Search Results (772)

This article describes a new software architecture for the non-invasive detection of canine leishmaniasis disease. The proposed platform combines gas-sensing technologies, artificial intelligence (AI), and modular cloud-based software components to identify disease-specific volatile organic compounds (VOCs) found in dog breath and hair samples. The system, which has a multi-tier architecture that includes data collection, pre-processing, machine learning-based analysis, diagnosis-request processing, and user interfaces for veterinarians, faculty researchers, and dog owners, has been integrated into a Li-ion Power website plug-in. The primary goal of implementing the proposed platform is to detect parasites at any point they are infectious to a host. This includes detecting parasites at all stages of their life cycle, where they can infect a new host. In addition, this is crucial for accurate diagnosis, effective treatment, and preventing further transmission. Full article

The emission and presence of volatile organic compounds (VOCs) in the indoor air of houses and factories has been a growing topic of debate in the industry and related research fields. Given the extended times people in modern society spend indoors, monitoring VOCs is crucial due to the associated potential health hazards, with formaldehyde being particularly noteworthy. Wood and wood-based panels (WBPs) (the latter constituting a significant segment of the wood-transforming industry, being widely used in furniture, construction, and other applications) are known sources for the emission of VOCs to indoor air. In the case of the WBPs, the emission of VOCs depends on the type and species of wood, together with industrial processing and addition of additives. This review integrates perspectives on the production processes associated with WBPs, together with the evolving global regulations, and thoroughly examines VOC sources associated with WBPs, health risks from exposure, and current analytical methods utilized for VOC detection. It comprises an overview of the WBP industry, providing relevant definitions, descriptions of manufacturing processes and adhesive use, analysis of legal constraints, and explanations of VOC source identification and describing analysis techniques utilized for VOCs in WBPs. Full article

Knowledge about the composition (volatile and non-volatile) and functionality of natural extracts from Mediterranean plants serves as a basis for their further application. In this study, five selected plants were used for the extraction of plant metabolites. Leaves and flowers of Critmum maritimum, Rosmarinus officinalis, Olea europea, Phylliera latifolia and Mellisa officinalis were collected, and a total of 12 extracts were prepared. Extractions were performed under microwave-assisted conditions, with two solvent types: water (W) and a hydroalcoholic (ethanolic) solution (HA). Detailed extract analysis was conducted. Phenolics were analyzed by detecting individual bioactive compounds using high-performance liquid chromatography and by calculating total phenolic and total flavonoid content through spectrophotometric analysis. Higher concentrations of total phenolics and total flavonoids were obtained in the hydroalcoholic extracts, with the significantly highest total phenolic and flavonoid values in the rosemary hydroalcoholic extract (3321.21 mg_GAE/L) and sea fennel flower extract (1794.63 mg_QE/L), respectively; and the lowest phenolics in the water extract of olive leaves (204.55 mg_GAE/L) and flavonoids in the water extracts of sea fennel leaves, rosemary, olive and mock privet (around 100 mg_QE/L). Volatile organic compounds (VOC) were detected using HS-SPME/GC–MS (Headspace Solid-Phase Microextraction coupled with Gas Chromatography-Mass Spectrometry), and antioxidant capacity was estimated using DPPH (2,2-diphenyl-1-picrylhydrazyl assay) and FRAP (Ferric Reducing Antioxidant Power) methods. HS-SPME/GC–MS analysis of samples revealed that sea fennel had more versatile profile, with the presence of 66 and 36 VOCs in W and HA sea fennel leaf extracts, 52 and 25 in W and HA sea fennel flower extracts, 57 in rosemary W and 40 in HA, 20 in olive leaf W and 9 in HA, 27 in W mock privet and 11 in HA, and 35 in lemon balm W and 10 in HA extract. The lowest values of chlorophyll a were observed in sea fennel leaves (2.52 mg/L) and rosemary (2.21 mg/L), and chlorophyll b was lowest in sea fennel leaf and flower (2.47 and 2.25 mg/L, respectively), while the highest was determined in olive (6.62 mg/L). Highest values for antioxidant activity, determined via the FRAP method, were obtained in the HA plant extracts (up to 11,216 mg_AAE/L for lemon balm), excluding the sea fennel leaf (2758 mg_AAE/L) and rosemary (2616 mg_AAE/L). Considering the application of these plants for fresh fish preservation, antimicrobial activity of water extracts was assessed against Vibrio fischeri JCM 18803, Vibrio alginolyticus 3050, Aeromonas hydrophila JCM 1027, Moraxella lacunata JCM 20914 and Yersinia ruckeri JCM 15110. No activity was observed against Y. ruckeri and P. aeruginosa, while the sea fennel leaf showed inhibition against V. fisheri (inhibition zone of 24 mm); sea fennel flower was active against M. lacunata (inhibition zone of 14.5 mm) and A. hydrophila (inhibition zone of 20 mm); and rosemary and lemon balm showed inhibition only against V. fisheri (inhibition zone from 18 to 30 mm). This study supports the preparation of natural extracts from Mediterranean plants using green technology, resulting in extracts rich in polyphenolics with strong antioxidant potential, but with no clear significant antimicrobial efficiency at the tested concentrations. Full article

