Electronic Nose Technology Research Articles

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges.

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Analysis of volatile compounds in L. edodes blanched by hot water and microwave

AbstractThe comprehensive flavour characterisation and volatile compounds of raw L. edodes, hot water blanching (HB) sample and microwave blanching (MB) sample were comparatively analysed by electronic nose technology and headspace solid‐phase micro‐extraction combined with gas chromatography‐mass spectrometry (HS‐SPME‐GC‐MS). Results indicated that volatile components in L. edodes markedly changed after HB and MB. Volatile compounds of raw L. edodes consisted mainly ketones, sulphide and alcohols, and 3‐octanone, as well as 1‐octen‐3‐one, were the major compounds. The content of ketones and sulphides in blanched samples markedly decreased, while the relative content of aldehydes, hydrocarbons and esters increased, which became the major volatile compounds of treatment samples. In addition, the percentage contents of esters, alcohols and sulphides in MB samples were significantly (P < 0.05) higher than that in HB samples, especially 1‐octen‐3‐ol, which contributes more to mushroom flavour. Therefore, MB is a better pretreatment method of L. edodes processing and cooking according to the results of experiment.

Volatile components of white Hypsizygus marmoreus detected by electronic nose and HS-SPME-GC-MS: Influence of four drying methods

ABSTRACTFour drying methods, viz hot air drying (HD), vacuum drying (VD), microwave-vacuum drying (MVD), and vacuum freeze-drying (FD), were used to dry white Hypsizygus marmoreus (WHM). The volatile and taste components of the dried WHM were comparatively analysed by electronic nose technology (E-nose) and head-space solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GS-MS). Principal component analysis (PCA) showed that E-nose could distinguish fresh WHM and four dried products clearly. Dominant volatile components in fresh WHM were found to be 1-octen-3-ol, 3-octanone, and (E)-2-nonenal. However, it was found that the drying methods caused damage to the 8-carbon compounds (C8), especially to 8-carbon alcohol greatly. For instance, HD produced more ester materials, MVD yielded more aldehyde compounds, FD brought more hydrocarbon products, and VD produced more ketone substances.

Electronic Nose Technology in Respiratory Diseases.

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.

Discrimination of Different Types Damage of Tomato Seedling by Electronic Nose

The profiles of volatile compounds emitted by plants varies in response to different damage. The potential of electronic nose technology to monitor such changes, with the aim of diagnosing plant health was investingted. An electronic nose(E-nose)was used to analyse tomato seedlings that were subjected to differnent types of damage(infection by Early blight disease, infection by Gray mold disease, mechanically damage, and undamaged). Principal component analysis(PCA), linear discrimination analysis(LDA), back-propagation neural network(BPNN), and support vector machine (SVM) network were used to evaluate the E-nose data. The results indicated that the E-nose can successfully discriminate between tomato seedling with different types of damage. The results of PCA and LDA showed that clusters of data were divided into 3 groups (ZP, HP, and CP/MP). Samples from groups CP and MP overlapped partially. Back-propagation neural network (BPNN) and support vector machine (SVM) network were used to evaluate the E-nose data. Good discrimination results were obtained using SVM and BPNN. The results demonstrate that it is plausible to use E-nose technology as a method for monitoring damage in tomato seedling.

Electronic Nose Technology Based on Quantum Dot Filters

The electronic nose with chemical dyes as sensor can react with target gas and have specific color changes. In general, RGB camera collects a group of images to record these changes used for pattern recognition. RGB filters are not sensitive to the slight color changes, which limits the performance of this kind of electronic nose. This paper demonstrates using quantum dot spec-troscopy technology to solve this problem. Multiple quantum dot filters are placed on the surface of image sensor. When capturing images, there are more response channels of the same incident light than RGB filters. Simulation and experiment both prove that quantum dot filters with appropriate processing are more sensitive to color changes than RGB filters.

