E-nose Technology Research Articles

Breath Analysis Using eNose and Ion Mobility Technology to Diagnose Inflammatory Bowel Disease-A Pilot Study.

Early diagnosis of inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), remains a clinical challenge with current tests being invasive and costly. The analysis of volatile organic compounds (VOCs) in exhaled breath and biomarkers in stool (faecal calprotectin (FCP)) show increasing potential as non-invasive diagnostic tools. The aim of this pilot study is to evaluate the efficacy of breath analysis and determine if FCP can be used as an additional non-invasive parameter to supplement breath results, for the diagnosis of IBD. Thirty-nine subjects were recruited (14 CD, 16 UC, 9 controls). Breath samples were analysed using an in-house built electronic nose (Wolf eNose) and commercial gas chromatograph–ion mobility spectrometer (G.A.S. BreathSpec GC-IMS). Both technologies could consistently separate IBD and controls [AUC ± 95%, sensitivity, specificity], eNose: [0.81, 0.67, 0.89]; GC-IMS: [0.93, 0.87, 0.89]. Furthermore, we could separate CD from UC, eNose: [0.88, 0.71, 0.88]; GC-IMS: [0.71, 0.86, 0.62]. Including FCP did not improve distinction between CD vs. UC; eNose: [0.74, 1.00, 0.56], but rather, improved separation of CD vs. controls and UC vs. controls; eNose: [0.77, 0.55, 1.00] and [0.72, 0.89, 0.67] without FCP, [0.81, 0.73, 0.78] and [0.90, 1.00, 0.78] with FCP, respectively. These results confirm the utility of breath analysis to distinguish between IBD-related diagnostic groups. FCP does not add significant diagnostic value to breath analysis within this study.

Volatile organic compounds of biofluids for detecting lung cancer by an electronic nose based on artificial neural network.

Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.

Breath analysis using eNose technology to diagnose inflammatory bowel disease – early results

This study aimed to determine if exhaled volatile organic compounds (VOCs), analysed with electronic nose (eNose) and gas chromatograph – ion mobility spectrometer (GC-IMS) technology, can be used to distinguish inflammatory bowel disease (IBD) from controls and Crohn’s disease (CD) from ulcerative colitis (UC). Early diagnosis of IBD, including CD and UC, remains a clinical challenge with current tests being invasive. About 0.5–1% of people in Europe have IBD. Colonoscopy …

Discrimination of Cocoa Liquors Based on Their Odor Fingerprint: a Fast GC Electronic Nose Suitability Study

With the rising interest by consumers for high-quality cocoa products from a clear geographical origin, a rapid analytical method for quality control, authenticity and traceability assessment is of paramount importance. However, the complex mixture of volatiles present in cocoa liquor, the main ingredient for the chocolate production, complicates reaching this purpose. Hence, an analytical fingerprint approach using advanced electronic nose (E-nose) technology may offer a suitable technique. This study aimed to verify the suitability of an E-nose based on ultra-fast gas chromatography (GC) for the rapid discrimination between cocoa liquors from different origins. Fourteen cocoa liquors, produced of cocoa beans from ten different geographical origins, were analyzed. The obtained odor fingerprints were investigated using principal component analysis (PCA) which successfully discriminated most cocoa liquors, within one continent, according to their geographical origin. Besides, discriminant factor analysis (DFA) showed the possibility to differentiate between bulk and fine cocoa. Further tentative identification of predominant volatile compounds allowed the detection of compounds within a wide range of chemical classes occurring in cocoa products, such as acids, alcohols, aldehydes, ketones, esters, pyrazines, pyrones, and pyrroles. Most odorant compounds were previously described in literature as key volatiles in cocoa flavor, notable examples are acetic acid, 2-heptanol, 2/3-methylbutanal, acetophenone, isoamyl acetate, tetramethylpyrazine, maltol, and 2-acetyl-1-pyrroline. This study proves for the first time the usefulness of the GC E-nose for effective and rapid aroma profiling and discrimination between single origin cocoa liquors, which can be easily applied in the cocoa industry.

Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases

The development of electronic-nose (e-nose) technologies for disease diagnostics was initiated in the biomedical field for detection of biotic (microbial) causes of human diseases during the mid-1980s. The use of e-nose devices for disease-diagnostic applications subsequently was extended to plant and animal hosts through the invention of new gas-sensing instrument types and disease-detection methods with sensor arrays developed and adapted for additional host types and chemical classes of volatile organic compounds (VOCs) closely associated with individual diseases. Considerable progress in animal disease detection using e-noses in combination with metabolomics has been accomplished in the field of veterinary medicine with new important discoveries of biomarker metabolites and aroma profiles for major infectious diseases of livestock, wildlife, and fish from both terrestrial and aquaculture pathology research. Progress in the discovery of new e-nose technologies developed for biomedical applications has exploded with new information and methods for diagnostic sampling and disease detection, identification of key chemical disease biomarkers, improvements in sensor designs, algorithms for discriminant analysis, and greater, more widespread testing of efficacy in clinical trials. This review summarizes progressive advancements in utilizing these specialized gas-sensing devices for numerous diagnostic applications involving noninvasive early detections of plant, animal, and human diseases.

Odour Detection Method of Silk Products Based on Electronic Nose Technology

Silk is a natural animal protein with special odour. Polyester yarns, similar in appearance to silk, are often used in silk production. Electronic nose (e-nose) technology can distinguish different gases by different pattern recognition methods. In this paper, e-nose technology was applied to obtain data sets of silk/polyester yarns with different mixing ratios, and cluster analysis was carried out for silk samples. The experimental results show that the results of e-nose response are related to the headspace, sample quality, and headspace generation time etc.; as the response time increases, the relative standard deviation of each sensor turns to be smaller, and the response value is more stable. There are significant differences in the fluctuations between the ten metal sensors, and different samples have significant effects on the response of sensors No. 2, 7, and. 9. The e-nose sensor has a higher prediction accuracy when identifying different ratios of silk/polyester yarns at the accuracy rate up to 90.286%.

Fire Resistance Detection of Flammable Materials Based on Electronic Nose Technology

The electronic nose system can identify and detect the toxic gases released by flammable materials during combustion. This paper studies the application of the e-nose technology in the early fire detection of flammable materials. Based on the brief introduction of the e-nose system and the associated pattern recognition technology, this paper builds a high-throughput flammable material fire simulation platform, and then, with the PVC material of the wires and cables in the electrical equipment which are prone to fire as an example, it detects the volatile substances within the safe operating temperature range (100-180°C) and at the warning temperature point (200°C) during the combustion process. In order to verify the anti-interference ability of the platform, this paper selects liquor and cigarette as interferences, which are also subject to detection in the experiment. It uses discriminant function analysis (DFA) and the BP neural network method to perform statistical analysis of the collected data and the results show that both methods have good discriminant effects. At the same time, it also optimizes the sensor array by the load analysis method. Through comparison and analysis, it is found that the eight-sensor array has a better discriminant effect. The research results show that the electronic nose technology can realize the early detection of flammable materials.

Applying Electronic Nose Based on Odour Classification and Identification Technology in Detecting the Shelf Life of Fresh Fruits

The purpose of this study is to improve the market competitiveness and food safety of fresh fruit products. To this end, the methods of literature analysis, comparative analysis and experimental detection were adopted and the electronic nose (e-nose) technology based on odour classification and identification was introduced to study the non-destructive testing (NDT) method for the shelf life of fresh fruits. Besides, according to the optimized e-nose sensor array, the support vector machine (SVM), BP neural network model and the chlorophyll prediction model for the shelf life detection of fresh fruits were established respectively to make spinach freshness grade discrimination and chlorophyll quantitative prediction by taking the spinach as example. Finally, the experimental results show that the e-nose technology based on odour classification and identification can better realize the shelf life detection of fresh fruit.

