Flexible Impedimetric Electronic Nose for High-Accurate Determination of Individual Volatile Organic Compounds by Tuning the Graphene Sensitive Properties

We investigated functionalized graphene materials to create highly sensitive sensors for volatile organic compounds (VOCs) such as formaldehyde, methanol, ethanol, acetone, and isopropanol. First, we prepared VOC-sensitive films consisting of mechanically exfoliated graphene (eG) and chemical graphene oxide (GO), which have different concentrations of structural defects. We deposited the films on silver interdigitated electrodes on Kapton substrate and submitted them to thermal treatment. Next, we measured the sensitive properties of the resulting sensors towards specific VOCs by impedance spectroscopy. We obtained the eG- and GO-based electronic nose composed of two eG films- and four GO film-based sensors with variable sensitivity to individual VOCs. The smallest relative change in impedance was 5% for the sensor based on eG film annealed at 180 °C toward 10 ppm formaldehyde, whereas the highest relative change was 257% for the sensor based on two-layers deposited GO film annealed at 200 °C toward 80 ppm ethanol. At 10 ppm VOC, the GO film-based sensors were sensitive enough to distinguish between individual VOCs, which implied excellent selectivity, as confirmed by Principle Component Analysis (PCA). According to a PCA-Support Vector Machine-based signal processing method, the electronic nose provided identification accuracy of 100% for individual VOCs. The proposed electronic nose can be used to detect multiple VOCs selectively because each sensor is sensitive to VOCs and has significant cross-selectivity to others.

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Evaluation of Semanotus bifasciatus (Motschulsky) Infestation in Platycladus orientalis Plants Using E-Nose and GC-MS

Transactions of the ASABE ◽

10.13031/trans.13145 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1629-1637

Author(s):

Zhenhe Wang ◽

Yubing Sun ◽

Jun Wang ◽

Yongwei Wang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

Electronic Nose ◽

Support Vector ◽

List Type ◽

Linear Discriminant ◽

Platycladus Orientalis ◽

Volatile Organic

HighlightsE-nose was employed for evaluation of Semanotus bifasciatus infestation based on four time-domain features.Plant VOCs were analyzed by GC-MS, and the results proved the feasibility of E-nose detection.PNN, BPNN, SVM, and PLSR were introduced to classify and predict Semanotus bifasciatus infestation numbers.Abstract. Trunk-boring insects such as Semanotus bifasciatus (Motschulsky) are difficult to detect because the larvae are hidden inside the trunks. In this study, the variation of volatile organic compounds (VOCs) in Platycladus orientalis after S. bifasciatus infestation was evaluated using an electronic nose (E-nose). VOCs from sample plants were observed with gas chromatography - mass spectrometry (GC-MS), and the results indicated that uninfected and infected groups differed both qualitatively and quantitatively, which proves the feasibility of E-nose evaluation. To extract features of the E-nose response signals, four feature extraction methods were applied, and their performances were compared based on linear discriminant analysis (LDA). Three classification models, including back-propagation neural network (BPNN), support vector machine (SVM), and probabilistic neural network (PNN), were established to identify the severity of infestation based on the optimal feature extraction method (75th second value). The classification results of BPNN, PNN, and SVM based on the calibration set were 96.43%, 91.07%, and 100%, respectively, and the results based on the validation set were 91.67%, 91.67%, and 100%, respectively. In addition, partial least squares regression (PLSR) and BPNN were used to predict the larvae density and achieved highly reliable results. It can be concluded that combining E-nose with GC-MS is a potential technique for evaluating trunk-borer infestation and can be used for pest management. Keywords: Electronic nose, Feature extraction, Pest evaluation, Semanotus bifasciatus, Volatile organic compounds.

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Non-Invasive Diagnosis of Diabetes by Volatile Organic Compounds in Urine Using FAIMS and Fox4000 Electronic Nose

Biosensors ◽

10.3390/bios8040121 ◽

2018 ◽

Vol 8 (4) ◽

pp. 121 ◽

Cited By ~ 15

Author(s):

