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Electronic Noses II

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 635

Special Issue Editor

Dr. Jose V. Ros-Lis

E-Mail Website
Guest Editor
REDOLí Research Group, Inorganic Chemistry Department, Universitat de València, 46010 Valencia, Spain
Interests: nanomaterial; microwaves; sensors; enzyme inhibition; mesoporous
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of electronic tongues and, recently, noses has become a popular trend in recent decades, with several groups worldwide preparing novel sensing systems. In comparison to traditional analytical instrumental methods of analysis based in expensive and complex equipment, electronic noses are relatively cheap and easy to handle. Electronic noses usually integrate an array of non-specific sensors, together with statistical tools for the analysis of data. Ideally, each sensor differs in their response to the volatile compounds generated by the sample, creating a characteristic fingerprint. This kind of system can be applied even to complex samples, such as food or biological samples for quantification, classification, sensorial analysis, and quality evaluation purposes.

This Special Issue is intended to be a timely and comprehensive issue on recent and emerging concepts and technologies in the area of electronic noses including metal–oxide semiconductors and polymers. Topics include but are not limited to systems based on metal–oxide sensors, polymers, color changes, other variations in optical properties, quartz crystal microbalance, or surface acoustic wave sensors. Furthermore, other areas such as data analysis and pattern recognition methodologies can be discussed. Research papers, short communications, and reviews are all welcome. If the author is interested in submitting a review, it would be helpful to discuss this with the Guest Editor before submission.

Dr. Jose V. Ros-Lis
Guest Editor

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15 pages, 3837 KiB  
Communication
Acetone Detection and Classification as Biomarker of Diabetes Mellitus Using a Quartz Crystal Microbalance Gas Sensor Array
by Marcos Rodríguez-Torres, Víctor Altuzar, Claudia Mendoza-Barrera, Georgina Beltrán-Pérez, Juan Castillo-Mixcóatl and Severino Muñoz-Aguirre
Sensors 2023, 23(24), 9823; https://0-doi-org.brum.beds.ac.uk/10.3390/s23249823 - 14 Dec 2023
Viewed by 723
Abstract
A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing [...] Read more.
A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol. Full article
(This article belongs to the Collection Electronic Noses)
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16 pages, 4517 KiB  
Article
UV-Light-Tunable p-/n-Type Chemiresistive Gas Sensors Based on Quasi-1D TiS3 Nanoribbons: Detection of Isopropanol at ppm Concentrations
by Victor V. Sysoev, Andrey V. Lashkov, Alexey Lipatov, Ilya A. Plugin, Michael Bruns, Dirk Fuchs, Alexey S. Varezhnikov, Mustahsin Adib, Martin Sommer and Alexander Sinitskii
Sensors 2022, 22(24), 9815; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249815 - 14 Dec 2022
Cited by 2 | Viewed by 2050
Abstract
The growing demand of society for gas sensors for energy-efficient environmental sensing stimulates studies of new electronic materials. Here, we investigated quasi-one-dimensional titanium trisulfide (TiS3) crystals for possible applications in chemiresistors and on-chip multisensor arrays. TiS3 nanoribbons were placed as [...] Read more.
