Application Of Breath Analysis Research Articles

How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment

Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between ‘healthy’ samples (breath without additives) and ‘cancer’ samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.

Collision Enhanced Raman Scattering (CERS): An Ultra-High Efficient Raman Enhancement Technique for Hollow Core Photonic Crystal Fiber Based Raman Spectroscopy Gas Analyzer.

Raman enhancement techniques are essential for gas analysis to increase the detection sensitivity of a Raman spectroscopy system. We have developed an efficient Raman enhancement technique called the collision-enhanced Raman scattering (CERS), where the active Raman gas as the analyte is mixed with a buffer gas inside the hollow-core photonic-crystal fiber (HCPCF) of a fiber-enhanced Raman spectroscopy (FERS) system. This results in an enhanced Raman signal from the analyte gas. In this study, we first showed that the intensity of the 587 cm-1 stimulated Raman scattering (SRS) peak of H2 confined in an HCPCF is enhanced by as much as five orders of magnitude by mixing with a buffer gas such as helium or N2. Secondly, we showed that the magnitudes of Raman enhancement depend on the type of buffer gas, with helium being more efficient compared to N2. This makes helium a favorable buffer gas for CERS. Thirdly, we applied CERS for Raman measurements of propene, a metabolically interesting volatile organic compound (VOC) with an association to lung cancer. CERS resulted in a substantial enhancement of propene Raman peaks. In conclusion, the CERS we developed is a simple and efficient Raman-enhancing mechanism for improving gas analysis. It has great potential for application in breath analysis for lung cancer detection.

Acetone sensing characteristics of TiO2-LaFeO3 nanocomposites

To enhance the acetone sensing capabilities of LaFeO3 nanoparticles for practical applications in breath analysis, commercial n-type rutile TiO2 nanoparticles were utilized as additives and directly mixed with LaFeO3 nanoparticles synthesized via a sol–gel method. The obtained TiO2-LaFeO3 nanocomposites were characterized by XRD, SEM, BET, and XPS. Compared to pure LaFeO3 nanoparticles, the resulting TiO2-LaFeO3 nanocomposites exhibited higher surface reactivity, as evidenced by their larger specific surface areas and higher oxygen vacancy contents. The introduction of TiO2 also facilitated the formation of p-n heterojunctions, which enhanced the regulation of acetone molecules on internal carrier conduction within the nanocomposites. Therefore, the TiO2-LaFeO3 nanocomposites demonstrated improved acetone sensing performance, including higher vapor response values, selectivity, and faster response/recovery speeds. Among the series of nanocomposites, those with an addition of 10 wt% TiO2 exhibited the highest vapor response value (37.3 to 100 ppm) and the greatest selectivity against methanol and ethylene glycol at the prime operating temperature of 144 °C.

SnO2–CuO Heterostructured Nanofibers for Enhanced NH3-Gas-Sensing Performance and Potential Application in Breath Analysis

SnO₂–CuO Heterostructured Nanofibers for Enhanced NH₃-Gas-Sensing Performance and Potential Application in Breath Analysis

Electronic nose based on multiple electrospinning nanofibers sensor array and application in gas classification

To mimic the human olfactory system, an electronic nose (E-nose, also known as artificial olfactory) has been proposed based on a multiple gas sensor array and a pattern recognition algorithm. Detection of volatile organic components (VOCs) has many potential applications in breath analysis, food quality estimation, and indoor and outdoor air quality monitoring, etc. In this study, a facile single-needle electrospinning technology was applied to develop the four different semiconductor metal oxide (MOS) nanofibers sensor arrays (SnO2, CuO, In2O3 and ZnO, respectively). The array shows a smooth surface and constant diameter of nanofiber (average of 150 nm) resulting in high sensitivity to multiple target analyte gases. Five human health related VOCs gases were measured by fabricated E-nose and different response patterns were obtained from four MOS nanofibers sensors. Combined with feature extraction from the response curves, a principal component analysis (PCA) algorithm was applied to reduce the dimension of feature matrix, Thus, the fabricated E-nose system successfully discriminated five different VOCs gases. Real-time and non-invasive gas monitoring by E-nose is very promising for application in human health monitoring, food monitoring, and other fields.

