Technologies for Clinical Diagnosis Using Expired Human Breath Analysis

High-precision Helicobacter pylori infection diagnosis using a dual-element multimodal gas sensor array

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope 13C or 14C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH2-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Highly Selective and Reversible Detection of Simulated Breath Hydrogen Sulfide Using Fe-Doped CuO Hollow Spheres: Enhanced Surface Redox Reaction by Multi-Valent Catalysts

The precise and reversible detection of hydrogen sulfide (H2S) at high humidity condition, a malodorous and harmful volatile sulfur compound, is essential for the self-assessment of oral diseases, halitosis, and asthma. However, the selective and reversible detection of trace concentrations of H2S (≈0.1 ppm) in high humidity conditions (exhaled breath) is challenging because of irreversible H2S adsorption/desorption at the surface of chemiresistors. The study reports the synthesis of Fe-doped CuO hollow spheres as H2S gas-sensing materials via spray pyrolysis. 4 at.% of Fe-doped CuO hollow spheres exhibit high selectivity (response ratio ≥ 34.4) over interference gas (ethanol, 1 ppm) and reversible sensing characteristics (100% recovery) to 0.1 ppm of H2S under high humidity (relative humidity 80%) at 175 °C. The effect of multi-valent transition metal ion doping into CuO on sensor reversibility is confirmed through the enhancement of recovery kinetics by doping 4 at.% of Ti- or Nb ions into CuO sensors. Mechanistic details of these excellent H2S sensing characteristics are also investigated by analyzing the redox reactions and the catalytic activity change of the Fe-doped CuO sensing materials. The selective and reversible detection of H2S using the Fe-doped CuO sensor suggested in this work opens a new possibility for halitosis self-monitoring.

Enhance Ethanol Sensing Performance of Fe-Doped Tetragonal SnO2 Films on Glass Substrate with a Proposed Mathematical Model for Diffusion in Porous Media

This research enhances ethanol sensing with Fe-doped tetragonal SnO2 films on glass, improving gas sensor reliability and sensitivity. The primary objective was to improve the sensitivity and operational efficiency of SnO2 sensors through Fe doping. The SnO2 sensors were synthesized using a flexible and adaptable method that allows for precise doping control, with energy-dispersive X-ray spectroscopy (EDX) confirming homogeneous Fe distribution within the SnO2 matrix. A morphological analysis showed a surface structure ideal for gas sensing. The results demonstrated significant improvement in ethanol response (1 to 20 ppm) and lower temperatures compared to undoped SnO2 sensors. The Fe-doped sensors exhibited higher sensitivity, enabling the detection of low ethanol concentrations and showing rapid response and recovery times. These findings suggest that Fe doping enhances the interaction between ethanol molecules and the sensor surface, improving performance. A mathematical model based on diffusion in porous media was employed to further analyze and optimize sensor performance. The model considers the diffusion of ethanol molecules through the porous SnO2 matrix, considering factors such as surface morphology and doping concentration. Additionally, the choice of electrode material plays a crucial role in extending the sensor's lifespan, highlighting the importance of material selection in sensor design.

A Prototype of Graphene E-Nose for Exhaled Breath Detection and Label-Free Diagnosis of Helicobacter Pylori Infection

Helicobacter pylori (HP), a common microanaerobic bacteria that lives in the human mouth and stomach, is reported to infect ≈50% of the global population. The current diagnostic methods for HP are either invasive, time-consuming, or harmful. Therefore, a noninvasive and label-free HP diagnostic method needs to be developed urgently. Herein, reduced graphene oxide (rGO) is composited with different metal-based materials to construct a graphene-based electronic nose (e-nose), which exhibits excellent sensitivity and cross-reactive response to several gases in exhaled breath (EB). Principal component analysis (PCA) shows that four typical types of gases in EB can be well discriminated. Additionally, the potential of the e-nose in label-free detection of HP infection is demonstrated through the measurement and analysis of EB samples. Furthermore, a prototype of an e-nose device is designed and constructed for automatic EB detection and HP diagnosis. The accuracy of the prototype machine integrated with the graphene-based e-nose can reach 92% and 91% in the training and validation sets, respectively. These results demonstrate that the highly sensitive graphene-based e-nose has great potential for the label-free diagnosis of HP and may become a novel tool for non-invasive disease screening and diagnosis.

