Current Status of Methods and Techniques for Breath Analysis

Ammonia detection: A pathway towards potential point-of-care diagnostics

Invasive methods such as blood collection and biopsy are commonly used for testing liver and kidney function, which are painful, time-consuming, require trained personnel, and may not be easily accessible to people for their routine checkup. Early diagnosis of liver and kidney diseases can prevent severe symptoms and ensure better management of these patients. Emerging approaches such as breath and sweat analysis have shown potential as non-invasive methods for disease diagnosis. Among the many markers, ammonia is often used as a biomarker for the monitoring of liver and kidney functions. In this review we provide an insight into the production and expulsion of ammonia gas in the human body, the different diseases that could potentially use ammonia as biomarker and analytical devices such as chemiresistive gas sensors for non-invasive monitoring of this gas. The review also provides an understanding into the different materials, doping agents and substrates used to develop such multifunctional sensors. Finally, the current challenges and the possible future trends have been discussed.

Mid-infrared optical frequency comb spectroscopy using an all-silica antiresonant hollow-core fiber

We present the first mid-infrared optical frequency comb spectrometer employing an absorption cell based on self-fabricated, all-silica antiresonant hollow-core fiber (ARHCF). The spectrometer is capable of measuring sub-mL sample volumes with 26 m interaction length and noise equivalent absorption sensitivity of 8.3 × 10-8 cm-1 Hz-1/2 per spectral element in the range of 2900 cm-1 to 3100 cm-1. Compared to a commercially available multipass cell, the ARHCF offers a similar interaction length in a 1000 times lower gas sample volume and a 2.8 dB lower transmission loss, resulting in better absorption sensitivity. The broad transmission windows of ARHCFs, in combination with a tunable optical frequency comb, make them ideal for multispecies detection, while the prospect of measuring samples in small volumes makes them a competitive technique to photoacoustic spectroscopy along with the robustness and prospect of coiling the ARHCFs open doors for miniaturization and out-of-laboratory applications.

Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip

Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.

Volatile organic compound sensing in breath using conducting polymer coated chemi-resistive filter paper sensors

In this work, a disposable sensor array was designed based on the chemi-resistive behavior of the conducting polymers to detect three volatile organic compounds (VOCs), i.e., acetone, ethanol, and methanol in air and breath. Four disposable resistive sensors were designed by coating polypyrrole and polyaniline (in their doped and de-doped forms) on filter paper substrates and tested against VOCs in air. Change in conductivity of the polymer resulting from exposure to various VOC concentration was measured as percentage resistance change using a standard multimeter. The lowest concentration detected for acetone, ethanol, and methanol vapors was 400 ppb, 150 ppb, and 300 ppb, respectively within 2 min. These VOC-responsive sensors, housed in an indigenous inert chamber, showed good stability, repeatability, and reversibility while sensing, thus making it suitable for environmental pollutant detection at room temperature. Furthermore, the non-specific nature of these easy to fabricate sensors towards all VOCs is considered favorable and upon classifying with principal component analysis (PCA), the gases were qualitatively distinguished in separate clusters. These developed sensors were also tested and analyzed using VOC spiked real breath samples as proof of concept.

Continuous real-time monitoring of carbon dioxide emitted from human skin by quartz-enhanced photoacoustic spectroscopy

In this study, a skin gas detection system based on quartz enhanced photoacoustic spectroscopy (QEPAS) with a constant temperature collection chamber and an automatic frequency adjustment function was used to collect and monitor carbon dioxide (CO₂) emissions from human skin. The detection element of the system is an on-beam structure assembled by a 30.72 kHz quartz tuning fork (QTF). A laser with a wavelength of 4991.26 cm^-1 is emitted (with a wavelength adjustment range of 10 cm^-1) to excite the QTF. When the integration time is 365 s, the system can achieve a minimum detection limit (MDL) of 2.6 ppmv. The sensitivity of the system is 636.9 ppmv/V. The gas detection system is used to monitor the concentration of CO₂ emissions from different parts of the skin and the same part covered by different cosmetics. The CO₂ emission rate is defined as the ratio of the skin gas monitoring time of 25 min to the CO₂ concentration variable in the gas chamber (volume of 8 mL). The results were collected from three healthy volunteers. Among the six different parts, the cheeks emitted the fastest rate (the average rate was 365.5 ppmv/min) of CO₂, and the thighs emitted the slowest rate (the average rate was 56.4 ppmv/min) of CO₂. Comparing the experimental results of the six sites at different times, the order of the CO₂ emission rate is identical for all six sites. In the experiments with the three cosmetic products (experimental site: forearm), comparing the CO₂ emission rate from clean skin with the CO₂ emission rate from cosmetic-covered skin shows that sunscreen is the most breathable, followed by barrier cream, and foundation is the least breathable.