Volatile organic compounds (VOCs) are important for teas’ quality and act as a critical evaluative criterion in teas. The distinctive aromatic profile of tea not only facilitates tea classification but also has potential applications in aroma-driven product innovation. In this review, we summarized the tea aroma from tea classification, VOCs extraction methodologies, and VOCs detection techniques. Moreover, the potential utilization of tea aroma in the future, such as applications in essential oil refinement, food flavor enhancement, and functional fragrance for personal health care, was proposed. Our review will provide a solid foundation for further investigations in tea aroma and offer significant insights into the development and application of tea fragrance. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article

Background: Insulin resistance (IR) is an underlying pathophysiology for type 2 diabetes (T2D). The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is the simplest method for evaluating IR. At the same time, volatile organic compounds (VOCs) detected in human respiration can be correlated with specific diseases. To date, machine learning (Mach-L) has yet to be used to examine potential relationships between VOCs and IR. The present study has two aims: (1) to identify the VOCs most relevant to HOMA-IR, and (2) to use Shapley addictive explanation (SHAP) to determine the impacts of the distributions and directions of each feature in Taiwanese women. Methods: A total of 1432 Taiwanese women between the ages of 19 and 84 years were enrolled, and 344 VOCs were measured. Traditional multiple linear regression (MLR) was used as a benchmark for comparison, applying three Mach-L methods. Finally, SHAP was used to evaluate the directions of impacts of the features on HOMA-IR. Results: Six VOCs were identified as important: dimethylfuran, propanamine, aniline, butoxyethanol, and isopropyltoluene, in order from most to least important. SHAP found that dimethylfuran, isopropyltoluene, and dodecane were positively correlated to HOMA-IR, while butoxyethanol, aniline, and propanamine were negatively correlated. Conclusions: Using three different Mach-L methods, six VOCs were selected to be related to IR in Taiwanese women. According to their importance, dimethylfuran, propanamine, aniline, butoxyethanol, and isopropyltoluene could be used to help diagnose HOMA-IR. Furthermore, by using SHAP, dimethylfuran, isopropyltoluene, and dodecane had a positive and the other three had a negative influence. Full article

Volatile organic compounds (VOCs) are presently posing a rather considerable threat to both human health and environmental sustainability. Among these, n-butanol is commonly identified as bringing potential hazards to environmental integrity and individual health. This study presents the creation of a highly sensitive n-butanol gas sensor utilizing cobalt-doped zinc oxide (ZnO) derived from a metal–organic framework (MOF). A series of x-Co/MOF-ZnO (x = 1, 3, 5, 7 wt%) nanomaterials with varying Co ratios were generated using the homogeneous co-precipitation method and assessed for their gas-sensing performances under a low operating temperature (191 °C) and UV excitation (220 mW/cm²). These findings demonstrated that the 5-Co/MOF-ZnO sensor presented the highest oxygen vacancy (O_v) concentration and the largest specific surface area (SSA), representing the optimal reactivity, selectivity, and durability for n-butanol detection. Regarding the sensor’s response to 100 ppm n-butanol under UV excitation, it achieved a value of 1259.06, 9.80 times greater than that of pure MOF-ZnO (128.56) and 2.07 times higher than that in darkness (608.38). Additionally, under UV illumination, the sensor achieved a rapid response time (11 s) and recovery rate (23 s). As a strategy to transform the functionality of ZnO-based sensors for n-butanol gas detection, this study also investigated potential possible redox reactions occurring during the detection process. Full article

