Detection Of Volatile Organic Compounds Research Articles

Advancements in miniaturized infrared spectroscopic-based volatile organic compound sensors: A systematic review

The global trends of urbanization and industrialization have given rise to critical environmental and air pollution issues that often receive insufficient attention. Among the myriad pollution sources, volatile organic compounds (VOCs) stand out as a primary cluster, posing a significant threat to human society. Addressing VOCs emissions requires an effective mitigation action plan, placing technological development, especially in detection, at the forefront. Photonic sensing technologies rooted in the infrared (IR) light and matter interaction mechanism offer nondestructive, fast-response, sensitive, and selective chemical measurements, making them a promising solution for VOC detection. Recent strides in nanofabrication processes have facilitated the development of miniaturized photonic devices and thus sparked growing interest in the creation of low-cost, highly selective, sensitive, and fast-response IR optical sensors for VOC detection. This review work thus serves a timely need to provide the community a comprehensive understanding of the state of the art in this field and illuminate the path forward in addressing the pressing issue of VOC pollution.

Synthesis of ZnO@Fe2O3 microflowers with enhanced performance in volatile organic compound detection

Metal-oxide semiconductor is widely applied in gas sensor for volatile organic compound detection. However, these sensors usually exhibit poor selectivity and inferior sensitivity. Here, ZnO@Fe2O3 microflowers were synthesized using FeOOH microflowers as precursors via a simple solution reaction route, followed by heat treatment. The ZnO@Fe2O3 microflowers were characterized using a series of techniques, and their sensing responses to volatile organic compounds (VOCs) were evaluated in comparison with those of pristine Fe2O3. The ZnO@Fe2O3 microflower sensor exhibited better responses to several gases, especially acetone, with a response of 74.3 towards 100 ppm acetone, which is 2.85 times higher than that of the Fe2O3 microflower sensor. The sensor also presented high selectivity, reproducibility, and stability in sensing different VOCs. The large specific surface area, heterostructure, favorable hierarchical flower-like nanostructure assembled by the nanosheets, and high porosity of the ZnO@Fe2O3 microflowers contributed to its enhanced gas-sensing responses. Overall, this synthetic strategy for fabricating flower-like ZnO@Fe2O3 nanostructures can be widely applied.

Non-invasive VOCs detection to monitor the gut microbiota metabolism in-vitro

This work implemented a non-invasive volatile organic compounds (VOCs) monitoring approach to study how food components are metabolised by the gut microbiota in-vitro. The fermentability of a model food matrix rich in dietary fibre (oat bran), and a pure prebiotic (inulin), added to a minimal gut medium was compared by looking at global changes in the volatilome. The substrates were incubated with a stabilised human faecal inoculum over a 24-h period, and VOCs were monitored without interfering with biological processes. The fermentation was performed in nitrogen-filled vials, with controlled temperature, and tracked by automated headspace-solid-phase microextraction coupled with gas chromatography–mass spectrometry. To understand the molecular patterns over time, we applied a multivariate longitudinal statistical framework: repeated measurements—ANOVA simultaneous component analysis. The methodology was able to discriminate the studied groups by looking at VOCs temporal profiles. The volatilome showed a time-dependency that was more distinct after 12 h. Short to medium-chain fatty acids showed increased peak intensities, mainly for oat bran and for inulin, but with different kinetics. At the same time, alcohols, aldehydes, and esters showed distinct trends with discriminatory power. The proposed approach can be applied to study the intertwined pathways of gut microbiota food components interaction in-vitro.

ZIF-8-Based Surface Plasmon Resonance and Fabry-Pérot Sensors for Volatile Organic Compounds.

This work explores the use of ZIF-8, a metal-organic framework (MOF) material, for its use in the optical detection of volatile organic compounds (VOCs) in Fabry-Pérot and surface plasmon resonance (SPR)-based sensors. The experiments have been carried out with ethanol (EtOH) and show response times as low as 30 s under VOC-saturated atmospheres, and the estimated limit of detection is below 4000 ppm for both sensor types. The selectivity towards other VOCs is relatively poor, although the dynamics of adsorption/desorption differ for each VOC and could be used for selectivity purposes. Furthermore, the hydrophobicity of ZIF-8 has been confirmed and the fabricated sensors are insensitive to this compound, which is a very attractive result for its practical use in gas sensing devices.