Breath Tests Used in the Context of Bariatric Surgery

This review article focuses on the use of breath tests in the field of bariatrics and obesitology. The first part of the review is an introduction to breath test problematics with a focus on their use in bariatrics. The second part provides a brief history of breath testing. Part three describes how breath tests are used for monitoring certain processes in various organs and various substances in exhaled air and how the results are analyzed and evaluated. The last part covers studies that described the use of breath tests for monitoring patients that underwent bariatric treatments. Although the number of relevant studies is small, this review could promote the future use of breath testing in the context of bariatric treatments.

Challenges and Opportunities for Printed Electrical Gas Sensors

Printed electrical gas sensors are a low-cost, lightweight, low-power, and potentially disposable alternative to gas sensors manufactured using conventional methods such as photolithography, etching, and chemical vapor deposition. The growing interest in Internet-of-Things, smart homes, wearable devices, and point-of-need sensors has been the main driver fueling the development of new classes of printed electrical gas sensors. In this Perspective, we provide an insight into the current research related to printed electrical gas sensors including materials, methods of fabrication, and applications in monitoring food quality, air quality, diagnosis of diseases, and detection of hazardous gases. We further describe the challenges and future opportunities for this emerging technology.

Two-Dimensional Ti3C2 MXene-Based Novel Nanocomposites for Breath Sensors for Early Detection of Diabetes Mellitus

The rates of diabetes throughout the world are rising rapidly, impacting nearly every country. New research is focused on better ways to monitor and treat this disease. Breath acetone levels have been defined as a biomarker for diabetes. The development of a method to monitor and diagnose diabetes utilizing breath acetone levels would provide a fast, easy, and non-invasive treatment option. An ideal material for point-of-care diabetes management would need to have a high response to acetone, high acetone selectivity, low interference from humidity, and be able to operate at room temperature. Chemiresistive gas sensors are a promising method for sensing breath acetone due to their simple fabrication and easy operation. Certain semiconductor materials in chemiresistive sensors can react to acetone in the air and produce changes in resistance that can be correlated with acetone levels. While these materials have been developed and show strong responses to acetone with good selectivity, most of them must operate at high temperatures (compared to RT), causing high power consumption, unstable device operation, and complex device design. In this paper, we systematically studied a series of 2-dimensional MXene-based nanocomposites as the sensing materials in chemiresistive sensors to detect 2.86 ppm of acetone at room temperature. Most of them showed great sensitivity and selectivity for acetone. In particular, the 1D/2D CrWO/Ti₃C₂ nanocomposite showed the best sensing response to acetone: nine times higher sensitivity than 1D KWO nanowires. To determine the sensing selectivity, a CrWO/Ti₃C₂ nanocomposite-based sensor was exposed to various common vapors in human breath. The result revealed that it has excellent selectivity for acetone, and far lower responses to other vapors. All these preliminary results indicate that this material is a promising candidate for the creation of a point-of-care diabetes management device.

Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min

Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.

A DNA-derived phage nose using machine learning and artificial neural processing for diagnosing lung cancer

There is a growing interest in electronic nose-based diagnostic systems that are fast and portable. However, existing technologies are suitable only for operation in the laboratory, making them difficult to apply in a rapid, non-face-to-face, and field-suitable manner. Here, we demonstrate a DNA-derived phage nose (D²pNose) as a portable respiratory disease diagnosis system requiring no pretreatment. D²pNose was produced based on phage colour films implanted with DNA sequences from mammalian olfactory receptor cells, and as a result, it possesses the comprehensive reactivity of these cells. The manipulated surface chemistry of the genetically engineered phages was verified through a correlation analysis between the calculated and the experimentally measured reactivity. Breaths from 31 healthy subjects and 31 lung cancer patients were collected and exposed to D²pNose without pretreatment. With the help of deep learning and neural pattern separation, D²pNose has achieved a diagnostic success rate of over 75% and a classification success rate of over 86% for lung cancer based on raw human breath. Based on these results, D²pNose can be expected to be directly applicable to other respiratory diseases.

Acetone Sensor Based on FAIMS-MEMS

In this paper, to address the problems of large blood draws, long testing times, and the inability to achieve dynamic detection of invasive testing for diabetes, stemming from the principle that type 1 diabetic patients exhale significantly higher levels of acetone than normal people, a FAIMS-MEMS gas sensor was designed to detect acetone, which utilizes the characteristics of high sensitivity, fast response, and non-invasive operation. It is prepared by MEMS processes, such as photolithography, etching, and sputtering, its specific dimensions are 4000 μm in length, 3000 μm in width and 800 μm in height and the related test system was built to detect acetone gas. The test results show that when acetone below 0.8 ppm is introduced, the voltage value detected by the sensor basically does not change, while when acetone gas exceeds 1.8 ppm, the voltage value detected by the sensor increases significantly. The detection accuracy of the sensor prepared by this method is about 0.02 ppm/mV, and the voltage change can reach 1 V with a response time of 3 s and a recovery time of 4 s when tested under 20 ppm acetone environment; this has good repeatability and stability, and has great prospects in the field of non-invasive detection of type 1 diabetes.

Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods

BACKGROUND AND AIMS

The chemical gas sensor array based electronic-nose (e-nose) devices with machine learning algorithms can detect and differentiate expelled breath samples of patients with various respiratory ailments and controls. It is by the recognition of levels and variations of volatile organic compounds (VOC) in the exhaled air. Here, we aimed to differentiate chronic obstructive pulmonary disease (COPD) and lung cancer from controls.

MATERIALS AND METHODS

This work presents the details of the developed e-nose system, selection of the study subjects, exhaled breath sampling method and detection, and the data analysis algorithms. The developed device is tested in 199 participants including 93 controls, 55 COPD patients, and 51 lung cancer patients. The main advantage of the device is robustness and portability and cost-effectiveness.

RESULTS

In the training phase and model validation phase, the ensemble learning method XGBoost outperformed the other two models. In the prediction of lung cancer, XGBoost method attained a classification accuracy of 79.31%. In COPD prediction also the same method had given the better results with 76.67% accuracy.

CONCLUSION

The e-nose system developed with TGS gas sensors was portable, low cost, and gave a rapid response. It has been demonstrated that the VOC profiles of patients with pulmonary diseases and healthy controls are different and hence the e-nose system can be used as a potential diagnostic device for patients with lung diseases.

Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose

INTRODUCTION

This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls.

MATERIALS AND METHODS

This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls.

RESULTS

In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy.

CONCLUSION

The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

Medical diagnosis at the point-of-care by portable high-field asymmetric waveform ion mobility spectrometry: a systematic review and meta-analysis

Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%-85%,I2= 62%), and specificity of 78% (95% CI, 70%-84%,I2= 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.

Electroless deposition of Pd/Pt nanoparticles on electrochemically grown TiO2 nanotubes for ppb level sensing of ethanol at room temperature

This work presents a comparative sensing study of three sensors based on pristine TiO₂ nanotubes, Pd loaded TiO₂ nanotubes, and Pt loaded TiO₂ nanotubes. Pristine TiO₂ nanotubes were synthesized using an electrochemical anodization method and an electroless plating method was used for the uniform deposition of noble metal nanoparticles of either Pd or Pt over the surface of TiO₂ nanotubes. The samples were thoroughly characterized by XRD, FESEM, EDS, TEM, and XPS techniques. The sensitivity of all three sensors was investigated at room temperature (300 K) for different volatile organic compounds like ethanol, methanol, 2-propanol, acetone, and benzene. The results revealed that loading of Pd and Pt nanoparticles improved the response magnitude of the sensor remarkably as these noble metals possess better oxygen dissociation capability than pristine TiO₂. The Pd-TiO₂ nanotube sensor exhibited a maximum response magnitude of 20-98% towards 100-1000 ppb of ethanol at room temperature. Notably, the formation of Pd/Pt-TiO₂ discrete heterojunctions on the surface of TiO₂ nanotubes was found to be responsible for enhanced sensitivity of the sensors.

Role of the oral microbiome, metabolic pathways, and novel diagnostic tools in intra-oral halitosis: a comprehensive update

Halitosis or oral malodor is one of the most common reasons for the patients' visit to the dental clinic, ranking behind only dental caries and periodontitis. In the present times, where social and professional communications are becoming unavoidable, halitosis has become a concern of growing importance. Oral malodor mostly develops due to the putrefaction of substrates by the indigenous bacterial populations. Although culture-based studies have provided adequate information on halitosis, the high throughput omics technologies have amplified the resolution at which oral microbial community can be examined and has led to the detection of a broader range of taxa associated with intra-oral halitosis (IOH). These microorganisms are regulated by the interactions of their ecological processes. Thus to develop effective treatment strategies, it is important to understand the microbial basis of halitosis. In the current review, we provide an update on IOH in context to the role of the oral microbiome, metabolic pathways involved, and novel diagnostic tools, including breathomics. Understanding oral microbiota associated with halitosis from a broader ecological perspective can provide novel insights into one's oral and systemic health. Such information can pave the way for the emergence of diagnostic tools that can revolutionize the early detection of halitosis and various associated medical conditions.

pn-Heterojunction of the SWCNT/ZnO nanocomposite for temperature dependent reaction with hydrogen

A hydrogen breath test is a non-invasive and safe diagnostic tool to explore the functional gastrointestinal disorders. For the diagnosis of small intestinal bacterial overgrowth syndrome as well as carbohydrate malabsorption such as fructose, lactose, and sorbitol malabsorption, a hydrogen breath test is considered one of the gold criterions. Since the more sensitive hydrogen sensor enables the more accurate prediction about the disease, many efforts have been to the development of the high performance H₂ sensor. Herein, we fabricate the pn-junction type composite sensors using single wall carbon nanotube (SWCNT) and zinc oxide and thoroughly investigate their hydrogen sensing properties at various temperatures. We discuss the origin of sensing performance enhancement mechanism in the composite sensors, while the composite sensor with high H₂ sensing performance, linearity, repeatability, and selectivity can be prepared.

