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Electronic Tongues, Electronic Noses, and Electronic Eyes

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 26641

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Special Issue Editors

Prof. Dr. Laura Pigani

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Guest Editor

Dipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Modena e Reggio Emilia, Via G. Campi, 103, 41125 Modena, Italy
Interests: amperometric sensors; electronic tongue; electroanalysis; food analysis; modified electrodes; electrodic materials

Dr. Rosalba Calvini

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Guest Editor

Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Padiglione Besta, Via Amendola, 2, 42122 Reggio Emilia, Italy
Interests: artificial sensors; electronic eye; optical sensors; data analysis; chemometrics; data fusion; signal processing

Special Issue Information

Dear Colleagues,

The application of sensing systems to the in situ study of real matrices, requiring minimal or no manipulation of the sample at all, is a very urgent task. To this aim, the development of devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) is of chief importance for the fast quantitative determination of one or more analytes, or for the estimation of overall quality parameters, which may also be related to sensory characteristics, such as ‘smell’, ‘taste’, and ‘color’. ENs and ETs are nonspecific sensor systems able to interact with volatile compounds and analytes dispersed in solution, respectively. EE is designed to analyze the color- and aspect-related attributes of a sample that are detected by the human eye, and it is usually based on computer vision, colorimetry or spectrophotometry. In order to determine the parameters of interest, the output of these sensing systems is analyzed through a “blind analysis” approach, without any prior assumption about the chemical species responsible for the measured signals. Indeed, the application of multivariate data analysis strategies allows relating specific patterns in the signals derived from the considered sensors with the sought information

Furthermore, when dealing with complex matrices, the combination of datasets resulting from multiple sensors can provide a more comprehensive characterization of the analyzed samples, which cannot be acquired by analyzing the different blocks of data separately from each other

The aim of this Special Issue is to collect recent advances in the development of ET, EN, and EE systems, used alone or in combination, including innovative sensing materials and new data processing strategies, with a particular attention to data fusion techniques. Submitted papers can include applications in different research fields, such as food, pharmaceutical, and medical analysis or forensic and environmental sciences, among others

Prof. Dr. Laura Pigani
Dr. Rosalba Calvini
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

Electronic noses
Electronic tongues
Electronic eyes
Chemometrics
Data fusion
Pattern recognition
Sensing materials
Electrochemical sensors
Optical sensors
Sensors array

Published Papers (5 papers)

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Research

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16 pages, 1728 KiB

Open AccessArticle

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

by Piotr Borowik, Leszek Adamowicz, Rafał Tarakowski, Przemysław Wacławik, Tomasz Oszako, Sławomir Ślusarski and Miłosz Tkaczyk

Sensors 2021, 21(4), 1326; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041326 - 13 Feb 2021

Cited by 19 | Viewed by 2985

Abstract

Compared with traditional gas chromatography–mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors’ response to the odors’ exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models’ performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose. Full article

(This article belongs to the Special Issue Electronic Tongues, Electronic Noses, and Electronic Eyes)

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13 pages, 1316 KiB

Open AccessArticle

Electronic Tongue Recognition with Feature Specificity Enhancement

by Tao Liu, Yanbing Chen, Dongqi Li, Tao Yang and Jianhua Cao

Sensors 2020, 20(3), 772; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030772 - 31 Jan 2020

Cited by 8 | Viewed by 2643

Abstract

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well. Full article

(This article belongs to the Special Issue Electronic Tongues, Electronic Noses, and Electronic Eyes)

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Review

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17 pages, 868 KiB

Open AccessReview

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes

by Rosalba Calvini and Laura Pigani

Sensors 2022, 22(2), 577; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020577 - 12 Jan 2022

Cited by 40 | Viewed by 3905

Abstract

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the “smell”, “taste”, and “color” of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review’s purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied. Full article

(This article belongs to the Special Issue Electronic Tongues, Electronic Noses, and Electronic Eyes)

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26 pages, 2811 KiB

Open AccessReview

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

by Alfonso Sierra-Padilla, Juan José García-Guzmán, David López-Iglesias, José María Palacios-Santander and Laura Cubillana-Aguilera

Sensors 2021, 21(15), 4976; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154976 - 22 Jul 2021

Cited by 16 | Viewed by 3455

Abstract

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered. Full article

(This article belongs to the Special Issue Electronic Tongues, Electronic Noses, and Electronic Eyes)

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40 pages, 30857 KiB

Open AccessReview

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

by Usman Yaqoob and Mohammad I. Younis

Sensors 2021, 21(8), 2877; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082877 - 20 Apr 2021

Cited by 93 | Viewed by 12628

Abstract

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors. Full article

(This article belongs to the Special Issue Electronic Tongues, Electronic Noses, and Electronic Eyes)

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