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Electronic Noses and Their Application

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

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 56087

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Special Issue Editors

Prof. Dr. Jacek Namiesnik

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Guest Editor

Chemical Faculty, Department of Analytical Chemistry, Gdańsk University of Technology, 80 233 Gdańsk, Poland
Interests: electronic noses and their broad spectrum of application, green technologies and analytical techniques, environmental protection, industrial analytics, sample preparation techniques

Dr. Tomasz Marcin Dymerski

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Guest Editor

Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Interests: two-dimensional gas chromatography; mass spectrometry; electronic noses; application of instrumental techniques in food analytics
Special Issues, Collections and Topics in MDPI journals

Dr. Jacek Gebicki

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Guest Editor

Chemical Faculty, Department of Analytical Chemistry, Gdańsk University of Technology, 80 233 Gdańsk, Poland
Interests: electronic noses in environmental monitoring, construction of electronic noses, costruction of chemical sensors, deodorization methods, control of technological processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Two parallel approaches contributed to the significant development of electronic olfaction in the last 30 years. The first was the gradual improvement of chemical sensors and in particular their metrological parameters such as the limit of detection, the linearity of the response signal, sensitivity, selectivity, response time and repeatability. The second involved the development of advanced data analysis techniques. Both these approaches need to be used when analysing samples characterised by complex composition in order to obtain comprehensive information. One of the main advantages of the use of electronic noses is the possibility to conduct a holistic analysis, without the need for prior separation of the particular components of a gaseous mixture, which significantly reduces the time of a single analysis. For that reason, the area of possible applications of electronic olfaction has been increasing over time. This Special Issue is devoted to the most recent technical developments in the area of electronic nose technology, including their design, the chemical sensors and detectors used in their construction, innovative data processing techniques and also their implementation, in particular in the food industry, environmental monitoring and medicine, as well as in other fields. We would like to invite researchers to submit both original and review papers. The contributions should be related to the listed topics.

Scope of the thematic issue:

new sensor solutions applied in electronic noses,
novel construction solutions dedicated for electronic noses,
new methodology approaches in the use of advanced data processing methods,
miniaturization of electronic noses,
application of electronic noses in food analysis,
application of electronic noses in analysis of various alcoholic beverages,
application of electronic noses in agriculture,
application of electronic noses in environmental monitoring,
application of electronic noses in medicine,
application of electronic noses in criminology,
application of electronic noses dedicated for military purposes.

Prof. Dr. Jacek Namiesnik
Dr. Tomasz Dymerski
Dr. Jacek Gebicki
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

electronic noses
chemical sensors
device miniaturization
statistical data analysis methods
food analysis
environmental monitoring medical diagnostics
criminal trace detection
explosives identification

Published Papers (10 papers)

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11 pages, 1492 KiB

Open AccessArticle

Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples

by Wenshen Jia, Gang Liang, Hui Tian, Jing Sun and Cihui Wan

Sensors 2019, 19(7), 1526; https://doi.org/10.3390/s19071526 - 29 Mar 2019

Cited by 73 | Viewed by 6379

Abstract

In this study, the PEN3 electronic nose was used to detect and recognize fresh and moldy apples inoculated with Penicillium expansum and Aspergillus niger, taking Golden Delicious apples as the model subject. Firstly, the apples were divided into two groups: individual apple inoculated only with/without different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then, the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined to simplify the analysis process and improve the accuracy of the results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM), and radial basis function neural network (RBFNN), were applied to analyze the data obtained from the characteristic sensors, aiming at establishing the prediction model of the flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W, and W2W in the PEN3 electronic nose exhibited a strong signal response to the flavor information, indicating most were closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors were used for modeling. The results of the four pattern recognition methods showed that BPNN had the best prediction performance for the training and testing sets for both Groups A and B, with prediction accuracies of 96.3% and 90.0% (Group A), 77.7% and 72.0% (Group B), respectively. Therefore, we demonstrate that the PEN3 electronic nose not only effectively detects and recognizes fresh and moldy apples, but also can distinguish apples inoculated with different molds. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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11 pages, 2530 KiB

Open AccessArticle

Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose

by Jun Chen, Juanhong Gu, Rong Zhang, Yuezhong Mao and Shiyi Tian

Sensors 2019, 19(3), 605; https://doi.org/10.3390/s19030605 - 31 Jan 2019

Cited by 65 | Viewed by 6994

Abstract

The aim of this study was to use an electronic nose set up in our lab to detect and predict the freshness of pork, beef and mutton. Three kinds of freshness, including fresh, sub-fresh and putrid, was established by human sensory evaluation and was used as a reference for the electronic nose’s discriminant factor analysis. The principal component analysis results showed the electronic nose could distinguish well pork, beef and mutton samples with different storage times. In the PCA figures, three kinds of meats samples all presented an approximate parabola trend during 7 days’ storage time. The discriminant factor analysis showed electronic nose could distinguish and judge well the freshness of samples (accuracy was 89.5%, 84.2% and 94.7% for pork, beef and mutton, respectively). Therefore, the electronic nose is promising for meat fresh detection application. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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14 pages, 2982 KiB

Open AccessArticle

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

by Dongdong Du, Jun Wang, Bo Wang, Luyi Zhu and Xuezhen Hong

Sensors 2019, 19(2), 419; https://doi.org/10.3390/s19020419 - 21 Jan 2019

Cited by 55 | Viewed by 5682

Abstract

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R² = 0.9928; testing: R² = 0.9928), SSC (training: R² = 0.9749; testing: R² = 0.9143) and firmness (training: R² = 0.9814; testing: R² = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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14 pages, 1844 KiB

