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E-noses: Sensors and Applications

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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 (31 March 2016) | Viewed by 81787

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Prof. Dr. Mari Carmen Horrillo Güemes

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Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), CSIC, Serrano 144, 28006 Madrid, Spain
Interests: chemical and biological sensors; electronic noses; nanomaterials; sensor technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The artificial olfaction, based on electronic systems (e-noses), includes three basic functions that operate on an odorant: a sample handler, a system of gas sensors, and a signal-processing method. The response of these artificial systems can be the identity of the odorant, an estimate concentration of the odorant, or characteristic properties of the odor as might be perceived by a human. These electronic noses are bioinspired instruments that mimic the sense of smell.

The e-nose often consists of non-selective sensors that interact with volatile molecules (gases or liquids) that result in a physical or chemical change that sends a signal to a computer which makes a classification based on a calibration and training process leading to pattern recognition. The non-selectivity of the sensors results in many possibilities for unique signal combinations, patterns, or fingerprints. The interpretation of the complex data sets is accomplished by use of multivariate statistics including, mainly, principal component analysis (PCA), linear discriminant analysis (LDA), discriminant function analysis (DFA), partial least squares (PLS), and artificial neural networks (NAA) for non-linear responses.

Moreover, they are easy to build, provide short analysis times in real time and on line, and, in addition, they are a non-destructive technology. These features make e-noses very useful for diverse applications in the food, cosmetic, and pharmaceutical industries, as well as in environmental control or clinical diagnostics of interest for society in general.

This Special Issue will be mainly concentrated on the recent advances in e-noses from the point of view of the sensors and of the applications above mentioned, because, although this instrumentation has received considerable attention over the past twenty years, there is a great deal of research still to be done, especially with regard to new materials (nanowires, nanofibers, etc.), sensor technology, data processing, interpretation, and validation of results. Therefore, both experimental and theoretical investigations are welcome in this Special Issue.

Prof. Dr. M. Carmen Horrillo Güemes
Guest Editor

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Keywords

Sensors
Sensitive materials (mainly nanostructured materials)
Pattern recognition
Electronic detection (aroma, gases, vapors, liquids)
Applications
System miniaturization
Electronic noses
Artificial olfaction

Published Papers (12 papers)

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Research

1122 KiB

Open AccessArticle

Electronic Nose Testing Procedure for the Definition of Minimum Performance Requirements for Environmental Odor Monitoring

by Lidia Eusebio, Laura Capelli and Selena Sironi

Sensors 2016, 16(9), 1548; https://doi.org/10.3390/s16091548 - 21 Sep 2016

Cited by 41 | Viewed by 8789

Abstract

Despite initial enthusiasm towards electronic noses and their possible application in different fields, and quite a lot of promising results, several criticalities emerge from most published research studies, and, as a matter of fact, the diffusion of electronic noses in real-life applications is still very limited. In general, a first step towards large-scale-diffusion of an analysis method, is standardization. The aim of this paper is describing the experimental procedure adopted in order to evaluate electronic nose performances, with the final purpose of establishing minimum performance requirements, which is considered to be a first crucial step towards standardization of the specific case of electronic nose application for environmental odor monitoring at receptors. Based on the experimental results of the performance testing of a commercialized electronic nose type with respect to three criteria (i.e., response invariability to variable atmospheric conditions, instrumental detection limit, and odor classification accuracy), it was possible to hypothesize a logic that could be adopted for the definition of minimum performance requirements, according to the idea that these are technologically achievable. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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2170 KiB

Open AccessArticle

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

by Li Wang, Pengfei Jia, Tailai Huang, Shukai Duan, Jia Yan and Lidan Wang

Sensors 2016, 16(8), 1275; https://doi.org/10.3390/s16081275 - 12 Aug 2016

Cited by 10 | Viewed by 5790

Abstract

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C₆H₆), toluene (C₇H₈), formaldehyde (CH₂O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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2016 KiB

Open AccessArticle

Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose

by You Wang, Jiacheng Miao, Xiaofeng Lyu, Linfeng Liu, Zhiyuan Luo and Guang Li

Sensors 2016, 16(7), 1088; https://doi.org/10.3390/s16071088 - 13 Jul 2016

Cited by 6 | Viewed by 4531

Abstract

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt’s method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt’s method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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2425 KiB

Open AccessArticle

A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections

by Pengfei Jia, Tailai Huang, Li Wang, Shukai Duan, Jia Yan and Lidan Wang

Sensors 2016, 16(7), 1019; https://doi.org/10.3390/s16071019 - 30 Jun 2016

Cited by 10 | Viewed by 5758

Abstract

An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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1202 KiB

Open AccessArticle

Electronic Noses for Well-Being: Breath Analysis and Energy Expenditure

by Julian W. Gardner and Timothy A. Vincent

Sensors 2016, 16(7), 947; https://doi.org/10.3390/s16070947 - 23 Jun 2016

Cited by 22 | Viewed by 8510

Abstract

The wealth of information concealed in a single human breath has been of interest for many years, promising not only disease detection, but also the monitoring of our general well-being. Recent developments in the fields of nano-sensor arrays and MEMS have enabled once bulky artificial olfactory sensor systems, or so-called “electronic noses”, to become smaller, lower power and portable devices. At the same time, wearable health monitoring devices are now available, although reliable breath sensing equipment is somewhat missing from the market of physical, rather than chemical sensor gadgets. In this article, we report on the unprecedented rise in healthcare problems caused by an increasingly overweight population. We first review recently-developed electronic noses for the detection of diseases by the analysis of basic volatile organic compounds (VOCs). Then, we discuss the primary cause of obesity from over eating and the high calorific content of food. We present the need to measure our individual energy expenditure from our exhaled breath. Finally, we consider the future for handheld or wearable devices to measure energy expenditure; and the potential of these devices to revolutionize healthcare, both at home and in hospitals. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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1461 KiB

