Identification Of Volatile Organic Compounds Research Articles

Behavioural Response of Whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) Towards Volatile Organic Compounds Emitted from Eggplant Leaves

The whitefly Bemisia tabaci is considered one of the most destructive insect pests that can attack several vegetable crops worldwide. The behavior of choosing a host plant by whitefly can be significantly influenced by volatile organic compounds. The aim of this investigation is to identify and bioassay the volatile organic compounds emitted from uninfested and infested eggplant leaves with B. tabaci. The current study focuses on the interaction between whitefly and eggplant with the identification of volatile organic compounds emitted from leaves that were collected by headspace solid phase microextraction fiber and gas chromatography mass spectrum analyzed. Both uninfested and infested eggplant leaves released a total of 11 compounds. The amount of volatile organic compounds emitted from affected eggplant leaves by whitefly was higher than from uninfested eggplant leaves. Using a Y-shaped olfactometer bioassay, whitefly preferred to attract toward volatile emitted by eggplant leaves in both uninfested and infested eggplant leaves. Our results suggest these chemical compounds can play a key mediator in host finding behavior. The exploitation of one or more of these compounds can apply in an integrated management program of this pest as a lure in the sticky trap or manipulating insect behavior.

Unique Photoactivated Time-Resolved Response in 2D GeS for Selective Detection of Volatile Organic Compounds.

Volatile organic compounds (VOCs) sensors have a broad range of applications including healthcare, process control, and air quality analysis. There are a variety of techniques for detecting VOCs such as optical, acoustic, electrochemical, and chemiresistive sensors. However, existing commercial VOC detectors have drawbacks such as high cost, large size, or lack of selectivity. Herein, a new sensing mechanism is demonstrated based on surface interactions between VOC and UV-excited 2D germanium sulfide (GeS), which provides an effective solution to distinguish VOCs. The GeS sensor shows a unique time-resolved electrical response to different VOC species, facilitating identification and qualitative measurement of VOCs. Moreover, machine learning is utilized to distinguish VOC species from their dynamic response via visualization with high accuracy. The proposed approach demonstrates the potential of 2D GeS as a promising candidate for selective miniature VOCs sensors in critical applications such as non-invasive diagnosis of diseases and health monitoring.

Graphene Oxide Thin Films for Detection and Quantification of Industrially Relevant Alcohols and Acetic Acid

Industrial environments are frequently composed of potentially toxic and hazardous compounds. Volatile organic compounds (VOCs) are one of the most concerning categories of analytes commonly existent in the indoor air of factories' facilities. The sources of VOCs in the industrial context are abundant and a vast range of human health conditions and pathologies are known to be caused by both short- and long-term exposures. Hence, accurate and rapid detection, identification, and quantification of VOCs in industrial environments are mandatory issues. This work demonstrates that graphene oxide (GO) thin films can be used to distinguish acetic acid, ethanol, isopropanol, and methanol, major analytes for the field of industrial air quality, using the electronic nose concept based on impedance spectra measurements. The data were treated by principal component analysis. The sensor consists of polyethyleneimine (PEI) and GO layer-by-layer films deposited on ceramic supports coated with gold interdigitated electrodes. The electrical characterization of this sensor in the presence of the VOCs allows the identification of acetic acid in the concentration range from 24 to 120 ppm, and of ethanol, isopropanol, and methanol in a concentration range from 18 to 90 ppm, respectively. Moreover, the results allows the quantification of acetic acid, ethanol, and isopropanol concentrations with sensitivity values of (3.03±0.12)∗104, (-1.15±0.19)∗104, and (-1.1±0.50)∗104 mL-1, respectively. The resolution of this sensor to detect the different analytes is lower than 0.04 ppm, which means it is an interesting sensor for use as an electronic nose for the detection of VOCs.

