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11 pages, 2188 KiB

Open AccessArticle

A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures

by Andreu González-Calabuig, Xavier Cetó and Manel Del Valle

Sensors 2018, 18(1), 216; https://doi.org/10.3390/s18010216 - 13 Jan 2018

Cited by 16 | Viewed by 4201

Abstract

This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of [...] Read more.

This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L⁻¹, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948. Full article

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

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2230 KiB

Open AccessArticle

Quantitative Determination of Spring Water Quality Parameters via Electronic Tongue

by Noèlia Carbó, Javier López Carrero, F. Javier Garcia-Castillo, Isabel Tormos, Estela Olivas, Elisa Folch, Miguel Alcañiz Fillol, Juan Soto, Ramón Martínez-Máñez and M. Carmen Martínez-Bisbal

Sensors 2018, 18(1), 40; https://doi.org/10.3390/s18010040 - 25 Dec 2017

Cited by 15 | Viewed by 5097

Abstract

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in spring water is proposed here. The electronic voltammetric tongue consisted of a set of four noble electrodes (iridium, rhodium, platinum, and gold) housed inside a stainless steel cylinder. [...] Read more.

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in spring water is proposed here. The electronic voltammetric tongue consisted of a set of four noble electrodes (iridium, rhodium, platinum, and gold) housed inside a stainless steel cylinder. These noble metals have a high durability and are not demanding for maintenance, features required for the development of future automated equipment. A pulse voltammetry study was conducted in 83 spring water samples to determine concentrations of nitrate (range: 6.9–115 mg/L), sulfate (32–472 mg/L), fluoride (0.08–0.26 mg/L), chloride (17–190 mg/L), and sodium (11–94 mg/L) as well as pH (7.3–7.8). These parameters were also determined by routine analytical methods in spring water samples. A partial least squares (PLS) analysis was run to obtain a model to predict these parameter. Orthogonal signal correction (OSC) was applied in the preprocessing step. Calibration (67%) and validation (33%) sets were selected randomly. The electronic tongue showed good predictive power to determine the concentrations of nitrate, sulfate, chloride, and sodium as well as pH and displayed a lower R² and slope in the validation set for fluoride. Nitrate and fluoride concentrations were estimated with errors lower than 15%, whereas chloride, sulfate, and sodium concentrations as well as pH were estimated with errors below 10%. Full article

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

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6990 KiB

Open AccessArticle

Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification

by Wei Zhao, Qing-Hao Meng, Ming Zeng and Pei-Feng Qi

Sensors 2017, 17(12), 2855; https://doi.org/10.3390/s17122855 - 8 Dec 2017

Cited by 30 | Viewed by 5349

Abstract

This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods [...] Read more.

This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods for e-noses. However, these steps are complicated and empirical because there is no uniform rule for choosing appropriate methods from many different options. The main advantage of SSAE is that it can automatically learn features from the original sensor data without the steps of preprocessing and feature extraction; which can greatly simplify data processing procedures for e-noses. To identify different brands of Chinese liquors; an SSAE based multi-layer back propagation neural network (BPNN) is constructed. Seven kinds of strong-flavor Chinese liquors were selected for a self-designed e-nose to test the performance of the proposed method. Experimental results show that the proposed method outperforms the traditional methods. Full article

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

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16396 KiB

Open AccessArticle

Chemical Selectivity and Sensitivity of a 16-Channel Electronic Nose for Trace Vapour Detection

by Drago Strle, Bogdan Štefane, Mario Trifkovič, Marion Van Miden, Ivan Kvasić, Erik Zupanič and Igor Muševič

Sensors 2017, 17(12), 2845; https://doi.org/10.3390/s17122845 - 8 Dec 2017

Cited by 15 | Viewed by 7049

Abstract

Good chemical selectivity of sensors for detecting vapour traces of targeted molecules is vital to reliable detection systems for explosives and other harmful materials. We present the design, construction and measurements of the electronic response of a 16 channel electronic nose based on [...] Read more.