Recent Advances in Ammonia Gas Sensors Based on Carbon Nanomaterials

This review paper is devoted to an extended analysis of ammonia gas sensors based on carbon nanomaterials. It provides a detailed comparison of various types of active materials used for the detection of ammonia, e.g., carbon nanotubes, carbon nanofibers, graphene, graphene oxide, and related materials. Different parameters that can affect the performance of chemiresistive gas sensors are discussed. The paper also gives a comparison of the sensing characteristics (response, response time, recovery time, operating temperature) of gas sensors based on carbon nanomaterials. The results of our tests on ammonia gas sensors using various techniques are analyzed. The problems related to the recovery of sensors using various approaches are also considered. Finally, the impact of relative humidity on the sensing behavior of carbon nanomaterials of various different natures was estimated.

A review of breath analysis techniques in head and neck cancer

Cancers of the head and neck region are a severely disabling group of diseases with no method for early detection. Analysis of exhaled breath volatile organic compounds shows promise as biomarkers for early detection and disease monitoring. This article reviews breath analysis in the setting of head and neck cancer, with a practical focus on breath sampling techniques, detection technologies and valid data analysis methods. Title and abstract keyword searches were conducted on PubMed and Embase databases to identify English language studies without a time-period limitation. The main inclusion criteria were human studies comparing head and neck cancer patients to healthy controls using exhaled breath analysis. Multiple breath collection techniques, three major detection technologies and multiple data analysis methods were identified. However, the variability in techniques and lack of methodological standardization does not allow for adequate study replication or data pooling. Twenty-two volatile organic compounds identified in five studies have been reported to discriminate head and neck cancer patients from healthy controls. Breath analysis for detection of head and neck cancer shows promise as a non-invasive detection tool. However, methodological standardization is paramount for future research study design to provide the potential for translating these techniques into routine clinical use.

Breath-based non-invasive diagnosis of Alzheimer's disease: a pilot study

Early detection of Alzheimer's disease (AD) will help researchers to better understand the disease and develop improved treatments. Recent developments have thus focused on identifying biomarkers for mild cognitive impairment due to AD (MCI) and AD during the preclinical phase. The aim of this pilot study is to determine whether exhaled volatile organic compounds (VOCs) can be used as a non-invasive method to distinguish controls from MCI, controls from AD and to determine whether there are differences between MCI and AD. The study used gas chromatography-ion mobility spectrometry (GC-IMS) techniques. Confounding factors, such as age, smoking habits, gender and alcohol consumption are investigated to demonstrate the efficacy of results. One hundred subjects were recruited including 50 controls, 25 AD and 25 MCI patients. The subject cohort was age- and gender-matched to minimise bias. Breath samples were analysed using a commercial GC-IMS instrument (G.A.S. BreathSpec, Dortmund, Germany). Data analysis indicates that the GC-IMS signal was consistently able to separate between diagnostic groups [AUC ± 95%, sensitivity, specificity], controls versus MCI: [0.77 (0.64-0.90), 0.68, 0.80], controls versus AD: [0.83 (0.72-0.94), 0.60, 0.96], and MCI versus AD: [0.70 (0.55-0.85), 0.60, 0.84]. VOC analysis indicates that six compounds play a crucial role in distinguishing between diagnostic groups. Analysis of possible confounding factors indicate that gender, age, smoking habits and alcohol consumption have insignificant influence on breath content. This pilot study confirms the utility of exhaled breath analysis to distinguish between AD, MCI and control subjects. Thus, GC-IMS offers great potential as a non-invasive, high-throughput, diagnostic technique for diagnosing and potentially monitoring AD in a clinical setting.

Recent advances in breath analysis to track human health by new enrichment technologies

Detection of biomarkers in exhaled breath has been gaining increasing attention as a tool for diagnosis of specific diseases. However, rapid and accurate quantification of biomarkers associated with specific diseases requires the use of analytical methods capable of fast sampling and preconcentration from breath matrix. In this regard, solid phase microextraction and needle trap technology are becoming increasingly popular in the field of breath analysis due to the unique benefits imparted by such methods, such as the integration of sampling, extraction, and preconcentration in a single step. This review discusses recent advances in breath analysis using these sample preparation techniques, providing a summary of recent developments of analytical methods based on breath volatile organic compounds analysis, including the successful identification of various biomarkers related to human diseases.