Identification of phenol 2,2-methylene bis, 6 [1,1-D] as breath biomarker of hepatocellular carcinoma (HCC) patients and its electrochemical sensing: E-nose biosensor for HCC

BackgroundAccording to WHO, Hepatocellular cancer (HCC) was the second leading cause of death in 2019 and is gradually increasing. The lipid peroxidation mechanism in cancer cells causes the emission of VOCs in the breath. Volatile organic compounds (VOCs) in breath are becoming favorable biomarkers, especially for cancers, for their sample retrieval and specific association with early metabolic changes. Since both diagnosis and prognosis of the disease depend on the quantity and kind of circulatory biomarkers to be detected, sensitive and selective biosensors with the possibility for portability are constantly in demand. ResultsIn this study, breath samples of HCC patients were screened for identification of VOCs via GCMS and later verified by applying unsupervised machine learning models. Phenol 2,2 methylene bis [6-(1,1-dimethyl ethyl)-4-methyl] (MBMBP) was found to be significant VOC in the breath of HCC patients, with a minimum concentration of 2100 ppm. Thiol-modified AuNPs were synthesized, as we reported earlier, and immobilized on the working electrode surface to electrochemically sense MBMBP in purified form and later from clinical breath samples. During the electrochemical experiment of AuNPs with MPMBP, the analyte gets electro-oxidized, whereas the Au (III) ions get reduced to the phenoxy radical's species. The electrochemical analysis of MBMBP detection using hexane thiol AuNPs showed a LOD of 0.005 molL 1. The thiolated AuNPs-based biosensor for HCC diagnosis via VOC detection confirmed MPMBP in lab standards and raw clinical breath samples of HCC patients. SignificanceThis study reveals that GCE modified with hexanethiol AuNPs for the adsorption of significant breath biomarker, is a potential platform for the development of e-nose sensor for the detection of HCC at early stage.

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

Piezoelectric Gas Sensors with Polycomposite Coatings in Biomedical Application

When developing methods for diagnosing pathologies and diseases in humans and animals using electronic noses, one of the important trends is the miniaturization of devices, while maintaining significant information for diagnostic purposes. A combination of several sorbents that have unique sorption features of volatile organic compounds (VOCs) on one transducer is a possible option for the miniaturization of sensors for gas analysis. This paper considers the principles of creating polycomposite coatings on the electrodes of piezoelectric quartz resonators, including the choice of sorbents for the formation of sensitive layers, determining the mass and geometry of the formation of sensitive layers in a polycomposite coating, as well as an algorithm for processing the output data of sensors to obtain maximum information about the qualitative and quantitative composition of the gas phase. A comparative analysis of the efficiency and kinetics of VOC vapor sorption by sensors with polycomposite coatings and a set of sensors with relevant single coatings has been carried out. Regression equations have been obtained to predict the molar-specific sensitivity of the microbalance of VOC vapors by a sensor with a polycomposite coating of three sorbents with an error of 5-15% based on the results of the microbalance of VOC vapors on single coatings. A method for creating "visual prints" of sensor signals with polycomposite coatings is shown, with results comparable to those from an array of sensors. The parameters Aij∑ are proposed for obtaining information on the qualitative composition of the gas phase when processing the output data of sensors with polycomposite coatings. A biochemical study of exhaled breath condensate (EBC) samples, a microbiological investigation of calf tracheal washes, and a clinical examination were conducted to assess the presence of bovine respiratory disease (BRD). An analysis of the gas phase over EBC samples with an array of sensors with polycomposite coatings was also carried out. The "visual prints" of the responses of sensors with polycomposite coatings and the results of the identification of VOCs in the gas phase over EBC samples were compared to the results of bacteriological studies of tracheal washes of the studied calves. A connection was found between the parameters Aij∑ of a group of sensors with polycomposite coatings and the biochemical parameters of biosamples. The adequacy of replacing an array of piezoelectric sensors with single coatings by the sensors with polycomposite coatings is shown.

Identification of volatile organic compounds related to the eating quality of cooked japonica rice