Good chemical selectivity of sensors for detecting vapour traces of targeted molecules is vital to reliable detection systems for explosives and other harmful materials. We present the design, construction and measurements of the electronic response of a 16 channel electronic nose based on 16 differential microcapacitors, which were surface-functionalized by different silanes. The e-nose detects less than 1 molecule of TNT out of 10⁺¹² N₂ molecules in a carrier gas in 1 s. Differently silanized sensors give different responses to different molecules. Electronic responses are presented for TNT, RDX, DNT, H₂S, HCN, FeS, NH₃, propane, methanol, acetone, ethanol, methane, toluene and water. We consider the number density of these molecules and find that silane surfaces show extreme affinity for attracting molecules of TNT, DNT and RDX. The probability to bind these molecules and form a surface-adsorbate is typically 10⁺⁷ times larger than the probability to bind water molecules, for example. We present a matrix of responses of differently functionalized microcapacitors and we propose that chemical selectivity of multichannel e-nose could be enhanced by using artificial intelligence deep learning methods. Full article

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

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2106 KiB

Open AccessArticle

Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints

by Marta Ferreiro-González, Gerardo F. Barbero, Miguel Palma, Jesús Ayuso, José A. Álvarez and Carmelo G. Barroso

Sensors 2017, 17(11), 2544; https://doi.org/10.3390/s17112544 - 5 Nov 2017

Cited by 22 | Viewed by 4753

Abstract

Characterization of petroleum-derived products is an area of continuing importance in environmental science, mainly related to fuel spills. In this study, a non-separative analytical method based on E-Nose (Electronic Nose) is presented as a rapid alternative for the characterization of several different petroleum-derived [...] Read more.

Characterization of petroleum-derived products is an area of continuing importance in environmental science, mainly related to fuel spills. In this study, a non-separative analytical method based on E-Nose (Electronic Nose) is presented as a rapid alternative for the characterization of several different petroleum-derived products including gasoline, diesel, aromatic solvents, and ethanol samples, which were poured onto different surfaces (wood, cork, and cotton). The working conditions about the headspace generation were 145 °C and 10 min. Mass spectroscopic data (45–200 m/z) combined with chemometric tools such as hierarchical cluster analysis (HCA), later principal component analysis (PCA), and finally linear discriminant analysis (LDA) allowed for a full discrimination of the samples. A characteristic fingerprint for each product can be used for discrimination or identification. The E-Nose can be considered as a green technique, and it is rapid and easy to use in routine analysis, thus providing a good alternative to currently used methods. Full article

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

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6157 KiB

Open AccessArticle

Improved Durability and Sensitivity of Bitterness-Sensing Membrane for Medicines

by Xiao Wu, Hideya Onitake, Zhiqin Huang, Takeshi Shiino, Yusuke Tahara, Rui Yatabe, Hidekazu Ikezaki and Kiyoshi Toko

Sensors 2017, 17(11), 2541; https://doi.org/10.3390/s17112541 - 4 Nov 2017

Cited by 12 | Viewed by 4632

Abstract

This paper reports the improvement of a bitterness sensor based on a lipid polymer membrane consisting of phosphoric acid di-n-decyl ester (PADE) as a lipid and bis(1-butylpentyl) adipate (BBPA) and tributyl o-acetylcitrate (TBAC) as plasticizers. Although the commercialized bitterness sensor (BT0) has high [...] Read more.

This paper reports the improvement of a bitterness sensor based on a lipid polymer membrane consisting of phosphoric acid di-n-decyl ester (PADE) as a lipid and bis(1-butylpentyl) adipate (BBPA) and tributyl o-acetylcitrate (TBAC) as plasticizers. Although the commercialized bitterness sensor (BT0) has high sensitivity and selectivity to the bitterness of medicines, the sensor response gradually decreases to almost zero after two years at room temperature and humidity in a laboratory. To reveal the reason for the deterioration of the response, we investigated sensor membranes by measuring the membrane potential, contact angle, and adsorption amount, as well as by performing gas chromatography-mass spectrometry (GC-MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS). We found that the change in the surface charge density caused by the hydrolysis of TBAC led to the deterioration of the response. The acidic environment generated by PADE promoted TBAC hydrolysis. Finally, we succeeded in fabricating a new membrane for sensing the bitterness of medicines with higher durability and sensitivity by adjusting the proportions of the lipid and plasticizers. Full article

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

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4293 KiB

Open AccessArticle

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

by Zhebo Wei, Xize Xiao, Jun Wang and Hui Wang

Sensors 2017, 17(11), 2500; https://doi.org/10.3390/s17112500 - 31 Oct 2017

Cited by 33 | Viewed by 5335

Abstract

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and [...] Read more.

