessReview

Methods for Early Detection of Microbiological Infestation of Buildings Based on Gas Sensor Technologies

by Monika Garbacz, Agnieszka Malec, Sylwia Duda-Saternus, Zbigniew Suchorab, Łukasz Guz and Grzegorz Łagód

Chemosensors 2020, 8(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors8010007 - 06 Jan 2020

Cited by 22 | Viewed by 5265

Abstract

In this review, the problem of microbiological infestation of buildings was discussed. The techniques of detection were described as well, with special attention drawn to the rapid-early detection methods based on gas sensor arrays. The physical and chemical conditions of the building environment conducive to the development of microorganisms and the technical conditions influencing the problem of microbiological infestation were investigated. Additionally, the harmful effects on human health caused by the microbiological contamination were discussed, with a short review of particular groups of microorganisms causing sick building syndrome. Among the detection techniques, the traditional microbiological techniques as well as the molecular and chemical methods were presented. Different designs of the gas sensor arrays together with the various techniques of analyzing the received multidimensional signal were described, analyzed, and compared in detail. Full article

(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)

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