An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N2O

A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) was demonstrated for simultaneous measurements of exhaled carbon monoxide (CO) and nitrous oxide (N₂O). The sampling pressure was optimized to improve the sensitivity, the optimal pressure was determined to be 150 mbar based on an optical density analysis of simulated and measured absorption spectra. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network was developed and proposed for real-time exhaled breath analysis in order to perform fast and high precision on-line measurements. The detection limits (1σ) of 1.14 ppb and 1.12 ppb were experimentally achieved for CO and N₂O detection, respectively. Typical concentrations of exhaled CO and N₂O from smokers and non-smokers were analyzed. The experimental results indicated that the state-of-the-art CW-QCL based sensor has a great potential for non-invasive, on-line identification and quantification of biomarkers in human breath.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

Ultrasensitive NOX Detection in Simulated Exhaled Air: Enhanced Sensing via Alumina Modification of In-Situ Grown WO3 Nanoblocks

Seedless growth of vertically aligned nanostructures, which can induce smoother transport and minimize Ohmic contact between substrate and semiconductor, can be fabricated by in situ growth utilizing modified hydrothermal methods. Such devices can be useful in designing non-invasive ultrasensitive hand-held sensors for diagnostic identification of volatile organic compounds (VOCs) in exhaled air, offering pain-free and easier detection of long-term diseases such as asthma. In the present work, WO₃ nanoblocks, with a high surface area and porosity, have been grown directly over transparent conducting oxide to minimize Ohmic resistance, facilitating smoother electron transfer and enhanced current response. Further modification with porous alumina (γ-Al₂ O₃ ), by electrodeposition, resulted in the selective and ultrasensitive detection of NO_X in simulated exhaled air. Crystal phase purity of as-fabricated pristine as well modified samples is validated with X-ray diffraction analysis. Morphological and microstructural analyses reveal the successful deposition of porous alumina over the surface of WO₃ . Improved surface area and porosity is presented by porous alumina in the modified WO₃ device, suggesting more active sites for the gas molecules to get adsorbed and diffuse through the pores. Oxygen vacancies, which are detrimental in the transport phenomenon in the presented sensors, have been studied using X-ray photoelectron spectroscopic (XPS) analysis. Gas sensing studies have been performed by fabricating chemiresistor devices based on bare WO₃ and Al₂ O₃ -modified WO₃ . The higher sensitivity for NO_X gas in case of γ-Al₂ O₃ -modified WO₃ based devices, as compared to bare WO₃ -based devices, is attributed to the better surface area and charge transport kinetics. The presented device strategy offers crucial understanding in the design and development of non-invasive, hand-held devices for NO gas present in the human breath, with potential application in medical diagnostics.

Development of low-cost hybrid multi-walled carbon nanotube-based ammonia gas-sensing strips with an integrated sensor read-out system for clinical breath analyzer applications

This work demonstrates the development of Ag@polyaniline/multi-walled carbon nanotube nanocomposite-based sensor strips and a suitable integrated electronic read-out system for the measurement of trace-level concentrations of ammonia (NH₃). The sensor is optimized under various operating conditions and the resulting sensor exhibited an enhanced response (32% for 2 ppm) with excellent selectivity. Stable performance was observed towards NH₃ in the presence of high concentrations of CO₂ (>40 000 ppm), simulated and real breath samples. A suitable electronic sensor read-out system has also been designed and developed based on multi-scale resistance-to-voltage conversion architecture, processed by a 32-bit microcontroller which is operatable over a wide range of sensor resistance (1 kΩ to 200 MΩ). As a proof of concept, integration of gas-sensing strips with the electronic read-out system was tested with various levels of NH₃ (<2 ppm as normal, >2 ppm as critical and 2 ppm as threshold), which is important for clinical breath analyzer applications. The developed prototype device can be readily incorporated into a portable, low-cost and non-invasive point-of-care breath NH₃ detection unit for portable pre-diagnostic breath analyzer applications.

Breath Analysis: A Systematic Review of Volatile Organic Compounds (VOCs) in Diagnostic and Therapeutic Management of Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare neoplasm related to asbestos exposure and with high mortality rate. The management of patients with MPM is complex and controversial, particularly with regard to early diagnosis. In the last few years, breath analysis has been greatly implemented with this aim. In this review the strengths of breath analysis and preliminary results in searching breath biomarkers of MPM are highlighted and discussed, respectively. Through a systematic electronic literature search, collecting papers published from 2000 until December 2018, fifteen relevant scientific papers were selected. All papers considered were prospective, comparative, observational case-control studies although every single one pilot and based on a relatively small number of samples. The identification of diagnostic VOCs pattern, through breath sample characterization and the statistical data treatment, allows to obtain a strategic information for clinical diagnostics. To date the collected data provide just preliminary information and, despite the promising results and diagnostic accuracy, conclusions cannot be generalized due to the limited number of individuals included in each cohort study. Furthermore none of studies was externally validated, although validation process is a necessary step towards clinical implementation. Breathomics-based biomarker approach should be further explored to confirm and validate preliminary findings and to evaluate its potential role in monitoring the therapeutic response.

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.