Janus PdSTe nanosheet as promising contender for detection of volatile organic compounds (VOCs) in human breath: A first principles investigation

Using the density functional theory and nonequilibrium Green’s function formalism, recent work report brings the interaction between the two-dimensional (2D) Janus PdSTe nanosheet and volatile organic compounds (VOCs) (i.e., acetone, ethanol, methanol, and propanol) to investigate the latent application of Janus PdSTe for excellent performance of VOCs sensors used for human breath analysis. The pristine Janus PdSTe monolayer is semiconducting with band gap of 0.32 eV, having the valence band maxima (VBM) at the Γ-point while, conduction band minima (CBM) at k-point in the Brillouin zone. The adsorption energies are revealed that all the targeted VOCs molecule are physiosorbed on the Janus PdSTe monolayer. We found that the acetone has greater sensitivity compare to other considered VOCs. To check the sensing mechanism of VOCs, the Mulliken charge transfer (CT), interaction distance and type of adsorption were analyzed. The calculated Mulliken charge is also reveals that the stronger electron exchange interation between ethanol and Janus PdSTe monolayer. Simultaneously, the I-V relation for before and after adsorption of VOCs molecules were also obtained using the Green’s function (NEGF) for experimental set up. Finally, our outcome indicates that Janus PdSTe monolayer is a potential and feasible candidate for aformentioned VOCs sensors for applications in room temperature breath analysis of VOCs.

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for “breath chemovapor fingerprinting” for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Crystal superlattices for versatile and sensitive quantum spectroscopy.

Nonlinear interferometers with quantum correlated photons have been demonstrated to improve optical characterization and metrology. These interferometers can be used in gas spectroscopy, which is of particular interest for monitoring greenhouse gas emissions, breath analysis and industrial applications. Here, we show that gas spectroscopy can be further enhanced via the deployment of crystal superlattices. This is a cascaded arrangement of nonlinear crystals forming interferometers, allowing the sensitivity to scale with the number of nonlinear elements. In particular, the enhanced sensitivity is observed via the maximum intensity of interference fringes that scales with low concentration of infrared absorbers, while for high concentration the sensitivity is better in interferometric visibility measurements. Thus, a superlattice acts as a versatile gas sensor since it can operate by measuring different observables, which are relevant to practical applications. We believe that our approach offers a compelling path towards further enhancements for quantum metrology and imaging using nonlinear interferometers with correlated photons.

Sensor Technology for Opening New Pathways in Diagnosis and Therapeutics of Breast, Lung, Colorectal and Prostate Cancer

This study analyzes the interaction between sensor research and technology and different types of cancer (breast, lung, colorectal, and prostate) with the goal of detecting new directions for improving diagnosis and therapeutics in medicine. This study develops an approach to computational scientometrics based on data from the Web of Science from the 1991 to 2021 period. The results of this analysis show the vital role of biosensors and electrochemical biosensors applied in breast cancer, lung cancer, and prostate cancer research. Instead, scientific research of optical sensors is developing main technological trajectories in breast, prostate, and colorectal cancer for improving diagnostics. Finally, oxygen sensor research has a main technological development in breast and lung cancer for new applications in breath analysis directed to treatment processes. Preliminary results presented here clearly illustrate the evolutionary paths of sensor research and technologies that have great potential for developing incremental and radical innovations in cancer diagnosis and therapies. These conclusions are, of course, tentative. There is a need for much more detailed research based on other aspects and factors for detecting stable technological trajectories that can foster the technology transfer of new sensor in cancer research for improving diagnosis and therapeutics, reducing, whenever possible, world-wide mortality of cancer in society.JEL Classification: I10, O30, O31, O32; O33. Doi: 10.28991/HIJ-2022-03-03-010 Full Text: PDF

Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics.

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.