The early detection of liver cirrhosis (LC) is crucial due to its high morbidity and mortality in advanced stages. Reliable, non-invasive diagnostic tools are essential for timely intervention. Exhaled human breath, reflecting metabolic changes, offers significant potential for disease diagnosis. This paper focuses on the emerging role of sensor array-based volatile organic compounds (VOCs) analysis of exhaled breath, particularly using electronic nose (e-nose) technology to differentiate LC patients from healthy controls (HCs). This study included 55 participants: 27 LC patients and 28 HCs. Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. The diagnostic accuracy was thoroughly assessed through receiver operating characteristic (ROC) curve analysis, with specific emphasis on assessing sensitivity and specificity metrics. The e-nose effectively distinguished LC from HC, with PCA explaining 92.50% variance and SVMs achieving 100% classification accuracy. This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. This research provides a foundation for advancing breath-based diagnostic technologies for early detection and monitoring of liver cirrhosis. Full article

Tomicus yunnanensis Kirkendall and Faccoli, a native bark beetle species and key pest of Pinus yunnanensis Franch. in southwestern China, relies on host-derived volatile organic compounds (VOCs) for host selection. To unravel these mechanisms, we collected VOCs from P. yunnanensis trunks across four infestation stages (healthy, early-infested, weakened, near-dead) using dynamic headspace sampling. Chemical profiling via gas chromatography–mass spectrometry (GC-MS) identified 51 terpenoids, with α-pinene as the most abundant component. VOC profiles differed markedly between healthy and early-infested trees, while gradual shifts in compound diversity and abundance occurred from the weakened to near-dead stages. Bioactive compounds were screened using gas chromatography–electroantennographic detection (GC-EAD) and a Y-tube olfactometer. Electrophysiological responses in T. yunnanensis were triggered by α-pinene, β-pinene, 3-carene, 2-thujene, and 4-allylanisole. Behavioral tests revealed that α-pinene, 3-carene, and 2-thujene acted as attractants, whereas β-pinene and 4-allylanisole functioned as repellents. These results indicate that infestation-induced VOC dynamics guide beetle behavior, with attractants likely promoting host colonization during early infestation and repellents signaling deteriorating host suitability in later stages. By mapping these chemical interactions, our study identifies potential plant-derived semiochemicals for targeted pest management. Integrating these compounds with pheromones could enhance the monitoring and control strategies for T. yunnanensis, offering ecologically sustainable solutions for pine ecosystems. Full article

The organic volatile compound gases (VOCs) emitted by the rubber running tracks in the park pose a threat to human health. Currently, the challenge lies in how to detect the VOC gas concentration to ensure it is below the level that is harmful to human health. This study developed a low-power acetone gas sensor based on SnO₂/Nb₂C MXene composites, designed for monitoring acetone gas in pocket park rubber tracks at room temperature. Nb₂C MXene was combined with SnO₂ nanoparticles through a hydrothermal method, and the results showed that the SnO₂/Nb₂C MXene composite sensor (SnM-2) exhibited a response value of 146.5% in detecting 1 ppm acetone gas, with a response time of 155 s and a recovery time of 295 s. This performance was significantly better than that of the pure SnO₂ sensor, with a 6-fold increase in response value. Additionally, the sensor exhibits excellent selectivity against VOCs, such as ethanol, formaldehyde, and isopropanol, with good stability (~20 days) and reversibility (~50). It can accurately recognize acetone gas concentrations and has been successfully used to simulate rubber track environments and provide accurate acetone concentration data. This study provides a feasible solution for monitoring VOCs in rubber tracks and the foundation for the development of low-power, high-performance, and 2D MXene gas sensors. Full article