The growing demand of society for gas sensors for energy-efficient environmental sensing stimulates studies of new electronic materials. Here, we investigated quasi-one-dimensional titanium trisulfide (TiS3) crystals for possible applications in chemiresistors and on-chip multisensor arrays. TiS3 nanoribbons were placed as a mat over a multielectrode chip to form an array of chemiresistive gas sensors. These sensors were exposed to isopropanol as a model analyte, which was mixed with air at low concentrations of 1–100 ppm that are below the Occupational Safety and Health Administration (OSHA) permissible exposure limit. The tests were performed at room temperature (RT), as well as with heating up to 110 °C, and under an ultraviolet (UV) radiation at λ = 345 nm. We found that the RT/UV conditions result in a n-type chemiresistive response to isopropanol, which seems to be governed by its redox reactions with chemisorbed oxygen species. In contrast, the RT conditions without a UV exposure produced a p-type response that is possibly caused by the enhancement of the electron transport scattering due to the analyte adsorption. By analyzing the vector signal from the entire on-chip multisensor array, we could distinguish isopropanol from benzene, both of which produced similar responses on individual sensors. We found that the heating up to 110 °C reduces both the sensitivity and selectivity of the sensor array. Full article
(This article belongs to the Collection Electronic Noses)
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14 pages, 1986 KiB  
Article
An Electronic Nose as a Non-Destructive Analytical Tool to Identify the Geographical Origin of Portuguese Olive Oils from Two Adjacent Regions
by Nuno Rodrigues, Nuno Ferreiro, Ana C. A. Veloso, José A. Pereira and António M. Peres
Sensors 2022, 22(24), 9651; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249651 - 09 Dec 2022
Cited by 4 | Viewed by 1509
Abstract
The geographical traceability of extra virgin olive oils (EVOO) is of paramount importance for oil chain actors and consumers. Oils produced in two adjacent Portuguese regions, Côa (36 oils) and Douro (31 oils), were evaluated and fulfilled the European legal thresholds for EVOO [...] Read more.
The geographical traceability of extra virgin olive oils (EVOO) is of paramount importance for oil chain actors and consumers. Oils produced in two adjacent Portuguese regions, Côa (36 oils) and Douro (31 oils), were evaluated and fulfilled the European legal thresholds for EVOO categorization. Compared to the Douro region, oils from Côa had higher total phenol contents (505 versus 279 mg GAE/kg) and greater oxidative stabilities (17.5 versus 10.6 h). The majority of Côa oils were fruity-green, bitter, and pungent oils. Conversely, Douro oils exhibited a more intense fruity-ripe and sweet sensation. Accordingly, different volatiles were detected, belonging to eight chemical families, from which aldehydes were the most abundant. Additionally, all oils were evaluated using a lab-made electronic nose, with metal oxide semiconductor sensors. The electrical fingerprints, together with principal component analysis, enabled the unsupervised recognition of the oils’ geographical origin, and their successful supervised linear discrimination (sensitivity of 98.5% and specificity of 98.4%; internal validation). The E-nose also quantified the contents of the two main volatile chemical classes (alcohols and aldehydes) and of the total volatiles content, for the studied olive oils split by geographical origin, using multivariate linear regression models (0.981 ≤ R2 ≤ 0.998 and 0.40 ≤ RMSE ≤ 2.79 mg/kg oil; internal validation). The E-nose-MOS was shown to be a fast, green, non-invasive and cost-effective tool for authenticating the geographical origin of the studied olive oils and to estimate the contents of the most abundant chemical classes of volatiles. Full article
(This article belongs to the Collection Electronic Noses)
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18 pages, 4488 KiB  
Article
Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen–Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling
by Hanyue Feng, Claudia Gonzalez Viejo, Niloofar Vaghefi, Paul W. J. Taylor, Eden Tongson and Sigfredo Fuentes
Sensors 2022, 22(22), 8645; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228645 - 09 Nov 2022
Cited by 7 | Viewed by 1666
Abstract
The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection [...] Read more.
The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil samples according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4–96.8% for tomato plants and between 94.81% and 96.22% for soil samples. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97–0.99, with very significant slope values in the range 0.97–1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors. Full article
(This article belongs to the Collection Electronic Noses)
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13 pages, 3789 KiB  
Article
Portable Electronic Nose Based on Digital and Analog Chemical Sensors for 2,4,6-Trichloroanisole Discrimination
by Félix Meléndez, Patricia Arroyo, Jaime Gómez-Suárez, Sergio Palomeque-Mangut, José Ignacio Suárez and Jesús Lozano
Sensors 2022, 22(9), 3453; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093453 - 30 Apr 2022
Cited by 7 | Viewed by 2595
Abstract
2,4,6-trichloroanisole (TCA) is mainly responsible for cork taint in wine, which causes significant economic losses; therefore, the wine and cork industries demand an immediate, economic, noninvasive and on-the-spot solution. In this work, we present a novel prototype of an electronic nose (e-nose) using [...] Read more.
