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Special Issue "Biomimetic Sensor Arrays"

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

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editor

Dr. Alisa Rudnitskaya
Website
Guest Editor
CESAM and Department of Chemistry, University of Aveiro, Aveiro, Portugal
Interests: multisensor systems; electronic tongues; electroanalysis; chemometrics; food analysis; environmental analysis; electrochemical sensors and biosensors
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Electronic noses and tongues, analytical devices based on an array of partially selective chemical sensors or biosensors and multivariate data processing tools, are often called biomimetic sensor systems or arrays as their design was inspired by biological sensing systems, primarily olfaction. They became popular analytical instruments during the last two decades and a wide range of applications has been reported, including the classification of samples according to the properties of interest, quantification, process control, and taste and flavour assessment.

Most of the sensors used in the electronic noses and electronic tongues are based on sensing materials that have nothing in common with biological receptors, which can result in the unsatisfactory performance of the sensor systems in some applications. Recently, the use of recognition elements of biological origin such as cells or tissues or based on bioinspired materials such as olfactory proteins or molecularly imprinted polymers have been proposed for sensor development, with the expectation that sensor sensitivity and selectivity characteristics would replicate that of the natural receptors.

In order to highlight the recent advances in the development of electronic tongues and noses and their applications, I would like to invite you to consider submitting a manuscript to our upcoming Special Issue, “Biomimetic Sensor Arrays”. The aim of this Special Issue is to gather a collection of papers dedicated to all aspects of biomimetic sensor systems, their design and applications with a particular emphasis on novel biomimetic sensing materials.

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 papers will be 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 2000 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 tongue
  • Electronic nose
  • Sensor arrays
  • Taste sensor
  • Biomimetic sensing materials
  • Aptamers
  • Olfaction proteins
  • Cell sensors
  • Molecularly imprinted polymers
  • Sensory analysis
  • Food analysis
  • Environmental analysis
  • Biomedical applications

Published Papers (7 papers)