Investigation of everyday influencing factors on the variability of exhaled breath profiles in healthy subjects

Abstract Introduction: The human breath is an accurate but complex read-out of many physiological processes in the organism that can be monitored via volatile organic compounds (VOCs) in the exhaled air. However, there are many confounding variables that limit the transfer and application of breath analysis to become a clinical procedure. Method: This work aims to establish a systematic procedure for sampling and characterization of various everyday influences of healthy subjects using proton transfer reaction-mass spectrometry (PTR-MS). In order to limit the influencing factors on the breath profile, a standard analysis procedure for sampling and evaluation of the exhaled breath samples was developed. The correlations between the selected experimental conditions and the resulting VOC profiles were investigated using a non-parametric Wilcoxon rank sum test. Results: In addition to the relevant influence of methodological experimental parameters, interesting insights into the effect of everyday factors on the exhalat gas were obtained and discussed. Furthermore, subject and condition-specific differences were found in the exhaled air of male and female subjects. Conclusion: With a more robust, standardized and reproducible breath sampling protocol, breath analysis is a promising non-invasive tool towards a system-wide understanding and personalized diagnosis and treatment of a wide range of diseases.

Quartz-enhanced photoacoustic NH3 sensor exploiting a large-prong-spacing quartz tuning fork and an optical fiber amplifier for biomedical applications

A sensor system for exhaled ammonia (NH3) monitoring exploiting quartz-enhanced photoacoustic spectroscopy (QEPAS) was demonstrated. An erbium-doped fiber amplifier (EDFA) with an operating frequency band targeting an NH3 absorption line falling at 1531.68 nm and capable to emit up to 3 W of optical power was employed. A custom T-shaped grooved QTF with prong spacing of 1 mm was designed and realized to allow a proper focusing of the high-power optical beam exiting the EDFA between the prongs. The performance of the realized sensor system was optimized in terms of spectrophone parameters, laser power and modulation current, resulting in a NH3 minimum detectable concentration of 14 ppb at 1 s averaging time, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 8.15 × 10−9 cm−1 W/√Hz. Continuous measurements of the NH3 level exhaled by 3 healthy volunteers was carried out to demonstrate the potentiality of the developed sensor for breath analysis applications.

High Sensitivity, Humidity-Independent Ethanol Gas Sensors Based on PFDS-ZnSnO₃ Hollow Microsphere

Breath analysis using gas sensors to detect disease-related exhaled breath for diagnosing human health has huge application potential. However, the humidity changing in both the surrounding environment and exhaled breath have great damage to the actual sensing results, so the humidity dependence of gas sensor is a major obstacle to breath analysis applications. Here, 1H, 1H, 2H, 2H-perfluorodecyltriethoxysilane (PFDS) is used to hydrophobic the prepared ZnSnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> sensor to obtain a high sensitivity and humidity-independent ethanol sensor for breath analysis. The ethanol sensing characteristics of PFDS-treated sensors were studied under dry and high humidity (RH (relative humidity) = 80%) conditions. The response value of pure ZnSnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> sensor to ethanol dropped from 33.7 to 3.2 as the condition change form dry to 80% RH. On the contrary, the response of the 3h PFDS-ZnSnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> sensor to 100 ppm ethanol was 7.5 under 80% RH, which was basically the same as the response (7.3) under dry conditions, indicating the same sensing behavior under dry and humidity conditions without being affected by moisture. In addition, the PFDS-ZnSnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> sensors ignore the interference of other volatile gases under dry and humid conditions, while maintaining excellent selectivity to ethanol. The humidity-independent properties are attributed to the fact that PFDS with low-energy groups make the sensor surface hydrophobic, thus the material resists the contact of water molecules with the sensor and competition with ethanol in the gas response. Therefore, regardless of humidity fluctuations, the sensor shows great practicability in detecting ethanol and could be used for medical breath analysis.