2,4,6-trichloroanisole (TCA) is mainly responsible for cork taint in wine, which causes significant economic losses; therefore, the wine and cork industries demand an immediate, economic, noninvasive and on-the-spot solution. In this work, we present a novel prototype of an electronic nose (e-nose) using an array of digital and analog metal-oxide gas sensors with a total of 31 signals, capable of detecting TCA, and classifying cork samples with low TCA concentrations (≤15.1 ng/L). The results show that the device responds to low concentrations of TCA in laboratory conditions. It also differentiates among the inner and outer layers of cork bark (81.5% success) and distinguishes among six different samples of granulated cork (83.3% success). Finally, the device can predict the concentration of a new sample within a ±10% error margin. Full article
(This article belongs to the Collection Electronic Noses)
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18 pages, 8238 KiB  
Article
Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani
by Piotr Borowik, Leszek Adamowicz, Rafał Tarakowski, Przemysław Wacławik, Tomasz Oszako, Sławomir Ślusarski and Miłosz Tkaczyk
Sensors 2021, 21(17), 5868; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175868 - 31 Aug 2021
Cited by 17 | Viewed by 3039
Abstract
Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first [...] Read more.
Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors’ responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions. Full article
(This article belongs to the Collection Electronic Noses)
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19 pages, 5718 KiB  
Article
Classifying the Biological Status of Honeybee Workers Using Gas Sensors
by Jakub T. Wilk, Beata Bąk, Piotr Artiemjew, Jerzy Wilde and Maciej Siuda
Sensors 2021, 21(1), 166; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010166 - 29 Dec 2020
Cited by 2 | Viewed by 1912
Abstract
Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The [...] Read more.
Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification. Full article
(This article belongs to the Collection Electronic Noses)
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16 pages, 8267 KiB  
Article
Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques
by Tiziano Zarra, Mark Gino K. Galang, Florencio C. Ballesteros, Jr., Vincenzo Belgiorno and Vincenzo Naddeo
Sensors 2021, 21(1), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010114 - 27 Dec 2020
Cited by 8 | Viewed by 3700
Abstract
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the [...] Read more.
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS. Full article
(This article belongs to the Collection Electronic Noses)
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16 pages, 4807 KiB  
Article
Development of a Tuneable NDIR Optical Electronic Nose
by Siavash Esfahani, Akira Tiele, Samuel O. Agbroko and James A. Covington
Sensors 2020, 20(23), 6875; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236875 - 01 Dec 2020
Cited by 22 | Viewed by 4361
Abstract
Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array [...] Read more.
Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO2 and CH4), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO2 and CH4. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications. Full article
(This article belongs to the Collection Electronic Noses)
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17 pages, 3537 KiB  
Article
Characterization and Analysis of Okoume and Aiele Essential Oils from Gabon by GC-MS, Electronic Nose, and Their Antibacterial Activity Assessment
by Youssra Aghoutane, Mohammed Moufid, Soukaina Motia, Guy Stephane Padzys, Linda Priscilia Omouendze, Eduard Llobet, Benachir Bouchikhi and Nezha El Bari
Sensors 2020, 20(23), 6750; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236750 - 26 Nov 2020
Cited by 10 | Viewed by 2781
Abstract
Essential oil resins of Aucoumea klaineana (Okoume) and Canarium schweinfurthii (Aiele) species, of the Burseraceae family, were studied to investigate their bioactive constituents and their antibacterial activities. Aiele resin had a higher yield (6.86%) of essential oil than Okoume (3.62%). Twenty-one compounds for [...] Read more.