Eating quality (EQ) of rice has a complex nature composed of physicochemical properties. Nevertheless, breeding programs evaluating EQ through sensory test or taste-evaluation instruments have been laborious, time-consuming and inefficient. EQ is affected by both taste and aroma. However, in actual breeding programs, aroma of cooked rice has been considered the least due to lack of information. Here we identified a total of 41 volatile compounds potentially affecting the EQ of non-aromatic, cooked japonica rice, identified by GC–MS, sensory panel test, and Toyo taste-meter analyses. Partial least squares discriminant analysis demonstrated an outstanding classification effect of the identified volatile compounds on eating-quality discrimination. Several volatile compounds related to lipid oxidation and fatty acid degradation were identified to affect the EQ in japonica rice. Of them, 1-octen-3-ol, 1-ethyl-3,5-dimethylbenzene, 2,6,11-trimethyldodecane, 3-ethyloctane, 2,7,10-trimethyldodecane, methyl salicylate, 2-octanone, and heptanal were selected as important compounds. The discriminant model for the classification of the quality of cultivars was robust and accurate, an r-squared value was 0.91, a q squared value was 0.85, and an accuracy was 1.0. Overall, the results of this study characterize EQ of rice cultivars based on volatile compounds, suggesting the application of metabolite profiling data for rice breeding of high eating quality.

Profiling of volatile and non-volatile compounds in Dianhong by a combined approach of static headspace GC-MS and UPLC-MS

ABSTRACT The systematic identification of volatile organic compounds (VOCs) and non-VOCs will facilitate understanding the characteristic aroma and bioactive constituents of Dianhong. This article reported for the first time the volatile and non-volatile composition profiles of Dianhong from four main producing areas. A combined total of 42 VOCs were identified from four Dianhong samples by static headspace GC-MS. Thirteen VOCs were found to be important common components contributing to the characteristic aroma of Dianhong. The non-VOCs of Dianhong were evaluated by UPLC-ESI-MS, and Dianhong samples from four producing areas shared almost the same component profiles. Thirty-four components including derivatives of quinic acid, gallic acid, and catechin, flavone C-glycosides, flavonol O-glycosides, and alkaloids were characterized. Finally, catechin, gallic acid, 5-O-galloylquininc acid, and theobromine were characterized as the main non-VOCs in Dianhong. Dianhong samples from four main producing areas showed similar VOCs and non-VOCs profiles under our optimized GC-MS and LC-MS conditions.

Evaluating polyvinylidene fluoride - carbon black composites as solid phase microextraction coatings for the detection of urinary volatile organic compounds by gas chromatography-mass spectrometry

Volatile organic compounds (VOCs) are biomarkers of disease, which can be utilized for accurate diagnostics. The gold standard for VOC identification is gas chromatography-mass spectrometry (GC-MS) as it allows for structure elucidation and quantification. Headspace solid phase microextraction (HS-SPME) is often used in biomarker discovery due to its ability to preconcentrate VOCs prior to GC-MS analysis. However, HS-SPME GC-MS is time-consuming, expensive and requires trained personnel. Gas sensor arrays can detect VOC biomarkers at a point-of-care and therefore are more suitable for disease diagnostics in the clinic. Nevertheless, qualification and optimization of sensing layers is tedious as each VOC of interest needs to be tested individually. Therefore, using SPME fibers to extract VOCs and GC-MS to quantitate the analytes may be an efficient strategy with high throughput to tune sensing layers and increase analyte affinity. To investigate this, suspensions of polyvinylidene fluoride (PVDF) and polyvinylidene fluoride-carbon black (PVDF-CB) fabricated at varying concentration were immobilized on SPME fibers through physical deposition, used to extract urinary VOCs and were subject to GC-MS analysis. The addition of CB shows increased fiber performance in terms of total integrated signal and sensitivity toward individual VOCs. PVDF-CB fibers were compared to a commercial polydimethylsiloxane (PDMS) SPME fiber run using the same method. The PVDF-CB fiber outperformed the commercial fiber in detecting numerous urinary VOCs of interest. Results of this study show not only that custom SPME fiber performance can be evaluated through GC-MS analysis, but the capability of custom fibers to adsorb urinary VOCs can be tuned based on properties of interest. Hence, this method may be utilized as an analytical tool to characterize and tune gas sensing layers with high analytical throughput.