A solid state electrolyte based enzymatic acetone sensor

This paper introduces a novel solid-state electrolyte-based enzymatic sensor designed for the detection of acetone, along with an examination of its performance under various surface modifications aimed at optimizing its sensing capabilities. To measure acetone concentrations in both liquid and vapor states, cyclic voltammetry and amperometry techniques were employed, utilizing disposable screen-printed electrodes consisting of a platinum working electrode, a platinum counter electrode, and a silver reference electrode. Four different surface modifications, involving different combinations of Nafion (N) and enzyme (E) layers (N + E; N + E + N; N + N + E; N + N + E + N), were tested to identify the most effective configuration for a sensor that can be used for breath acetone detection. The sensor's essential characteristics, including linearity, sensitivity, reproducibility, and limit of detection, were thoroughly evaluated through a range of experiments spanning concentrations from 1 µM to 25 mM. Changes in acetone concentration were monitored by comparing currents readings at different acetone concentrations. The sensor exhibited high sensitivity, and a linear response to acetone concentration in both liquid and gas phases within the specified concentration range, with correlation coefficients ranging from 0.92 to 0.98. Furthermore, the sensor achieved a rapid response time of 30-50 s and an impressive detection limit as low as 0.03 µM. The results indicated that the sensor exhibited the best linearity, sensitivity, and limit of detection when four layers were employed (N + N + E + N).

MOF-Based Platform for Kidney Diseases: Advances, Challenges, and Prospects

Kidney diseases are important diseases that affect human health worldwide. According to the 2020 World Health Organization (WHO) report, kidney diseases have become the top 10 causes of death. Strengthening the prevention, primary diagnosis, and action of kidney-related diseases is of great significance in maintaining human health and improving the quality of life. It is increasingly challenging to address clinical needs with the present technologies for diagnosing and treating renal illness. Fortunately, metal-organic frameworks (MOFs) have shown great promise in the diagnosis and treatment of kidney diseases. This review summarizes the research progress of MOFs in the diagnosis and treatment of renal disease in recent years. Firstly, we introduce the basic structure and properties of MOFs. Secondly, we focus on the utilization of MOFs in the diagnosis and treatment of kidney diseases. In the diagnosis of kidney disease, MOFs are usually designed as biosensors to detect biomarkers related to kidney disease. In the treatment of kidney disease, MOFs can not only be used as an effective adsorbent for uremic toxins during hemodialysis but also as a precise treatment of intelligent drug delivery carriers. They can also be combined with nano-chelation technology to solve the problem of the imbalance of trace elements in kidney disease. Finally, we describe the current challenges and prospects of MOFs in the diagnosis and treatment of kidney diseases.

Electrochemical Detection of Gasotransmitters: Status and Roadmap

Gasotransmitters, including nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S), are a class of gaseous, endogenous signaling molecules that interact with one another in the regulation of critical cardiovascular, immune, and neurological processes. The development of analytical sensing mechanisms for gasotransmitters, especially multianalyte mechanisms, holds vast importance and constitutes a growing area of study. This review provides an overview of electrochemical sensing mechanisms with an emphasis on opportunities in multianalyte sensing. Electrochemical methods demonstrate good sensitivity, adequate selectivity, and the most well-developed potential for the multianalyte detection of gasotransmitters. Future research will likely address challenges with sensor stability and biocompatibility (i.e., sensor lifetime and cytotoxicity), sensor miniaturization, and multianalyte detection in biological settings.

Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Ultra-broadband spectroscopy using a 2-11.5 µm IDFG-based supercontinuum source

Supercontinuum sources based on intrapulse difference frequency generation (IDFG) from mode-locked lasers open new opportunities in mid-infrared gas spectroscopy. These sources provide high power and ultra-broadband spectral coverage in the molecular fingerprint region with very low relative intensity noise. Here, we demonstrate the performance of such a light source in combination with a multipass cell and a custom-built Fourier transform spectrometer (FTS) for multispecies trace gas detection. The light source provides a low-noise, ultra-broad spectrum from 2-11.5 µm with ∼3 W output power, outperforming existing mid-infrared supercontinuum sources in terms of noise, spectral coverage, and output power. This translates to an excellent match for spectroscopic applications, establishing (sub-)ppb sensitivity for molecular hydrocarbons (e.g., CH4, C2H4), oxides (e.g., SO2, NOx), and small organic molecules (e.g., acetone, ethyl acetate) over the spectral range of the supercontinuum source with a measurement time varying from seconds to minutes. We demonstrate a practical application by measuring the off-gas composition of a bioreactor containing an acidic ammonia-oxidizing culture with the simultaneous detection of multiple nitrogen oxides (NO, NO2, N2O, etc.). As the different species absorb various parts of the spectrum, these results highlight the functionality of this spectroscopic system for biological and environmental applications.

Overall perspective of electrospun semiconductor metal oxides as high-performance gas sensor materials for NOx detection

Gas sensors based on nanostructured semiconductor metal oxide (SMO) materials have been extensively investigated as key components due to their advantages over other materials, namely, high sensitivity, stability, affordability, rapid response and simplicity. However, the difficulty of working at high temperatures, response in lower concentration and their selectivity are huge challenges of SMO materials for detecting gases. Therefore, researchers have not stopped their quest to develop new gas sensors based on SMOs with higher performance. This paper begins by highlighting the importance of nitrogen monoxide (NO) and nitrogen dioxide (NO2) detection for human health and addresses the challenges associated with existing methods in effectively detecting them. Subsequently, the mechanism of SMO gas sensors, analysis of their structure and fabrication techniques focusing on electrospinning technique, as well as their advantages, difficulties, and structural design challenges are discussed. Research on enhancing the sensing performance through tuning the fabrication parameters are summarized as well. Finally, the problems and potential of SMO based gas sensors to detect NOx are revealed, and the future possibilities are stated.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