Feasibility of electronic nose technology for discriminating between head and neck, bladder, and colon carcinomas

Electronic nose (e-nose) technology has the potential to detect cancer at an early stage and can differentiate between cancer origins. Our objective was to compare patients who had head and neck squamous cell carcinoma (HNSCC) with patients who had colon or bladder cancer to determine the distinctive diagnostic characteristics of the e-nose. Feasibility study An e-nose device was used to collect samples of exhaled breath from patients who had HNSCC and those who had bladder or colon cancer, after which the samples were analyzed and compared. One hundred patients with HNSCC, 40 patients with bladder cancer, and 28 patients with colon cancer exhaled through an e-nose for 5 min. An artificial neural network was used for the analysis, and double cross-validation to validate the model. In differentiating HNSCC from colon cancer, a diagnostic accuracy of 81 % was found. When comparing HNSCC with bladder cancer, the diagnostic accuracy was 84 %. A diagnostic accuracy of 84 % was found between bladder cancer and colon cancer. The e-nose technique using double cross-validation is able to discriminate between HNSCC and colon cancer and between HNSCC and bladder cancer. Furthermore, the e-nose technique can distinguish colon cancer from bladder cancer.

Breathomics in the setting of asthma and chronic obstructive pulmonary disease

Exhaled breath contains thousands of volatile organic compounds that reflect the metabolic process occurring in the host both locally in the airways and systemically. They also arise from the environment and airway microbiome. Comprehensive analysis of breath volatile organic compounds (breathomics) provides opportunities for noninvasive biomarker discovery and novel mechanistic insights. Applications in patients with obstructive lung diseases, such as asthma and chronic obstructive pulmonary disease, include not only diagnostics (especially in children and other challenging diagnostic areas) but also identification of clinical treatable traits, such as airway eosinophilia and risk of infection/exacerbation, that are not specific to diagnostic labels. Although many aspects of breath sampling and analysis are challenging, proof-of-concept studies with mass spectrometry and electronic nose technologies have provided independent studies with moderate-to-good diagnostic and phenotypic accuracies. The present review evaluates the data obtained by using breathomics in (1) predicting the inception of asthma or chronic obstructive pulmonary disease, (2) inflammatory phenotyping, (3) exacerbation prediction, and (4) treatment stratification. The current findings merit the current efforts of large multicenter studies using standardized sampling, shared analytic methods, and databases, including external validation cohorts. This will position this noninvasive technology in the clinical assessment and monitoring of chronic airways diseases.

Factors Influencing Continuous Breath Signal in Intubated and Mechanically-Ventilated Intensive Care Unit Patients Measured by an Electronic Nose.

Introduction: Continuous breath analysis by electronic nose (eNose) technology in the intensive care unit (ICU) may be useful in monitoring (patho) physiological changes. However, the application of breath monitoring in a non-controlled clinical setting introduces noise into the data. We hypothesized that the sensor signal is influenced by: (1) humidity in the side-stream; (2) patient-ventilator disconnections and the nebulization of medication; and (3) changes in ventilator settings and the amount of exhaled CO2. We aimed to explore whether the aforementioned factors introduce noise into the signal, and discuss several approaches to reduce this noise. Methods: Study in mechanically-ventilated ICU patients. Exhaled breath was monitored using a continuous eNose with metal oxide sensors. Linear (mixed) models were used to study hypothesized associations. Results: In total, 1251 h of eNose data were collected. First, the initial 15 min of the signal was discarded. There was a negative association between humidity and Sensor 1 (Fixed-effect β: −0.05 ± 0.002) and a positive association with Sensors 2–4 (Fixed-effect β: 0.12 ± 0.001); the signal was corrected for this noise. Outliers were most likely due to noise and therefore removed. Sensor values were positively associated with end-tidal CO2, tidal volume and the pressure variables. The signal was corrected for changes in these ventilator variables after which the associations disappeared. Conclusion: Variations in humidity, ventilator disconnections, nebulization of medication and changes of ventilator settings indeed influenced exhaled breath signals measured in ventilated patients by continuous eNose analysis. We discussed several approaches to reduce the effects of these noise inducing variables.

