Volatile Organic Compounds In Human Breath Research Articles

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose.

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.

Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening

The objective of this study is to validate reduced graphene oxide (RGO)-based volatile organic compounds (VOC) sensors, assembled by simple and low-cost manufacturing, for the detection of disease-related VOCs in human breath using machine learning (ML) algorithms. RGO films were functionalized by four different metalloporphryins to assemble cross-sensitive chemiresistive sensors with different sensing properties. This work demonstrated how different ML algorithms affect the discrimination capabilities of RGO–based VOC sensors. In addition, an ML-based disease classifier was derived to discriminate healthy vs. unhealthy individuals based on breath sample data. The results show that our ML models could predict the presence of disease-related VOC compounds of interest with a minimum accuracy and F1-score of 91.7% and 83.3%, respectively, and discriminate chronic kidney disease breath with a high accuracy, 91.7%.

Theory helps experiment to reveal VOCs in human breath

Volatile organic compounds (VOCs) present in human breath not only provide information about the internal chemistry of the body but can also be specific to diseases. Therefore, detection and analysis of specific VOCs can be used for medical diagnostics. However, up until today in spite of several existing VOC-based detection techniques and significant efforts, breath analysis is not a diagnostic method available for clinicians. Infrared absorption spectroscopy is a promising technique to fill this gap, with tens of identified VOCs in breath. Currently, a lack of digital spectral databases and several masking effects make difficult reliable molecular identification of observed absorption features. We demonstrate that calculations of rotational bands of vibrational spectra could serve as a basic method for molecular identification of spectral features observed in experiment. Results of comparison of several known VOCs spectra with the predictions of the theoretical model are presented.

Switchable sniff-cam (gas-imaging system) based on redox reactions of alcohol dehydrogenase for ethanol and acetaldehyde in exhaled breath

Measuring the volatile organic compounds (VOCs) released from a human is a promising method for noninvasive disease screening and metabolism assessment. Selectively imaging multiple VOCs derived from human breath and skin gas is expected to improve current gas analysis techniques. In this study, a gas-imaging system (sniff-cam) that can be used to simultaneously image the concentration distribution of multiple VOCs, namely, ethanol (EtOH) and acetaldehyde (AcH), was developed. The sniff-cam was based on the pH-dependent redox reactions of nicotinamide adenine dinucleotide (NAD)-dependent alcohol dehydrogenase (ADH). The sniff-cam was constructed with a camera, two ADH-immobilized meshes, and a UV-LED array sheet. The ADH-immobilized mesh containing a solution of the oxidized form of NAD (NAD+) or reduced form (NADH) was used as an EtOH-imaging mesh and an AcH-imaging mesh, respectively. The distributions of the EtOH and AcH concentrations were visualized through the fluorescence of NADH (the excitation wavelength was 340 nm; the emission wavelength was 490 nm) occurring by the ADH-mediated redox reaction. First, the influence of pH on the activity of the redox reaction of ADH was measured, and then the quantitativeness and selectivity of the sniff-cam were evaluated. The ADH-mediated reactions of EtOH and AcH showed maximum activities at pH 9.0 and pH 6.5, respectively. The sniff-cam demonstrated not only a dynamic range (0.1–1000 ppm for EtOH and 0.2–10 ppm for AcH) for measuring EtOH and AcH in breath after drinking alcohol, but also displayed a high selectivity against other breath VOCs. Finally, EtOH and AcH in breath after drinking alcohol were measured simultaneously. A group with high activity of aldehyde dehydrogenase type 2 (EtOH = 143.3 ± 13.5 ppm, AcH = 1.7 ± 0.2 ppm) and a group with low activity (EtOH = 163.3 ± 28.0 ppm, AcH = 8.4 ± 0.5 ppm) displayed differences in the concentrations of EtOH and AcH contained in their breath samples, and the effectiveness of the developed method was confirmed and compared with previous results. It is suggested that the multiplexed sniff-cam in the future may be capable of selectively and simultaneously imaging various VOCs in human breath and skin gas by using multiple NADH-dependent enzymes.

