Research

11 pages, 3620 KiB

Open AccessArticle

Effects of Center Metals in Porphines on Nanomechanical Gas Sensing

by Huynh Thien Ngo, Kosuke Minami, Gaku Imamura, Kota Shiba and Genki Yoshikawa

Sensors 2018, 18(5), 1640; https://doi.org/10.3390/s18051640 - 21 May 2018

Cited by 22 | Viewed by 4120

Porphyrin is one of the most promising materials for realizing a practical artificial olfactory sensor system. In this study, we focus on non-substituted porphyrins—porphines—as receptor materials of nanomechanical membrane-type surface stress sensors (MSS) to investigate the effect of center metals on gas sensing. [...] Read more.

Porphyrin is one of the most promising materials for realizing a practical artificial olfactory sensor system. In this study, we focus on non-substituted porphyrins—porphines—as receptor materials of nanomechanical membrane-type surface stress sensors (MSS) to investigate the effect of center metals on gas sensing. By omitting the substituents on the tetrapyrrole macrocycle of porphyrin, the peripheral interference by substituents can be avoided. Zinc, nickel, and iron were chosen for the center metals as these metalloporphines show different properties compared to free-base porphine. The present study revealed that iron insertion enhanced sensitivity to various gases, while zinc and nickel insertion led to equivalent or less sensitivity than free-base porphine. Based on the experimental results, we discuss the role of center metals for gas uptake from the view point of molecular interaction. We also report the high robustness of the iron porphine to humidity, showing the high feasibility of porphine-based nanomechanical sensor devices for practical applications in ambient conditions. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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15 pages, 2051 KiB

Open AccessArticle

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

by Marco Abbatangelo, Estefanía Núñez-Carmona, Veronica Sberveglieri, Dario Zappa, Elisabetta Comini and Giorgio Sberveglieri

Sensors 2018, 18(5), 1617; https://doi.org/10.3390/s18051617 - 18 May 2018

Cited by 25 | Viewed by 3789

Abstract

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based [...] Read more.

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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9 pages, 382 KiB

Open AccessArticle

Key Odorants from Pig Production Based on Improved Measurements of Odor Threshold Values Combining Olfactometry and Proton-Transfer-Reaction Mass Spectrometry (PTR-MS)

by Michael Jørgen Hansen, Pernille Lund Kasper, Anders Peter S. Adamsen and Anders Feilberg

Sensors 2018, 18(3), 788; https://doi.org/10.3390/s18030788 - 06 Mar 2018

Cited by 24 | Viewed by 4278

Abstract

Analytical measurements of odorants in combination with odor threshold values is an alternative to sensory measurements that can be used to evaluate abatement technologies for pig production facilities. The purpose of the present study was to estimate odor threshold values for key odorants [...] Read more.

Analytical measurements of odorants in combination with odor threshold values is an alternative to sensory measurements that can be used to evaluate abatement technologies for pig production facilities. The purpose of the present study was to estimate odor threshold values for key odorants found in pig house air. A new method was applied where an olfactometer was used to dilute the sample air and the concentrations of odorants presented to the panelists at the dilutions steps were measured by proton-transfer-reaction mass spectrometry (PTR-MS). The results demonstrate that the odor threshold values of acetic acid, butanoic acid, and 4-methylphenol are considerably lower than reported previously, whereas the values of hydrogen sulfide, methanethiol and dimethylsulfide were comparable. Consequently, acetic acid, butanoic acid, and 4-methyl-phenol will have a larger influence on odor from pig production facilities than previously assumed. The results highlight the necessity for directly measuring exposure concentrations when determining odor threshold values. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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29 pages, 3897 KiB

Open AccessArticle

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

by Zhiyuan Ma, Guangchun Luo, Ke Qin, Nan Wang and Weina Niu

Sensors 2018, 18(3), 742; https://doi.org/10.3390/s18030742 - 01 Mar 2018

Cited by 25 | Viewed by 5734

Abstract

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. [...] Read more.

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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22 pages, 11745 KiB

Open AccessArticle

On the Temporal Stability of Analyte Recognition with an E-Nose Based on a Metal Oxide Sensor Array in Practical Applications

by Ilia Kiselev, Victor Sysoev, Igor Kaikov, Ilona Koronczi, Ruslan Adil Akai Tegin, Jamila Smanalieva, Martin Sommer, Coskan Ilicali and Michael Hauptmannl

Sensors 2018, 18(2), 550; https://doi.org/10.3390/s18020550 - 11 Feb 2018

Cited by 36 | Viewed by 6105

Abstract

The paper deals with a functional instability of electronic nose (e-nose) units which significantly limits their real-life applications. Here we demonstrate how to approach this issue with example of an e-nose based on a metal oxide sensor array developed at the Karlsruhe Institute [...] Read more.