Application of Electronic Nose in Detection of Fresh Vegetables Freezing Time Considering Odor Identification Technology

In this paper, the application of electronic nose (e-nose) technology in the detection of fresh vegetables freezing time is taken as the research goal. This paper uses the literature analysis method, test detection method and comparative analysis method, basing on a brief introduction of e-nose technology, it takes fresh beans stored for different days (1-6 days) at room temperature and -18°C freezing condition as the research object. According to the response of the sensor array to the volatile components and three model analysis methods, the freezing time of fresh vegetables is determined, among which, both PCA and LDA can accurately detect the freezing time of fresh vegetables. Loadings analysis shows that hydrogen sulfide and nitrogen oxides play a major role in detecting the freezing time of fresh vegetables. The results show that the e-nose can quickly and accurately detect the freezing time of fresh vegetables.

A novel method using MOS electronic nose and ELM for predicting postharvest quality of cherry tomato fruit treated with high pressure argon

Flavor characteristic, caused by a series of physicochemical changes, is an important part of fruit quality and freshness. The purpose of this study was to investigate the reliability and validity of using electronic nose (e-nose) technology to evaluate the quality and freshness of cherry tomatoes after high pressure argon treatments. Extreme learning machine (ELM) and Partial least squares (PLS) were applied for quality parameters prediction. The results showed that freshness of cherry tomatoes based on flavor characteristic over the entire storage time displayed four groups (fresh, acceptable, stale and very stale, respectively). High pressure argon at 0.8 MPa significantly maintained the freshness and quality of cherry tomatoes during cold storage. ELM models showed better performance than PLS models for evaluating the firmness, SSC and pH values according to their higher fitting correlation coefficients (R2 > 0.9500). The results demonstrated e-nose technology combined with ELM provided a reliable and valid method for evaluating quality and freshness of cherry tomatoes during cold storage.

Detection of Wastewater Treatment Process Disturbances in Bioreactors Using the E-Nose Technology

Abstract Wastewater treatment processes are subject to numerous disturbances during biological treatment of wastewater. In order to achieve and sustain suitable conditions of the process, basic wastewater parameters should be frequently monitored. While great improvements have been made in the automatization of treatment process, little is known about automatic measuring systems that can detect unusual process conditions in a bioreactor. Tracking these parameters can be difficult and the time required for the determination might vary from several minutes to few days. The objective of this study is to evaluate the use of an electronic nose in-house device (based on a non-selective gas sensor array) for the detection of process disturbances in a lab-scale sequencing batch reactor (SBR) during biological treatment of wastewater with activated sludge. Measurements were performed during a 12-hours working cycle. Continuous analyses of the headspace were performed using a sensor array based on the resistive Metal Oxide Semiconductor type (MOS) gas sensor. Based on the data obtained and the PCA analysis, this study showed that the e-nose technology can be used to predict or retrieve information about potential disruptions during wastewater processes using the e-nose technology.

Determination of Postharvest Quality of Cucumbers Using Nuclear Magnetic Resonance and Electronic Nose Combined with Chemometric Methods

The purpose of this study was to investigate changes in water status and flavor characteristics of cucumbers during postharvest storage and to trace the quality attributes using partial least squares (PLS) and support vector machine (SVM). The results showed that four distinct water populations were identified in cucumbers by nuclear magnetic resonance (NMR), and the changes of water mobility and distribution occurred mainly in pulp of cucumbers. Flavor characteristics of cucumbers at different storage stages were distinguished by electronic nose (e-nose), and four clusters could be achieved through hierarchical clustering analysis. Comparison of two models, excellent prediction performances for firmness, pH, SSC, and ΔE of postharvest cucumbers were obtained using a combination method of e-nose technology and SVM algorithm. This study indicated that there were significant changes in the quality parameters of cucumbers during postharvest storage, which were related to water status and flavor characteristics. The combination of e-nose technology with the SVM algorithm offers a promising technique to monitor cucumber quality.