Development of an organ-on-chip model for the detection of volatile organic compounds as potential biomarkers of tumour progression

Early detection of tumours remains a significant challenge due to their invasive nature and the limitations of current monitoring techniques. Liquid biopsies have emerged as a minimally invasive diagnostic approach, wherein volatile organic compounds (VOCs) show potential as compelling candidates. However, distinguishing tumour-specific VOCs is difficult due to the presence of gases from non-tumour tissues and environmental factors. Therefore, it is essential to develop preclinical models that accurately mimic the intricate tumour microenvironment to induce cellular metabolic changes and secretion of tumour-associated VOCs. In this study, a microfluidic device was used to recreate the ischaemic environment within solid tumours for the detection of tumour-derived VOCs. The system represents a significant advance in understanding the role of VOCs as biomarkers for early tumour detection and holds the potential to improve patient prognosis; particularly for inaccessible and rapidly progressing tumours such as glioblastoma.

Detection of volatile organic compounds as new paradigm to accelerate antimicrobial susceptibility testing: performance evaluation of VITEK® REVEAL™.

The measurement of VOCs release in the headspace of a bacterial culture represents a new approach to rapidly assess antimicrobial susceptibility. Herein, we evaluated the diagnostic performance of the VITEK® REVEAL™ system directly from a collection of Gram-negative positive blood cultures. One hundred and twenty-eight positive blood cultures were included in the analysis (Enterobacterales, n = 95; Pseudomonas aeruginosa, n = 21; Acinetobacter baumannii complex, n = 12). Samples were processed using VITEK® REVEAL™ according to the manufacturer's recommendations, and MICs of 22 antimicrobials were compared with those obtained using reference methods. Categorical agreement (CA), essential agreement (EA) and categorical errors were calculated. Overall, 2220 strain/antibiotic pair combinations were analysed. Of these, most were classified as resistant by reference antimicrobial susceptibility testing (1091/2220; 48.7%). The overall CA and EA were 97.6% and 97.7%, respectively. CA ranged from 97.5% in Enterobacterales to 97.9% in both P. aeruginosa and A. baumannii complex. The overall number of categorical discrepancies were: 18 very major errors (1.6%), 13 major errors (1.2%) and 22 minor errors (2.4%). EA ranged from 95.2% in P. aeruginosa to 98.1% in Enterobacterales. Screening test for ESBL phenotype was positive, indeterminate and negative in 13.7%, 32.6% and 27.4% of Enterobacterales isolates tested by both VITEK® REVEAL™ and the reference method, showing 100% CA. VITEK® REVEAL™ represents a reliable tool to obtain antimicrobial susceptibility results of the main Gram-negative species directly from positive blood cultures with time to results of less than 8 h.

Nanofibrous Conductive Sensor for Limonene: One-Step Synthesis via Electrospinning and Molecular Imprinting.

Detecting volatile organic compounds (VOCs) emitted from different plant species and their organs can provide valuable information about plant health and environmental factors that affect them. For example, limonene emission can be a biomarker to monitor plant health and detect stress. Traditional methods for VOC detection encounter challenges, prompting the proposal of novel approaches. In this study, we proposed integrating electrospinning, molecular imprinting, and conductive nanofibers to fabricate limonene sensors. In detail, polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) served here as fiber and cavity formers, respectively, with multiwalled carbon nanotubes (MWCNT) enhancing conductivity. We developed one-step monolithic molecularly imprinted fibers, where S(-)-limonene was the target molecule, using an electrospinning technique. The functional cavities were fixed using the UV curing method, followed by a target molecule washing. This procedure enabled the creation of recognition sites for limonene within the nanofiber matrix, enhancing sensor performance and streamlining manufacturing. Humidity was crucial for sensor working, with optimal conditions at about 50% RH. The sensors rapidly responded to S(-)-limonene, reaching a plateau within 200 s. Enhancing fiber density improved sensor performance, resulting in a lower limit of detection (LOD) of 137 ppb. However, excessive fiber density decreased accessibility to active sites, thus reducing sensitivity. Remarkably, the thinnest mat on the fibrous sensors created provided the highest selectivity to limonene (Selectivity Index: 72%) compared with other VOCs, such as EtOH (used as a solvent in nanofiber development), aromatic compounds (toluene), and two other monoterpenes (α-pinene and linalool) with similar structures. These findings underscored the potential of the proposed integrated approach for selective VOC detection in applications such as precision agriculture and environmental monitoring.