ENose technology can detect and classify human pathogenic molds in vitro: a proof-of-concept study of Aspergillus fumigatus and Rhizopus oryzae

Invasive pulmonary mold disease (IPMD) is often fatal in neutropenic patients. This is because IPMD is difficult to diagnose timely, especially when non-Aspergillus molds are the causative agent, as they are usually not associated with a positive galactomannan assay. In 2013 we showed that exhaled breath analysis might be used to diagnose invasive aspergillosis through profiling of patterns in exhaled volatile organic compounds (VOCs) by electronic nose (eNose) technology. The current study aimed to determine (1) whether molds can be discriminated from other microorganisms (using two mold species: Aspergillus fumigatus and a pathogenic mold not associated with a positive galactomannan assay, i.c. Rhizopus oryzae) and (2) whether both molds can be discriminated from each other.First, we cultured strains of Streptococcus pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, A. fumigatus and R. oryzae in separate airtight bottles. We examined whether an eNose (Cyranose 320) could discriminate the headspaces of bottles with molds from those with bacteria/yeasts. Second, we examined whether an eNose could discriminate A. fumigatus and R. oryzae. Diagnostic algorithms were created using canonical discriminant analysis after principle component analysis. Primary outcome parameter was the validated accuracy.The eNose discriminated A. fumigatus from bacteria/yeasts with a cross-validated accuracy of 92.9% (sensitivity 95.2%, specificity 91.9%). The eNose had an accuracy (validated using split-half analysis) of 100% in discriminating A. fumigatus from R. oryzae.Our study suggests that an eNose can identify and classify molds in vitro. This warrants prospective in vivo studies aimed at detecting and classifying IPMD using exhaled breath.

Correction for de Heer et al., Detection of Airway Colonization by Aspergillus fumigatus by Use of Electronic Nose Technology in Patients with Cystic Fibrosis.

Volume 54, no. 3, p. [569–575][1], 2016. Page 570, Results section, lines 18–19: “two false positively and one false negatively” should read “one false positively and two false negatively.” [1]: /lookup/doi/10.1128/JCM.02214-15

Removal of Rancid-Acid Odor of Expeller-Pressed Virgin Coconut Oil by Gamma Irradiation: Evaluation by Sensory and Electronic Nose Technology

Expeller-pressed virgin coconut oil, known to have disagreeable rancid-acid odor due to the presence of octanoic acid, was subjected to gamma (γ) irradiation for removal of the same. Sensory evaluation in tandem with electronic nose (Heracles and ENOVISION) analyses established that the oil irradiated at 4.2 kGy had no rancid-acid odor (absence of octanoic acid) at the end of 28 days of storage at 23 ± 2 °C. Therefore, 4.2 kGy was established as the “appropriate irradiation dose” for removal of rancid-acid odor of virgin coconut oil. A “deodorization index” with respect to rancid-acid odor was also developed using electronic nose for confirmation of this “appropriate dose” and thereby provided possible means of preventing detrimental effects of high dose of γ-irradiation in oils. GC–MS analysis of hydrocarbons extracted from oil irradiated at 4.2 kGy by supercritical carbon dioxide extraction revealed radiolysis of octanoic acid resulting in elimination of rancid-acid odor. Further, γ-irradiation at 4.2 kGy did not affect the antioxidant potency of coconut oil rendering it a safe and healthy alternative to conventional deodorization techniques for oils. Development of methodology for assessment of rancid-acid odor of coconut oil using electronic nose has been reported for the first time in this work.

Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model.

A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staphylococcu aureus, Escherichia coli and Pseudomonas aeruginosa) in this experiment. In addition, KELM is compared with five existing classification methods: Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), extreme learning machine (ELM), k-nearest neighbor (KNN) and support vector machine (SVM). Meanwhile, three traditional optimization methods including particle swarm optimization algorithm (PSO), genetic algorithm (GA) and grid search algorithm (GS) and four kernel functions (Gaussian kernel, linear kernel, polynomial kernel and wavelet kernel) for KELM are discussed in this experiment. Finally, the QPSO-KELM model is also used to deal with another two experimental E-nose datasets in the previous experiments. The experimental results demonstrate the superiority of QPSO-KELM in various E-nose applications.

