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Special Issue "Multisensor Systems and Signal Processing in Analytical Chemistry"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Larisa Lvova
Website
Guest Editor
Department of Chemical Sciences and Technology, University "Tor Vergata", Rome 00133, Italy
Interests: chemical sensors; multisensor analysis; chemometrics
Special Issues and Collections in MDPI journals
Dr. Małgorzata Jakubowska
Website
Guest Editor
Faculty of Materials Science and Ceramics, Department of Analytical Chemistry, AGH University of Science and Technology, Kraków, Poland
Interests: numerical analysis; chemometrics; analytical chemistry; electrochemistry; signal processing
Dr. Andrey Bogomolov
Website
Guest Editor
Samara State Technical University, Samara, Russia
Interests: chemometrics, i.e. multivariate analysis of complex data generated by modern analytical instruments, such as optical spectrometers and sensors; chemometrics application, including industrial process monitoring, food quality analysis, medical diagnostics, and ecology

Special Issue Information

Dear Colleagues,

Signal processing has become an integral part of the modern analytical measurement systems and plays a critical role in assuring the quality of measurements. In analytical chemistry, it is used in qualitative and quantitative analyses, to study the physical and chemical properties of compounds and mixtures of various materials. The purpose of signal processing is to make signals useful for specific purposes, to increase the selectivity of information, to make further modeling less complicated, or to provide improved prediction. Different processing methods use different rules and therefore are efficient at extracting information in diverse situations. The processed data space can be one dimensional for typical chemical signals; two dimensional for images; and multidimensional in modern, complex experiments.

The digital revolution impacted signal processing; therefore, new procedures and approaches offer the possibility to resolve more complicated problems. Over the years, the progress in chemical measuring systems has been based on advances in electronics; sensors, and, more recently, multisensor systems, by the application of new materials and better microfabrication capabilities, and modernized acquisition techniques. Hence, an intelligent signal processing algorithm is often the central aspect of an optimized measuring procedure; it may expand applicability, improve efficiency, and maximize the performance of the measuring system.

This Special Issue on “Multisensor Systems and Signal Processing in Analytical
Chemistry” will include but is not limited to the following topics:

  • Novel chemical sensors’ and multisensor systems’ development and application;
  • Analytical signals sampling and quantization;
  • Signal normalization and standardization, baseline correction;
  • Detection of the important features of the signals;
  • Separation of overlapping useful components;
  • Compression of signals and images;
  • Influence of the adequate and optimized signal processing for analytical parameters ;
  • Chemometric approaches in multivariate signal processing;
  • Software for signal processing.

New research and ideas for novel chemical sensors and multisensor systems development and application comprising signal processing details are strongly invited to be a part of this Special Issue.

Dr. Larisa Lvova
Dr. Małgorzata Jakubowska
Dr. Andrey Bogomolov
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 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.

Published Papers (6 papers)

