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Gas Recognition in E-nose System
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Gas Recognition in E-nose System

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

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 5001

Special Issue Editors

Dr. Antonio Pardo Martínez

E-Mail Website
Guest Editor
Department of Electronics and Biomedical Engineering, University of Barcelona, 08028 Barcelona, Spain
Interests: signal processing for chemical gas sensors; system identification; pattern recognition and machine learning; applications in chemical measurements; electronic noses and machine olfaction; hardware and software development for volatile measurements
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Fernandez Romero

E-Mail Website
Guest Editor
Department of Electronics and Biomedical Engineering, University of Barcelona, 08028 Barcelona, Spain
Interests: gas sensors; chemical sensing; signal pre-processing; multivariate analysis; chemometrics; metabolomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

"Electronic noses" refer to instruments that utilize a mechanism for detecting volatile chemicals and incorporate pattern recognition and machine learning. Since the early 1980s, they have undergone significant advancements in terms of sensor technology, machine learning tools, and an expanding range of potential applications. While gas sensors have traditionally served as the sensing mechanism for electronic noses, there is a growing trend to broaden the concept, including instruments, such as ultra-fast chromatography and ion mobility spectrometry, among others. This broader definition enhances gas recognition capabilities, expanding possibilities, but also increases the need for signal processing. Gas recognition algorithms and workflows play a crucial role, and their ability to extract valuable information is correlated with the correct implementation of preprocessing workflows (denoising, baseline correction, peak alignment, outlier detection, etc.) and processing tools (Principal Component Analysis, Linear Discriminant Analysis, Partial Least Squares, k-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks, etc.). However, on the other hand, numerous challenging issues arise when dealing with gas recognition in new electronic noses, including the high dimensionality of raw data, the balance between simplicity and performance of algorithms, managing short- and long-term drifts, facing nonlinear responses, multi-gas recognition in noisy environments, and more.

The topics covered in this Special Issue will include both recent advances in gas recognition and improvements in the practical application of electronic noses. Original research articles are welcomed from a broad diversity of disciplines, such as engineering, computer science, machine learning, medicine, analytical science, environmental science, sensors technologies, and chemometrics, to highlight the latest developments in the topic of gas recognition with electronic noses.

This Special Issue will cover, but is not limited to, the following topics:

  • Gas recognition for electronic noses;
  • Chemometrics, pattern recognition, and machine learning for e-nose instruments;
  • Electronic nose application solutions;
  • Tools and workflows for preprocessing e-nose raw data.

Dr. Antonio Pardo Martínez
Prof. Dr. Luis Fernandez Romero
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

  • gas recognition
  • electronic noses
  • machine olfaction
  • chemical sensing
  • chemometrics and signal processing
  • pattern recognition and machine learning

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Published Papers (4 papers)

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Research

Jump to: Review

19 pages, 2273 KiB  
Article
Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control
by Luis Fernandez, Sergio Oller-Moreno, Jordi Fonollosa, Rocío Garrido-Delgado, Lourdes Arce, Andrés Martín-Gómez, Santiago Marco and Antonio Pardo
Sensors 2025, 25(3), 737; https://doi.org/10.3390/s25030737 - 25 Jan 2025
Viewed by 617
Abstract
Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with [...] Read more.
Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance. Full article
(This article belongs to the Special Issue Gas Recognition in E-nose System)
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Figure 1

