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Electronic Noses and Tongues for Environmental Monitoring

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

Deadline for manuscript submissions: closed (1 April 2021) | Viewed by 15059

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Dr. Alisa Rudnitskaya

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Guest Editor

CESAM and Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: multisensor systems; electronic tongues; electroanalysis; chemometrics; food analysis; environmental analysis; electrochemical sensors and biosensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, growth in human activities and occurrence of natural events related to the climate change have led to a release of contaminants causing significant impacts on the environment. In this context, there is growing interest in in situ and real time detection of the contaminants present in water, air, and soil. Sensor arrays for gas and liquid analysis, i.e., electronic noses and tongues, can provide a fast screening of specific contaminants in the environment. Sensor arrays have been demonstrated to be attractive tools for this purpose as they combine advantages of chemical sensors, i.e., low cost and easy automation, with superior performance by accounting for the insufficient selectivity plaguing most chemical sensors in complex media.

The aim of our upcoming Special Issue on “Electronic Noses and Tongue for Environmental Monitoring” is to highlight the latest developments in this field. We welcome submissions spanning all aspects of development and application of the electronic noses and tongues based on chemical and biosensors to the detection of environmentally relevant compounds. Both reviews and original papers are welcome.

Dr. Alisa Rudnitskaya
Guest Editor

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.

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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

sensor arrays
electronic noses
electronic tongues
chemical sensors
biosensors
environmental monitoring
contaminants

Published Papers (5 papers)

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Research

15 pages, 18421 KiB

Open AccessArticle

Keeping Track of Phaeodactylum tricornutum (Bacillariophyta) Culture Contamination by Potentiometric E-Tongue

by Saverio Savio, Corrado di Natale, Roberto Paolesse, Larisa Lvova and Roberta Congestri

Sensors 2021, 21(12), 4052; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124052 - 12 Jun 2021

Cited by 1 | Viewed by 1853

Abstract

The large-scale cultivation of microalgae provides a wide spectrum of marketable bioproducts, profitably used in many fields, from the preparation of functional health products and feed supplement in aquaculture and animal husbandry to biofuels and green chemistry agents. The commercially successful algal biomass production requires effective strategies to maintain the process at desired productivity and stability levels. Hence, the development of effective early warning methods to timely indicate remedial actions and to undertake countermeasures is extremely important to avoid culture collapse and consequent economic losses. With the aim to develop an early warning method of algal contamination, the potentiometric E-tongue was applied to record the variations in the culture environments, over the whole growth process, of two unialgal cultures, Phaeodactylum tricornutum and a microalgal contaminant, along with those of their mixed culture. The E-tongue system ability to distinguish the cultures and to predict their growth stage, through the application of multivariate data analysis, was shown. A PLS regression method applied to the E-tongue output data allowed a good prediction of culture growth time, expressed as growth days, with R² values in a range from 0.913 to 0.960 and RMSEP of 1.97–2.38 days. Moreover, the SIMCA and PLS-DA techniques were useful for cultures contamination monitoring. The constructed PLS-DA model properly discriminated 67% of cultures through the analysis of their growth media, i.e., environments, thus proving the potential of the E-tongue system for a real time monitoring of contamination in microalgal intensive cultivation. Full article

(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)

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13 pages, 6784 KiB

Open AccessArticle

Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring

by Rachid Laref, Etienne Losson, Alexandre Sava and Maryam Siadat

Sensors 2021, 21(11), 3581; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113581 - 21 May 2021

Cited by 14 | Viewed by 2348

Abstract

This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO₂]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors’ sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO₂] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration. Full article

(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)

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14 pages, 2127 KiB

Open AccessArticle

Non-Invasive Method to Detect Infection with Mycobacterium tuberculosis Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System

by Kelvin de Jesús Beleño-Sáenz, Juan Martín Cáceres-Tarazona, Pauline Nol, Aylen Lisset Jaimes-Mogollón, Oscar Eduardo Gualdrón-Guerrero, Cristhian Manuel Durán-Acevedo, Jose Angel Barasona, Joaquin Vicente, María José Torres, Tesfalem Geremariam Welearegay, Lars Österlund, Jack Rhyan and Radu Ionescu

Sensors 2021, 21(2), 584; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020584 - 15 Jan 2021

Cited by 3 | Viewed by 2992

Abstract

More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12–24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations. Full article

(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)

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10 pages, 1752 KiB

Open AccessCommunication

Monitoring the BTEX Volatiles during 3D Printing with Acrylonitrile Butadiene Styrene (ABS) Using Electronic Nose and Proton Transfer Reaction Mass Spectrometry

by Wojciech Wojnowski, Kaja Kalinowska, Jacek Gębicki and Bożena Zabiegała

Sensors 2020, 20(19), 5531; https://0-doi-org.brum.beds.ac.uk/10.3390/s20195531 - 27 Sep 2020

Cited by 10 | Viewed by 2507

Abstract

We describe a concept study in which the changes of concentration of benzene, toluene, ethylbenzene, and xylene (BTEX) compounds and styrene within a 3D printer enclosure during printing with different acrylonitrile butadiene styrene (ABS) filaments were monitored in real-time using a proton transfer reaction mass spectrometer and an electronic nose. The quantitative data on the concentration of the BTEX compounds, in particular the concentration of carcinogenic benzene, were then used as reference values for assessing the applicability of an array of low-cost electrochemical sensors in monitoring the exposure of the users of consumer-grade fused deposition modelling 3D printers to potentially harmful volatiles. Using multivariate statistical analysis and machine learning, it was possible to determine whether a set threshold limit value for the concentration of BTEX was exceeded with a 0.96 classification accuracy and within a timeframe of 5 min based on the responses of the chemical sensors. Full article

(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)

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17 pages, 3381 KiB

Open AccessEditor’s ChoiceArticle

Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

by Sabina Licen, Alessia Di Gilio, Jolanda Palmisani, Stefania Petraccone, Gianluigi de Gennaro and Pierluigi Barbieri

Sensors 2020, 20(7), 1887; https://doi.org/10.3390/s20071887 - 29 Mar 2020

Cited by 17 | Viewed by 4404

Abstract

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses. Full article

(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)

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