Ligand-Capped Ultrapure Metal Nanoparticle Sensors for the Detection of Cutaneous Leishmaniasis Disease in Exhaled Breath

Human cutaneous leishmaniasis, although designated as one of the most neglected tropical diseases, remains underestimated due to its misdiagnosis. The diagnosis is mainly based on the microscopic detection of amastigote forms, isolation of the parasite, or the detection of Leishmania DNA, in addition to its differential clinical characterization; these tools are not always available in routine daily practice, and they are expensive and time-consuming. Here, we present a simple-to-use, noninvasive approach for human cutaneous leishmaniasis diagnosis, which is based on the analysis of volatile organic compounds in exhaled breath with an array of specifically designed chemical gas sensors. The study was realized on a group of n = 28 volunteers diagnosed with human cutaneous leishmaniasis and a group of n = 32 healthy controls, recruited in various sites from Tunisia, an endemic country of the disease. The classification success rate of human cutaneous leishmaniasis patients achieved by our sensors test was 98.2% accuracy, 96.4% sensitivity, and 100% specificity. Remarkably, one of the sensors, based on CuNPs functionalized with 2-mercaptobenzoxazole, yielded 100% accuracy, 100% sensitivity, and 100% specificity for human cutaneous leishmaniasis discrimination. While AuNPs have been the most extensively used in metal nanoparticle-ligand sensing films for breath sensing, our results demonstrate that chemical sensors based on ligand-capped CuNPs also hold great potential for breath volatile organic compounds detection. Additionally, the chemical analysis of the breath samples with gas chromatography coupled to mass spectrometry identified nine putative breath biomarkers for human cutaneous leishmaniasis.

Confounding effect of benign pulmonary diseases in selecting volatile organic compounds as markers of lung cancer

Lung cancer (LC) is a leading cause of cancer-related morbidity and mortality globally, and exhaled breath testing has been considered as a fast, convenient and non-invasive way to diagnose LC in its early stages. Volatile organic compounds (VOCs), as markers of LC in exhaled breath, have been widely investigated for cancer diagnosis. However, few studies have reported on the interference of benign pulmonary diseases (BPD) in the selection of VOC markers for LC. During this study, 207 samples were analyzed using thermal desorption instrumentation/gas chromatography/mass spectrometry (TD-GCMS) to detect C₆-C₃₀ VOCs, and all samples were divided into four groups: LC group, BPD group, lung disease (LD) group (including LC group and BPD group) and healthy group. To make up for the deficiency of detecting low carbon hydrocarbons (<C₆), 277 samples were analyzed using solid-phase micro-extraction/gas chromatography/mass spectrometry (SPME-GCMS), divided among the four groups. VOC markers were selected by reference to the receiver operating characteristics curve. With the comparisons among the LC group, BPD group and healthy group from TD-GCMS and SPME-GCMS results, we found that exhaled VOCs are capable of discriminating LC group versus healthy group and BPD group versus healthy group with a consistency of 70%-80%. However, no VOCs can be selected with good discrimination capability between the LC group and BPD group, indicating that BPD interferes significantly in VOC marker selection for LC. To discriminate breath samples from the LD group and healthy group, 11 VOCs, including ten selected from TD-GCMS and one from SPME-GCMS, were chosen as markers for LD diagnosis. The sensitivity, specificity and overall accuracy of the diagnostic model established using ten VOCs were 80.8%, 84% and 82.7%, and those of the model established by using one VOC were 75.6%, 78.9% and 76.7%. These results validate that LD patients can be effectively discriminated and diagnosed using exhaled VOC analysis. (Clinical trial registration number: ChiCTR-DCD-15007106.).

Thermoelectric Array Sensors with Selective Combustion Catalysts for Breath Gas Monitoring

Inflammable breath gases such as H₂ and CH₄ are used as bio markers for monitoring the condition of the colon. However, their typical concentrations of below 100 ppm pose sensitivity and selectivity challenges to current gas sensing systems without the use of chromatography. We fabricated a compact, gas-selective thermoelectric array sensor (TAS) that uses micro-machined sensor devices with three different combustion catalysts to detect gases such as H₂, CO, and CH₄ in breath. Using Pt/Pt-W thin-film micro-heater meanders, Pd/Al₂O₃, Pt,Pd,Au/Co₃O₄, and Pt/Al₂O₃ catalysts were heated to 320, 200, and 125 °C, respectively, and the gas sensing performances of the TAS for each gas and for a model breath gas mixture of 100 ppm H₂, 25 ppm CO, 50 ppm CH₄, and 199 ppm CO₂ in air were investigated. Owing to its high catalyst temperature, the Pd/Al₂O₃ catalyst burned all three gases, while the Pt,Pd,Au/Co₃O₄ burned CO and H₂ and the Pt/Al₂O₃ burned H₂ selectively. To calibrate the gas concentration of the mixture gas without the use of a gas separation tool, linear discriminant analysis was applied to measure the sensing performance of TAS. To enhance the gas selectivity against H₂, a double catalyst structure was integrated into the TAS sensor.