Selection of Classifiers to Enhance Efficacy of Metal/Organic Hybrid Sensor Array for VOC and Toxic Gas Identification

Modern developments in gas sensor technology include a decrease in size and an increase in sensitivity and selectivity. These improvements, paired with postprocessing tools, such as machine learning, are pushing gas detection toward viability for complex tasks, such as volatile organic compound (VOC) analysis in human breath. In our research, we use a sensor array fabricated in our lab featuring a hybrid combination of metals and organic polymers [palladium (Pd), zinc oxide (ZnO), polypyrrole (PPy), and polyaniline (PANI)] designed to detect a range of VOCs and toxic gases (CO, H2, CH3OH, and NO2). An exhaustive analysis of 25 machine learning classifiers using three different feature sets was completed to find the best classifier and feature set combinations for one versus rest gas classification. We determined that ensemble classifiers, using normalized sensor data as a feature set, yield the best classification results. From these results, we demonstrated that Pd, PPy, and PANI are best suited to identify H2, NO2, and CH3OH, respectively. Furthermore, PANI is best suited to identify CO, so we correctly identified four gases from three sensor materials with sensitivity values all above 85%. These promising classification results could allow us to expand our set of gases and, therefore, make this sensor array viable for real-world applications.

Microbial volatile organic compounds 2-heptanol and acetoin control Fusarium crown and root rot of tomato.

Some microbial volatile organic compounds (mVOCs) can act as antagonistic weapons against plant pathogens, but little information is available on the contribution of individual mVOC to biocontrol and how they interact with plant pathogens. In this study, the Bacillus subtilis strain N-18 isolated from the rhizosphere of healthy plants grown in areas where Fusarium crown and root rot (FCRR) of tomatooccurs could reduce the 30% of the incidence of FCRR. Moreover, the volatile organic compounds (VOCs) produced by N-18 had inhibitory effects on Fusarium oxysporum f. sp. radicis-lycopersici (FORL). The identification of VOCs of N-18 was analyzed by the solid-phase microextraction coupled to gas chromatography-mass spectrometry.Meanwhile, we conducted sensitivity tests with these potential active ingredients and found that the volatile substances acetoin and 2-heptanol can reduce the 41.33% and 35% of the incidence of FCRR in tomato plants. In addition, the potential target protein of acetoin, found in the cheminformatics and bioinformatics database, was F. oxysporum of hypothetical protein AU210_012600 (FUSOX). Molecular docking results further predicted that acetoin interacts with FUSOX protein. These results reveal the VOCs of N-18 and their active ingredients in response to FORL and provide a basis for further research on regulating and controlling FCRR.

Practical Application of a Multi-Bed Adsorbent Tube Coupled with GC-MS for Low-Level VOCs Identification to Achieve Comprehensive Odor Management

We investigated the effectiveness of a multi-sorbent tube (M-Tube) to improve the sampling method for a wide range of odor-causing volatile organic compounds (VOCs) at low concentrations. The M-Tube applied in this study was a fixed tube packed with three adsorbents (Carboxen®, Carbopack™, and Tenax®-TA) in series. To verify the efficiency of the M-Tube, a comparative study was conducted against a single tube (S-Tube, Tenax®-TA). A total of 42 target compounds were selected, including eight different chemical groups: terpenes, hydrocarbons, aldehydes, ketones, alcohols, esters, furans, and alkanes, to verify the effectiveness of the M-Tube. In the laboratory test, response factors, which are the ratios between output signals of gas chromatography by the sorbent-tube sample and the liquid-standard sample, for the M-Tube were higher than those for the S-Tube. The breakthrough volume of the M-Tube was higher than that of the S-Tube, indicating that the M-Tube had a relatively lower method detection limit. In the field application to the sewage treatment plant, 29 compounds were identified, and their concentration ranged from 0.3 to 98 ppbv (above MDL). The detected compounds were considered odor-causing VOCs, and the odor contribution of the compounds could be assessed. We ultimately concluded that comprehensive odor management could be established by determining trace VOCs.