The paper deals with a functional instability of electronic nose (e-nose) units which significantly limits their real-life applications. Here we demonstrate how to approach this issue with example of an e-nose based on a metal oxide sensor array developed at the Karlsruhe Institute of Technology (Germany). We consider the instability of e-nose operation at different time scales ranging from minutes to many years. To test the e-nose we employ open-air and headspace sampling of analyte odors. The multivariate recognition algorithm to process the multisensor array signals is based on the linear discriminant analysis method. Accounting for the received results, we argue that the stability of device operation is mostly affected by accidental changes in the ambient air composition. To overcome instabilities, we introduce the add-training procedure which is found to successfully manage both the temporal changes of ambient and the drift of multisensor array properties, even long-term. The method can be easily implemented in practical applications of e-noses and improve prospects for device marketing. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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14 pages, 1385 KiB

Open AccessArticle

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

by Tailai Wen, Jia Yan, Daoyu Huang, Kun Lu, Changjian Deng, Tanyue Zeng, Song Yu and Zhiyi He

Sensors 2018, 18(2), 388; https://doi.org/10.3390/s18020388 - 29 Jan 2018

Cited by 16 | Viewed by 4022

Abstract

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the [...] Read more.

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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6896 KiB

Open AccessArticle

Determination of Odour Interactions of Three-Component Gas Mixtures Using an Electronic Nose

by Bartosz Szulczyński, Jacek Namieśnik and Jacek Gębicki

Sensors 2017, 17(10), 2380; https://doi.org/10.3390/s17102380 - 18 Oct 2017

Cited by 45 | Viewed by 4997

Abstract

The paper presents an application of an electronic nose prototype comprised of six TGS-type sensors and one PID-type sensor to identify odour interaction phenomena in odorous three-component mixtures. The investigation encompassed eight odorous mixtures—toluene-acetone-triethylamine and formaldehyde-butyric acid-pinene—characterized by different odour intensity and hedonic [...] Read more.

The paper presents an application of an electronic nose prototype comprised of six TGS-type sensors and one PID-type sensor to identify odour interaction phenomena in odorous three-component mixtures. The investigation encompassed eight odorous mixtures—toluene-acetone-triethylamine and formaldehyde-butyric acid-pinene—characterized by different odour intensity and hedonic tone. A principal component regression (PCR) calibration model was used for evaluation of predicted odour intensity and hedonic tone. Correctness of identification of odour interactions in the odorous three-component mixtures was determined based on the results obtained with the electronic nose. The results indicated a level of 75–80% for odour intensity and 57–73% for hedonic tone. The average root mean square error of prediction amounted to 0.03–0.06 for odour intensity determination and 0.07–0.34 for hedonic tone evaluation of the odorous three-component mixtures. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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465 KiB

Open AccessArticle

Classification of Data from Electronic Nose Using Gradient Tree Boosting Algorithm

by Yuan Luo, Wenbin Ye, Xiaojin Zhao, Xiaofang Pan and Yuan Cao

Sensors 2017, 17(10), 2376; https://doi.org/10.3390/s17102376 - 18 Oct 2017

Cited by 28 | Viewed by 4694

Abstract

In this paper, an approach that can fast classify the data from the electronic nose is presented. In this approach the gradient tree boosting algorithm is used to classify the gas data and the experiment results show that the proposed gradient tree boosting [...] Read more.

In this paper, an approach that can fast classify the data from the electronic nose is presented. In this approach the gradient tree boosting algorithm is used to classify the gas data and the experiment results show that the proposed gradient tree boosting algorithm achieved high performance on this classification problem, outperforming other algorithms as comparison. In addition, electronic nose we used only requires a few seconds of data after the gas reaction begins. Therefore, the proposed approach can realize a fast recognition of gas, as it does not need to wait for the gas reaction to reach steady state. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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2463 KiB

Open AccessArticle

Effects of Dilution Systems in Olfactometry on the Recovery of Typical Livestock Odorants Determined by PTR-MS

by Pernille Lund Kasper, Dietmar Mannebeck, Arne Oxbøl, Jens Vinge Nygaard, Michael Jørgen Hansen and Anders Feilberg

Sensors 2017, 17(8), 1859; https://doi.org/10.3390/s17081859 - 11 Aug 2017

Cited by 22 | Viewed by 4609

Abstract

The present study provides an elaborate assessment of the performance of olfactometers in terms of odorant recovery for a selection of odorants emitted from livestock houses. The study includes three different olfactometer dilution systems, which have been in use at accredited odor laboratories. [...] Read more.

The present study provides an elaborate assessment of the performance of olfactometers in terms of odorant recovery for a selection of odorants emitted from livestock houses. The study includes three different olfactometer dilution systems, which have been in use at accredited odor laboratories. They consist of: (i) a custom-built olfactometer made of glass tubes, (ii) a TO8 olfactometer, and (iii) an Olfacton dilution system based on a mass flow controller. The odorants include hydrogen sulfide, methanethiol, dimethyl sulfide, acetic acid, butanoic acid, propanoic acid, 3-methylbutanoic acid, 4-methylphenol, and trimethylamine. Furthermore, n-butanol, as the reference gas in the European standard for olfactometry, EN13725, was included. All measurements were performed in real time with proton-transfer-reaction mass spectrometry (PTR-MS). The results show that only dimethyl sulfide was almost completely recovered in all cases, while for the remaining compounds, the performance was found to vary significantly (from 0 to 100%) depending on the chemical properties of the compounds, the concentration levels, the pulse duration, and the olfactometer material. To elucidate the latter, the recovery in different locations of the TO8 olfactometer and in tubes of different materials, that is, poly-tetrafluoroethylene (PTFE), perfluoroalkoxy (PFA), stainless steel and SilcoTek-coated steel, were tested. Significant saturation effects were observed when odorants were in contact with stainless steel. Full article

(This article belongs to the Special Issue Artificial Olfaction and Taste)

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