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Electronic Noses III
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Electronic Noses III

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2181

Special Issue Editor

Dr. Jose V. Ros-Lis

E-Mail Website
Guest Editor
REDOLí Research Group, Inorganic Chemistry Department, Universitat de València, 46010 Valencia, Spain
Interests: nanomaterial; microwaves; sensors; enzyme inhibition; mesoporous
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of electronic tongues and, recently, noses has been a popular trend in recent decades, with several groups worldwide preparing novel sensing systems. In comparison with the traditional analytical instrumental methods of analysis using expensive and complex equipment, electronic noses are relatively cheap and easy to handle. Electronic noses usually integrate an array of non-specific sensors, together with statistical tools for the analysis of data. Ideally, each sensor differs in their response to the volatile compounds generated by the sample, creating a characteristic fingerprint. This kind of system can be applied even to complex samples such as food or biological samples for quantification, classification, sensorial analysis, and quality evaluation purposes.

This Special Issue is intended to be a timely and comprehensive Issue on recent and emerging concepts and technologies in the area of electronic noses including metal-oxide semiconductors and polymers. Topics include but are not limited to systems based on metal-oxide sensors, polymers, color changes, other variations in optical properties, quartz crystal microbalance, and surface acoustic wave sensors. Furthermore, other areas such as data analysis and pattern recognition methodologies can be discussed. Research papers, short communications, and reviews are all welcome. If the author is interested in submitting a review, it would be helpful to discuss this with the Guest Editor before submission.

Dr. Jose V. Ros-Lis
Guest Editor

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

  • e-nose
  • electronic nose
  • pattern recognition
  • optoelectronic nose
  • array analysis

Related Special Issue

Published Papers (3 papers)

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Research

12 pages, 1812 KiB  
Article
Application of MOX Sensors to Determine the Emission of Volatile Compounds in Corn Groats as a Function of Vertical Pressure in the Silo and Moisture Content of the Bed
by Robert Rusinek, Aleksandra Żytek, Mateusz Stasiak, Joanna Wiącek and Marek Gancarz
Sensors 2024, 24(7), 2187; https://doi.org/10.3390/s24072187 - 28 Mar 2024
Viewed by 434
Abstract
This study was focused on the analysis of the emission of volatile compounds as an indicator of changes in the quality degradation of corn groats with 14% and 17% moisture content (wet basis) using an electronic nose (Agrinose) at changing vertical pressure values. [...] Read more.
This study was focused on the analysis of the emission of volatile compounds as an indicator of changes in the quality degradation of corn groats with 14% and 17% moisture content (wet basis) using an electronic nose (Agrinose) at changing vertical pressure values. The corn groats were used in this study in an unconsolidated state of 0 kPa (the upper free layer of bulk material in the silo) and under a consolidation pressure of 40 kPa (approximately 3 m from the upper layer towards the bottom of the silo) and 80 kPa (approximately 6 m from the upper layer towards the bottom of the silo). The consolidation pressures corresponded to the vertical pressures acting on the layers of the bulk material bed in medium-slender and low silos. Chromatographic determinations of volatile organic compounds were performed as reference tests. The investigations confirmed the correlation of the electronic nose response with the quality degradation of the groats as a function of storage time. An important conclusion supported by the research results is that, based on the determined levels of intensity of volatile compound emission, the electronic nose is able to distinguish the individual layers of the bulk material bed undergoing different degrees of quality degradation. Full article
(This article belongs to the Special Issue Electronic Noses III)
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14 pages, 1651 KiB  
Article
Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks
by Huichao Zhu, Yu Wu, Ge Yang, Ruijie Song, Jun Yu and Jianwei Zhang
Sensors 2024, 24(4), 1319; https://doi.org/10.3390/s24041319 - 18 Feb 2024
Viewed by 592
Abstract
Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the [...] Read more.
Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer—a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model’s generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques. Full article
(This article belongs to the Special Issue Electronic Noses III)
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20 pages, 1197 KiB  
Article
Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species
by Piotr Borowik, Valentyna Dyshko, Rafał Tarakowski, Miłosz Tkaczyk, Adam Okorski and Tomasz Oszako
Sensors 2023, 23(18), 7907; https://doi.org/10.3390/s23187907 - 15 Sep 2023
Cited by 2 | Viewed by 723
Abstract
Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be [...] Read more.
Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor’s heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor’s response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer. Full article
(This article belongs to the Special Issue Electronic Noses III)
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