Eco‐Friendly Colorimetric Nanofiber Design: Halochromic Sensors with Tunable pH‐Sensing Regime Based on 2‐Ethyl‐2‐Oxazoline and 2‐n‐Butyl‐2‐Oxazoline Statistical Copolymers Functionalized with Alizarin Yellow R

AbstractColorimetric nanofibers provide visual, easy‐to‐interpret sensors for personal use as well as advanced applications. The potential of 2‐n‐butyl‐2‐oxazoline (B) and 2‐ethyl‐2‐oxazoline (E) statistical copolymers as a universal, versatile support platform for nanofibrous halochromic sensor design is demonstrated. These polymers are electrospinnable from eco‐friendly solvent systems, while wettability, moist adsorption capacity, and water‐solubility of the membranes can be easily tuned by changing the B/E monomer ratio, ensuring wide applicability. The halochromic sensing functionality is introduced by incorporating the alizarin yellow R (AYR) chromophore, which is covalently modified with an ethyl ester‐group or a short poly(2‐n‐butyl‐2‐oxazoline) chain, which is demonstrated to simultaneously prevent dye‐leaching and allows tuning of the halochromic pH‐sensing window. The colorimetric nanofibrous sensors reversibly respond toward aqueous solutions of different pH, (hydrochloric) acid and alkaline (ammonia) vapors, and several biogenic amines with detection limits as low as 5 ppb. Tunability of sensor responsivity, sensitivity, and pKa via manipulation of dye–polymer interactions, determined by support polymer structure and semi‐crystallinity, as well as the chain length of the AYR‐modified polymer, are discussed. Preliminary proof‐of‐principle studies indicate the potential of the developed sensors for sub‐ppm biogenic amine vapor detection, which may serve as the basis for future applications in food packaging or breath analysis.

Selective and self-validating breath-level detection of hydrogen sulfide in humid air by gold nanoparticle-functionalized nanotube arrays

We demonstrate the selective detection of hydrogen sulfide at breath concentration levels under humid airflow, using a self-validating 64-channel sensor array based on semiconducting single-walled carbon nanotubes (sc-SWCNTs). The reproducible sensor fabrication process is based on a multiplexed and controlled dielectrophoretic deposition of sc-SWCNTs. The sensing area is functionalized with gold nanoparticles to address the detection at room temperature by exploiting the affinity between gold and sulfur atoms of the gas. Sensing devices functionalized with an optimized distribution of nanoparticles show a sensitivity of 0.122%/part per billion (ppb) and a calculated limit of detection (LOD) of 3 ppb. Beyond the self-validation, our sensors show increased stability and higher response levels compared to some commercially available electrochemical sensors. The cross-sensitivity to breath gases NH3 and NO is addressed demonstrating the high selectivity to H2S. Finally, mathematical models of sensors’ electrical characteristics and sensing responses are developed to enhance the differentiation capabilities of the platform to be used in breath analysis applications.

Breath Test: Clinical Application of Breath Analysis in Lower Respiratory Tract Infection Diagnosis.

Breath Test: Clinical Application of Breath Analysis in Lower Respiratory Tract Infection Diagnosis.

Self-Powered Respiration Monitoring Enabled By a Triboelectric Nanogenerator.

In mammals, physiological respiration involves respiratory cycles of inhaled and exhaled breaths, which has traditionally been an underutilized resource potentially encompassing a wealth of physiologically relevant information as well as clues to potential diseases. Recently, triboelectric nanogenerators (TENGs) have been widely adopted for self-powered respiration monitoring owing to their compelling features, such as decent biocompatibility, wearing comfort, low-cost, and high sensitivity to respiration activities in the aspect of low frequency and slight amplitude body motions. Physiological respiration behaviors and exhaled chemical regents can be precisely and continuously monitored by TENG-based respiration sensors for personalized health care. This article presents an overview of TENG enabled self-powered respiration monitoring, with a focus on the working principle, sensing materials, functional structures, and related applications in both physical respiration motion detection and chemical breath analysis. Concepts and approaches for acquisition of physical information associated with respiratory rate and depth are covered in the first part. Then the sensing mechanism, theoretical modeling, and applications related to detection of chemicals released from breathing gases are systemically summarized. Finally, the opportunities and challenges of triboelectric effect enabled self-powered respiration monitoring are comprehensively discussed and criticized.