Portable FAIMS: Applications and Future Perspectives

Miniaturized mass spectrometry (MMS) is optimal for a wide variety of applications that benefit from field-portable instrumentation. Like MMS, field asymmetric ion mobility spectrometry (FAIMS) has proven capable of providing in situ analysis, allowing researchers to bring the lab to the sample. FAIMS compliments MMS very well, but has the added benefit of operating at atmospheric pressure, unlike MS. This distinct advantage makes FAIMS uniquely suited for portability. Since its inception, FAIMS has been envisioned as a field-portable device, as it affords less expense and greater simplicity than many similar methods. Ideally, these are simple, robust devices that may be operated by non-professional personnel, yet still provide adequate data when in the field. While reducing the size and complexity tends to bring with it a loss of performance and accuracy, this is made up for by the incredibly high throughput and overall convenience of the instrument. Moreover, the FAIMS device used in the field can be brought back to the lab, and coupled to a conventional mass spectrometer to provide any necessary method development and compound validation. This work discusses the various considerations, uses, and applications for portable FAIMS instrumentation, and how the future of each applicable field may benefit from the development and acceptance of such a device.

Biomarkers for early diagnosis of malignant mesothelioma: Do we need another moonshot?

Early diagnosis of malignant pleural mesothelioma (MPM) is a challenge for clinicians. The disease is usually detected in an advanced stage which precludes curative treatment. We assume that only new and non-invasive biomarkers allowing earlier detection will result in better patient management and outcome. Many efforts have already been made to find suitable biomarkers in blood and pleural effusions, but have not yet resulted in a valid and reproducible diagnostic one. In this review, we will highlight the strengths and shortcomings of blood and fluid based biomarkers and highlight the potential of breath analysis as a non-invasive screening tool for MPM. This method seems very promising in the early detection of diverse malignancies, because exhaled breath contains valuable information on cell and tissue metabolism. Research that focuses on breath biomarkers in MPM is in its early days, but the few studies that have been performed show promising results. We believe a breathomics-based biomarker approach should be further explored to improve the follow-up and management of asbestos exposed individuals.

A Novel Medical E-Nose Signal Analysis System

It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy.

Real-Time Quantitative Analysis of Valproic Acid in Exhaled Breath by Low Temperature Plasma Ionization Mass Spectrometry

Real-time analysis of exhaled human breath is a rapidly growing field in analytical science and has great potential for rapid and noninvasive clinical diagnosis and drug monitoring. In the present study, an LTP-MS method was developed for real-time, in-vivo and quantitative analysis of γ-valprolactone, a metabolite of valproic acid (VPA), in exhaled breath without any sample pretreatment. In particular, the effect of working conditions and geometry of the LTP source on the ions of interest, protonated molecular ion at m/z 143 and ammonium adduct ion at m/z 160, were systematically characterized. Tandem mass spectrometry (MS/MS) with collision-induced dissociation (CID) was carried out in order to identify γ-valprolactone molecular ions (m/z 143), and the key fragment ion (m/z 97) was used for quantitation. In addition, the fragmentation of ammonium adduct ions to protonated molecular ions was performed in-source to improve the signal-to-noise ratio. At optimum conditions, signal reproducibility with an RSD of 8% was achieved. The concentration of γ-valprolactone in exhaled breath was determined for the first time to be 4.83 (±0.32) ng/L by using standard addition method. Also, a calibration curve was obtained with a linear range from 0.7 to 22.5 ng/L, and the limit of detection was 0.18 ng/L for γ-valprolactone in standard gas samples. Our results show that LTP-MS is a powerful analytical platform with high sensitivity for quantitative analysis of volatile organic compounds in human breath, and can have potential applications in pharmacokinetics or for patient monitoring and treatment. Graphical Abstract ᅟ.

Possible photoacoustic gas detection for a smart endoscope

Endogenous gas analysis is a potentially fast and convenient noninvasive diagnostic method for a variety of diseases. However, sampling and sample preparation are error-prone steps and must be optimized to achieve reliable results. A miniature photoacoustic system was developed to allow gas sampling using a smart endoscope. The photoacoustic system was demonstrated to have a 1% detection limit for CO2, which is too high. Many improvements, including modifying the structure of PA cell, must be considered for further development.