Essential oil resins of Aucoumea klaineana (Okoume) and Canarium schweinfurthii (Aiele) species, of the Burseraceae family, were studied to investigate their bioactive constituents and their antibacterial activities. Aiele resin had a higher yield (6.86%) of essential oil than Okoume (3.62%). Twenty-one compounds for Okoume and eighteen for Aiele essential oil were identified using a gas chromatography-mass spectrometry (Gp-C-MS) technique. The main compounds identified in Okoume essential oil were benzenemethanol, α, α,4-trimethyl (28.85%), (+)-3-carene (3,7,7-trimethyl bicyclo[4.1.0]hept-3-ene) (17.93%), D-Limonene ((4R)-1-methyl-4-prop-1-en-2-ylcyclohexene) (19.36%). With regard to the Aiele essential oil, we identified (1R,4S)-1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.64%), and 1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.83%). Two strains of bacteria, Escherichia coli and Staphylococcus aureus, were used in antibacterial tests. S. aureus was found to be more sensitive to Okoume and Aiele essential oils, with a high inhibition zone ranging from 20 to 16 mm. In comparison, the inhibition zone ranged from 6 to 12 mm for E. coli. An electronic nose (e-nose) combined with pattern analysis methods such as principal component analysis (PCA), discriminant function analysis (DFA), and hierarchical cluster analysis (HCA) were used to discriminate the essential oil samples. In summary, the e-nose and GC-MS allowed the identification of bioactive compounds in the essential oil samples, which have a strong antimicrobial activity, with satisfactory results. Full article
(This article belongs to the Collection Electronic Noses)
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20 pages, 1596 KiB  
Article
Odor Detection Using an E-Nose With a Reduced Sensor Array
by Piotr Borowik, Leszek Adamowicz, Rafał Tarakowski, Krzysztof Siwek and Tomasz Grzywacz
Sensors 2020, 20(12), 3542; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123542 - 23 Jun 2020
Cited by 35 | Viewed by 6519
Abstract
Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose [...] Read more.
Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model’s performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model. Full article
(This article belongs to the Collection Electronic Noses)
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12 pages, 4911 KiB  
Article
Visual Analysis of Odor Interaction Based on Support Vector Regression Method
by Luchun Yan, Chuandong Wu and Jiemin Liu
Sensors 2020, 20(6), 1707; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061707 - 19 Mar 2020
Cited by 6 | Viewed by 2446
Abstract
The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish [...] Read more.
The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches. Full article
(This article belongs to the Collection Electronic Noses)
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16 pages, 4955 KiB  
Article
Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements
by Patricia Arroyo, Félix Meléndez, José Ignacio Suárez, José Luis Herrero, Sergio Rodríguez and Jesús Lozano
Sensors 2020, 20(3), 786; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030786 - 31 Jan 2020
Cited by 60 | Viewed by 7110
Abstract
This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same [...] Read more.
This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations. To validate the system, it has been tested with different concentrations of NOx including some tests specifically developed to study the behavior of the device in different humidity conditions. Finally, a mobile application has been developed to provide classification services. In this regard, a neural network has been developed, trained, and integrated into a smartphone to process the information retrieved from e-nose devices. Full article
(This article belongs to the Collection Electronic Noses)
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13 pages, 4381 KiB  
Article
Portable Low-Cost Electronic Nose Based on Surface Acoustic Wave Sensors for the Detection of BTX Vapors in Air
by Daniel Matatagui, Fabio Andrés Bahos, Isabel Gràcia and María del Carmen Horrillo
Sensors 2019, 19(24), 5406; https://0-doi-org.brum.beds.ac.uk/10.3390/s19245406 - 08 Dec 2019
Cited by 23 | Viewed by 4843
Abstract
A portable electronic nose based on surface acoustic wave (SAW) sensors is proposed in this work to detect toxic chemicals, which have a great potential to threaten the surrounding natural environment or adversely affect the health of people. We want to emphasize that [...] Read more.
A portable electronic nose based on surface acoustic wave (SAW) sensors is proposed in this work to detect toxic chemicals, which have a great potential to threaten the surrounding natural environment or adversely affect the health of people. We want to emphasize that ferrite nanoparticles, decorated (Au, Pt, Pd) and undecorated, have been used as sensitive coatings for the first time in these types of sensors. Furthermore, the proposed electronic nose incorporates signal conditioning and acquisition and transmission modules. The electronic nose was tested to low concentrations of benzene, toluene, and xylene, exhibiting excellent performance in terms of sensitivity, selectivity, and response time, indicating its potential as a monitoring system that can contribute to the detection of toxic compounds. Full article
(This article belongs to the Collection Electronic Noses)
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29 pages, 2763 KiB  
Article
A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose
by Binchun Lu, Lidan Fu, Bo Nie, Zhiyun Peng and Hongying Liu
Sensors 2019, 19(23), 5333; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235333 - 03 Dec 2019
Cited by 17 | Viewed by 5004
Abstract
The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low [...] Read more.
