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Chemosensors
Special Issues
The Application and Advance of Electronic Nose
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The Application and Advance of Electronic Nose

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Electrochemical Devices and Sensors".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 8708

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, and their synonym terms (chemical sensing, odor analyzer, machine olfaction, among others), refers to an instrument based on a mechanism for chemical detection of volatiles and a mechanism for pattern recognition and machine learning. Since its appearance at the beginning of the 80s of the 20th century, electronic noses have been evolving in their sensor technology, machine learning tools and the diversity of possible applications. Quality control in the food, beverage and cosmetic industry, air quality and environment monitoring, healthcare and medical diagnosis, and civilian and military security, among others, are applications where electronic noses have progressively gained in popularity. Despite the advances and benefits of electronic nose instruments, there are still many technical and practical challenging issues to solve: short- and long-term drifts, lack of sensitivity, lack of reproducibility, non-linear responses, confounding factors, lack of standardization, and so on. The concurrence of multidisciplinary knowledge should help to overcome these issues. The topics covered in this Special Issue will include recent advances both in machine learning and sensor technology as well as improvements in the practical application of electronic noses. Original research articles are welcomed from a broad diversity of disciplines and fields of knowledge as engineering, computer science, machine learning, medicine, analytical science, environmental science, sensors technologies and chemometrics, to highlight the latest developments in the topic of electronic noses.

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

  • Gas sensors for electronic noses
  • Electronic noses and Instruments for volatile detection
  • Chemometrics, pattern recognition and machine learning
  • Electronic nose applications
  • Electronic noses standardization.

Dr. Antonio Pardo Martínez
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. Chemosensors is an international peer-reviewed open access monthly 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 2700 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 sensors
  • electronic noses
  • machine olfaction
  • chemical sensing
  • chemometrics and signal processing
  • pattern recognition and machine learning
  • gas, odor, and volatile organic compounds measurement

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Published Papers (4 papers)

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Research

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17 pages, 2741 KiB  
Article
Early Detection of Prostate Cancer: The Role of Scent
by Fabio Grizzi, Carmen Bax, Mohamed A. A. A. Hegazi, Beatrice Julia Lotesoriere, Matteo Zanoni, Paolo Vota, Rodolfo Fausto Hurle, Nicolò Maria Buffi, Massimo Lazzeri, Lorenzo Tidu, Laura Capelli and Gianluigi Taverna
Chemosensors 2023, 11(7), 356; https://doi.org/10.3390/chemosensors11070356 - 25 Jun 2023
Cited by 3 | Viewed by 2017
Abstract
Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to [...] Read more.
Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa. Full article
(This article belongs to the Special Issue The Application and Advance of Electronic Nose)
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20 pages, 5892 KiB  
Article
Discrimination of Diabetes Mellitus Patients and Healthy Individuals Based on Volatile Organic Compounds (VOCs): Analysis of Exhaled Breath and Urine Samples by Using E-Nose and VE-Tongue
by Omar Zaim, Benachir Bouchikhi, Soukaina Motia, Sònia Abelló, Eduard Llobet and Nezha El Bari
Chemosensors 2023, 11(6), 350; https://doi.org/10.3390/chemosensors11060350 - 19 Jun 2023
Cited by 5 | Viewed by 1777
Abstract
Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such [...] Read more.
Studies suggest that breath and urine analysis can be viable non-invasive methods for diabetes management, with the potential for disease diagnosis. In the present work, we employed two sensing strategies. The first strategy involved analyzing volatile organic compounds (VOCs) in biological matrices, such as exhaled breath and urine samples collected from patients with diabetes mellitus (DM) and healthy controls (HC). The second strategy focused on discriminating between two types of DM, related to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), by using a data fusion method. For this purpose, an electronic nose (e-nose) based on five tin oxide (SnO2) gas sensors was employed to characterize the overall composition of the collected breath samples. Furthermore, a voltametric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. To evaluate the diagnostic performance of the electronic sensing systems, algorithm tools including principal component analysis (PCA), discriminant function analysis (DFA) and receiver operating characteristics (ROC) were utilized. The results showed that the e-nose and VE-tongue could discriminate between breath and urine samples from patients with DM and HC with a success rate of 99.44% and 99.16%, respectively. However, discrimination between T1DM and T2DM samples using these systems alone was not perfect. Therefore, a data fusion method was proposed as a goal to overcome this shortcoming. The fusing of data from the two instruments resulted in an enhanced success rate of classification (i.e., 93.75% for the recognition of T1DM and T2DM). Full article
(This article belongs to the Special Issue The Application and Advance of Electronic Nose)
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24 pages, 4008 KiB  
Article
A Qualitative and Quantitative Analysis Strategy for Continuous Turbulent Gas Mixture Monitoring
by Yinsheng Chen, Wanyu Xia, Deyun Chen, Tianyu Zhang, Tingting Song, Wenjie Zhao and Kai Song
Chemosensors 2022, 10(12), 499; https://doi.org/10.3390/chemosensors10120499 - 24 Nov 2022
Cited by 6 | Viewed by 1676
Abstract
Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the [...] Read more.
Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction. Full article
(This article belongs to the Special Issue The Application and Advance of Electronic Nose)
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Review

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37 pages, 3556 KiB  
Review
Physical Confounding Factors Affecting Gas Sensors Response: A Review on Effects and Compensation Strategies for Electronic Nose Applications
by Stefano Robbiani, Beatrice Julia Lotesoriere, Raffaele L. Dellacà and Laura Capelli
Chemosensors 2023, 11(10), 514; https://doi.org/10.3390/chemosensors11100514 - 29 Sep 2023
Cited by 13 | Viewed by 2647
Abstract
Electronic noses (e-noses) are devices based on combining different gas sensors’ responses to a given sample for identifying specific odor fingerprints. In recent years, this technology has been considered a promising novel tool in several fields of application, but several issues still hamper [...] Read more.
Electronic noses (e-noses) are devices based on combining different gas sensors’ responses to a given sample for identifying specific odor fingerprints. In recent years, this technology has been considered a promising novel tool in several fields of application, but several issues still hamper its widespread use. This review paper describes how some physical confounding factors, such as temperature, humidity, and gas flow, in terms of flow direction and flow rate, can drastically influence gas sensors’ responses and, consequently, e-nose results. Among the software and hardware approaches adopted to address such issues, different hardware compensation strategies proposed in the literature were critically analyzed. Solutions related to e-nose sensors’ modification, design and readout, sampling system and/or chamber geometry design were investigated. A trade-off between the loss of volatile compounds of interest, the decrease of sensors’ sensitivity, and the lack of fast responses need to be pointed out. The existing body of knowledge suggests that the e-nose design needs to be highly tailored to the target application to exploit the technology potentialities fully and highlights the need for further studies comparing the several solutions proposed as a starting point for the application-driven design of e-nose-based systems. Full article
(This article belongs to the Special Issue The Application and Advance of Electronic Nose)
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