Fiber Optic Sensor Coated with Multiple Layers of Hexagonal Boron Nitride Nanosheets (BNNS) for the Detection of Volatile Organic Compounds.

Nowadays, volatile organic compound (VOC) detection is imperative to ensure environmental safety in industry and indoor environments, as well as to monitor human health in medical diagnosis. Gas sensors with the best sensor response, selectivity, and stability are in high demand. Simultaneously, the advancement of nanotechnology facilitates novel nanomaterial-based gas sensors with superior sensor characteristics and low power consumption. Recently, boron nitride, a 2D material, has emerged as an excellent candidate for gas sensing and demonstrated exceptional sensing characteristics for new-generation gas sensing devices. Herein, ultrathin porous boron nitride nanosheets (BNNSs) with large lateral sizes were synthesized using a facile synthesis approach, and their material characteristics were investigated utilizing a variety of analytical techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, ultraviolet spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. A BNNS-coated cladding-modified fiber optic sensor (FOS) probe was prepared and employed for VOC (ammonia, ethanol, and acetone) sensing across concentrations varying from 0 to 300 ppm. The BNNSs-coated FOS demonstrated better selectivity toward 300 ppm ammonia, and specifically annealed BNNSs displayed a maximum sensor response of 55% along with a response/recovery times of 15 s/34 s compared to its counterparts. The superior ammonia sensing performances could be attributed to the formation of ultrathin nanosheets and a porous surface with slit-like features in hexagonal boron nitride.

Research Progress of Using Canine Olfactory Search for Human Remains.

In the process of murder investigation, it is of great significance to find the discarded and buried human remains accurately. The main methods of searching for human remains include human visual search, aerial detection, geophysical technology, remote imaging technology and canine olfactory search technique. Canine olfactory search for human remains is a recognized time-effective and non-invasive search method, making dogs the most valuable search tool in forensic investigation. By systematically reviewing and summarizing relevant literature, and based on the theory of volatile organic compound produced by the decomposition of human remains, this paper explores the basic principle of the canine olfactory search technique for human remains. This paper also reviews the application of training canine search technique for human remains in forensic investigation by using human blood, tissue, cadaver putrefying fluid and odor substitutes as sniffing sources. The application prospect of canine olfactory search for human remains was prospected from the perspectives of detection of volatile organic compound during cadaver decay, development of odor substitutes and adsorption devices, and technology tactics used in canine training and use, to provide references for the relevant research of canine olfactory search for human remains in China.

Facile synthesis of porous Au-decorated SnO2 microflowers with abundant oxygen vacancies for trace VOCs detection

Hierarchical Au-decorated SnO2 (Au@SnO2) microflowers with a porous structure were successfully synthesized. The preparation process involves in a hydrothermal method, calcination treatment, as well as modification. The hierarchical structure consisted of a large number of porous, uniform nanosheets. Excellent gas-sensing performances were demonstrated by the gas sensors fabricated using Au@SnO2 microflowers, for detecting volatile organic compounds (VOCs). In particular, for the accurate and fast detection of formaldehyde, the 2%Au-decorated SnO2 microflowers sensors showed a strong sensing response (76.5) for formaldehyde (100 ppm) at 140 °C, approximately three times higher than that (28.2) observed for the pure porous SnO2 microflowers. The response and recovery times (10 s/16 s) were shorter than those (12 s/25 s) of the pure SnO2, respectively. The detection limit for the 2%Au@SnO2 sensor was 19.03 ppb. The sensing mechanism of Au@SnO2 sensors was also investigated. Favorable porous 3-dimensional structure, high catalytic activity, and electron sensitization effect of Au nanoparticles (NPs) improved the gas-sensing performance. Furthermore, the catalytic activity of Au NPs was maximized due to their uniform distribution on the surface of each porous SnO2 nanosheet. The Au-decorated SnO2 microflowers sensors have great potential for the accurate, fast, and highly sensitive response detection of formaldehyde gas.