Identification of volatile organic compounds in muscle tissues of different species based on Headspace-Gas-Chromatography Ion-Mobility spectrometry

Species identification of unknown biological samples is crucial for forensic applications, especially in cases of explosion, disaster accidents, and body mutilation after murdering, as well as poaching, illegal trade in endangered animals, and meat food fraud. In this study, we identified 60 volatile organic compounds (VOCs) in fresh skeletal muscle tissues of seven different animal species (cattle, sheep, pigs, rabbits, rats, chickens and carp) and a human dead body by headspace-gas-chromatography ion-mobility spectrometry (HS-GC-IMS), and compared their differences by retention time, drift time and molecular weight. The results showed that these VOCs formed different gallery plot fingerprints in the skeletal muscle tissues of the human dead body and seven animal species. Principal component analysis (PCA) showed significantly different fingerprints between these species, and these fingerprints maintained good stability between the species and within the same species. Some VOCs have high species specificity, while VOCs of human fresh muscle tissues from different individual sources have little difference, demonstrating that all tested muscle tissue samples could be distinguished based on different VOCs. HS-GC-IMS has proved to be a rapid, high-throughput, highly sensitive and specific species identification method, which can be used for forensic species identification in criminal cases and disaster accidents, as well as detection in the field of food safety, such as meat fraud and adulteration.

Miniaturized Photo-Ionization Fourier Deconvolution Ion Mobility Spectrometer for the Detection of Volatile Organic Compounds.

Because of its simplicity, reliability, and sensitivity, the drift tube ion mobility spectrometer (IMS) has been recognized as the equipment of choice for the on-site monitoring and identification of volatile organic compounds (VOCs). However, the performance of handheld IMS is often limited by the size, weight, and drift voltage, which heavily determine the sensitivity and resolving power that is crucial for the detection and identification of VOCs. In this work, we present a low-cost, miniaturized drift tube ion mobility spectrometer incorporated with a miniaturized UV ionization lamp and a relatively low drift voltage. The sensitivity and resolving power are boosted with the implementation of Fourier deconvolution multiplexing compared to the conventional signal averaging data acquisition method. The drift tube provides a high resolving power of up to 52 at a drift length of 41 mm, 10 mm ID dimensions, and a drift voltage of 1.57 kV. Acetone, benzene, dimethyl methyl phosphonate, methyl salicylate, and acetic acid were evaluated in the developed spectrometer and showed satisfactory performance.

Effects of incorporation of Chavir ultrasound and maceration extracts on the antioxidant activity and oxidative stability of ordinary virgin olive oil: identification of volatile organic compounds

Effects of incorporation of Chavir ultrasound and maceration extracts on the antioxidant activity and oxidative stability of ordinary virgin olive oil: identification of volatile organic compounds

Extraction and Identification of Volatile Organic Compounds in Scentless Flowers of 14 Tillandsia Species Using HS-SPME/GC-MS.

VOCs emitted by flowers play an important role in plant ecology. In the past few years, the Tillandsia genus has been scarcely studied according to the VOCs emitted by flowers. Hence, we decided to enlarge the VOCs composition study already undergone in our laboratory on fragrant 3 Tillandsia species to 12 unscented and 2 faint-scented Tillandsia species and hybrids. The headspace solid phase microextraction (HS-SPME) coupled with gas chromatography combined with the mass spectrometry (GC-MS) method was used to explore the chemical diversity of the VOCs. This study allowed the identification of 65 VOCs among the 14 species and between 6 to 25 compounds were identified in each of the species. The aromatic profile of 10 of the species and hybrids are similar to each other’s and show 8 predominant compounds: benzaldehyde, benzacetaldehyde, hexanol, hexanal, heptanal, octanal, nonanal, and furan-2-pentyl. Some specific compounds are present only in some unique species such as trans-calamenene, α-muurolene, and α-guaiene trans-β-bergamotene. The two faint-scented species studied present an original aromatic profile with a high number of monoterpenes or phenylpropanoids/benzenoids. Our studies allow a better understanding of the ecological role and function of these VOCs in the interactions between these plants with their environment.

Rapid identification of volatile organic compounds and their isomers in the atmosphere

Isomers are widely present in volatile organic compounds (VOCs), and it is a tremendous challenge to rapidly distinguish the isomers of VOCs in the atmosphere. In this work, laser-induced breakdown spectroscopy (LIBS) technology was developed to online distinguish VOCs and their isomers in the air. First, LIBS was used to directly detect halogenated hydrocarbons (a typical class of VOCs) and the characteristic peaks of the related halogens were observed in the LIBS spectra. Then, comparing the LIBS spectra of various samples, it was found that for VOCs with different molecular formulas, although the spectra are completely the same in elemental composition, there are still significant differences in the relative intensity of the spectral lines and other information. Finally, in light of the shortcomings of traditional LIBS technology in identifying isomers, machine learning algorithms were introduced to develop the LIBS technique to identify the isomers of atmospheric VOCs, and the recognition results were very good. It is proved that LIBS combined with machine learning algorithms is promising for online traceability of VOCs in the atmospheric environment.

