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Over the last few decades, breath analysis using electronic nose (eNose) technology has become a topic of intense research, as it is both non-invasive and painless, and is suitable for point-of-care use. To date, however, only a few studies have examined nasal air. As the air in the oral cavity and the lungs differs from the air in the nasal cavity, it is unknown whether aspirated nasal air could be exploited with eNose technology. Compared to traditional eNoses, differential mobility spectrometry uses an alternating electrical field to discriminate the different molecules of gas mixtures, providing analogous information. This study reports the collection of nasal air by aspiration and the subsequent analysis of the collected air using a differential mobility spectrometer. We collected nasal air from ten volunteers into breath collecting bags and compared them to bags of room air and the air aspirated through the device. Distance and dissimilarity metrics between the sample types were calculated and statistical significance evaluated with Kolmogorov-Smirnov test. After leave-one-day-out cross-validation, a shrinkage linear discriminant classifier was able to correctly classify 100% of the samples. The nasal air differed ( p < 0.05) from the other sample types. The results show the feasibility of collecting nasal air by aspiration and subsequent analysis using differential mobility spectrometry, and thus increases the potential of the method to be used in disease detection studies., (© 2021 IOP Publishing Ltd.)

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver-operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66-0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43-0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49-0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50-0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Background: Colorectal carcinoma (CRC) has a worldwide incidence of 1.4 million patients and a large share in cancer-related mortality. After curative treatment, the risk of recurrence is 30-65%. Early detection may result in curative treatment. However, current follow-up (FU) examinations have low sensitivity ranging from 49 to 85% and are associated with high costs. Therefore, the search for a new diagnostic tool is justified. Analysis of volatile organic compound in exhaled air through an electronic nose (eNose) is a promising new patient-friendly diagnostic tool. We studied whether the eNose under investigation, the Aeonose™, is able to detect local recurrence or metastases of CRC., Methods: In this cross-sectional study we included 62 patients, all of whom underwent curative treatment for CRC in the past 5 years. Thirty-six of them had no metastases and 26 had extraluminal local recurrence or metastases of CRC, detected during FU. Breath testing was performed and machine learning was used to predict extraluminal recurrences or metastases, and based on the receiver operating characteristics (ROC)-curve both sensitivity and specificity were calculated., Results: The eNose identified extra luminal local recurrences or metastases of CRC with a sensitivity and specificity of 0.88 (CI 0.69-0.97) and 0.75 (CI 0.57-0.87), respectively, with an overall accuracy of 0.81., Discussion: This eNose may be a promising tool in detecting extraluminal local recurrences or metastases in the FU of curatively treated CRC. However, a well-designed prospective study is warranted to show its accuracy and predictive value before it can be used in clinical practice., (Copyright © 2020 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.)

According to their materials and operating parameters, metal oxide (MOX) sensors respond to target gases only by a change in sensor resistance with a lack in selectivity. By the use of infrared spectroscopy, highly discriminatory information from samples at a molecular level can be obtained and the selectivity can be enhanced. A low-volume gas cell was developed for a commercially available semiconducting MOX methane gas sensor and coupled directly to a mid-infrared gas sensor based on substrate-integrated hollow waveguide (iHWG) technology combined with a Fourier transform infrared spectrometer. This study demonstrates a sensing process with combined orthogonal sensors for fast, time-resolved, and synergic detection of methane and carbon dioxide in gas samples.

In the present work, a novel, portable and innovative eNose composed of a surface acoustic wave (SAW) sensor array based on zeolitic imidazolate frameworks, ZIF-8 and ZIF-67 nanocrystals (pure and combined with gold nanoparticles), as sensitive layers has been tested as a non-invasive system to detect different disease markers, such as acetone, ethanol and ammonia, related to the diagnosis and control of diabetes mellitus through exhaled breath. The sensors have been prepared by spin coating, achieving continuous sensitive layers at the surface of the SAW device. Low concentrations (5 ppm, 10 ppm and 25 ppm) of the marker analytes were measured, obtaining high sensitivities, good reproducibility, short time response and fast signal recovery.

Breath-based metabolomics (breathomics) is an exciting developing area of biotechnology that centers on the capture, identification, and quantification of volatile organic compound (VOC) patterns in human breath and their utilization as tools in the diagnosis of a broad spectrum of medical problems. With the age of personalized medicines demanding rapid bespoke diagnosis and treatment, this area of molecular diagnostics is beginning to see an upsurge in biotechnological advancement. Here, we discuss recent improvements and directions in the development of breath VOC analysis and diagnosis platforms that offer the potential for disease biomarker discovery and disease prognosis., (Copyright © 2014 Elsevier Ltd. All rights reserved.)