Siavash Esfahani ◽

Alfian Wicaksono ◽

Ella Mozdiak ◽

Ramesh Arasaradnam ◽

James Covington

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Electronic Nose ◽

Area Under The Curve ◽

Urine Samples ◽

Support Vector ◽

Sparse Logistic Regression ◽

Non Invasive ◽

Volatile Organic ◽

Sample Age

The electronic nose (eNose) is an instrument designed to mimic the human olfactory system. Usage of eNose in medical applications is more popular than ever, due to its low costs and non-invasive nature. The eNose sniffs the gases and vapours that emanate from human waste (urine, breath, and stool) for the diagnosis of variety of diseases. Diabetes mellitus type 2 (DM2) affects 8.3% of adults in the world, with 43% being underdiagnosed, resulting in 4.9 million deaths per year. In this study, we investigated the potential of urinary volatile organic compounds (VOCs) as novel non-invasive diagnostic biomarker for diabetes. In addition, we investigated the influence of sample age on the diagnostic accuracy of urinary VOCs. We analysed 140 urine samples (73 DM2, 67 healthy) with Field-Asymmetric Ion Mobility Spectrometry (FAIMS); a type of eNose; and FOX 4000 (AlphaM.O.S, Toulouse, France). Urine samples were collected at UHCW NHS Trust clinics over 4 years and stored at −80 °C within two hours of collection. Four different classifiers were used for classification, specifically Sparse Logistic Regression, Random Forest, Gaussian Process, and Support Vector on both FAIMS and FOX4000. Both eNoses showed their capability of diagnosing DM2 from controls and the effect of sample age on the discrimination. FAIMS samples were analysed for all samples aged 0–4 years (AUC: 88%, sensitivity: 87%, specificity: 82%) and then sub group samples aged less than a year (AUC (Area Under the Curve): 94%, Sensitivity: 92%, specificity: 100%). FOX4000 samples were analysed for all samples aged 0–4 years (AUC: 85%, sensitivity: 77%, specificity: 85%) and a sub group samples aged less than 18 months: (AUC: 94%, sensitivity: 90%, specificity: 89%). We demonstrated that FAIMS and FOX 4000 eNoses can discriminate DM2 from controls using urinary VOCs. In addition, we showed that urine sample age affects discriminative accuracy.

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Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose

Applied Sciences ◽

10.3390/app11052337 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2337

Author(s):

Xiaohui Weng ◽

Cheng Kong ◽

Hongyang Jin ◽

Dongxue Chen ◽

Chunguang Li ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Electronic Nose ◽

Large Scale ◽

Gas Chromatography Mass Spectrometry ◽

Support Vector ◽

Classification Rate ◽

Linear Discriminant ◽

Volatile Organic ◽

Livestock Farms

The composition of volatile organic compounds (VOCs) in large-scale livestock farms is complex, which seriously affects the health of livestock and is difficult to evaluate. In order to quickly analyze the pollution degree of VOCs in livestock farms, electronic nose technology was used in this study to detect and analyze the gases in pig and chicken houses, respectively. Firstly, the gas chromatography–mass spectrometry (GC–MS) and electronic nose were used to analyze the VOCs in the pig and chicken houses at different time and locations. The types and relative contents of VOCs were obtained from different livestock farms by GC–MS analysis. The sensor array response of the electronic nose showed similar results. In addition, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machine (SVM) analyses were performed on the electrical signal that was generated by the sensors of electronic nose, respectively. Finally, the classification rate of different odor sources in livestock farms was the highest (>85%), which indicates that SVM is a more effective method suitable for volatile gases recognition in livestock farms. The results have shown that the developed electronic nose sensor is a promising and feasible instrument for characterizing volatile odors in livestock farms.

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Discriminative detection of volatile organic compounds using an electronic nose based on TiO2 hybrid nanostructures

Sensors and Actuators B Chemical ◽

10.1016/j.snb.2021.130124 ◽

2021 ◽

pp. 130124

Author(s):

Patrick P. Conti ◽

Rafaela S. Andre ◽

Luiza A. Mercante ◽

Lucas Fugikawa-Santos ◽

Daniel S. Correa

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Electronic Nose ◽

Hybrid Nanostructures ◽

Volatile Organic

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Non-Invasive Method to Detect Infection with Mycobacterium tuberculosis Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System

Sensors ◽

10.3390/s21020584 ◽

2021 ◽

Vol 21 (2) ◽

pp. 584

Author(s):

Kelvin de Jesús Beleño-Sáenz ◽

Juan Martín Cáceres-Tarazona ◽

Pauline Nol ◽

Aylen Lisset Jaimes-Mogollón ◽

Oscar Eduardo Gualdrón-Guerrero ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Wild Boar ◽

Organic Compounds ◽

Electronic Nose ◽

Bovine Tuberculosis ◽

Human Beings ◽

Non Invasive ◽

Testing Phase ◽

Volatile Organic ◽

Electronic Nose System

More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12–24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations.

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Influence of age and gender on the profile of exhaled volatile organic compounds analyzed by an electronic nose

Jornal Brasileiro de Pneumologia ◽

10.1590/s1806-37562015000000195 ◽

2016 ◽

Vol 42 (2) ◽

pp. 143-145 ◽

Cited By ~ 8

Author(s):

Silvano Dragonieri ◽

Vitaliano Nicola Quaranta ◽

Pierluigi Carratu ◽

Teresa Ranieri ◽

Onofrio Resta

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Electronic Nose ◽

Principal Component ◽

Exhaled Breath ◽

Age And Gender ◽

Volatile Organic ◽

Older Subjects ◽

Never Smokers ◽

And Gender

We aimed to investigate the effects of age and gender on the profile of exhaled volatile organic compounds. We evaluated 68 healthy adult never-smokers, comparing them by age and by gender. Exhaled breath samples were analyzed by an electronic nose (e-nose), resulting in "breathprints". Principal component analysis and canonical discriminant analysis showed that older subjects (≥ 50 years of age) could not be distinguished from younger subjects on the basis of their breathprints, as well as that the breathprints of males could not distinguished from those of females (cross-validated accuracy, 60.3% and 57.4%, respectively).Therefore, age and gender do not seem to affect the overall profile of exhaled volatile organic compounds measured by an e-nose.

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