Regulating the Electron Depletion Layer of Au/V2O5/Ag Thin Film Sensor for Breath Acetone as Potential Volatile Biomarker

Human exhaled breath has been utilized to identify biomarkers for diseases such as diabetes and cancer. The existence of these illnesses is indicated by a rise in the level of acetone in the breath. The development of sensing devices capable of identifying the onset of lung cancer or diabetes is critical for the successful monitoring and treatment of these diseases. The goal of this research is to prepare a novel breath acetone sensor made of Ag NPs/V₂O₅ thin film/Au NPs by combining DC/RF sputtering and post-annealing as synthesis methods. The produced material was characterized using X-ray diffraction (XRD), UV-Vis, Raman, and atomic force microscopy (AFM). The results revealed that the sensitivity to 50 ppm acetone of the Ag NPs/V₂O₅ thin film/Au NPs sensor was 96%, which is nearly twice and four times greater than the sensitivity of Ag NPs/V₂O₅ and pristine V₂O₅, respectively. This increase in sensitivity can be attributed to the engineering of the depletion layer of V₂O₅ through the double activation of the V₂O₅ thin films with uniform distribution of Au and Ag NPs that have different work function values.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Breath-by-breath measurement of exhaled ammonia by acetone-modifier positive photoionization ion mobility spectrometry via online dilution and purging sampling

Exhaled ammonia (NH₃) is an essential noninvasive biomarker for disease diagnosis. In this study, an acetone-modifier positive photoionization ion mobility spectrometry (AM-PIMS) method was developed for accurate qualitative and quantitative analysis of exhaled NH₃ with high selectivity and sensitivity. Acetone was introduced into the drift tube along with the drift gas as a modifier, and the characteristic NH₃ product ion peak of (C₃H₆O)₄NH₄⁺ (K₀ = 1.45 cm²/V·s) was obtained through the ion-molecule reaction with acetone reactant ions (C₃H₆O)₂H⁺ (K₀ = 1.87 cm²/V·s), which significantly increased the peak-to-peak resolution and improved the accuracy of exhaled NH₃ qualitative identification. Moreover, the interference of high humidity and the memory effect of NH₃ molecules were significantly reduced via online dilution and purging sampling, thus realizing breath-by-breath measurement. As a result, a wide quantitative range of 5.87-140.92 μmol/L with a response time of 40 ms was achieved, and the exhaled NH₃ profile could be synchronized with the concentration curve of exhaled CO₂. Finally, the analytical capacity of AM-PIMS was demonstrated by measuring the exhaled NH₃ of healthy subjects, demonstrating its great potential for clinical disease diagnosis.

Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring

Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO₂ nanopillars covered with a 15 nm metal-organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5-80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.

Millimeter-wave gas spectroscopy for breath analysis of COPD patients in comparison to GC-MS

The analysis of human breath is a very active area of research, driven by the vision of a fast, easy, and non-invasive tool for medical diagnoses at the point of care. Millimeter-wave gas spectroscopy (MMWGS) is a novel, well-suited technique for this application as it provides high sensitivity, specificity and selectivity. Most of all, it offers the perspective of compact low-cost systems to be used in doctors' offices or hospitals. In this work, we demonstrate the analysis of breath samples acquired in a medical environment using MMWGS and evaluate validity, reliability, as well as limitations and perspectives of the method. To this end, we investigated 28 duplicate samples from chronic obstructive lung disease patients and compared the results to gas chromatography-mass spectrometry (GC-MS). The quantification of the data was conducted using a calibration-free fit model, which describes the data precisely and delivers absolute quantities. For ethanol, acetone, and acetonitrile, the results agree well with the GC-MS measurements and are as reliable as GC-MS. The duplicate samples deviate from the mean values by only 6% to 18%. Detection limits of MMWGS depend strongly on the molecular species. For example, acetonitrile can be traced down to 1.8 × 10^-12mol by the MMWGS system, which is comparable to the GC-MS system. We observed correlations of abundances between formaldehyde and acetaldehyde as well as between acetonitrile and acetaldehyde, which demonstrates the potential of MMWGS for breath research.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Breath volatile organic compound analysis: an emerging method for gastric cancer detection

Gastric cancer is a common malignancy, being the fifth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Diagnosis of gastric cancer at the early stage is critical to effectively improve the survival rate. However, a substantial proportion of patients with gastric cancer in the early stages lack specific symptoms or are asymptomatic. Moreover, the imaging techniques currently used for gastric cancer screening, such as computed tomography and barium examination, are usually radioactive and have low sensitivity and specificity. Even though endoscopy has high accuracy for gastric cancer screening, its application is limited by the invasiveness of the technique. Breath analysis is an economic, effective, easy to perform, non-invasive detection method, and has no undesirable side effects on subjects. Extensive worldwide research has been conducted on breath volatile organic compounds (VOCs), which reveals its prospect as a potential method for gastric cancer detection. Many interesting results have been obtained and innovative methods have been introduced in this subject; hence, an extensive review would be beneficial. By providing a comprehensive list of breath VOCs identified by gastric cancer would promote further research in this field. This review summarizes the commonly used technologies for exhaled breath analysis, focusing on the application of analytical instruments in the detection of breath VOCs in gastric cancers, and the alterations in the profile of breath biomarkers in gastric cancer patients are discussed as well.

