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Signal and Information Processing in Chemical Sensing

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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 (30 January 2018) | Viewed by 56054

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Special Issue Editor

Prof. Dr. Santiago Marco

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Guest Editor

1. Department of Electronics and Biomedical Engineering, University of Barcelona, Marti I Franqués 1, 08028 Barcelona, Spain
2. Signal and Information Processing in Sensor Systems, Institute for Bioengineering of Catalonia, The Barcelona Institute of Science and Technology, Baldiri Rexac 10-12, 08028 Barcelona, Spain
Interests: chemical sensors; signal processing; machine learning; chemometrics; microsystems; ion mobility spectrometry; metabolomics
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Special Issue Information

Dear Colleagues,

Chemical sensing technology has witnessed astonishing advances in the last decade, leading to powerful portable instrumentation for fast diagnostics. These instruments are characterized by fast evaluations, in the order of few seconds, and minimal or absent sample preparations. Examples of these technologies are chemical sensors, but also miniature spectrometers based on Ion Mobility, direct injection mass spectrometry, and spectroscopies, such as NIR, Visible or Raman. Recent technological advances allow that the principles of measurement, only available in desktops, in analytical lab instrumentation, are now handheld instruments, operating in the field, with on-board signal and data processing. These systems are being applied to very diverse fields, ranging from environmental monitoring, to food quality evaluation, to health applications. This novel instrumentation provides signals or spectral time-series that require online signal and data processing in order to obtain the final desired results.

A variety of problems are targeted that require different signal and data processing tools. Chemical sensing may be aimed at qualitative (classification) or quantitative evaluations (regression). Beyond that, systems require solutions for drift compensation, calibration transfer, or chemical noise (interferants) rejection. In terms of self-diagnostics, we need techniques for fault detection, identification, and diagnosis. In addition, optimization techniques, required for optimal feature extraction or selection, are also required during algorithmic development. The development of methodologies for system validation to ensure proper generalization to new samples is equally important. Last, but not least, the use of design of experiment techniques in system calibration or recalibration also require further research. Finally, some application scenarios may require specific signal and data processing needs, such as chemical mapping and source localization with mobile platforms.

We solicit contributions focused on signal and information processing for chemical sensing in the following, or similar, areas:

- Signal pre-processing: Digital filters, baseline corrections, peak detection, alignment

- Feature Extraction and Selection

- Classification and Regression techniques

- Validation techniques

- Novelty detection

- Drift compensation

- Chemical mapping from mobile platforms of fixed chemical sensor networks

- Odour robots

- Calibration transfer

- Fault detection, identification and diagnosis

- Orthogonal Projection Filters: Component Correction, OSC, OPLS, O2PLS

- Signal and Data Processing solutions for specific applications in environmental monitoring, food evaluation, biomedical analysis, safety, and security

Dr. Santiago Marco
Guest Editor

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Keywords

Chemometrics
Pattern Recognition
Machine Learning
Machine Olfaction
Electronic Nose
Odour

Published Papers (6 papers)

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Research

14 pages, 2254 KiB

Open AccessArticle

Facile Quantification and Identification Techniques for Reducing Gases over a Wide Concentration Range Using a MOS Sensor in Temperature-Cycled Operation

by Caroline Schultealbert, Tobias Baur, Andreas Schütze and Tilman Sauerwald

Sensors 2018, 18(3), 744; https://doi.org/10.3390/s18030744 - 01 Mar 2018

Cited by 17 | Viewed by 5012

Abstract

Dedicated methods for quantification and identification of reducing gases based on model-based temperature-cycled operation (TCO) using a single commercial MOS gas sensor are presented. During high temperature phases the sensor surface is highly oxidized, yielding a significant sensitivity increase after switching to lower temperatures (differential surface reduction, DSR). For low concentrations, the slope of the logarithmic conductance during this low-temperature phase is evaluated and can directly be used for quantification. For higher concentrations, the time constant for reaching a stable conductance during the same low-temperature phase is evaluated. Both signals represent the reaction rate of the reducing gas on the strongly oxidized surface at this low temperature and provide a linear calibration curve, which is exceptional for MOS sensors. By determining these reaction rates on different low-temperature plateaus and applying pattern recognition, the resulting footprint can be used for identification of different gases. All methods are tested over a wide concentration range from 10 ppb to 100 ppm (4 orders of magnitude) for four different reducing gases (CO, H₂, ammonia and benzene) using randomized gas exposures. Full article

(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)

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17 pages, 4528 KiB

Open AccessArticle

Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks

by Bartosz Szulczyński, Krzysztof Armiński, Jacek Namieśnik and Jacek Gębicki

Sensors 2018, 18(2), 519; https://doi.org/10.3390/s18020519 - 08 Feb 2018

Cited by 45 | Viewed by 5088

Abstract

This paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected to investigation. Evaluation of predicted odour intensity and hedonic tone was performed with selected artificial neural network structures with the activation functions tanh and Leaky rectified linear units (Leaky ReLUs) with the parameter

a = 0.03

. Correctness of identification of odour interactions in the odorous mixtures was determined based on the results obtained with the electronic nose instrument and non-linear data analysis. This value (average) was at the level of 88% in the case of odour intensity, whereas the average was at the level of 74% in the case of hedonic tone. In both cases, correctness of identification depended on the number of components present in the odorous mixture. Full article