A novel polyaniline/polypyrrole/graphene oxide fiber for the determination of volatile organic compounds in headspace gas of lung cell lines

Exploration of volatile organic compounds (VOCs) generated by lung cell lines is a powerful and non-invasive tool for the detection of potential volatile biomarkers of lung cancer. In this study, a simple and sensitive solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) method was developed for the determination of VOCs in the headspace gas of lung cell lines. For the purpose of preconcentration, a novel polyaniline/polypyrrole/graphene oxide (PANI/PPy/GO) coating was prepared on the surface of stainless steel fiber via in-situ electrochemical deposition for the first time. The characteristic properties of the coating were studied and the results revealed that the coating possessed large surface area, high extraction efficiency, excellent thermal and mechanical stability as well as long lifespan. Some parameters affecting the extraction efficiency such as synthesis conditions, extraction and desorption conditions were optimized. Under the optimal conditions, the method displayed relatively wide linear range (three or four orders of magnitude) with correlation coefficients above 0.9916. Low detection limits from 1.0 to 12ngL^-1 were obtained. Relative standard deviations ranged from 1.2% to 18.0% indicating good repeatability and reproducibility of the method. This method has been successfully applied to analyze VOCs in the headspace gas of lung adenocarcinoma epithelial cell line (A549) and human embryonic fibroblast cell line (MRC-5).

Gas-phase broadband spectroscopy using active sources: progress, status, and applications

Broadband spectroscopy is an invaluable tool for measuring multiple gas-phase species simultaneously. In this work we review basic techniques, implementations, and current applications for broadband spectroscopy. We discuss components of broad-band spectroscopy including light sources, absorption cells, and detection methods and then discuss specific combinations of these components in commonly-used techniques. We finish this review by discussing potential future advances in techniques and applications of broad-band spectroscopy.

Quantum cascade lasers (QCLs) in biomedical spectroscopy

This review focuses on the recent applications of QCLs in mid-IR spectroscopy of clinically relevant samples.

Applications of external cavity diode laser-based technique to noninvasive clinical diagnosis using expired breath ammonia analysis: chronic kidney disease, epilepsy

An external cavity laser (ECL)-based off-axis cavity-enhanced absorption spectroscopy was applied to noninvasive clinical diagnosis using expired breath ammonia analysis: (1) the correlation between breath ammonia levels and blood parameters related to chronic kidney disease (CKD) was investigated and (2) the relationship between breath ammonia levels and blood concentrations of valproic acid (VAP) was studied. The concentrations of breath ammonia in 15 healthy volunteers, 10 epilepsy patients (before and after taking VAP), and 27 patients with different stages of CKD were examined. The range of breath ammonia levels was 120 to 530 ppb for healthy subjects and 710 to 10,400 ppb for patients with CKD. There was a statistically significant positive correlation between breath ammonia concentrations and urea, blood urea nitrogen, creatinine, or estimated glomerular filtration rate in 27 patients. It was demonstrated that taking VAP gave rise to increasing breath ammonia levels. A statistically significant difference was found between the levels of exhaled ammonia (NH3) in healthy subjects and in patients with epilepsy before and after taking VAP. The results suggest that our breath ammonia measurement system has great potential as an easy, noninvasive, real-time, and continuous monitor of the clinical parameters related to epilepsy and CKD.

Feasibility Study of Using Breath Ammonia Analysis Based on Off-Axis Cavity-Enhanced Absorption Spectroscopy with External Cavity Diode Laser for Noninvasive Real-Time Diagnosis of Helicobacter Pylori

The purpose of this study is to assess the feasibility of using breath ammonia analysis based on off-axis cavity-enhanced absorption spectroscopy (OA-CEAS) with an external-cavity diode laser (ECL) for noninvasive, real-time diagnosis of Helicobacter pylori (HP) infection. Analyses are performed for the breath of 15 healthy volunteers, and eight children and 19 adults with HP infection. The range of ammonia levels for healthy participants is determined to be between 178 and 610 ppb, whereas the ranges for child and adult patients with HP infection are measured to be 457-2470 ppb and 450-2990 ppb, respectively. The ammonia concentrations for patients with HP infection are significantly higher than the concentrations for healthy volunteers. However, no sharp boundary between the ammonia concentrations in the breath of patients with HP infection and healthy volunteers is observed. No correlation between breath ammonia and either body mass index (BMI) or age is found. The reported results suggest that our breath ammonia measurement system has the potential for future use in easy, noninvasive diagnosis of HP infection.

Techniques and issues in breath and clinical sample headspace analysis for disease diagnosis

Analysis of volatile organic compounds (VOCs) from breath or clinical samples for disease diagnosis is an attractive proposition because it is noninvasive and rapid. There are numerous studies showing its potential, yet there are barriers to its development. Sampling and sample handling is difficult, and when coupled with a variety of analytical instrumentation, the same samples can give different results. Background air and the environment a person has been exposed to can greatly affect the VOCs emitted by the body; however, this is not an easy problem to solve. This review investigates the use of VOCs in disease diagnosis, the analytical techniques employed and the problems associated with sample handling and standardization. It then suggests the barriers to future development.

Hierarchical growth of ZnFe2O4 for sensing applications

Selective sensing of toxic heavy metals Hg(ii) and environmentally hazardous acetone vapour using mesoporous ZnFe₂O₄ NFs, synthesized from our laboratory developed modified hydrothermal technique.