Detection of Volatile Plant Chemicals Using Novel Enzymatic Electrochemical Biosensor

Volatile organic compounds released by pathogen-infected and pest-infested plants can be used as fingerprints for plant disease and infestation detection via electronic nose technologies 1,2. p-ethylphenol (EP) and methyl salicylate (MeSA) were discovered as two most important volatile organic compounds. In this work, enzymatic based electrochemical biosensors were developed for the detection of EP and MeSA. For EP detection, a tyrosinase (TYR)-immobilized amperometric biosensor was developed. Cyclic voltammetry (CV) was used to study the electrochemical reaction. CV indicated that the sensitivity reached to 8.528 µA·cm-2·µM while limit of detection (LOD) reached to 0.210 µM. Steady state constant potential amperometry displayed lower sensitivity (4.052 µA·cm-2·µM), but better LOD (0.095 µM) than CV. For MeSA detection, alcohol oxidase and horseradish peroxidase were applied for detection of methanol that produced after hydrolysis of MeSA. CV and constant potential amperometry techniques were used to determine sensitivity of 0.112 and 0.283 µA·cm-2·µM, and LOD of 23 and 1 µM for the CV and DPV respectively. In order to obtain better sensitivity and higher specificity for MeSA detection, salicylate hydroxylase (SH) and TYR were immobilized on the multiwalled carbon nanotube modified glassy-carbon electrode. After hydrolysis of MeSA, salicylate reacts with SH and TYR to generate amperometric signal. The sensitivity and LOD generated from CV were reported to be 21.29 µA·cm-2·µM and 0.137 µM respectively. Constant potential amperometry was also applied for MeSA detection. The results display that sensitivity of 30.61 µA·cm-2·µM and LOD of 0.013 µM can be achieved. Interference and simulated analyte based studies indicate that the biosensor is suitable for application in agricultural sensing. Reference: T. Baldwin, A. Kessler and R. Halitschke, Curr. Opin. Plant Biol., 2002, 5, 351.P. W. Par´e and J. H. Tumlinson, Plant Physiol., 1999, 121, 325. Figure 1

Robust Bayesian Inference for Gas Identification in Electronic Nose Applications by Using Random Matrix Theory

Finding a rapid gas identification algorithm with high accuracy and a closed-form solution that does not require any manual tuning of parameters is the major challenge to overcome in adopting electronic nose technology in daily life applications. Recently, bio-inspired rank-order-based classifiers have been proposed to meet this challenge by transforming multidimensional sensitivity vectors into temporally ordered spike sequences for target gases. The performance of these classifiers, however, is limited when the spike sequences corresponding to all the target gases do not contain sufficient discriminatory information to identify them. Moreover, their identification decision is delayed up to the computation of the sensitivity vectors at steady state, which incurs a long waiting time. In this paper, we adopt a Bayesian parametric method with a normal distribution model as an alternative approach that provides a closed-form solution with only second-order statistics, i.e., mean and covariance. However, for electronic nose applications, a reliable estimation of the covariance matrix is a major challenge with the commonly used maximum likelihood estimate in Bayesian inference because of the limited number of available measurements for each target gas. We exploit random matrix theory principles to reduce randomness in the sample covariance matrix for its reliable estimation. Moreover, transient features are used to accelerate the gas identification. In order to validate the effectiveness of this approach, data of eight gases, namely, C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O, CL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , CO, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , are acquired in the laboratory. We achieve a 7.73% performance improvement as compared with Bayesian inference using the maximum likelihood estimate, and an overall accuracy rate of 99.40% on the experimental data set.

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

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.

Graphene-zinc Oxide Based Nanomaterials for Gas Sensing Devices

Herein, we report the preparation of a hybrid material by combination of modified graphene and ZnO. The morphological and compositional analyses of the obtained material have been performed by means of scanning electron microscopy and energy dispersive X-ray analysis. The functional properties of the prepared structures have been investigated for their application in gas sensor devices. The gas sensing performance of the hybrid material show that the structure can be used for fabrication of chemical sensors, as well as in electronic nose technology.

