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Multisensor Systems and Signal Processing in Analytical Chemistry

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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 (31 December 2020) | Viewed by 25552

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Special Issue Editors

Dr. Larisa Lvova

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Guest Editor

Department of Chemical Sciences and Technology, University "Tor Vergata", Via della Ricerca Scientifica, 00133 Rome, Italy
Interests: chemical sensors; multisensor analysis; chemometrics
Special Issues, Collections and Topics in MDPI journals

Dr. Małgorzata Jakubowska

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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

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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 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.

Published Papers (9 papers)

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Research

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12 pages, 2345 KiB

Open AccessArticle

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

by Barbara Falatová, Marta Ferreiro-González, José Luis P. Calle, José Ángel Álvarez and Miguel Palma

Sensors 2021, 21(3), 801; https://doi.org/10.3390/s21030801 - 26 Jan 2021

Cited by 11 | Viewed by 2672

Abstract

Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials’ guidelines (ASTM E1618) based on gas chromatography–mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace–mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates—four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate. Full article

(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)

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18 pages, 2839 KiB

Open AccessArticle

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

by Thaddäus Hocotz, Olga Bibikova, Valeria Belikova, Andrey Bogomolov, Iskander Usenov, Lukasz Pieszczek, Tatiana Sakharova, Olaf Minet, Elena Feliksberger, Viacheslav Artyushenko, Beate Rau and Urszula Zabarylo

Sensors 2020, 20(22), 6706; https://doi.org/10.3390/s20226706 - 23 Nov 2020

Cited by 6 | Viewed by 2808

Abstract

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more efficient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool. Full article

(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)

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20 pages, 7762 KiB

Open AccessArticle

Influence of the Flow Rate in an Automated Microfluidic Electronic Tongue Tested for Sucralose Differentiation

by Maria L. Braunger, Igor Fier, Flávio M. Shimizu, Anerise de Barros, Varlei Rodrigues and Antonio Riul, Jr.

Sensors 2020, 20(21), 6194; https://doi.org/10.3390/s20216194 - 30 Oct 2020

Cited by 5 | Viewed by 2193

Abstract

Incorporating electronic tongues into microfluidic devices brings benefits as dealing with small amounts of sample/discharge. Nonetheless, such measurements may be time-consuming in some applications once they require several operational steps. Here, we designed four collinear electrodes on a single printed circuit board, further comprised inside a straight microchannel, culminating in a robust e-tongue device for faster data acquisition. An analog multiplexing circuit automated the signal’s routing from each of the four sensing units to an impedance analyzer. Both instruments and a syringe pump are controlled by dedicated software. The automated e-tongue was tested with four Brazilian brands of liquid sucralose-based sweeteners under 20 different flow rates, aiming to systematically evaluate the influence of the flow rate in the discrimination among sweet tastes sold as the same food product. All four brands were successfully distinguished using principal component analysis of the raw data, and despite the nearly identical sucralose-based taste in all samples, all brands’ significant distinction is attributed to small differences in the ingredients and manufacturing processes to deliver the final food product. The increasing flow rate improves the analyte’s discrimination, as the silhouette coefficient reaches a plateau at ~3 mL/h. We used an equivalent circuit model to evaluate the raw data, finding a decrease in the double-layer capacitance proportional to improvements in the samples’ discrimination. In other words, the flow rate increase mitigates the formation of the double-layer, resulting in faster stabilization and better repeatability in the sensor response. Full article

(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)

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15 pages, 2685 KiB

Open AccessFeature PaperArticle

Evaluation of Discrimination Performance in Case for Multiple Non-Discriminated Samples: Classification of Honeys by Fluorescent Fingerprinting

by Elizaveta A. Rukosueva, Valeria A. Belikova, Ivan N. Krylov, Vladislav S. Orekhov, Evgenii V. Skorobogatov, Andrei V. Garmash and Mikhail K. Beklemishev

Sensors 2020, 20(18), 5351; https://doi.org/10.3390/s20185351 - 18 Sep 2020

Cited by 3 | Viewed by 1936

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. 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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19 pages, 3303 KiB

Open AccessArticle

Materials Contamination and Indoor Air Pollution Caused by Tar Products and Fungicidal Impregnations: Intervention Research in 2014–2019

by Mateusz Kozicki and Adam Niesłochowski

Sensors 2020, 20(15), 4099; https://doi.org/10.3390/s20154099 - 23 Jul 2020

Cited by 6 | Viewed by 2562

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 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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11 pages, 1887 KiB

Open AccessFeature PaperArticle

Plutonium (IV) Quantification in Technologically Relevant Media Using Potentiometric Sensor Array

by Julia Savosina, Marina Agafonova-Moroz, Irina Yaroshenko, Julia Ashina, Vasily Babain, Alexander Lumpov, Andrey Legin and Dmitry Kirsanov

Sensors 2020, 20(6), 1604; https://doi.org/10.3390/s20061604 - 13 Mar 2020

Cited by 9 | Viewed by 2782

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 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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10 pages, 1676 KiB

Open AccessCommunication

Potentiometric E-Tongue System for Geosmin/Isoborneol Presence Monitoring in Drinkable Water

by Larisa Lvova, Igor Jahatspanian, Luiz H.C. Mattoso, Daniel S. Correa, Ekaterina Oleneva, Andrey Legin, Corrado Di Natale and Roberto Paolesse

Sensors 2020, 20(3), 821; https://doi.org/10.3390/s20030821 - 04 Feb 2020

Cited by 19 | Viewed by 3467

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 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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11 pages, 4857 KiB

Open AccessArticle

Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends

by Shichao Zhu, Zhuoming Song, Shengyu Shi, Mengmeng Wang and Gang Jin

Sensors 2019, 19(16), 3463; https://doi.org/10.3390/s19163463 - 08 Aug 2019

Cited by 16 | Viewed by 3691

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 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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12 pages, 2598 KiB

Open AccessLetter

Towards a Multi-Enzyme Capacitive Field-Effect Biosensor by Comparative Study of Drop-Coating and Nano-Spotting Technique

by Denise Molinnus, Stefan Beging, Carsten Lowis and Michael J. Schöning

Sensors 2020, 20(17), 4924; https://doi.org/10.3390/s20174924 - 31 Aug 2020

Cited by 15 | Viewed by 2814

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 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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