Advances in chemical sensing technologies for VOCs in breath for security/threat assessment, illicit drug detection, and human trafficking activity

On-site chemical sensing of compounds associated with security and terrorist attacks is of worldwide interest. Other related bio-monitoring topics include identification of individuals posing a threat from illicit drugs, explosive manufacturing, as well as searching for victims of human trafficking and collapsed buildings. The current status of field analytical technologies is directed towards the detection and identification of vapours and volatile organic compounds (VOCs). Some VOCs are associated with exhaled breath, where research is moving from individual breath testing (volatilome) to cell breath (microbiome) and most recently to crowd breath metabolites (exposome). In this paper, an overview of field-deployable chemical screening technologies (both stand-alone and those with portable characteristics) is given with application to early detection and monitoring of human exposome in security operations. On-site systems employed in exhaled breath analysis, i.e. mass spectrometry (MS), optical spectroscopy and chemical sensors are reviewed. Categories of VOCs of interest include (a) VOCs in human breath associated with exposure to threat compounds, and (b) VOCs characteristic of, and associated with, human body odour (e.g. breath, sweat). The latter are relevant to human trafficking scenarios. New technological approaches in miniaturised detection and screening systems are also presented (e.g. non-scanning digital light processing linear ion trap MS (DLP-LIT-MS), nanoparticles, mid-infrared photo-acoustic spectroscopy and hyphenated technologies). Finally, the outlook for rapid and precise, real-time field detection of threat traces in exhaled breath is revealed and discussed.

A rapid method for breath analysis in cystic fibrosis patients.

For easy handling and speed of lung diseases diagnostics, approaches based on volatile organic compounds (VOCs), including those emitted by pathogenic microorganisms, are considered but currently require considerable sampling efforts. We tested whether easy-to-handle and fast detection of lung infections is possible using solid-phase microextraction (SPME) of 100ml of exhaled breath. An analytical procedure for the detection of VOCs from the headspace of epithelial lung cells infected with four human pathogens was developed. The feasibility of this method was tested in a cystic fibrosis (CF) outpatient clinic in vivo. Exhaled breath was extracted by SPME and analyzed by gas chromatography-mass spectrometry (GC-MS). The compositions of VOCs released in the infection model were characteristic for all individual pathogens tested. Exhaled breath of CF patients allowed clear distinction of CF patients and controls by their VOC compositions using multivariate analyses. Interestingly, the major specific VOCs detected in the exhaled breath of infected CF patients in vivo differed from those monitored during bacterial in vitro growth. SPME extraction of VOCs from 100ml of human breath allowed the distinction between CF patients and healthy probands. Our results highlight the importance of assessing the entire pattern of VOCs instead of selected biomarkers for diagnostic purposes, as well as the need to use clinical samples to identify reliable biomarkers. This study provides the proof-of-concept for the approach using the composition of exhaled VOCs in human breath for the rapid identification of infectious agents in patients with lower respiratory tract infections.

Breath analysis in disease diagnosis: methodological considerations and applications.

Breath analysis is a promising field with great potential for non-invasive diagnosis of a number of disease states. Analysis of the concentrations of volatile organic compounds (VOCs) in breath with an acceptable accuracy are assessed by means of using analytical techniques with high sensitivity, accuracy, precision, low response time, and low detection limit, which are desirable characteristics for the detection of VOCs in human breath. “Breath fingerprinting”, indicative of a specific clinical status, relies on the use of multivariate statistics methods with powerful in-built algorithms. The need for standardisation of sample collection and analysis is the main issue concerning breath analysis, blocking the introduction of breath tests into clinical practice. This review describes recent scientific developments in basic research and clinical applications, namely issues concerning sampling and biochemistry, highlighting the diagnostic potential of breath analysis for disease diagnosis. Several considerations that need to be taken into account in breath analysis are documented here, including the growing need for metabolomics to deal with breath profiles.

Determination of volatile organic compounds as potential markers of lung cancer by gas chromatography–mass spectrometry versus trained dogs

The main aim of the study was qualitative and quantitative analysis of volatile organic compounds (VOCs) occurring in biological samples such as exhaled air obtained from 108 patients with lung cancer, 121 healthy volunteers, and 24 persons with other lung diseases. For determination of VOCs in human breath, solid phase microextraction and gas chromatography–mass spectrometry (SPME/GC–MS) were applied. Statistical methods such as artificial neural network and chi-squared automatic interaction detector (CHAID) were applied for data evaluation. The concentration of acetone, isoprene, ethanol, 1-propanol, 2-propanol, hexanal, and dimethyl sulfide were higher in patients with lung cancer than in case of healthy volunteers and persons with other lung diseases.The second goal was the application of trained dogs for detection of the same breath samples as in GC–MS research and evaluate the sensitivity and specificity of canine scent detection using 5 station scent lineup. Among lung cancer patients and complementary samples, overall sensitivity of canine scent detection was 86%, while specificity was 72%.There is a possibility that in the future these methods could allow for a fast, painless, and noninvasive diagnosis of cancer.