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Research

Open AccessArticle
A Barcoded Polymer-Based Cross-Reactive Spectroscopic Sensor Array for Organic Volatiles
Sensors 2019, 19(17), 3683; https://doi.org/10.3390/s19173683 - 24 Aug 2019
Cited by 1
Abstract
The development of cross-reactive sensor arrays for volatile organics (electronic noses, e-noses) is an active area of research. In this manuscript, we present a new format for barcoded polymer sensor arrays based on porous polymer beads. An array of nine self-encoded polymers was [...] Read more.
The development of cross-reactive sensor arrays for volatile organics (electronic noses, e-noses) is an active area of research. In this manuscript, we present a new format for barcoded polymer sensor arrays based on porous polymer beads. An array of nine self-encoded polymers was analyzed by Raman spectroscopy before and after exposure to a series of volatile organic compounds, and the changes in the vibrational fingerprints of their polymers was recorded before and after exposure. Our results show that the spectroscopic changes experienced by the porous spectroscopically encoded beads after exposure to an analyte can be used to identify and classify the target analytes. To expedite this analysis, analyte-specific changes induced in the sensor arrays were transformed into a response pattern using multivariate data analysis. These studies established the barcoded bead array format as a potentially effective sensing element in e-nose devices. Devices such as these have the potential to advance personalized medicine, providing a platform for non-invasive, real-time volatile metabolite detection. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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Open AccessArticle
An E-Nose for the Monitoring of Severe Liver Impairment: A Preliminary Study
Sensors 2019, 19(17), 3656; https://doi.org/10.3390/s19173656 - 22 Aug 2019
Abstract
Biologically inspired to mammalian olfactory system, electronic noses became popular during the last three decades. In literature, as well as in daily practice, a wide range of applications are reported. Nevertheless, the most pioneering one has been (and still is) the assessment of [...] Read more.
Biologically inspired to mammalian olfactory system, electronic noses became popular during the last three decades. In literature, as well as in daily practice, a wide range of applications are reported. Nevertheless, the most pioneering one has been (and still is) the assessment of the human breath composition. In this study, we used a prototype of electronic nose, called Wize Sniffer (WS) and based it on an array of semiconductor gas sensor, to detect ammonia in the breath of patients suffering from severe liver impairment. In the setting of severely impaired liver, toxic substances, such as ammonia, accumulate in the systemic circulation and in the brain. This may result in Hepatic Encephalopathy (HE), a spectrum of neuro–psychiatric abnormalities which include changes in cognitive functions, consciousness, and behaviour. HE can be detected only by specific but time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. In the presented proof-of-concept study, we aimed at investigating the possibility of discriminating the severity degree of liver impairment on the basis of the detected breath ammonia, in view of the detection of HE at its early stage. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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Open AccessCommunication
Quantum Dots—Assisted 2D Fluorescence for Pattern Based Sensing of Amino Acids, Oligopeptides and Neurotransmitters
Sensors 2019, 19(17), 3655; https://doi.org/10.3390/s19173655 - 22 Aug 2019
Cited by 1
Abstract
Quantum dots (QDs) are very attractive nanomaterials for analytical chemistry, due to high photostability, large surface area featuring numerous ways of bioconjugation with biomolecules, usually high quantum yield and long decay times. Their broad absorption spectra and narrow, sharp emission spectra of size-tunable [...] Read more.
Quantum dots (QDs) are very attractive nanomaterials for analytical chemistry, due to high photostability, large surface area featuring numerous ways of bioconjugation with biomolecules, usually high quantum yield and long decay times. Their broad absorption spectra and narrow, sharp emission spectra of size-tunable fluorescence make them ideal tools for pattern-based sensing. However, almost always they are applied for specific sensing with zero-dimensional (0D) signal reporting (only peak heights or peak shifts are considered), without taking advantage of greater amount of information hidden in 1D signal (emission spectra), or huge amount of information hidden in 2D fluorescence maps (Excitation-Emission Matrixes, EEMs). Therefore, in this work we propose opposite strategy—non-specific interactions of QDs, which are usually avoided and regarded as their disadvantage, were exploited here for 2D fluorescence fingerprinting. Analyte-specific multivariate fluorescence response of QDs is decoded with the use of Partial Least Squares—Discriminant Analysis. Even though only one type of QDs is studied, the proposed pattern-based method enables to obtain satisfactory accuracy for all studied compounds—various neurotransmitters, amino-acids and oligopeptides. This is a proof of principle of the possibility of the identification of various bioanalytes by such fluorescence fingerprinting with the use of QDs. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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Open AccessFeature PaperArticle
Simultaneous Voltammetric Determination of Acetaminophen, Ascorbic Acid and Uric Acid by Use of Integrated Array of Screen-Printed Electrodes and Chemometric Tools
Sensors 2019, 19(15), 3286; https://doi.org/10.3390/s19153286 - 26 Jul 2019
Cited by 7
Abstract
In the present work, ternary mixtures of Acetaminophen, Ascorbic acid and Uric acid were resolved using the Electronic tongue (ET) principle and Cyclic voltammetry (CV) technique. The screen-printed integrated electrode array having differentiated response for the three oxidizable compounds was formed by Graphite, [...] Read more.
In the present work, ternary mixtures of Acetaminophen, Ascorbic acid and Uric acid were resolved using the Electronic tongue (ET) principle and Cyclic voltammetry (CV) technique. The screen-printed integrated electrode array having differentiated response for the three oxidizable compounds was formed by Graphite, Prussian blue (PB), Cobalt (II) phthalocyanine (CoPc) and Copper oxide (II) (CuO) ink-modified carbon electrodes. A set of samples, ranging from 0 to 500 µmol·L−1, was prepared, using a tilted (33) factorial design in order to build the quantitative response model. Subsequently, the model performance was evaluated with an external subset of samples defined randomly along the experimental domain. Partial Least Squares Regression (PLS) was employed to construct the quantitative model. Finally, the model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 1.00 and 0.99 for the training and test subsets, respectively, and R2 ≥ 0.762 for the obtained vs. expected comparison graphs. In this way, a screen-printed integrated electrode platform can be successfully used for voltammetric ET applications. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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Open AccessArticle
Rapid Evaluation of Integral Quality and Safety of Surface and Waste Waters by a Multisensor System (Electronic Tongue)
Sensors 2019, 19(9), 2019; https://doi.org/10.3390/s19092019 - 29 Apr 2019
Cited by 4
Abstract
The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of [...] Read more.
The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of waste water purification processes at urban water treatment plants. In total, 25 natural surface water samples were collected around St. Petersburg (Russia), analyzed as is, and after ultrasonic treatment. Toxicity of the samples was evaluated using bioassay and MS. Relative errors of toxicity assessment with MS in these samples were below 20%. The system was also applied for fast determination of integral water quality using chemical oxygen demand (COD) values in 20 samples of water from river and ponds in Kolkata (India) and performed with an acceptable precision of 20% to 22% in this task. Furthermore, the MS was applied for fast simultaneous evaluation of COD, biochemical oxygen demand, inorganic phosphorous, ammonia, and nitrate nitrogen at two waste water treatment plants (over 320 samples). Reasonable precision (within 25%) of such analysis is acceptable for rapid water safety evaluation and enables fast control of the purification process. MS proved to be a practicable analytical instrument for various real-world tasks related to water safety monitoring. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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Open AccessArticle
Prediction of the Biogenic Amines Index of Poultry Meat Using an Electronic Nose
Sensors 2019, 19(7), 1580; https://doi.org/10.3390/s19071580 - 01 Apr 2019
Cited by 5
Abstract
The biogenic amines index of fresh chicken meat samples during refrigerated storage was predicted based on the headspace analysis using an electronic nose equipped with an array of electrochemical sensors. The reference biogenic amines index values were obtained using dispersive liquid–liquid microextraction–gas chromatography–mass [...] Read more.
The biogenic amines index of fresh chicken meat samples during refrigerated storage was predicted based on the headspace analysis using an electronic nose equipped with an array of electrochemical sensors. The reference biogenic amines index values were obtained using dispersive liquid–liquid microextraction–gas chromatography–mass spectrometry. A prototype electronic nose with modular construction and a dedicated sample chamber was used to rapidly analyze the volatile fraction of chicken meat samples, with a single measurement time of five minutes. Back-propagation artificial neural network was used to estimate the biogenic amines index of the samples with a determination coefficient of 0.954 based on ten-fold stratified cross-validation. The results indicate that the determination of the biogenic amines index is a good reference method for studies in which the freshness of meat products is assessed based on headspace analysis and fingerprinting, and that the described electronic device can be used to assess poultry meat freshness based on this value with high accuracy. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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Open AccessArticle
Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection
Sensors 2019, 19(1), 45; https://doi.org/10.3390/s19010045 - 22 Dec 2018
Cited by 17
Abstract
In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. [...] Read more.
In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm. Full article
(This article belongs to the Special Issue Biomimetic Sensor Arrays)
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