Foraging behavior of Ganaspis brasiliensis in response to temporal dynamics of volatile release by the fruit–Drosophila suzukii complex

The lineage G1 of Ganaspis brasiliensis is a larval parasitoid of the worldwide pest Drosophila suzukii and one of its most effective natural enemies in the native area. Because of its high degree of host specificity, G. brasiliensis G1 is considered a suitable species for introduction in areas invaded by D. suzukii following a classical biological control approach. Indeed, the release of the parasitoid is currently implemented in the USA and Italy. G1 females attack only host larvae developing in ripening fresh fruits on the plant and not larvae that develop in decaying fruits. To date, virtually no information is available on the cues regulating the foraging behavior of G1. In this study, we therefore aimed to find out whether chemical cues are exploited by G1 females to: (i) locate host fruits; (ii) locate suitable host larvae within infested fruit; (iii) discriminate between infested fresh fruits and infested rotting ones. We used a model system composed of blueberries and D. suzukii tested in two-choice olfactometer bioassays (with D. suzukii-infested fruits, healthy fruits, and pure air as odor targets), followed by the collection and the characterization of volatile organic compounds (VOCs) released by the tested targets. The results showed a clear time-dependent choice made by G1 females of infested versus healthy fruits related to the concomitant development of D. suzukii larvae and fruit degradation. Attraction to infested fruits was recorded during the early stages of infestation, followed by a repellent phase coinciding with fruits largely degraded by larval feeding. We found that the attractiveness of G. brasiliensis G1 towards fruits infested by young larvae was associated with the detection of VOCs released by the infested blueberries, and host’s cuticular hydrocarbons. Conversely, the repellence of older and deteriorated fruits hosting developed D. suzukii larvae was associated with the detection of a fermentation compound produced by microorganisms likely carried inside the fruit by the flies. The response of G1 females to the temporal dynamics of chemical cues emitted by the fruit–host larvae complex was consistent with the high degree of specificity of the parasitoid towards the ripening host fruits and towards D. suzukii.

Canine olfactory detection and its relevance for the medical identification of patients with COVID-19

Introduction The assessment of Volatile Organic Compounds (VOCs) in exhaled breath or sweat represents a potential non-invasive and rapid diagnostic tool for respiratory diseases. Objective To determine if trained dogs can reliably identify the odour associated with COVID19. Methods This is a monocentric prospective study carried out in the Emergency Department (ED) of a university hospital fromJulyto November 2021.Axillary sweat samples from all patients were collected bytwo trained health care professionals. The samples were collected in the form of sterile gauze swabs placed under the armpits for at least 4 h for each patient.Then, Tubes wereshiftedto the double-blind dog training centre for VOC detection by two individuals. Results Dogs were tested using a total of 129 axillary sweat samples; 69 of the 107 patients who tested positive for COVID-19 based on their odours had a positive PCR/Antigen test and 19 of the 22 patients who were tested negative for COVID-19 by the dogs had a negative PCR test. The sniffer dog infection detection method had a sensitivity of 95.83% and a specificity of 33.33%. The PPV was 64.49% and the NPVwas 86.36%. The measurement of the intensity of the connection between the two variables (disease/sign) was very strong (Q = 0.84). This link is statistically significant (X2 = 19.13) with a probability p ≤ 0.001. Conclusion Overall, the use of trained detection dogs as a screening method for SARS-CoV-2 is an interesting avenue of research that warrants further exploration and validation.

Comparative study of volatile organic compound profiles in aromatic and non-aromatic rice cultivars using HS-GC–IMS and their correlation with sensory evaluation

Analyzing of volatile organic compounds (VOCs) is important in rice quality control and breeding aromatic cultivars. This study utilized HS-GC–IMS, known for its simplicity, high sensitivity, and low cost, to analyze VOCs in 59 aromatic and 6 non-aromatic rice cultivars. The aromatic intensity of each rice cultivar was determined by artificial sensory evaluation, thus the correlation between sensory evaluation scores and VOC peak volumes was explored. A total of 77 VOCs were identified, with aromatic cultivars exhibiting prevalent short-chain aliphatic aldehydes, alcohols, and ketones, while non-aromatic samples contained higher concentrations of esters, furans, terpenes, and benzene derivatives. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) based on VOC data effectively distinguished the two rice types, and 35 VOCs were regarded as the marker VOCs contributing to the volatile variations. Furthermore, the correlation analysis between sensory evaluation and VOC markers revealed that most of these VOCs had a positive correlation with the sensory evaluation scores, with 1-pentanol, 2-acetyl-1-pyrroline (D), and 1-hexanol (M) exhibiting the highest correlation coefficients. Meanwhile, most VOC markers also exhibited positive correlations with other volatiles. The results confirm the suitability of HS-GC–IMS for VOC detection and rice category discrimination.

AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer.

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Room temperature detection of aspergillus flavus volatile organic compounds (VOCs) under simulated conditions using graphene oxide and tin oxide Nanorods (SnO2 NRs-GO)

This study investigated the application of a hybrid nanocomposite of tin oxide nanorods (SnO2 NRs) and graphene oxide (GO) for the chemoresistive detection of some volatile compounds (hexanal, benzaldehyde, octanal, 1-octanol, and ethyl acetate vapours) emitted by Aspergillus flavus under simulated conditions. The synthesised materials were characterised using various analytical techniques, including high-resolution transmission electron microscopy (HR-TEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) analysis, and Fourier transform infrared spectroscopy (FTIR). Three sensors were fabricated: individual nanomaterials (i.e., SnO2 and GO) and composites (SnO2-GO). The results showed that SnO2 NRs had limited sensitivity as a sensor, while GO-based sensors responded to various analyte vapours. However, the incorporation of SnO2 NRs into GO layers resulted in synergistic effects and improved sensor performance. The sensors' sensitivity, selectivity, recovery, and response times were quantitatively determined from the sensors' response curves. The nanocomposite sensor demonstrated superior sensitivity and selectivity for analyte vapours with acceptable response and recovery times. In addition, the sensor was insensitive to humidity and showed robust performance up to 62% RH, although sensor drift occurred at 70% RH. This study highlights the promising potential of using SnO2 NRs-GO composite-based sensor for sensitive and selective detection of analyte vapours, which has significant implications for food safety and environmental monitoring applications.

Nanoclay biosensor for rapidly detecting lung cancer biomarkers at room temperature: A first principles study

The detection of volatile organic compounds (VOCs) in exhaled gas is crucial for noninvasive diagnostic applications in lung cancer. Herein, we systematically employed first-principle calculations to investigate the utilization of nanoclay as a sensing material for the development of sensitive biosensors for VOCs. In this study, the effect of VOCs adsorption on the structural and electronic properties of pristine kaolinite (Kaol) and transition metal [TM(II/III)] doped kaolinite [TM(II/III)-Kaol, TM = Fe, Co, and Ni] was investigated. The calculation results show that TM doping induces a slight structural perturbation, and VOCs lead to a decrease in the bandgap of pristine TM(II/III)-Kaol. Additionally, we have comprehensively discussed that the adsorption of VOCs causes significant changes in the electronic behavior of TM(II/III)-Kaol, including density of states, charge transfer, molecular front orbitals, and work functions. In particular, we also have calculated the recovery time to determine the reusability of the designed sensor material. Our results specifically support the fact that TM(II/III)-Kaol can be an attractive sensing material for VOCs biosensors.

Dynamic Interchain Motion in 1D Tetrathiafulvalene-Based Coordination Polymers for Highly Sensitive Molecular Recognition.

The application of electrically conductive 1D coordination polymers (1D CPs) in nanoelectronic molecular recognition is theoretically promising yet rarely explored due to the challenges in their synthesis and optimization of electrical properties. In this regard, two tetrathiafulvalene-based 1D CPs, namely [Co(m-H2TTFTB)(DMF)2(H2O)]n (Co-m-TTFTB), and {[Ni(m-H2TTFTB)(CH3CH2OH)1.5(H2O)1.5]·(H2O)0.5}n (Ni-m-TTFTB) are successfully constructed. The shorter S···S contacts between the [M(solvent)3(m-H2TTFTB)]n chains contribute to a significant improvement in their electrical conductivities. The powder X-ray diffraction (PXRD) under different organic solvents reveals the flexible and dynamic structural characteristic of M-m-TTFTB, which, combined with the 1D morphology, lead to their excellent performance for sensitive detection of volatile organic compounds. Co-m-TTFTB achieves a limit of detection for ethanol vapor down to 0.5ppm, which is superior to the state-of-the-art chemiresistive sensors based on metal-organic frameworks or organic polymers at room temperature. In situ diffuse reflectance infrared Fourier transform spectroscopy, PXRD measurements and density functional theory calculations reveal the molecular insertion sensing mechanism and the corresponding structure-function relationship. This work expands the applicable scenario of 1D CPs and opens a new realm of 1D CP-based nanoelectronic sensors for highly sensitive room temperature gas detection.