Experimental Investigation on Water Adsorption Using Laser Photoacoustic Spectroscopy and Numerical Simulations

In the present research we propose a model to assess the water vapors adsorption capacity of a SiO₂ trap in the breathing circuit, aiming to reduce the loading of interfering compounds in human breath samples. In this study we used photoacoustic spectroscopy to analyze the SiO₂ adsorption of interfering compounds from human breath and numerical simulations to study the flow of expired breath gas through porous media. As a result, the highest adsorption rate was achieved with a flow rate of 300 sccm, while the lowest rate was achieved with a flow rate of 600 sccm. In the procedure of H₂O removal from the human breath air samples, we determined a quantity of 213 cm³ SiO₂ pearls to be used for a 750 mL sampling bag, in order to keep the detection of ethylene free of H₂O interference. The data from this study encourages the premise that the SiO₂ trap is efficient in the reduction of interfering compounds (like water vapors) from the human breath.

Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient's exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

Socio-economic demands and challenges for non-invasive disease diagnosis through a portable breathalyzer by the incorporation of 2D nanosheets and SMO nanocomposites

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials. The trace level selective detection of volatile organic compounds (VOCs) in breath facilitates the study of physiological disorder and real-time health monitoring. This review focuses on advancements in chemiresistive gas sensor technology for biomarker detection associated with different diseases. Emphasis is placed on selective biomarker detection by semiconducting metal oxide (SMO) nanostructures, 2-dimensional nanomaterials (2DMs) and nanocomposites through various optimization strategies and sensing mechanisms. Their synergistic properties for incorporation in a portable breathalyzer have been elucidated. Furthermore, the socio-economic demands of a breathalyzer in terms of recent establishment of startups globally and challenges of a breathalyzer are critically reviewed. This initiative is aimed at highlighting the challenges and scope for improvement to realize a high performance chemiresistive gas sensor for non-invasive disease diagnosis.

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials.

Rapid in vitro differentiation of bacteria by ion mobility spectrometry

Rapid screening of infected people plays a crucial role in interrupting infection chains. However, the current methods for identification of bacteria are very tedious and labor intense. Fast on-site screening for pathogens based on volatile organic compounds (VOCs) by ion mobility spectrometry (IMS) could help to differentiate between healthy and potentially infected subjects. As a first step towards this, the feasibility of differentiating between seven different bacteria including resistant strains was assessed using IMS coupled to multicapillary columns (MCC-IMS). The headspace above bacterial cultures was directly drawn and analyzed by MCC-IMS after 90 min of incubation. A cluster analysis software and statistical methods were applied to select discriminative VOC clusters. As a result, 63 VOC clusters were identified, enabling the differentiation between all investigated bacterial strains using canonical discriminant analysis. These 63 clusters were reduced to 7 discriminative VOC clusters by constructing a hierarchical classification tree. Using this tree, all bacteria including resistant strains could be classified with an AUC of 1.0 by receiver-operating characteristic analysis. In conclusion, MCC-IMS is able to differentiate the tested bacterial species, even the non-resistant and their corresponding resistant strains, based on VOC patterns after 90 min of cultivation. Although this result is very promising, in vivo studies need to be performed to investigate if this technology is able to also classify clinical samples. With a short analysis time of 5 min, MCC-IMS is quite attractive for a rapid screening for possible infections in various locations from hospitals to airports.

Key Points

• Differentiation of bacteria by MCC-IMS is shown after 90-min cultivation.

• Non-resistant and resistant strains can be distinguished.

• Classification of bacteria is possible based on metabolic features.

Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry Analysis of Exhaled Breath Compounds after Whole Grain Diets

Exhaled breath is a potential noninvasive matrix to give new information about metabolic effects of diets. In this pilot study, non-targeted analysis of exhaled breath volatile organic compounds (VOCs) was made by comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS) to explore compounds relating to whole grain (WG) diets. Nine healthy subjects participated in the dietary intervention with parallel crossover design, consisting of two high-fiber diets containing whole grain rye bread (WGR) or whole grain wheat bread (WGW) and 1-week control diets with refined wheat bread (WW) before both diet periods. Large interindividual differences were detected in the VOC composition. About 260 VOCs were detected from exhaled breath samples, in which 40 of the compounds were present in more than half of the samples. Various derivatives of benzoic acid and phenolic compounds, as well as some furanones existed in exhaled breath samples only after the WG diets, making them interesting compounds to study further.

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.