Open AccessArticle

Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose

by Tao Liu, Dongqi Li, Jianjun Chen, Yanbing Chen, Tao Yang and Jianhua Cao

Sensors 2018, 18(11), 4028; https://doi.org/10.3390/s18114028 - 19 Nov 2018

Cited by 19 | Viewed by 3733

Abstract

Gas sensors are the key components of an electronic nose (E-nose) in violated odour analysis. Gas-sensor drift is a kind of physical change on a sensor surface once an E-nose works. The perturbation of gas-sensor responses caused by drift would deteriorate the performance of the E-nose system over time. In this study, we intend to explore a suitable approach to deal with the drift effect in an online situation. Considering that the conventional drift calibration is difficult to implement online, we use active learning (AL) to provide reliable labels for online instances. Common AL learning methods tend to select and label instances with low confidence or massive information. Although this action clarifies the ambiguity near the classification boundary, it is inadequate under the influence of gas-sensor drift. We still need the samples away from the classification plane to represent drift variations comprehensively in the entire data space. Thus, a novel drift counteraction method named AL on adaptive confidence rule (AL-ACR) is proposed to deal with online drift data dynamically. By contrast with conventional AL methods selecting instances near the classification boundary of a certain category, AL-ACR collects instances distributed evenly in different categories. This action implements on an adjustable rule according to the outputs of classifiers. Compared with other reference methods, we adopt two drift databases of E-noses to evaluate the performance of the proposed method. The experimental results indicate that the AL-ACR reaches higher accuracy than references on two E-nose databases, respectively. Furthermore, the impact of the labelling number is discussed to show the trend of performance for the AL-type methods. Additionally, we define the labelling efficiency index (LEI) to assess the contribution of certain labelling numerically. According to the results of LEI, we believe AL-ACR can achieve the best effect with the lowest cost among the AL-type methods in this work. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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11 pages, 1769 KiB

Open AccessArticle

Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring

by Rachid Laref, Etienne Losson, Alexandre Sava and Maryam Siadat

Sensors 2018, 18(11), 3716; https://doi.org/10.3390/s18113716 - 01 Nov 2018

Cited by 36 | Viewed by 5272

Abstract

Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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21 pages, 4645 KiB

Open AccessArticle

Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation

by Hong Men, Yanan Jiao, Yan Shi, Furong Gong, Yizhou Chen, Hairui Fang and Jingjing Liu

Sensors 2018, 18(10), 3387; https://doi.org/10.3390/s18103387 - 10 Oct 2018

Cited by 9 | Viewed by 3313

Abstract

In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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11 pages, 3728 KiB

Open AccessArticle

Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification

by Li-Ying Chen, Cheng-Chun Wu, Ting-I. Chou, Shih-Wen Chiu and Kea-Tiong Tang

Sensors 2018, 18(10), 3256; https://doi.org/10.3390/s18103256 - 27 Sep 2018

Cited by 39 | Viewed by 8082

Abstract

Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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11 pages, 963 KiB

Open AccessArticle

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

by Chi-Hsiang Huang, Chian Zeng, Yi-Chia Wang, Hsin-Yi Peng, Chia-Sheng Lin, Che-Jui Chang and Hsiao-Yu Yang

Sensors 2018, 18(9), 2845; https://doi.org/10.3390/s18092845 - 28 Aug 2018

Cited by 49 | Viewed by 5991

Abstract

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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14 pages, 1846 KiB

Open AccessArticle

Evaluation of Hydrocarbon Soil Pollution Using E-Nose

by Andrzej Bieganowski, Grzegorz Józefaciuk, Lidia Bandura, Łukasz Guz, Grzegorz Łagód and Wojciech Franus

Sensors 2018, 18(8), 2463; https://doi.org/10.3390/s18082463 - 30 Jul 2018

Cited by 48 | Viewed by 6139

Abstract

The possibility of detecting low levels of soil pollution by petroleum fuel using an electronic nose (e-nose) was studied. An attempt to distinguish between pollution caused by petrol and diesel oil, and its relation to the time elapsed since the pollution event was simultaneously performed. Ten arable soils, belonging to various soil groups from the World Reference Base (WRB), were investigated. The measurements were performed on soils that were moistened to field capacity, polluted separately with both hydrocarbons, and then allowed to dry slowly over a period of 180 days. The volatile fingerprints differed throughout the course of the experiment, and, by its end, they were similar to those of the unpolluted soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that the e-nose results could be used to detect soil contamination and distinguish between pollutants and contamination levels. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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3 pages, 170 KiB

Open AccessLetter

The Electronic Nose’s Emerging Role in Respiratory Medicine

by Roberto Gasparri, Giulia Sedda and Lorenzo Spaggiari

Sensors 2018, 18(9), 3029; https://doi.org/10.3390/s18093029 - 10 Sep 2018

Cited by 12 | Viewed by 3096

Abstract

New interest has grown in the respiratory disorder diagnosis and monitoring, throughout electronic nose technologies. This technology has several advantages compared to classic approach. In this short letter, we aim to emphasize electronic nose role in respiratory medicine. Full article

(This article belongs to the Special Issue Electronic Noses and Their Application)

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