Figure 1
<p>MCC-IMS data acquired from an olive oil sample. An MCC chromatogram and an IMS spectrum are also shown. IMS spectra show prominent peak (RIP) close to 6 ms. The region of the image in which most of the peaks appear is also shown in a three-dimensional plot, showing the complexity of the captured data. Note the non-uniform color scale to highlight the peaks in data.</p> Full article ">Figure 2
<p>Steps involved in the development of calibration models for MCC-IMS data 2.2.1 preprocessing.</p> Full article ">Figure 3
<p>Double cross-validation scheme utilized to evaluate the classification performance of models.</p> Full article ">Figure 4
<p>(<b>a</b>) Segment of a spectrum before and after applying a second derivative Savitzky–Golay filter with window sizes of n = 7 and n = 9; (<b>b</b>) a different segment of the same spectrum filtered with window sizes of n = 35 and n = 39. Note the presence of an optimal window size that minimizes noise while preserving the spectral shape; (<b>c</b>) filtered spectrum and baseline estimation using AsLS after various iterations, showing rapid convergence toward accurate baseline estimation; (<b>d</b>) filtered spectrum and the resulting spectrum after baseline correction; (<b>e</b>) three spectra (acquired at tret = 104 s), each corresponding to one of the olive oil classes (LOO, VOO, and EVOO) after noise removal and baseline correction, demonstrating misaligned peaks; (<b>f</b>) the same spectra after peak alignment.</p> Full article ">Figure 5
<p>The selection of latent variables was based on optimizing classification accuracy during internal validation. The figure indicates that data preprocessing reduces model complexity while maintaining performance. Baseline removal followed by peak alignment are the preprocessing steps that contribute most to this improvement.</p> Full article ">Figure 6
<p>Scores for the first two latent variables of the training set. The same projection is used for the test samples. EVOO samples tend to exhibit higher scores on LV1.</p> Full article ">Figure 7
<p>Average VIP scores of the final PLS-DA models. Relevant features for samples’ class separation (VIP score higher than 1) are colored in red hues.</p> Full article ">
21 pages, 10599 KiB  
Article
Optimizing Low-Cost Gas Analysis with a 3D Printed Column and MiCS-6814 Sensor for Volatile Compound Detection
by Nela Skowronkova, Martin Adamek, Magdalena Zvonkova, Jiri Matyas, Anna Adamkova, Stepan Dlabaja, Martin Buran, Veronika Sevcikova, Jiri Mlcek, Zdenek Volek and Martina Cernekova
Sensors 2024, 24(20), 6594; https://doi.org/10.3390/s24206594 - 13 Oct 2024
Viewed by 1029
Abstract
This paper explores an application of 3D printing technology on the food industry. Since its inception in the 1980s, 3D printing has experienced a huge rise in popularity. This study uses cost-effective, flexible, and sustainable components that enable specific features of certain gas [...] Read more.
This paper explores an application of 3D printing technology on the food industry. Since its inception in the 1980s, 3D printing has experienced a huge rise in popularity. This study uses cost-effective, flexible, and sustainable components that enable specific features of certain gas chromatography. This study aims to optimize the process of gas detection using a 3D printed separation column and the MiCS-6814 sensor. The principle of the entire device is based on the idea of utilizing a simple capillary chromatographic column manufactured by 3D printing for the separation of samples into components prior to their measurement using inexpensive chemiresistive sensors. An optimization of a system with a 3D printed PLA block containing a capillary, a mixing chamber, and a measuring chamber with a MiCS-6814 sensor was performed. The optimization distributed the sensor output signal in the time domain so that it was possible to distinguish the peak for the two most common alcohols, ethanol and methanol. The paper further describes some optimization types and their possibilities. Full article
(This article belongs to the Special Issue Gas Recognition in E-nose System)
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Figure 1

Figure 1
<p>Views of the PLA capillary block model in Prusa Slicer 2.7.4+win64 software (Prusa Research a.s., Prague, Czech Republic). (<b>a</b>) General view of the capillary block model. (<b>b</b>) Side views of the capillary block model showing the mixing chamber, measuring chamber, and four-layer capillary and their connections.</p> Full article ">Figure 2
<p>A simple Ishikawa diagram of the optimization of the experimental equipment.</p> Full article ">Figure 3
<p>Modifications and improvements of the measuring chamber (internal square dimension 27 × 27 × 4 mm): (<b>a</b>) an empty chamber; (<b>b</b>) a canal chamber with baffle; (<b>c</b>) a chamber with a channel and its constriction in the sensor area; (<b>d</b>) a chamber with a channel, narrowing in the sensor area, and cutouts to improve analyte drainage; (<b>e</b>) a layered green lined chamber with a molded channel and rubber leak guard.</p> Full article ">Figure 4
<p>Overall positions of the measuring chamber and the sensor plate. The entrance to the chamber is on the left from the capillary, on the right the exit to the open space.</p> Full article ">Figure 5
<p>Response of CO (<b>a</b>) and NH<sub>3</sub> (<b>b</b>) sensor to a 1 mL Vodka sample for different measuring chamber configurations. The labeling of the curves (letters a–e) is identical to the labeling of the measuring chamber configurations in <a href="#sensors-24-06594-f003" class="html-fig">Figure 3</a>. The data were preprocessed before standardization.</p> Full article ">Figure 6
<p>Time response of the sensor (raw data from the A/D converter) to the departure of the analyte Vodka, butane, and toluene from the sensor area. The starting time point is 0 s—the first recorded signal rise at the CO sensor.</p> Full article ">Figure 7
<p>Time response of the sensor (raw data from the A/D converter) to the departure of the analyte Vodka, methanol, and Tuzemsky from the sensor area. The starting time point is 0 s—the first recorded signal rise at the CO sensor.</p> Full article ">Figure 8
<p>Example of the start of a measurement with a clean air sample (values after a centered moving averaging m = 11).</p> Full article ">Figure 9
<p>Example of CO sensor response when a second syringe is connected, and the flow rate is changed from 0.0177 mL/s to 0.0830 mL/s for a 1 mL sample of a 1:1 mixture of natural gas (methane) and food grade ethanol. The data were preprocessed before standardization.</p> Full article ">Figure 10
<p>Response of the CO (<b>a</b>) and NH<sub>3</sub> (<b>b</b>) sensor to a 1 mL Vodka sample at different motor voltages. The data were preprocessed before standardization.</p> Full article ">Figure 11
<p>Noise elimination using the NO<sub>2</sub> sensor signal.</p> Full article ">Figure 12
<p>Example of the whole measurement process with air.</p> Full article ">Figure 13
<p>The results for the CO signal. (<b>a</b>) The resulting signal after standardization. (<b>b</b>) The resulting signal after standardization and difference calculation (m = 11).</p> Full article ">Figure 14
<p>The results for the NH<sub>3</sub> signal. (<b>a</b>) The resulting signal after standardization. (<b>b</b>) The resulting signal after standardization and difference calculation (m = 11).</p> Full article ">Figure 15
<p>The results for the CO signal—detailed view. (<b>a</b>) The resulting signal after standardization. (<b>b</b>) The resulting signal after standardization and difference calculation (m = 11).</p> Full article ">Figure 16
<p>The results for the NH<sub>3</sub> signal—detailed view. (<b>a</b>) The resulting signal after standardization. (<b>b</b>) The resulting signal after standardization and difference calculation (m = 11).</p> Full article ">
20 pages, 1252 KiB  
Article
Distinguishing between Wheat Grains Infested by Four Fusarium Species by Measuring with a Low-Cost Electronic Nose
by Piotr Borowik, Miłosz Tkaczyk, Przemysław Pluta, Adam Okorski, Marcin Stocki, Rafał Tarakowski and Tomasz Oszako
Sensors 2024, 24(13), 4312; https://doi.org/10.3390/s24134312 - 2 Jul 2024
Cited by 2 | Viewed by 1532
Abstract
An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: [...] Read more.
An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: F. avenaceum, F. langsethiae, F. poae, and F. sporotrichioides. The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested. Full article
(This article belongs to the Special Issue Gas Recognition in E-nose System)
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Figure 1