The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios. Full article
(This article belongs to the Collection Electronic Noses)
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15 pages, 1434 KiB  
Article
Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System
by Tao Liu, Dongqi Li, Jianjun Chen, Yanbing Chen, Tao Yang and Jianhua Cao
Sensors 2019, 19(16), 3601; https://0-doi-org.brum.beds.ac.uk/10.3390/s19163601 - 19 Aug 2019
Cited by 13 | Viewed by 3430
Abstract
Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter [...] Read more.
Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the drift effect in more challenging situations in which the category information (labels) of the drifted samples is difficult or expensive to obtain. Thus, only a few of the drifted samples can be used for label querying. To solve this problem, we propose an innovative methodology based on Active Learning (AL) that selectively provides sample labels for drift correction. Moreover, we utilize a dynamic clustering process to balance the sample category for label querying. In the experimental section, we set up two E-nose drift scenarios—a long-term and a short-term scenario—to evaluate the performance of the proposed methodology. The results indicate that the proposed methodology is superior to the other state-of-art methods presented. Furthermore, the increasing tendencies of parameter sensitivity and accuracy are analyzed. In addition, the Label Efficiency Index (LEI) is adopted to measure the efficiency and labelling cost of the AL methods. The LEI values indicate that our proposed methodology exhibited better performance than the other presented AL methods in the online drift correction of E-noses. Full article
(This article belongs to the Collection Electronic Noses)
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14 pages, 2599 KiB  
Article
A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose
by Henike Guilherme Jordan Voss, José Jair Alves Mendes Júnior, Murilo Eduardo Farinelli and Sergio Luiz Stevan, Jr.
Sensors 2019, 19(11), 2646; https://0-doi-org.brum.beds.ac.uk/10.3390/s19112646 - 11 Jun 2019
Cited by 38 | Viewed by 6835
Abstract
Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be [...] Read more.
Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages. Full article
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12 pages, 2436 KiB  
Article
Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO
by Hui Jiang, Weidong Xu and Quansheng Chen
Sensors 2019, 19(9), 2021; https://0-doi-org.brum.beds.ac.uk/10.3390/s19092021 - 30 Apr 2019
Cited by 10 | Viewed by 3175
Abstract
The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, [...] Read more.
The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture. Full article
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14 pages, 839 KiB  
Article
A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey
by María José Aliaño-González, Marta Ferreiro-González, Estrella Espada-Bellido, Miguel Palma and Gerardo F. Barbero
Sensors 2019, 19(7), 1621; https://0-doi-org.brum.beds.ac.uk/10.3390/s19071621 - 04 Apr 2019
Cited by 26 | Viewed by 4898
Abstract
Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this [...] Read more.
Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box–Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10–50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way. Full article
(This article belongs to the Collection Electronic Noses)
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15 pages, 1216 KiB  
Article
Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor
by You Wang, Zhan Wang, Junwei Diao, Xiyang Sun, Zhiyuan Luo and Guang Li
Sensors 2019, 19(4), 964; https://0-doi-org.brum.beds.ac.uk/10.3390/s19040964 - 25 Feb 2019
Cited by 8 | Viewed by 3769
Abstract
A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy [...] Read more.
A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising. Full article
(This article belongs to the Collection Electronic Noses)
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12 pages, 3939 KiB  
Letter
Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification
by Zhiyuan Wu, Hanying Zhang, Wentao Sun, Ning Lu, Meng Yan, Yi Wu, Zhongqiu Hua and Shurui Fan
Sensors 2020, 20(15), 4239; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154239 - 30 Jul 2020
Cited by 15 | Viewed by 4224
Abstract
In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we [...] Read more.
In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes. Full article
(This article belongs to the Collection Electronic Noses)
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