Real-time breath ammonia measurement using a novel cuprous bromide sensor device in patients with chronic liver disease: a feasibility and pilot study

We developed a small portable sensor device using a p-type semiconductor cuprous bromide (CuBr) thin film to measure breath ammonia in real time with highsensitivity and selectivity. Breath ammonia is reportedly associated with chronic liver disease (CLD). We aimed to assess the practical utility of the novel CuBr sensor device for exhaled breath ammonia and the correlation between breath and blood ammonia in CLD patients. This was a feasibility and pilot clinical study of 21 CLD patients and 18 healthy volunteers. Breath ammonia was directly and quickly measured using the novel CuBr sensor device and compared with blood ammonia measured at the same time. CLD patients had significantly higher breath ammonia levels than healthy subjects (p = 1.51 × 10^-3), with the level of significance being similar to that for blood ammonia levels (p= 0.024). Significant differences were found in breath and blood ammonia between the healthy and cirrhosis groups (p = 2.97 × 10^-3 and 3.76 × 10^-3, respectively). Significant, positive correlations between breath and blood ammonia were noted in the CLD group (R = 0.747, p = 1.00 × 10^-4), healthy/CLD group (R = 0.741, p = 6.75 × 10^-8), and cirrhosis group (R = 0.744, p = 9.52 × 10^-4). In conclusion, the newly developed, easy-to-use, and small portable CuBr sensor device was able to non-invasively measure breath ammonia in real time. Breath ammonia measured using the device was correlated with blood ammonia and the presence of liver cirrhosis, and might be an alternative surrogate biomarker to blood ammonia.

Non-Invasive Blood Glucose Monitoring Technology: A Review

In recent years, with the rise of global diabetes, a growing number of subjects are suffering from pain and infections caused by the invasive nature of mainstream commercial glucose meters. Non-invasive blood glucose monitoring technology has become an international research topic and a new method which could bring relief to a vast number of patients. This paper reviews the research progress and major challenges of non-invasive blood glucose detection technology in recent years, and divides it into three categories: optics, microwave and electrochemistry, based on the detection principle. The technology covers medical, materials, optics, electromagnetic wave, chemistry, biology, computational science and other related fields. The advantages and limitations of non-invasive and invasive technologies as well as electrochemistry and optics in non-invasives are compared horizontally in this paper. In addition, the current research achievements and limitations of non-invasive electrochemical glucose sensing systems in continuous monitoring, point-of-care and clinical settings are highlighted, so as to discuss the development tendency in future research. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market.

Real-time selected ion flow tube mass spectrometry to assess short- and long-term variability in oral and nasal breath

Breath-based non-invasive diagnostics have the potential to provide valuable information about a person's health status. However, they are not yet widely used in clinical practice due to multiple factors causing variability and the lack of standardized procedures. This study focuses on the comparison of oral and nasal breathing, and on the variability of volatile metabolites over the short and long term. Selected ion flow tube mass spectrometry (SIFT-MS) was used for online analysis of selected volatile metabolites in oral and nasal breath of 10 healthy individuals five times in one day (short-term) and six times spread over three weeks (long-term), resulting in nearly 100 breath samplings. Intra-class correlation coefficients (ICCs) were used to assess short- and long-term biological variability. Additionally, the composition of ambient air was analyzed at different samplings. The selected volatiles common in exhaled breath were propanol, 2,3-butanedione, acetaldehyde, acetone, ammonia, dimethyl sulfide, isoprene, pentane, and propanal. Additionally, environmental compounds benzene and styrene were analyzed as well. Volatile metabolite concentrations in ambient air were not correlated with those in exhaled breath and were significantly lower than in breath samples. All volatiles showed significant correlation between oral and nasal breath. Five were significantly higher in oral breath compared to nasal breath, while for acetone, propanal, dimethyl sulfide, and ammonia, concentrations were similar in both matrices. Variability depended on the volatile metabolite. Most physiologically relevant volatiles (acetone, isoprene, propanol, acetaldehyde) showed good to very good biological reproducibility (ICC > 0.61) mainly in oral breath and over a short-term period of one day. Both breathing routes showed relatively similar patterns; however, bigger differences were expected. Therefore, since sampling from the mouth is practically more easy, the latter might be preferred.

Constructing an E-Nose Using Metal-Ion-Induced Assembly of Graphene Oxide for Diagnosis of Lung Cancer via Exhaled Breath