Identification of Volatile Organic Compounds Produced by Xenorhabdus indica Strain AB and Investigation of Their Antifungal Activities.

Xenorhabdus spp. are symbiotic bacteria associated with entomopathogenic nematodes to form a model complex that is used for the biological control of insect pests. These bacteria also produce secondary metabolites that have commercial potential in the pharmaceutical and agroforestry industries. Volatile organic compounds (VOCs) produced by the Xenorhabdus indica "strain AB" have been shown to have significant antifungal activity against Fusarium oxysporum f. sp. cucumerinum. Using gas chromatography-mass spectrometry, we identified 61 volatiles in the mixture of VOCs emitted by strain AB compared to a control strain, 6 of which were investigated for their antifungal activities. Of these, methyl anthranilate exhibited the highest mycelial growth suppression toward F. oxysporum, with a minimum inhibitory volume (MIV) of 50 μL/plate. Fluorescence assays, scanning electron microscopy, and measurements of the leakage of intracellular components revealed that the use of methyl anthranilate changed cell wall and cell membrane integrity as well as the permeability of the plasma membrane. Furthermore, methyl anthranilate treatment upregulated the transcription level of target genes related to redox reactions and the cell wall integrity pathway. The results suggest a novel mechanism used by Xenorhabdus spp. to overcome competitors during its life cycle and open up a new approach to using these bacteria in biological control. IMPORTANCE Fungal phytopathogens, particularly Fusarium oxysporum, are a major problem worldwide, especially in the postharvest of vital economic crops. Concerns about negative effects on the environment and human health have led to increasing restrictions on the use of chemical fungicides, and therefore, biological control agents are now being considered alternatives. It is in this context that we investigated the antifungal activity of VOCs produced by X. indica strain AB against F. oxysporum. We found that AB VOCs have a strong effect on the growth of the fungal phytopathogen. In addition, 85% of the identified volatile compounds were determined to be new compounds, opening up new lines of research to discover their properties, effects, and potential for pharmaceutical and agricultural applications. Antifungal assays proved that four of the six compounds with a high concentration in the GC-MS profile had a significant inhibitory effect on pathogen growth. Accordingly, this study opens up a new approach for the use of these bacteria in biocontrol.

Identification of Volatiles of the Dinoflagellate Prorocentrum cordatum.

The dinoflagellate Prorocentrum cordatum, often called P. minimum, is a potentially toxic alga found in algal blooms. Volatile compounds released by the alga might carry important information, e.g., on its physiological state, and may act as chemical messengers. We report here the identification of volatile organic compounds emitted by two strains, xenic P. cordatum CCMP 1529 and axenic P. cordatum CCMP 1329. The volatiles released during culture were identified despite their low production rates, using sensitive methods such as open-system-stripping analysis (OSSA) on Tenax TA desorption tubes, thermodesorption, cryofocusing and GC/MS-analysis. The analyses revealed 16 compounds released from the xenic strain and 52 compounds from the axenic strain. The majority of compounds were apocarotenoids, aromatic compounds and small oxylipins, but new natural products such as 3,7-dimethyl-4-octanolide were also identified and synthesized. The large difference of compound composition between xenic and axenic algae will be discussed.

Profiling exhaled breath of smokers using mass spectrometry to identify a signature related to tobacco use for diagnostic perspectives

Breath analysis for the identification of volatile organic compounds by mass spectrometry is a very innovative and non-invasive technology, which represents a great opportunity for an early and personalised diagnosis. In this pilot study we recruited a series of volunteers, smokers and non-smokers, characterized from the respiratory point of view, and profiled their exhaled breath through SESI- HRMS technology. The aim of the study is to identify a volatile molecular signature associated with tobacco use. The supervised analysis highlighted 32 features that discriminate the breath of smokers and non-smoker subjects, at the baseline. We therefore identified a volatile molecular signature closely related to tobacco smoke, which will be characterized in subsequent studies.