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

This review presents information obtained over the past 10 years on the methods to control the widespread worldwide phytopathogen Pectobacterium carotovorum subsp. carotovorum (Pcc). This bacterium is among the ten most dangerous phytopathogens; it affects a wide range of cultivated plants: vegetables, ornamental and medicinal crops, both during vegetation and during the storage of fruits. Symptoms of Pcc damage include the wilting of plants, blackening of vessels on leaves, stems and petioles. At the flowering stage, the stem core gradually wilts and, starting from the root, the stem breaks and the plant dies. Pcc is a rod-shaped, non-capsule and endospore-forming facultative anaerobic Gram-negative bacterium with peritrichous flagellation. Pcc synthesizes bacteriocins—carocins. The main virulence factors of Pcc are the synthesis of N-acyl-homoserine lactone (AHL) and plant cell wall-degrading enzymes (PCWDEs) (pectinases, polygalacturonases, cellulases, and proteases). Diagnostic methods for this phytopathogen include polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), multilocus genotyping of strain-specific genes and detection of unique volatile organic compounds (VOCs). The main methods to control this microorganism include the use of various chemicals (acids, phenols, esters, salts, gases), plant extracts (from grasses, shrubs, trees, and algae), antagonistic bacteria (Bacillus, Pseudomonas, Streptomyces, and lactic acid bacteria), viruses (including a mixture of bacteriophages), and nanomaterials based on metals and chitosan. Full article

Moniliophthora roreri (MR, frosty pod rot) and M. perniciosa (MP, witches’ broom disease) pose critical threats to cacao production in Latin America. This study explores the biocontrol potential of Trichoderma spp. strains against these pathogens through exploratory analysis of mycoparasitism, chitinolytic activity, and volatile organic compound (VOC) production. Dual-culture assays revealed species-specific antagonism, but C2A/C4B showed a dual-pathogen efficacy (>93% of Monioliopthora inhibition). Chitinase activity revealed C4A/C1 strains as exceptional producers (72 mg/mL NAGA vs. MR and 94 mg/mL vs. MP, respectively). GC-MS analysis identified pathogen-modulated VOC dynamics: hexadecanoic acid dominated in 80% Trichoderma solo-cultures (up to 26.65% peak area in C3B). MP showed 18.4-fold higher abundance of hexadecanoic acid than MR (0.23%). In 90% of dual-culture with MR and MP, HDA was detected as the most abundant. Functional specialization was evident. C4A and C1 prioritized chitinase production growing on MR and MP cell walls (respectively), whereas C9 excelled in antifungal hexadecanoic acid synthesis in confrontation with both pathogens. Complementary strengths among strains—enzymatic activity in C4A/C4B versus volatile-mediated inhibition in C9—suggest niche partitioning, supporting a consortium-based approach for robust biocontrol. This study provides preliminary evidence for the biocontrol potential of several Trichoderma strains, showing possible complementary modes of action. Full article

Bee pollen’s aroma combined with other floral components serve various purposes, including attracting pollinators and signaling the availability of food sources. The present study aimed to comparatively analyze the volatile profiles of unifloral pollen taxa. Bee pollen loads were collected using pollen traps and sorted based on their botanical origin, determined by color and pollen grain morphology. The separated pollen samples were analyzed using a Purge & Trap/GC-MS system, identifying the volatile profiles of pollen from 16 plant species. The analysis revealed distinguished differences in the total volatile organic compounds (VOCs) among the various pollen species. Notably, the pollen from Erica manipuliflora, Papaver rhoeas, and Sisymbrium irio contained the highest number of VOCs, with 54, 51, and 42 substances detected, respectively. Certain volatile compounds appeared to correlate with increased bee visitation. For instance, 4-methyl-5-nonanone was uniquely found in E. manipuliflora pollen, while isothiocyanate compounds were exclusively present in species of the Brassicaceae family. Therefore, given the significant impact of VOCs on honey bees’ preferences, it is essential to consider not only the nutritional value of bee pollen when evaluating its beekeeping value, but also its aroma profile. Full article