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R² = 0.9942) worked much better than PLSR. Full article

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

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2932 KiB

Open AccessArticle

Selectivity Enhancement in Electronic Nose Based on an Optimized DQN

by Yu Wang, Jianguo Xing and Shu Qian

Sensors 2017, 17(10), 2356; https://doi.org/10.3390/s17102356 - 16 Oct 2017

Cited by 19 | Viewed by 5285

Abstract

In order to enhance the selectivity of metal oxide gas sensors, we use a flow modulation method to exploit transient sensor information. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the [...] Read more.

In order to enhance the selectivity of metal oxide gas sensors, we use a flow modulation method to exploit transient sensor information. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the sensor chamber. We present an active perception strategy by using a DQN which can optimize the flow modulation online. The advantage of DQN is not only that the classification accuracy is higher than traditional methods such as PCA, but also that it has a good adaptability under small samples and labeled data. From observed values of the sensors array and its past experiences, the DQN learns an action policy to change the flow speed dynamically that maximizes the total rewards (or minimizes the classification error). Meanwhile, a CNN is trained to predict sample class and reward according to current actions and observation of sensors. We demonstrate our proposed methods on a gases classification problem in a real time environment. The results show that the DQN learns to modulate flow to classify different gas and the correct rates of gases are: sesame oil 100%, lactic acid 80%, acetaldehyde 80%, acetic acid 80%, and ethyl acetate 100%, the average correct rate is 88%. Compared with the traditional method, the results of PCA are: sesame oil 100%, acetic acid 24%, acetaldehyde 100%, lactic acid 56%, ethyl acetate 68%, the average accuracy rate is 69.6%. DQN uses fewer steps to achieve higher recognition accuracy and improve the recognition speed, and to reduce the training and testing costs. Full article

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

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2778 KiB

Open AccessArticle

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

by Peilin He, Pengfei Jia, Siqi Qiao and Shukai Duan

Sensors 2017, 17(10), 2279; https://doi.org/10.3390/s17102279 - 7 Oct 2017

Cited by 22 | Viewed by 5028

Abstract

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into [...] Read more.

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy. Full article

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

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1077 KiB

Open AccessFeature PaperCommunication

Rational Design of Peptide-Functionalized Surface Plasmon Resonance Sensor for Specific Detection of TNT Explosive

by Jin Wang, Masaki Muto, Rui Yatabe, Takeshi Onodera, Masayoshi Tanaka, Mina Okochi and Kiyoshi Toko

Sensors 2017, 17(10), 2249; https://doi.org/10.3390/s17102249 - 30 Sep 2017

Cited by 17 | Viewed by 6419

Abstract

In this study, a rationally-designed 2,4,6-trinitrotoluene (TNT) binding peptide derived from an amino acid sequence of the complementarity-determining region (CDR) of an anti-TNT monoclonal antibody was used for TNT detection based on a maleimide-functionalized surface plasmon resonance (SPR) sensor. By antigen-docking simulation and [...] Read more.

In this study, a rationally-designed 2,4,6-trinitrotoluene (TNT) binding peptide derived from an amino acid sequence of the complementarity-determining region (CDR) of an anti-TNT monoclonal antibody was used for TNT detection based on a maleimide-functionalized surface plasmon resonance (SPR) sensor. By antigen-docking simulation and screening, the TNT binding candidate peptides were obtained as TNTHCDR1 derived from the heavy chain of CDR1, TNTHCDR2 derived from CDR2, and TNTHCDR3 from CDR3 of an anti-TNT antibody. The binding events between candidate peptides and TNT were evaluated using the SPR sensor by direct determination based on the 3-aminopropyltriethoxysilane (APTES) surface. The TNT binding peptide was directly immobilized on the maleimide-functionalized sensor chip surface from N-γ-maleimidobutyryl-oxysuccinimide ester (GMBS). The results demonstrated that peptide TNTHCDR3 was identified and selected as a TNT binding peptide among the other two candidate peptides. Five kinds of TNT analogues were also investigated to testify the selectivity of TNT binding peptide TNTHCDR3. Furthermore, the results indicated that the APTES-GMBS-based SPR sensor chip procedure featured a great potential application for the direct detection of TNT. Full article