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Open AccessFeature PaperArticle
Evaluation of Discrimination Performance in Case for Multiple Non-Discriminated Samples: Classification of Honeys by Fluorescent Fingerprinting
Sensors 2020, 20(18), 5351; https://doi.org/10.3390/s20185351 - 18 Sep 2020
Abstract
In this study we develop a variant of fluorescent sensor array technique based on addition of fluorophores to samples. A correct choice of fluorophores is critical for the successful application of the technique, which calls for the necessity of comparing different discrimination protocols. [...] Read more.
In this study we develop a variant of fluorescent sensor array technique based on addition of fluorophores to samples. A correct choice of fluorophores is critical for the successful application of the technique, which calls for the necessity of comparing different discrimination protocols. We used 36 honey samples from different sources to which various fluorophores were added (tris-(2,2′-bipyridyl) dichlororuthenium(II) (Ru(bpy)32+), zinc(II) 8-hydroxyquinoline-5-sulfonate (8-Ox-Zn), and thiazole orange in the presence of two types of deoxyribonucleic acid). The fluorescence spectra were obtained within 400–600 nm and treated by principal component analysis (PCA). No fluorophore allowed for the discrimination of all samples. To evaluate the discrimination performance of fluorophores, we introduced crossing number (CrN) calculated as the number of mutual intersections of confidence ellipses in the PCA scores plots, and relative position (RP) characterized by the pairwise mutual location of group centers and their most distant points. CrN and RP parameters correlated with each other, with total sensitivity (TS) calculated by Mahalanobis distances, and with the overall rating based on all metrics, with coefficients of correlation over 0.7. Most of the considered parameters gave the first place in the discrimination performance to Ru(bpy)32+ fluorophore. Full article
(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)
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Open AccessArticle
Materials Contamination and Indoor Air Pollution Caused by Tar Products and Fungicidal Impregnations: Intervention Research in 2014–2019
Sensors 2020, 20(15), 4099; https://doi.org/10.3390/s20154099 - 23 Jul 2020
Abstract
Construction materials containing tar products are a source of indoor air pollution in buildings. This particularly concerns old buildings, in which wooden structures were impregnated with tar compositions (creosote oil and Xylamite oil containing tar products) and buildings in which bituminous seal containing [...] Read more.
Construction materials containing tar products are a source of indoor air pollution in buildings. This particularly concerns old buildings, in which wooden structures were impregnated with tar compositions (creosote oil and Xylamite oil containing tar products) and buildings in which bituminous seal containing hydrocarbon solvents was used. During the 1970s and 1980s, an impregnant known as Xylamite was commonly used in Polish buildings. This material still emits organic vapors into the building’s environment, significantly worsening indoor air quality (IAQ). Xylamites and other impregnating materials are a source of indoor air pollution through toxic organic compounds, such as phenol, cresols, naphthalenes, chlorophenols (CPs), and chloronaphthalenes (CNs), which emit specific odors. TD-GC/MS enables detailed identification of the reasons behind chemical indoor air pollution. The results of laboratory tests on the chemical emissions of bitumen-impregnated materials were presented in 32 case studies. In turn, the results of indoor air pollution by volatile bitumen components were presented on 11 reference rooms and 14 case studies, including residential buildings, office buildings, and others. Laboratory tests of samples of construction products confirmed the main emission sources into indoor air. The research results for the period 2014–2019 are tabulated and described in detail in this manuscript. Full article
(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)
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Open AccessFeature PaperArticle
Plutonium (IV) Quantification in Technologically Relevant Media Using Potentiometric Sensor Array
Sensors 2020, 20(6), 1604; https://doi.org/10.3390/s20061604 - 13 Mar 2020
Abstract
The quantification of plutonium in technological streams during spent nuclear fuel (SNF) reprocessing is an important practical task that has to be solved to ensure the safety of the process. Currently applied methods are tedious, time-consuming and can hardly be implemented in on-line [...] Read more.
The quantification of plutonium in technological streams during spent nuclear fuel (SNF) reprocessing is an important practical task that has to be solved to ensure the safety of the process. Currently applied methods are tedious, time-consuming and can hardly be implemented in on-line mode. A fast and simple quantitative plutonium (IV) analysis using a potentiometric sensor array based on extracting agents is suggested in this study. The response of the set of specially designed PVC-plasticized membrane sensors can be related to plutonium content in solutions simulating real SNF-reprocessing media through multivariate regression modeling, providing 30% higher precision of plutonium quantification than optical spectroscopy. Full article
(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)
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Open AccessCommunication
Potentiometric E-Tongue System for Geosmin/Isoborneol Presence Monitoring in Drinkable Water