A novel strategy for rapid identification of the fruits of Illicium verum and Illicium anisatum using electronic nose and tongue technology

Purpose: To develop an effective and rapid strategy for the identification of fruits of I. verum and I. anisatum based on their odor and taste.Methods: Electronic nose (E-nose) and electronic tongue (E-tongue) technology was used to identify the fruits of I. verum (FIV) and I. anisatum (FIA). Samples of FIA, FIV, and FIA : FIV mixtures in different proportions (1 : 3, 1 : 1, and 3 : 1) were prepared to evaluate the identification abilities of E-nose and Etongue methods. Samples were powdered and sifted through a standard sieve (aperture size 355 ± 13 μm) for E-nose analysis. Each sample was refluxed with water for 1 h before E-tongue analysis. The acquired data were analyzed by principal component analysis (PCA) and discriminant factor analysis (DFA).Results: Based on the signals acquired by E-nose and E-tongue analyses, a total of 90 data points each were used for PCA. The three principal component values for E-nose analysis were PC1 = 93.89 %, PC2 = 6.08 %, and PC3 = 0.03 %, and those for E-tongue analysis were PC1 = 98.72 %, PC2 = 0.68 %, and PC3 = 0.57 %. The sample data were significantly divided into two groups representing FIV and FIA. Furthermore, E-nose and E-tongue assessments combined with PCA and DFA analyses effectively identified FIV, FIA and their mixtures.Conclusion: The use of E-nose and E-tongue technology is an effective and rapid strategy to identify the fruits of I. verum and I. anisatum and their mixtures. This strategy may also offer an effective method for detection of adulterants.Keywords: Illicium verum, Illicium anisatum, Discrimination, Electronic nose, Electronic tongue, Safety, Principal component analysis, Discriminant factor analysis

A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS

BackgroundVolatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.MethodThe performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.ResultsThis study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.ConclusionThe findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.

Efficient Solutions for Discreteness, Drift, and Disturbance (3D) in Electronic Olfaction

In this paper, we aim at presenting the new challenges of electronic noses (E-noses) and proposing effective methods for handling the new challenging scientific issues to be solved, such as signal discreteness (reproducibility), systematical drift and nontarget disturbances. We first review the progress of E-noses in applications, systems, and algorithms during the past two decades. Recall a number of significant achievements and motivated by the current issues that hinder large-scale application pace of E-nose technology, we propose to address three key issues: 1) discreteness; 2) drift; and 3) disturbance (simplified as 3D issues), which are sensor induced and sensor specific. For each issue, a highly effective and efficient method is proposed. Specifically, for discreteness issue, a global affine transformation method is introduced for E-nose instruments batch calibration; for drift issue, an unsupervised feature adaptation model is proposed to achieve effective drift adaptation; additionally, for disturbance issue, we proposed a simple targets-to-targets self-representation classifier method for fast nontargets detection, without knowing any prior knowledge of thousands of nontarget disturbances in real world. For each method, a closed form solution can be analytically determined and the simplicity is guaranteed. Experiments demonstrate the effectiveness and efficiency of the proposed methods for addressing the proposed 3D issues in real applications of E-noses.

Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label.

Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath.Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set.This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression.Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R2=0.581) and neutrophilic (R2=0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set.Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner.

Developing Electronic-nose Technologies for Clinical Practice

The development of relatively simple gas-sensing devices with sensor arrays capable of constructing molecular profiles of complex Volatile Organic Compound (VOC) metabolites from clinical samples, based on production of healthy or disease-specific digital signature patterns, may soon help to revolutionize diagnostic procedures in clinical practice. This mini-review summarizes the development of recent portable electronic-nose (e-nose) technologies as new diagnostic tools with the potential for accelerating clinical procedures to facilitate non-invasive early disease diagnoses, improving the speed and accuracy of Point-Of-Care Testing (POCT); yielding more effective treatments and significantly improving prognoses.

Screening of Obstructive Sleep Apnea Syndrome by Electronic-Nose Analysis of Volatile Organic Compounds