Highly selective and sensitive detection of volatile organic compounds using long wavelength InAs-based quantum cascade lasers through quartz-enhanced photoacoustic spectroscopy

The precise detection of volatile organic compounds plays a pivotal role in addressing environmental concerns, industrial safety, and medical diagnostics. The accurate identification and quantification of these compounds because of their ubiquity and potential health hazards has fueled the development of advanced sensing technologies. This work presents a sensing system in the realm of long-wavelength infrared spectroscopy for achieving enhanced selectivity and sensitivity of benzene, toluene, and propane detection through quartz-enhanced photoacoustic spectroscopy. High-resolution gas spectroscopy is made possible by the use of specially designed InAs/AlSb-based quantum cascade lasers, emitting in the wavelength range 13–15 μm, and quartz tuning forks. The sensor system, characterized by its robustness and precision, demonstrates exceptional capabilities in benzene, toluene, and propane detection. The system's capacity for practical applications in environmental monitoring and medical diagnostics is demonstrated by its ability to distinguish these volatile organic compounds with a minimum detection limit of 113 ppb, 3 ppb, and 3 ppm for toluene, benzene, and propane at an integration time of 10 s, even in complex gas matrices. This work advances gas sensing technology while also offering insightful information on spectral interferences, a persistent problem in the field. The results usher in a new era of sophisticated and reliable gas sensing techniques meeting the growing demand for precise volatile organic compounds detectors for environmental monitoring purposes.

Gas phase growth of metal-organic frameworks on microcantilevers for highly sensitive detection of volatile organic compounds

A gas-phase technique, known as chemical vapor deposition of metal-organic frameworks (MOF-CVD), is used for sensitizing silicon cantilevers. These cantilevers are coated with a uniform and compact Zn(EtIm)2 (MAF-6) film, enabling the detection of volatile organic compounds (VOCs) through a change in the resonance frequency of the cantilever. The MOF-coated sensor exhibits remarkable sensitivity to VOCs within the 0.33–0.71 Hz/ppm range and a limit of detection (LOD) spanning from 4 to 9 ppb. Notably, these sensitivities surpass those achieved by ZnO-coated cantilevers by two orders of magnitude. This high sensitivity is attributed to the high porosity and large surface area of MAF-6. The approach employed in this work is compatible with conventional microfabrication techniques and offers an advantageous avenue for the development of highly sensitive gas sensors.

A compact breath breathalyzer for identifying the non-alcoholic fatty liver disease biomarker

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disease worldwide. Currently, its diagnosis relies primarily on imaging and histological examinations, which are invasive and prone to misdiagnosis in the early stage. To address these limitations, detection and analysis of volatile organic compounds (VOCs) in human breath can be a rapid and non-invasive screening method for NAFLD. In this study, a compact breath breathalyzer was developed, utilizing a miniaturized gas chromatography chip with the STM32 microcontroller as the main control chip to manage airflow, temperature, and receive terminal signals from the photoionization detector. In the experiment, a gas mixture comprising five VOCs (pentane, acetone, toluene, octane, and decane) was selected as the simulated typical disease biomarkers in human breath to investigate the breathalyzer's performance and optimize testing conditions for multi-polar and wide-boiling-range breath samples. Results show that the breathalyzer can detect low-boiling components (< 100°C) such as the isoprene and acetone, with a detection limit less than 50 ppb which are two commonly biomarkers of NAFLD. Furthermore, breath samples were collected from 35 non-diseased individuals, and NAFLD early-stage patient samples were simulated by increasing the isoprene concentration by 10 ppb. Convolutional neural network (CNN) were used to identify the VOC signatures in gas chromatograms with predictive accuracy of 85% for the classification model. Therefore, the compact breath breathalyzer has potential application in the rapid and early screening of NAFLD.