Development of a Wireless Electronic Nose Capable of Measuring Odors Both in Open and Closed Systems

This paper presents an electronic nose (e-nose) technology for the odor measurement in open and closed systems, i.e. ambient air and the bottle headspace, respectively. Solenoid valves and an air pump control the air flow in the e-nose. Metal oxide semiconductor (MOS-type) gas sensors are used for the detection of odor. A humidity-temperature sensor is also equipped for monitoring the humidity and temperature of the air being measured. The e-nose also implements a wireless system for the real-time data transmission to a remote computer. The wireless system will realize the e-nose applications in the field work. In this paper, we demonstrate that the developed e-nose can measure an odor both in open and closed systems and precisely transmit the odor data via the wireless system.

Recent progress in the design and clinical development of electronic-nose technologies

Recent progress in the design and clinical development of electronic-nose technologies Alphus D Wilson Forest Insect and Disease Research (FIDR), Southern Hardwoods Laboratory, Center for Bottomland Hardwoods Research, Southern Research Station, USDA Forest Service, Stoneville, MS, USA Abstract: Electronic-nose (e-nose) devices are instruments designed to detect and discriminate between precise complex gaseous mixtures of volatile organic compounds derived from specific organic sources, such as clinical test samples from patients, based on electronic aroma signature patterns (distinct digital sensor responses) resulting from the combined outputs of a multisensor array. E-nose tools allow the identification of complex gaseous samples at relatively low costs, making them useful in a diverse range of clinical situations, particularly for assessments of a patients&#39; metabolic (physiological) health and for diagnostic applications. The acquisition and analysis of e-nose clinical &quot;breathprints&quot; or &quot;smellprints&quot;, taken from patients&#39; exhaled air, allow for the potential detection of a wide range of human ailments due to abnormal metabolic activities, disorders, and diseases that may be present in the lungs, other organs, and various compartments of the human body. E-nose instruments have shown strong capabilities for detecting inflammatory lung diseases, various types of cancers, patients&#39; exposure to toxins and harmful drugs, organ failures, and many other abnormal human conditions. The increasing trend of medical procedures to move toward the use of noninvasive diagnostic methods, such as breath analysis of complex gases and metabolic biomarkers using e-nose devices, is likely to continue because these methods are quicker, more efficient, and cause less stress, anxiety, and pain to patients. The need for standardization of e-nose analysis methods for use in clinical applications will be resolved with new research, eventually allowing the use of portable e-nose devices in clinical practice to become routine and accepted as the preferred tools of choice by physicians for many clinical tasks. This review provides a brief summary of recent progress in the development of e-nose applications for clinical examinations, general medical practice, and for other branches of medical science and research. Keywords: breath analysis, electronic aroma detection, e-nose, point-of-care diagnostic testing, volatile organic compounds

Detection of Airway Colonization by Aspergillus fumigatus by Use of Electronic Nose Technology in Patients with Cystic Fibrosis.

Currently, there is no noninvasive test that can reliably diagnose early invasive pulmonary aspergillosis (IA). An electronic nose (eNose) can discriminate various lung diseases through an analysis of exhaled volatile organic compounds. We recently published a proof-of-principle study showing that patients with prolonged chemotherapy-induced neutropenia and IA have a distinct exhaled breath profile (or breathprint) that can be discriminated with an eNose. An eNose is cheap and noninvasive, and it yields results within minutes. We determined whether Aspergillus fumigatus colonization may also be detected with an eNose in cystic fibrosis (CF) patients. Exhaled breath samples of 27 CF patients were analyzed with a Cyranose 320. Culture of sputum samples defined the A. fumigatus colonization status. eNose data were classified using canonical discriminant analysis after principal component reduction. Our primary outcome was cross-validated accuracy, defined as the percentage of correctly classified subjects using the leave-one-out method. The P value was calculated by the generation of 100,000 random alternative classifications. Nine of the 27 subjects were colonized by A. fumigatus. In total, 3 subjects were misclassified, resulting in a cross-validated accuracy of the Cyranose detecting IA of 89% (P = 0.004; sensitivity, 78%; specificity, 94%). Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89. The results indicate that A. fumigatus colonization leads to a distinctive breathprint in CF patients. The present proof-of-concept data merit external validation and monitoring studies.