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

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4243 KiB

Open AccessArticle

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

by Mohammad Mizanur Rahman, Chalie Charoenlarpnopparut, Prapun Suksompong, Pisanu Toochinda and Attaphongse Taparugssanagorn

Sensors 2017, 17(9), 2089; https://doi.org/10.3390/s17092089 - 12 Sep 2017

Cited by 10 | Viewed by 5984

Abstract

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor ( [...] Read more.

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms. Full article

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

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1900 KiB

Open AccessArticle

Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection

by Enric Climent, Jose Pelegri-Sebastia, Tomas Sogorb, J. B. Talens and Jose Chilo

Sensors 2017, 17(8), 1917; https://doi.org/10.3390/s17081917 - 20 Aug 2017

Cited by 16 | Viewed by 6047

Abstract

In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable for IoT technology. The system architecture consists of four stages: [...] Read more.

In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable for IoT technology. The system architecture consists of four stages: data acquisition, data storage, data processing, and user interfacing. The designed eNose was tested with experiment for detection of volatile components in water pollution, as a dimethyl disulphide or dimethyl diselenide or sulphur. Therefore, the results provide evidence that odor information can be recognized with around 86% efficiency, detecting smells unwanted in the water and improving the quality control in bottled water factories. Full article

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

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494 KiB

Open AccessArticle

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

by Zhan Wang, Xiyang Sun, Jiacheng Miao, You Wang, Zhiyuan Luo and Guang Li

Sensors 2017, 17(8), 1869; https://doi.org/10.3390/s17081869 - 14 Aug 2017

Cited by 19 | Viewed by 5221

Abstract

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic [...] Read more.

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity. Full article

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

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2965 KiB

Open AccessArticle

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

by Hong Men, Yan Shi, Songlin Fu, Yanan Jiao, Yu Qiao and Jingjing Liu

Sensors 2017, 17(7), 1656; https://doi.org/10.3390/s17071656 - 19 Jul 2017

Cited by 57 | Viewed by 7112

Abstract

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based [...] Read more.

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively. Full article

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

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3744 KiB

Open AccessArticle

Practical Use of Metal Oxide Semiconductor Gas Sensors for Measuring Nitrogen Dioxide and Ozone in Urban Environments

by Philip J. D. Peterson, Amrita Aujla, Kirsty H. Grant, Alex G. Brundle, Martin R. Thompson, Josh Vande Hey and Roland J. Leigh

Sensors 2017, 17(7), 1653; https://doi.org/10.3390/s17071653 - 19 Jul 2017

Cited by 102 | Viewed by 16397

Abstract

The potential of inexpensive Metal Oxide Semiconductor (MOS) gas sensors to be used for urban air quality monitoring has been the topic of increasing interest in the last decade. This paper discusses some of the lessons of three years of experience working with [...] Read more.

The potential of inexpensive Metal Oxide Semiconductor (MOS) gas sensors to be used for urban air quality monitoring has been the topic of increasing interest in the last decade. This paper discusses some of the lessons of three years of experience working with such sensors on a novel instrument platform (Small Open General purpose Sensor (SOGS)) in the measurement of atmospheric nitrogen dioxide and ozone concentrations. Analytic methods for increasing long-term accuracy of measurements are discussed, which permit nitrogen dioxide measurements with 95% confidence intervals of 20.0