(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)

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15 pages, 2152 KiB

Open AccessArticle

Low Power Operation of Temperature-Modulated Metal Oxide Semiconductor Gas Sensors

by Javier Burgués and Santiago Marco

Sensors 2018, 18(2), 339; https://doi.org/10.3390/s18020339 - 25 Jan 2018

Cited by 88 | Viewed by 13213

Abstract

Mobile applications based on gas sensing present new opportunities for low-cost air quality monitoring, safety, and healthcare. Metal oxide semiconductor (MOX) gas sensors represent the most prominent technology for integration into portable devices, such as smartphones and wearables. Traditionally, MOX sensors have been continuously powered to increase the stability of the sensing layer. However, continuous power is not feasible in many battery-operated applications due to power consumption limitations or the intended intermittent device operation. This work benchmarks two low-power, duty-cycling, and on-demand modes against the continuous power one. The duty-cycling mode periodically turns the sensors on and off and represents a trade-off between power consumption and stability. On-demand operation achieves the lowest power consumption by powering the sensors only while taking a measurement. Twelve thermally modulated SB-500-12 (FIS Inc. Jacksonville, FL, USA) sensors were exposed to low concentrations of carbon monoxide (0–9 ppm) with environmental conditions, such as ambient humidity (15–75% relative humidity) and temperature (21–27 °C), varying within the indicated ranges. Partial Least Squares (PLS) models were built using calibration data, and the prediction error in external validation samples was evaluated during the two weeks following calibration. We found that on-demand operation produced a deformation of the sensor conductance patterns, which led to an increase in the prediction error by almost a factor of 5 as compared to continuous operation (2.2 versus 0.45 ppm). Applying a 10% duty-cycling operation of 10-min periods reduced this prediction error to a factor of 2 (0.9 versus 0.45 ppm). The proposed duty-cycling powering scheme saved up to 90% energy as compared to the continuous operating mode. This low-power mode may be advantageous for applications that do not require continuous and periodic measurements, and which can tolerate slightly higher prediction errors. Full article

(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)

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16 pages, 5716 KiB

Open AccessArticle

Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring

by Peng Wei, Zhi Ning, Sheng Ye, Li Sun, Fenhuan Yang, Ka Chun Wong, Dane Westerdahl and Peter K. K. Louie

Sensors 2018, 18(2), 59; https://doi.org/10.3390/s18020059 - 23 Jan 2018

Cited by 106 | Viewed by 11423

Abstract

The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), and oxidants (O_x) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO₂ and ozone on a newly introduced oxidant sensor. Full article

(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)

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11 pages, 383 KiB

Open AccessArticle

Gas Classification Using Deep Convolutional Neural Networks

by Pai Peng, Xiaojin Zhao, Xiaofang Pan and Wenbin Ye

Sensors 2018, 18(1), 157; https://doi.org/10.3390/s18010157 - 08 Jan 2018

Cited by 137 | Viewed by 16171

Abstract

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). Full article

(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)

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1782 KiB

Open AccessArticle

Comparison of Benchtop Fourier-Transform (FT) and Portable Grating Scanning Spectrometers for Determination of Total Soluble Solid Contents in Single Grape Berry (Vitis vinifera L.) and Calibration Transfer

by Hui Xiao, Ke Sun, Ye Sun, Kangli Wei, Kang Tu and Leiqing Pan

Sensors 2017, 17(11), 2693; https://doi.org/10.3390/s17112693 - 22 Nov 2017

Cited by 22 | Viewed by 4396

Abstract

Near-infrared (NIR) spectroscopy was applied for the determination of total soluble solid contents (SSC) of single Ruby Seedless grape berries using both benchtop Fourier transform (VECTOR 22/N) and portable grating scanning (SupNIR-1500) spectrometers in this study. The results showed that the best SSC prediction was obtained by VECTOR 22/N in the range of 12,000 to 4000 cm⁻¹ (833–2500 nm) for Ruby Seedless with determination coefficient of prediction (R_p²) of 0.918, root mean squares error of prediction (RMSEP) of 0.758% based on least squares support vector machine (LS-SVM). Calibration transfer was conducted on the same spectral range of two instruments (1000–1800 nm) based on the LS-SVM model. By conducting Kennard-Stone (KS) to divide sample sets, selecting the optimal number of standardization samples and applying Passing-Bablok regression to choose the optimal instrument as the master instrument, a modified calibration transfer method between two spectrometers was developed. When 45 samples were selected for the standardization set, the linear interpolation-piecewise direct standardization (linear interpolation-PDS) performed well for calibration transfer with R_p² of 0.857 and RMSEP of 1.099% in the spectral region of 1000–1800 nm. And it was proved that re-calculating the standardization samples into master model could improve the performance of calibration transfer in this study. This work indicated that NIR could be used as a rapid and non-destructive method for SSC prediction, and provided a feasibility to solve the transfer difficulty between totally different NIR spectrometers. Full article

(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)

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