A flexible electronic-nose (E-nose) was constructed by assembling graphene oxide (GO) using different types of metal ions (M^x+) with different ratio of GO to M^x+. Owing to the cross-linked networks, the M^x+-induced assembly of graphene films resulted in different porous structures. A chemi-resistive sensor array was constructed by coating the GO-M hybrid films on PET substrate patterned with 8 interdigited electrodes, followed by in situ reduction of GO to rGO with hydrazine vapor. Each of the sensing elements on the sensor array showed a cross-reactive response toward different types of gases at room temperature. Compared to bare rGO, incorporation of metal species into rGO significantly improved sensitivity owing to the additional interaction between metal species and gas analyte. Principle component analysis (PCA) showed that four types of exhaled breath (EB) biomarkers including acetone, isoprene, ammonia, and hydrothion in sub-ppm concentrations can be discriminated well. To overcome the interference from humidity in EB, a protocol to collect and analyze EB gases was established and further validated by simulated EB samples. Finally, clinical EB samples collected from patients with lung cancer and healthy controls were analyzed. In a 106 case study, the healthy group can be accurately distinguished from lung cancer patients by linear discrimination analysis. With the assistance of an artificial neural network, a sensitivity of 95.8% and specificity of 96.0% can be achieved in the diagnosis of lung cancer based on the E-nose. We also find that patients with renal failure can be distinguished through comparison of dynamic response curves between patient and healthy samples on some specific sensing elements. These results demonstrate the proposed E-nose will have great potential in noninvasive disease screening and personalized healthcare management.

Development of a new system to collect and dry patient exhalation samples for laser photo-acoustic gas analyzer

The present work is devoted to the development of a new system for collecting and drying patient exhalation samples for a laser photo-acoustic gas analyzer for express analysis of exhaled air, as well as the possibility of ozone sterilization of the gas tract after use. It is proposed to collect the patient's exhalation in a disposable sterile plastic bag, place it in a low-temperature freezer chamber to freeze the water vapor, and then transfer part of the dried exhaled sample to the gas analyzer using a disposable syringe. It is proposed to use ozone purge for 10-15 min to sterilize the gas path. Experimentally, it is shown that the water vapor content in the exhalation samples decreased by ~ 20 times when the packet with samples was kept at a temperature of - 18 °C during 10 min. Cooling the exhalation sample in the packet to - 45 °С and lower will allow reducing the water vapor content at least 200 times from the initial level. A new universal system for collecting and drying of patient exhalation samples for a medical laser photo-acoustic gas analyzer has been developed and tested. A gas path for a medical laser gas analyzer has been designed, which allows sterilization of the internal gas path surfaces using ozone purging.

Applications of Near Infrared Photoacoustic Spectroscopy for Analysis of Human Respiration: A Review

In this review, applications of near-infrared photoacoustic spectroscopy are presented as an opportunity to evaluate human respiration because the measurement of breath is fast, intact and simple to implement. Recently, analytical methods for measuring biomarkers in exhaled air have been extensively developed. With laser-based photoacoustic spectroscopy, volatile organic compounds can be identified with high sensitivity, at a high rate, and with very good selectivity. The literature review has shown the applicability of near-infrared photoacoustic spectroscopy to one of the problems of the real world, i.e., human health. In addition, the review will consider and explore different breath sampling methods for human respiration analysis.

From Pre-Diabetes to Diabetes: Diagnosis, Treatments and Translational Research

Diabetes, a silent killer, is one of the most widely prevalent conditions of the present time. According to the 2017 International Diabetes Federation (IDF) statistics, the global prevalence of diabetes among the age group of 20-79 years is 8.8%. In addition, 1 in every 2 persons is unaware of the condition. This unawareness and ignorance lead to further complications. Pre-diabetes is the preceding condition of diabetes, and in most of the cases, this ultimately leads to the development of diabetes. Diabetes can be classified into three types, namely type 1 diabetes, type 2 diabetes mellitus (T2DM) and gestational diabetes. The diagnosis of both pre-diabetes and diabetes is based on glucose criteria; the common modalities used are fasting plasma glucose (FPG) test and oral glucose tolerance test (OGTT). A glucometer is commonly used by diabetic patients to measure blood glucose levels with fast and rather accurate measurements. A few of the more advanced and minimally invasive modalities include the glucose-sensing patch, SwEatch, eyeglass biosensor, breath analysis, etc. Despite a considerable amount of data being collected and analyzed regarding diabetes, the actual molecular mechanism of developing type 2 diabetes mellitus (T2DM) is still unknown. Both genetic and epigenetic factors are associated with T2DM. The complications of diabetes can predominantly be classified into two categories: microvascular and macrovascular. Retinopathy, nephropathy, and neuropathy are grouped under microvascular complications, whereas stroke, cardiovascular disease, and peripheral artery disease (PAD) belong to macrovascular complications. Unfortunately, until now, no complete cure for diabetes has been found. However, the treatment of pre-diabetes has shown significant success in preventing the further progression of diabetes. To prevent pre-diabetes from developing into T2DM, lifestyle intervention has been found to be very promising. Various aspects of diabetes, including the aforementioned topics, have been reviewed in this paper.

VOC biomarkers identification and predictive model construction for lung cancer based on exhaled breath analysis: research protocol for an exploratory study

INTRODUCTION

Lung cancer is the most common cancer and the leading cause of cancer death in China, as well as in the world. Late diagnosis is the main obstacle to improving survival. Currently, early detection methods for lung cancer have many limitations, for example, low specificity, risk of radiation exposure and overdiagnosis. Exhaled breath analysis is one of the most promising non-invasive techniques for early detection of lung cancer. The aim of this study is to identify volatile organic compound (VOC) biomarkers in lung cancer and to construct a predictive model for lung cancer based on exhaled breath analysis.