Figure 1
<p>Measurement setup of the electronic nose. (1)—sensor chamber, (2)—control unit, (3)—measured sample in a Petri dish, (4)—charcoal air filter, (5)—laptop controlling the measurements.</p> Full article ">Figure 2
<p>Examples of shapes of sensor electronic nose response during one measurement cycle of a sample. (blue)—the shape of the response of the TGS 2602 sensor, (green)—the shape of the response of all other types of used sensors (<a href="#sensors-24-04312-t001" class="html-table">Table 1</a>). Various stages of the measurement cycle are indicated inside the figure. The red dot at the beginning of the gas adsorption phase and at the beginning of the sensor temperature modulation phase represent the baseline response level, which is different for each of the response phases.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>i</b>) Distribution of observations obtained by electronic nose measurements as LDA projection of features extracted from sensor response curves. The rows of the sub-figures represent different sets of features: extracted from the adsorption phase, extracted from the temperature modulation phase, and extracted from both phases of the response, as indicated on the right side of the figure. The columns of the sub-figures represent different projections onto the LDA components c1–c2, c1–c3, and c2–c3, as indicated at the top of the figure and in the axis labels. The percentage of variance explained by the selected components is indicated in the axis labels. Confidence ellipses for two standard deviations are shown.</p> Full article ">Figure 4
<p>Measures of the performance of classification models obtained by Logistic Regression and Support Vector Machine algorithms estimated with the leave-one-out cross-validation method. (<b>a</b>) Accuracy, (<b>b</b>) Precision, (<b>c</b>) Recall, as indicated in y-axis captions. Comparison of different sets of predictors extracted from the adsorption phase (A), the temperature modulation phase (T), both phases of the sensor response (AT). For Precision and Recall, the average of the performance for all species is compared with the performance of <span class="html-italic">F. poae</span> alone. The numerical values of the metrics with corresponding confidence intervals are presented in <a href="#sensors-24-04312-t004" class="html-table">Table 4</a>.</p> Full article ">Figure A1
<p>Chemical compounds detected by the GC-MS analysis [<a href="#B56-sensors-24-04312" class="html-bibr">56</a>]. The numbers (rounded to whole percentages) indicate the percent of the total ion current collected during the measurement of a sample. The bars are plotted to facilitate visual comparison of data. Empty cells in the table indicate that the component has not been detected, and zero indicates that the found percentage of the detected compounds in the sample was below one percent.</p> Full article ">Figure A2
<p>Principal Components Analysis transformation of the proportion of chemical compounds identified in the GC-MS analysis of measured samples. The sub-figure columns represent different projections onto the PCA components, as indicated in the axis labels. The variability explained by the components is indicated in the axis labels. Confidence ellipses for two standard deviations are shown.</p> Full article ">

Review

Jump to: Research

21 pages, 1976 KiB  
Review
Non-Invasive Diagnostic Approaches for Kidney Disease: The Role of Electronic Nose Systems
by Francesco Sansone and Alessandro Tonacci
Sensors 2024, 24(19), 6475; https://doi.org/10.3390/s24196475 - 8 Oct 2024
Viewed by 1299
Abstract
Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the [...] Read more.
Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease’s presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field. Full article
(This article belongs to the Special Issue Gas Recognition in E-nose System)
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Figure 1

Figure 1
<p>Causes (in red boxes), symptoms (yellow), and treatment opportunities (green) for CKD.</p> Full article ">Figure 2
<p>Analogies between biological and electronic noses.</p> Full article ">Figure 3
<p>The PRISMA flowchart.</p> Full article ">
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