μ g m^{- 3}

and ozone precision of 26.8

μ g m^{- 3}

, for measurements over a period one month away from calibration, averaged over 18 months of such calibrations. Beyond four months from calibration, sensor drift becomes significant, and accuracy is significantly reduced. Successful calibration schemes are discussed with the use of controlled artificial atmospheres complementing deployment on a reference weather station exposed to the elements. Manufacturing variation in the attributes of individual sensors are examined, an experiment possible due to the instrument being equipped with pairs of sensors of the same kind. Good repeatability (better than 0.7 correlation) between individual sensor elements is shown. The results from sensors that used fans to push air past an internal sensor element are compared with mounting the sensors on the outside of the enclosure, the latter design increasing effective integration time to more than a day. Finally, possible paths forward are suggested for improving the reliability of this promising sensor technology for measuring pollution in an urban environment. Full article

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

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6017 KiB

Open AccessArticle

The Regular Interaction Pattern among Odorants of the Same Type and Its Application in Odor Intensity Assessment

by Luchun Yan, Jiemin Liu, Shen Jiang, Chuandong Wu and Kewei Gao

Sensors 2017, 17(7), 1624; https://doi.org/10.3390/s17071624 - 13 Jul 2017

Cited by 39 | Viewed by 5290

Abstract

The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture’s [...] Read more.

The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture’s odor intensity. This study investigated the odor interaction principle in odor mixtures of aldehydes and esters, respectively. Then, a modified vector model (MVM) was proposed and it successfully demonstrated the similarity of the odor interaction pattern among odorants of the same type. Based on the regular interaction pattern, unlike a determined empirical model only fit for a specific odor mixture in conventional approaches, the MVM distinctly simplified the odor intensity prediction of odor mixtures. Furthermore, the MVM also provided a way of directly converting constituents’ chemical concentrations to their mixture’s odor intensity. By combining the MVM with usual data-processing algorithm of e-nose, a new e-nose system was established for an odor intensity rating. Compared with instrumental analysis and human assessor, it exhibited accuracy well in both quantitative analysis (Pearson correlation coefficient was 0.999 for individual aldehydes (n = 12), 0.996 for their binary mixtures (n = 36) and 0.990 for their ternary mixtures (n = 60)) and odor intensity assessment (Pearson correlation coefficient was 0.980 for individual aldehydes (n = 15), 0.973 for their binary mixtures (n = 24), and 0.888 for their ternary mixtures (n = 25)). Thus, the observed regular interaction pattern is considered an important foundation for accelerating extensive application of olfactory evaluation in odor pollution monitoring. Full article

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

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Open AccessArticle

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

by Yulin Jian, Daoyu Huang, Jia Yan, Kun Lu, Ying Huang, Tailai Wen, Tanyue Zeng, Shijie Zhong and Qilong Xie

Sensors 2017, 17(6), 1434; https://doi.org/10.3390/s17061434 - 19 Jun 2017

Cited by 29 | Viewed by 6655

Abstract

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme [...] Read more.

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. Full article

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

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1551 KiB

Open AccessArticle

A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment

by Ruicong Zhi, Lei Zhao and Dezheng Zhang

Sensors 2017, 17(5), 1007; https://doi.org/10.3390/s17051007 - 3 May 2017

Cited by 82 | Viewed by 6931

Abstract

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part [...] Read more.

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste. Full article

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

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Review

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23 pages, 2916 KiB

Open AccessReview

Applications and Advances in Bioelectronic Noses for Odour Sensing

by Tran Thi Dung, Yunkwang Oh, Seon-Jin Choi, Il-Doo Kim, Min-Kyu Oh and Moonil Kim

Sensors 2018, 18(1), 103; https://doi.org/10.3390/s18010103 - 1 Jan 2018

Cited by 70 | Viewed by 13778

Abstract

A bioelectronic nose, an intelligent chemical sensor array system coupled with bio-receptors to identify gases and vapours, resembles mammalian olfaction by which many vertebrates can sniff out volatile organic compounds (VOCs) sensitively and specifically even at very low concentrations. Olfaction is undertaken by [...] Read more.