METHODS AND ANALYSIS

The study will recruit 389 lung cancer patients in one cancer centre and 389 healthy subjects in two lung cancer screening centres. Bio-VOC breath sampler and Tedlar bag will be used to collect breath samples. Gas chromatography-mass spectrometry coupled with solid phase microextraction technique will be used to analyse VOCs in exhaled breath. VOC biomarkers with statistical significance and showing abilities to discriminate lung cancer patients from healthy subjects will be selected for the construction of predictive model for lung cancer.

ETHICS AND DISSEMINATION

The study was approved by the Ethics Committee of Sichuan Cancer Hospital on 6 April 2017 (No. SCCHEC-02-2017-011). The results of this study will be disseminated in presentations at academic conferences, publications in peer-reviewed journals and the news media.

TRIAL REGISTRATION NUMBER

ChiCTR-DOD-17011134; Pre-results.

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.

The Use of Selected Ion Flow Tube-Mass Spectrometry Technology to Identify Breath Volatile Organic Compounds for the Detection of Head and Neck Squamous Cell Carcinoma: A Pilot Study

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide, with approximately 630,000 new cases diagnosed each year. The development of low-cost and non-invasive tools for the detection of HNSCC using volatile organic compounds (VOCs) in the breath could potentially improve patient care. The aim of this study was to investigate the feasibility of selected ion flow tube mass spectrometry (SIFT-MS) technology to identify breath VOCs for the detection of HNSCC. Materials and Methods: Breath samples were obtained from HNSCC patients (N = 23) and healthy volunteers (N = 21). Exhaled alveolar breath samples were collected into FlexFoil^® PLUS (SKC Limited, Dorset, UK) sampling bags from newly diagnosed, histologically confirmed, untreated patients with HNSCC and from non-cancer participants. Breath samples were analyzed by Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) (Syft Technologies, Christchurch, New Zealand) using Selective Ion Mode (SIM) scans that probed for 91 specific VOCs that had been previously reported as breath biomarkers of HNSCC and other malignancies. Results: Of the 91 compounds analyzed, the median concentration of hydrogen cyanide (HCN) was significantly higher in the HNSCC group (2.5 ppb, 1.6–4.4) compared to the non-cancer group (1.1 ppb, 0.9–1.3; Benjamini–Hochberg adjusted p < 0.05). A receiver operating curve (ROC) analysis showed an area under the curve (AUC) of 0.801 (95% CI, 0.65952–0.94296), suggesting moderate accuracy of HCN in distinguishing HNSCC from non-cancer individuals. There were no statistically significant differences in the concentrations of the other compounds of interest that were analyzed. Conclusions: This pilot study demonstrated the feasibility of SIFT-MS technology to identify VOCs for the detection of HNSCC.

Analysis of Human Breath by Millimeter-Wave/Terahertz Spectroscopy

Breath gas analysis is a promising tool for medical research and diagnosis. A particularly powerful technological approach is millimeter-wave/terahertz (mmW/THz) spectroscopy, because it is a very sensitive and highly selective technique. In addition, it offers the potential for compact and affordable sensing systems for wide use. In this work, we demonstrate the capability of a mmW/THz spectrometer for breath analysis. Samples from three volunteers and a sample from ambient air were analyzed with respect to 31 different molecular species. High-resolution absorption spectra were measured by scanning two absorption lines from each species. Out of the 31, a total of 21 species were detected. The results demonstrate the potential of mmW/THz spectroscopy for breath analysis.

Hybrid Analytical Platform Based on Field-Asymmetric Ion Mobility Spectrometry, Infrared Sensing, and Luminescence-Based Oxygen Sensing for Exhaled Breath Analysis

The reliable online analysis of volatile compounds in exhaled breath remains a challenge, as a plethora of molecules occur in different concentration ranges (i.e., ppt to %) and need to be detected against an extremely complex background matrix. Although this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three different detector types based on truly orthogonal measurement principles as an alternative solution: Field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). By carefully aligning the experimental needs and measurement protocols of all three methods, they were successfully integrated into a single compact analytical platform suitable for online measurements. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N₂), oxygen (O₂), carbon dioxide (CO₂), and acetone as a model volatile organic compound (VOC) commonly present in breath. All three target analytes could be detected within their respectively breath-relevant concentration range, i.e., CO₂ and O₂ at 3-5 % and at ~19.6 %, respectively, while acetone could be detected with LOQs as low as 165-405 ppt. Orthogonality of the three methods operating in concert was clearly proven, which is essential to cover a possibly wide range of detectable analytes. Finally, the remaining challenges toward the implementation of the developed hybrid FAIMS-FTIR-LS system for exhaled breath analysis for metabolic studies in small animal intensive care units are discussed.