Obstructive Sleep Apnea Syndrome (OSAS) carries important social and economic implications. Once the suspicion of OSAS has arisen, Polysomnography (PSG) represents the diagnostic gold standard. However, about 45% of people who have undergone PSG are free from OSAS. Thus, efforts should be made to improve the selection of subjects. We verified whether the pattern of Volatile Organic Compounds (VOCs) helps to select patients amenable to PSG. We studied 136 subjects (20 obese non-OSAS, 20 hypoxic OSAS, 20 non-hypoxic OSAS, and 20 non-hypoxic Chronic Obstructive Pulmonary Disease (COPD) vs 56 healthy controls) without any criteria of exclusion for comorbidity to deal with a real-life population. VOCs patterns were analyzed using electronic-nose (e-nose) technology. A Discriminant Analysis (Partial Least Square-Discriminant Analysis) was performed to predict respiratory functions and PSG parameters. E-nose distinguished controls (100% correct classification) from others and identified 60% of hypoxic, and 35% of non-hypoxic OSAS patients. Similarly, it identified 60% of COPD patients. One-by-one group comparison yielded optimal discrimination of OSAS vs controls and of COPD vs controls (100% correct classification). In conclusion, e-nose technology applied to breath-analysis can discriminate non-respiratory from respiratory diseased populations in real-life multimorbid populations and exclude OSAS. If confirmed, this evidence may become pivotal for screening purposes.

Assessment of Shelf Lives of Black Pepper and Small Cardamom Cookies by Metal Oxide-Based Electronic Nose Using Spoilage Index

E-nose technology has been successfully employed in determination of rancidity in nutraceutical-rich drop cookies. The cookies were formulated with extracts obtained by supercritical carbon dioxide extractions and post-extraction sample matrices of black pepper and small cardamom. Rancidity in cookies was estimated by comparing the odor profiles of stored cookies with the profiles of deliberately rancid cookies (training sets), using e-nose. PCA plots along with spoilage indices obtained from Mahalanobis distances calculated from e-nose responses revealed that the extracts and post-extraction sample matrices of these spices performed as natural antioxidants in preventing rancidity in cookies. Both the extracts enhanced the shelf lives of cookies by at least 120 days; post-extraction matrices of black pepper and small cardamom enhanced the same by 80 and 40 days, respectively. These findings were further affirmed by biochemical analyses and linear regression equations were generated for prediction of FFA content, PV, and MDA values from the spoilage indices of each type of cookie. Thus, spoilage indices can circumvent the requirement of conducting cumbersome biochemical assays for estimation of rancidity in cookies. Similar applications of e-nose technology and spoilage indices can be envisaged for other bakery products also.

Exhaled breath profiles in the monitoring of loss of control and clinical recovery in asthma.

Asthma is a chronic inflammatory airway disease, associated with episodes of exacerbations. Therapy with inhaled corticosteroids (ICS) targets airway inflammation, which aims to maintain and restore asthma control. Clinical features are only modestly associated with airways inflammation. Therefore, we hypothesized that exhaled volatile metabolites identify longitudinal changes between clinically stable episodes and loss of asthma control. To determine whether exhaled volatile organic compounds (VOCs) as measured by gas-chromatography/mass-spectrometry (GC/MS) and electronic nose (eNose) technology discriminate between clinically stable and unstable episodes of asthma. Twenty-three patients with (partly) controlled mild to moderate persistent asthma using ICS were included in this prospective steroid withdrawal study. Exhaled metabolites were measured at baseline, during loss of control and after recovery. Standardized sampling of exhaled air was performed, after which samples were analysed by GC/MS and eNose. Univariate analysis of covariance (ANCOVA), followed by multivariate principal component analysis (PCA) was used to reduce data dimensionality. Next paired t tests were utilized to analyse within-subject breath profile differences at the different time-points. Finally, associations between exhaled metabolites and sputum inflammation markers were examined. Breath profiles by eNose showed 95% (21/22) correct classification for baseline vs loss of control and 86% (19/22) for loss of control vs recovery. Breath profiles using GC/MS showed accuracies of 68% (14/22) and 77% (17/22) for baseline vs loss of control and loss of control vs recovery, respectively. Significant associations between exhaled metabolites captured by GC/MS and sputum eosinophils were found (Pearson r≥.46, P<.01). Loss of asthma control can be discriminated from clinically stable episodes by longitudinal monitoring of exhaled metabolites measured by GC/MS and particularly eNose. Part of the uncovered biomarkers was associated with sputum eosinophils. These findings provide proof of principle for monitoring and identification of loss of asthma control by breathomics.