Open AccessArticle

Evaluating Soil Moisture Status Using an e-Nose

by Andrzej Bieganowski, Katarzyna Jaromin-Glen, Łukasz Guz, Grzegorz Łagód, Grzegorz Jozefaciuk, Wojciech Franus, Zbigniew Suchorab and Henryk Sobczuk

Sensors 2016, 16(6), 886; https://doi.org/10.3390/s16060886 - 22 Jun 2016

Cited by 37 | Viewed by 6094

Abstract

The possibility of distinguishing different soil moisture levels by electronic nose (e-nose) was studied. Ten arable soils of various types were investigated. The measurements were performed for air-dry (AD) soils stored for one year, then moistened to field water capacity and finally dried within a period of 180 days. The volatile fingerprints changed during the course of drying. At the end of the drying cycle, the fingerprints were similar to those of the initial AD soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that e-nose results can be used to distinguish soil moisture. It was also shown that different soils can give different e-nose signals at the same moistures. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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3102 KiB

Open AccessArticle

Love Acoustic Wave-Based Devices and Molecularly-Imprinted Polymers as Versatile Sensors for Electronic Nose or Tongue for Cancer Monitoring

by Corinne Dejous, Hamida Hallil, Vincent Raimbault, Jean-Luc Lachaud, Bernard Plano, Raphaël Delépée, Patrick Favetta, Luigi Agrofoglio and Dominique Rebière

Sensors 2016, 16(6), 915; https://doi.org/10.3390/s16060915 - 20 Jun 2016

Cited by 24 | Viewed by 7624

Abstract

Cancer is a leading cause of death worldwide and actual analytical techniques are restrictive in detecting it. Thus, there is still a challenge, as well as a need, for the development of quantitative non-invasive tools for the diagnosis of cancers and the follow-up care of patients. We introduce first the overall interest of electronic nose or tongue for such application of microsensors arrays with data processing in complex media, either gas (e.g., Volatile Organic Compounds or VOCs as biomarkers in breath) or liquid (e.g., modified nucleosides as urinary biomarkers). Then this is illustrated with a versatile acoustic wave transducer, functionalized with molecularly-imprinted polymers (MIP) synthesized for adenosine-5′-monophosphate (AMP) as a model for nucleosides. The device including the thin film coating is described, then static measurements with scanning electron microscopy (SEM) and electrical characterization after each step of the sensitive MIP process (deposit, removal of AMP template, capture of AMP target) demonstrate the thin film functionality. Dynamic measurements with a microfluidic setup and four targets are presented afterwards. They show a sensitivity of 5 Hz·ppm⁻¹ of the non-optimized microsensor for AMP detection, with a specificity of three times compared to PMPA, and almost nil sensitivity to 3′AMP and CMP, in accordance with previously published results on bulk MIP. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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1553 KiB

Open AccessArticle

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

by Abbas Gorji-Chakespari, Ali Mohammad Nikbakht, Fatemeh Sefidkon, Mahdi Ghasemi-Varnamkhasti, Jesús Brezmes and Eduard Llobet

Sensors 2016, 16(5), 636; https://doi.org/10.3390/s16050636 - 04 May 2016

Cited by 21 | Viewed by 6579

Abstract

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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306 KiB

Open AccessArticle

Identification of a Large Pool of Microorganisms with an Array of Porphyrin Based Gas Sensors

by Nicola M. Zetola, Chawangwa Modongo, Keikantse Matlhagela, Enoch Sepako, Ogopotse Matsiri, Tsaone Tamuhla, Bontle Mbongwe, Eugenio Martinelli, Giorgio Sirugo, Roberto Paolesse and Corrado Di Natale

Sensors 2016, 16(4), 466; https://doi.org/10.3390/s16040466 - 01 Apr 2016

Cited by 13 | Viewed by 4844

Abstract

The association between volatile compounds (VCs) and microorganisms, as demonstrated by several studies, may offer the ground for a rapid identification of pathogens. To this regard, chemical sensors are a key enabling technology for the exploitation of this opportunity. In this study, we investigated the performance of an array of porphyrin-coated quartz microbalance gas sensors in the identification of a panel of 12 bacteria and fungi. The porphyrins were metal complexes and the free base of a functionalized tetraphenylporphyrin. Our results show that the sensor array distinguishes the VC patterns produced by microorganisms in vitro. Besides being individually identified, bacteria are also sorted into Gram-positive and Gram-negative. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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1947 KiB

Open AccessArticle

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

by Pengfei Jia, Tailai Huang, Shukai Duan, Lingpu Ge, Jia Yan and Lidan Wang

Sensors 2016, 16(3), 370; https://doi.org/10.3390/s16030370 - 14 Mar 2016

Cited by 7 | Viewed by 5270

Abstract

When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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558 KiB

Open AccessArticle

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network

by M. Fatih Adak and Nejat Yumusak

Sensors 2016, 16(3), 304; https://doi.org/10.3390/s16030304 - 27 Feb 2016

Cited by 64 | Viewed by 8974

Abstract

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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669 KiB

Open AccessArticle

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

by Zhaozhou Lin, Qiao Zhang, Ruixin Liu, Xiaojie Gao, Lu Zhang, Bingya Kang, Junhan Shi, Zidan Wu, Xinjing Gui and Xuelin Li

Sensors 2016, 16(2), 151; https://doi.org/10.3390/s16020151 - 25 Jan 2016

Cited by 20 | Viewed by 5574

Abstract

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data. Full article

(This article belongs to the Special Issue E-noses: Sensors and Applications)

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