Use of Electronic Noses for Diagnosis of Digestive and Respiratory Diseases through the Breath

The increased occurrence of chronic diseases related to lifestyle or environmental conditions may have a detrimental effect on long-term health if not diagnosed and controlled in time. For this reason, it is important to develop new noninvasive early diagnosis equipment that allows improvement of the current diagnostic methods. This, in turn, has led to an exponential development of technology applied to the medical sector, such as the electronic nose. In addition, the appearance of this type of technology has allowed the possibility of studying diseases from another point of view, such as through breath analysis. This paper presents a bibliographic review of past and recent studies, selecting those investigations in which a patient population was studied with electronic nose technology, in order to identify potential applications of this technology in the detection of respiratory and digestive diseases through the analysis of volatile organic compounds present in the breath.

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.

Breath based volatile organic compounds in the detection of breast, lung, and colorectal cancers: A systematic review

BACKGROUND

Detecting volatile organic compounds (VOCs) could provide a rapid, noninvasive, and inexpensive screening tool for detecting cancer.

OBJECTIVE

In this systematic review, we identified specific exhaled breath VOCs correlated with lung, colorectal, and breast cancer.

METHODS

We identified relevant studies published in 2015 and 2016 by searching Pubmed and Web of Science. The protocol for this systematic review was registered in PROSPERO and the PRISMA guidelines were used in reporting. VOCs and performance data were extracted.

RESULTS

Three hundred and thirty three records were identified and 43 papers were included in the review, of which 20 were review articles themselves. We identified 17 studies that listed the VOCs with at least a subset of statistics on detection cutoff levels, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and gradient.

CONCLUSIONS

Breath analysis for cancer screening and early detection shows promise, because samples can be collected easily, safely, and frequently. While gas chromatography-mass spectrometry is considered the gold standard for identifying specific VOCs, breath analysis has moved into analyzing patterns of VOCs using a variety of different multiple sensor techniques, such as eNoses and nanomaterials. Further development of VOCs for early cancer detection requires clinical trials with standardized breath sampling methods.

Compact devices for generation of reference trace VOC mixtures: a new concept in assuring quality at chemical and biochemical laboratories

Volatile organic compounds (VOCs) in gas mixtures at trace level (nmol/mol) are routinely measured by chemical and biochemical laboratories as climate indicators, indoor air quality pollutants from building materials emissions, contaminants in food and beverages, and biomarkers in body fluids (blood, urine, breath) of occupational exposure or human diseases. Current analytical instruments used for measurements are gas chromatographs equipped with various injector and detector configurations. The assurance of measurement quality is done by using a huge amount of certified liquid VOC standard solutions (or gaseous VOC standard cylinders) with multiple dilutions to reach the required trace level. This causes high standard uncertainty in instrument calibrations, high cost, and high consumption of analysis and laboratory personal time. In this paper, we present the implementation of portable generators producing VOC gas standards at trace level for automatic and direct calibration of VOC detectors employed in various contexts, removing the need for preparation of matrix calibration standards in cylinders. Two compact devices in-house developed by two national metrology institutes-the Istituto Nazionale di Ricerca Metrologica (INRIM) and the Federal Institute of Metrology (METAS)-are here used to dynamically generate reference gas mixtures in an SI traceable way. The two devices are based on different technologies: diffusion and permeation, for INRIM and METAS, respectively. A metrological characterization is given and the practical implementation at chemical and biochemical laboratories is discussed. Graphical abstract Onsite calibration with transportable generation system with similar performances to primary laboratory devices.

Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring

The review describes the technologies used in the field of breath analysis to diagnose and monitor diabetes mellitus. Currently the diagnosis and monitoring of blood glucose and ketone bodies that are used in clinical studies involve the use of blood tests. This method entails pricking fingers for a drop of blood and placing a drop on a sensitive area of a strip which is pre-inserted into an electronic reading instrument. Furthermore, it is painful, invasive and expensive, and can be unsafe if proper handling is not undertaken. Human breath analysis offers a non-invasive and rapid method for detecting various volatile organic compounds thatare indicators for different diseases. In patients with diabetes mellitus, the body produces excess amounts of ketones such as acetoacetate, beta-hydroxybutyrate and acetone. Acetone is exhaled during respiration. The production of acetone is a result of the body metabolising fats instead of glucose to produce energy. There are various techniques that are used to analyse exhaled breath including Gas Chromatography Mass Spectrometry (GC-MS), Proton Transfer Reaction Mass Spectrometry (PTR-MS), Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS), laser photoacoustic spectrometry and so on. All these techniques are not portable, therefore this review places emphasis on how nanotechnology, through semiconductor sensing nanomaterials, has the potential to help individuals living with diabetes mellitus monitor their disease with cheap and portable devices.

Infrared spectroscopy based on broadly tunable quantum cascade lasers and polycrystalline diamond waveguides

Recently emerging broadly tunable quantum cascade lasers (tQCL) emitting in the mid-infrared (MIR) are a versatile alternative to well established thermal emitters in combination with interferometers as applied in Fourier transform infrared (FTIR) spectroscopy.