A bioelectronic nose, an intelligent chemical sensor array system coupled with bio-receptors to identify gases and vapours, resembles mammalian olfaction by which many vertebrates can sniff out volatile organic compounds (VOCs) sensitively and specifically even at very low concentrations. Olfaction is undertaken by the olfactory system, which detects odorants that are inhaled through the nose where they come into contact with the olfactory epithelium containing olfactory receptors (ORs). Because of its ability to mimic biological olfaction, a bio-inspired electronic nose has been used to detect a variety of important compounds in complex environments. Recently, biosensor systems have been introduced that combine nanoelectronic technology and olfactory receptors themselves as a source of capturing elements for biosensing. In this article, we will present the latest advances in bioelectronic nose technology mimicking the olfactory system, including biological recognition elements, emerging detection systems, production and immobilization of sensing elements on sensor surface, and applications of bioelectronic noses. Furthermore, current research trends and future challenges in this field will be discussed. Full article

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

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3525 KiB

Open AccessReview

Biomimetic Sensors for the Senses: Towards Better Understanding of Taste and Odor Sensation

by Chunsheng Wu, Ya-Wen Du, Liquan Huang, Yaron Ben-Shoshan Galeczki, Ayana Dagan-Wiener, Michael Naim, Masha Y. Niv and Ping Wang

Sensors 2017, 17(12), 2881; https://doi.org/10.3390/s17122881 - 11 Dec 2017

Cited by 17 | Viewed by 7883

Abstract

Taste and smell are very important chemical senses that provide indispensable information on food quality, potential mates and potential danger. In recent decades, much progress has been achieved regarding the underlying molecular and cellular mechanisms of taste and odor senses. Recently, biosensors have [...] Read more.

Taste and smell are very important chemical senses that provide indispensable information on food quality, potential mates and potential danger. In recent decades, much progress has been achieved regarding the underlying molecular and cellular mechanisms of taste and odor senses. Recently, biosensors have been developed for detecting odorants and tastants as well as for studying ligand-receptor interactions. This review summarizes the currently available biosensing approaches, which can be classified into two main categories: in vitro and in vivo approaches. The former is based on utilizing biological components such as taste and olfactory tissues, cells and receptors, as sensitive elements. The latter is dependent on signals recorded from animals’ signaling pathways using implanted microelectrodes into living animals. Advantages and disadvantages of these two approaches, as well as differences in terms of sensing principles and applications are highlighted. The main current challenges, future trends and prospects of research in biomimetic taste and odor sensors are discussed. Full article

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

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2058 KiB

Open AccessReview

Different Ways to Apply a Measurement Instrument of E-Nose Type to Evaluate Ambient Air Quality with Respect to Odour Nuisance in a Vicinity of Municipal Processing Plants

by Bartosz Szulczyński, Tomasz Wasilewski, Wojciech Wojnowski, Tomasz Majchrzak, Tomasz Dymerski, Jacek Namieśnik and Jacek Gębicki

Sensors 2017, 17(11), 2671; https://doi.org/10.3390/s17112671 - 19 Nov 2017

Cited by 58 | Viewed by 6757

Abstract

This review paper presents different ways to apply a measurement instrument of e-nose type to evaluate ambient air with respect to detection of the odorants characterized by unpleasant odour in a vicinity of municipal processing plants. An emphasis was put on the following [...] Read more.

This review paper presents different ways to apply a measurement instrument of e-nose type to evaluate ambient air with respect to detection of the odorants characterized by unpleasant odour in a vicinity of municipal processing plants. An emphasis was put on the following applications of the electronic nose instruments: monitoring networks, remote controlled robots and drones as well as portable devices. Moreover, this paper presents commercially available sensors utilized in the electronic noses and characterized by the limit of quantification below 1 ppm v/v, which is close to the odour threshold of some odorants. Additionally, information about bioelectronic noses being a possible alternative to electronic noses and their principle of operation and application potential in the field of air evaluation with respect to detection of the odorants characterized by unpleasant odour was provided. Full article

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

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794 KiB

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An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems

by Anup Vanarse, Adam Osseiran and Alexander Rassau

Sensors 2017, 17(11), 2591; https://doi.org/10.3390/s17112591 - 10 Nov 2017

Cited by 28 | Viewed by 6720

Abstract

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the [...] Read more.

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses. Full article

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

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