Sensors 2020, 20(3), 821; https://doi.org/10.3390/s20030821 - 04 Feb 2020
Cited by 4
Abstract
A potentiometric E-tongue system based on low-selective polymeric membrane and chalcogenide-glass electrodes is employed to monitor the taste-and-odor-causing pollutants, geosmin (GE) and 2-methyl-isoborneol (MIB), in drinkable water. The developed approach may permit a low-cost monitoring of these compounds in concentrations near the odor [...] Read more.
A potentiometric E-tongue system based on low-selective polymeric membrane and chalcogenide-glass electrodes is employed to monitor the taste-and-odor-causing pollutants, geosmin (GE) and 2-methyl-isoborneol (MIB), in drinkable water. The developed approach may permit a low-cost monitoring of these compounds in concentrations near the odor threshold concentrations (OTCs) of 20 ng/L. The experiments demonstrate the success of the E-tongue in combination with partial least squares (PLS) regression technique for the GE/MIB concentration prediction, showing also the possibility to discriminate tap water samples containing these compounds at two concentration levels: the same OTC order from 20 to 100 ng/L and at higher concentrations from 0.25 to 10 mg/L by means of PLS-discriminant analysis (DA) method. Based on the results, developed multisensory system can be considered a promising easy-to-handle tool for express evaluation of GE/MIB species and to provide a timely detection of alarm situations in case of extreme pollution before the drinkable water is delivered to end users. Full article
(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)
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Open AccessArticle
Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends
Sensors 2019, 19(16), 3463; https://doi.org/10.3390/s19163463 - 08 Aug 2019
Cited by 1
Abstract
Spectral measurement techniques, such as the near-infrared (NIR) and Raman spectroscopy, have been intensively researched. Nevertheless, even today, these techniques are still sparsely applied in industry due to their unpredictable and unstable measurements. This paper put forward two data fusion strategies (low-level and [...] Read more.
Spectral measurement techniques, such as the near-infrared (NIR) and Raman spectroscopy, have been intensively researched. Nevertheless, even today, these techniques are still sparsely applied in industry due to their unpredictable and unstable measurements. This paper put forward two data fusion strategies (low-level and mid-level fusion) for combining the NIR and Raman spectra to generate fusion spectra or fusion characteristics in order to improve the in-line measurement precision of component content of molten polymer blends. Subsequently, the fusion value was applied to modeling. For evaluating the response of different models to data fusion strategy, partial least squares (PLS) regression, artificial neural network (ANN), and extreme learning machine (ELM) were applied to the modeling of four kinds of spectral data (NIR, Raman, low-level fused data, and mid-level fused data). A system simultaneously acquiring in-line NIR and Raman spectra was built, and the polypropylene/polystyrene (PP/PS) blends, which had different grades and covered different compounding percentages of PP, were prepared for use as a case study. The results show that data fusion strategies improve the ANN and ELM model. In particular, mid-level fusion enables the in-line measurement of component content of molten polymer blends to become more accurate and robust. Full article
(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)
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Open AccessLetter
Towards a Multi-Enzyme Capacitive Field-Effect Biosensor by Comparative Study of Drop-Coating and Nano-Spotting Technique
Sensors 2020, 20(17), 4924; https://doi.org/10.3390/s20174924 - 31 Aug 2020
Cited by 1
Abstract
Multi-enzyme immobilization onto a capacitive field-effect biosensor by nano-spotting technique is presented. The nano-spotting technique allows to immobilize different enzymes simultaneously on the sensor surface with high spatial resolution without additional photolithographical patterning. The amount of applied enzymatic cocktail on the sensor surface [...] Read more.
Multi-enzyme immobilization onto a capacitive field-effect biosensor by nano-spotting technique is presented. The nano-spotting technique allows to immobilize different enzymes simultaneously on the sensor surface with high spatial resolution without additional photolithographical patterning. The amount of applied enzymatic cocktail on the sensor surface can be tailored. Capacitive electrolyte-insulator-semiconductor (EIS) field-effect sensors with Ta2O5 as pH-sensitive transducer layer have been chosen to immobilize the three different (pL droplets) enzymes penicillinase, urease, and glucose oxidase. Nano-spotting immobilization is compared to conventional drop-coating method by defining different geometrical layouts on the sensor surface (fully, half-, and quarter-spotted). The drop diameter is varying between 84 µm and 102 µm, depending on the number of applied drops (1 to 4) per spot. For multi-analyte detection, penicillinase and urease are simultaneously nano-spotted on the EIS sensor. Sensor characterization was performed by C/V (capacitance/voltage) and ConCap (constant capacitance) measurements. Average penicillin, glucose, and urea sensitivities for the spotted enzymes were 81.7 mV/dec, 40.5 mV/dec, and 68.9 mV/dec, respectively. Full article
(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)
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