Electronic Nose With Detection Method for Alcohol, Acetone, and Carbon Monoxide in Coronavirus Disease 2019 Breath Simulation Model.

Electronic nose technology may have the potential to substantially slow the spread of contagious diseases with rapid signal indication. As our understanding of infectious diseases such as Corona Virus Disease 2019 improves, we expect electronic nose technology to detect changes associated with pathogenesis of the disease such as biomarkers of immune response for respiratory symptoms, central nervous system injury, and/or peripheral nervous system injury in the breath and/or odor of an individual. In this paper, a design of an electronic nose was configured to detect the concentration of a COVID-19 breath simulation sample of alcohol, acetone, and carbon monoxide mixture. After preheating for 24 hours, the sample was carried into an internal bladder of the collection vessel for analysis and data was collected from three sensors to determine suitability of these sensors for the application of exhaled breath analysis. Test results show a detection range in parts-per-million within the sensor detection range of at least 10–300 ppm. The output response of an MQ-2 and an MQ-135 sensor to a diverse environment of target gasses show the MQ-2 taking a greater length of time to normalize baseline drift compared to an MQ-135 sensor due to cross interferences with other gasses. The COVID-19 breath simulation sample was established and validated based on preliminary data obtained from parallel COVID-19 breath studies based in Edinburgh and Dortmund. This detection method provides a non-invasive, rapid, and selective detection of gasses in a variety of applications in virus detection as well as agricultural and homeland security.

Detecting COVID-19 from Breath: A Game Changer for a Big Challenge.

Coronavirus disease 2019 (COVID-19) is probably the most commonly heard word of the last 12 months. The outbreak of this virus (SARS-CoV-2) is strongly compromising worldwide healthcare systems, social behavior, and everyone’s lives. The early diagnosis of COVID-19 and isolation of positive cases has proven to be fundamental in containing the spread of the infection. Even though the polymerase chain reaction (PCR) based methods remain the gold standard for SARS-CoV-2 detection, the urgent demand for rapid and wide-scale diagnosis precipitated the development of alternative diagnostic approaches. The millions of tests performed every day worldwide are still insufficient to achieve the desired goal, that of screening the population during daily life. Probably the most appealing approach to consistently monitor COVID-19 spread is the direct detection of SARS-CoV-2 from exhaled breath. For instance, the challenging incorporation of reliable, highly sensitive, and cost-efficient detection methods in masks could represent a breakthrough in the development of portable and noninvasive point-of-care diagnosis for COVID-19. In this perspective paper, we discuss the critical technical aspects related to the application of breath analysis in the diagnosis of viral infection. We believe that, if achieved, it could represent a game-changer in containing the pandemic spread.

High performance exhaled breath biomarkers for diagnosis of lung cancer and potential biomarkers for classification of lung cancer

Exhaled breath analysis has emerged as a promising non-invasive method for diagnosing lung cancer (LC), whereas reliable biomarkers are lacking. Herein, a standardized and systematic study was presented for LC diagnosis, classification and metabolism exploration. To improve the reliability of biomarkers, a validation group was included, and quality control for breath sampling and analysis, comprehensive pollutants analysis, and strict biomarker screening were performed. The performance of exhaled breath biomarkers was shown to be excellent in diagnosing LC even in early stages (stage I and II) with surpassing 0.930 area under the receiver operating characteristic (ROC) curve (AUC), 90% of sensitivity and 88% of specificity both in the discovery and validation analyses. Meanwhile, in these two groups, diagnosing subtypes of LC attained AUCs over 0.930 and reached 1.00 in the two subtypes of adenocarcinomas. It is demonstrated that the metabolism changes in LC are possibly related to lipid oxidation, gut microbial, cytochrome P450 and glutathione S-transferase, and glutathione pathways change in LC progression. Overall, the reliable biomarkers contribute to the clinical application of breath analysis in screening LC patients as well as those in early stages.