Volatile Organic Compounds Patterns Research Articles

Diagnosing gastrointestinal illnesses using fecal headspace volatile organic compounds.

Volatile organic compounds (VOCs) emitted from stool are the components of the smell of stool representing the end products of microbial activity and metabolism that can be used to diagnose disease. Despite the abundance of hydrogen, carbon dioxide, and methane that have already been identified in human flatus, the small portion of trace gases making up the VOCs emitted from stool include organic acids, alcohols, esters, heterocyclic compounds, aldehydes, ketones, and alkanes, among others. These are the gases that vary among individuals in sickness and in health, in dietary changes, and in gut microbial activity. Electronic nose devices are analytical and pattern recognition platforms that can utilize mass spectrometry or electrochemical sensors to detect these VOCs in gas samples. When paired with machine-learning and pattern recognition algorithms, this can identify patterns of VOCs, and thus patterns of smell, that can be used to identify disease states. In this review, we provide a clinical background of VOC identification, electronic nose development, and review gastroenterology applications toward diagnosing disease by the volatile headspace analysis of stool.

Differentiation between genetic mutations of breast cancer by breath volatolomics.

Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses.

Effects of Curative Colorectal Cancer Surgery on Exhaled Volatile Organic Compounds and Potential Implications in Clinical Follow-up.

The aim of this study was to determine whether the volatile organic compounds (VOCs) pattern in colorectal cancer (CRC) patients is modified by curative surgery for a potential application in the oncologic follow-up. CRC has been proved to induce metabolic derangements detectable by high through-output techniques in exhaled breath showing a specific pattern of VOCs. Forty-eight CRC patients and 55 healthy controls (HC) entered the study. Thirty-two patients (M/F: 1.4; mean age 63 years) attended the oncologic follow-up (mean 24 months) and were found disease-free. Breath samples were collected under similar environmental conditions into a Tedlar bags and processed offline by thermal-desorption gas chromatography-mass spectrometry (TD-GC-MS). VOCs were selected by U test to build a Probabilistic Neural Network (PNN) model to set-up a training phase, which was cross-validated using the leave-one out method. A total of 11 VOCs were finally selected for their excellent discriminant performance in identifying disease-free patients in follow-up from CRC patients before surgery, (sensitivity 100%, specificity 97.92%, accuracy 98.75%, and AUC: 1). The same VOCs pattern discriminated follow-up patients from HC, with a sensitivity of 100%, specificity of 90.91%, accuracy of 94.25%, and AUC 0.959. Exhaled VOCs pattern from CRC patients is modified by cancer removal confirming the tight relationship between tumor metabolism and exhaled VOCs. PNN analysis provides a high discriminatory tool to identify patients disease-free after curative surgery suggesting potential implications in CRC screening and secondary prevention.

The Breath Metabolome in Patients With Pouch Disorders

Introduction: Ileal Pouch Anal Anastomosis (IPAA) in patients with ulcerative colitis (UC) is associated with complications like pouchitis or Crohn's disease of the pouch (CD). Pouch disorders are diagnosed with a combination of pouchoscopy, histology, clinical findings and response to antibiotics. Our previous work indicates an inflammatory bowel disease (IBD) specific breath metabolome in patients naive to surgery. In a novel, non-invasive, approach we therefore analyzed volatile organic compounds (VOCs) in the breath and serum headspace of IPAA patients as biomarkers to diagnose and differentiate IPAA disorders. Methods: A prospective study of diagnostic testing was conducted at a tertiary referral center, recruiting subjects under the categories refractory pouchitis (RP) (refractory to or dependency on antibiotics), CD (small bowel mucosal involvement, perianal complications, pouch fistula at least 3 months after ileostomy closure) or normal pouch (NP). Exclusion criteria were surgical complications of IPAA. Clinical and endoscopic sub scores of the pouchitis disease activity score (PDAI) were obtained. The VOC profile was analyzed using selective ion flow tube-mass spectrometry in 1) the breath of subjects following a mouth rinse with water and 2) the headspace of serum samples warmed to 37°C after being nil orally for eight hours. Appropriate statistical tests were used (Table 1). Twenty two distinct VOCs were analyzed. Results: Thirty UC subjects with IPAA (NP (n=7), RP (n=10) or CD (n=13), (mean age 46.8 years, 50% female, mean disease duration 106 months) were included. We observed no difference in any VOCs when comparing NP with RP or RP with CD. Acrylonitrile was the only compound elevated in CD compared to NP (p < 0.05) (Table 1). None of the serum headspace VOCs were different between NP, RP and CD. There was no significant correlation (rho value) between the level of VOCs and clinical or endoscopic sub scores of the PDAI. Ethanol, nonene and trimethylamine showed significant (p < 0.05) correlation between serum and breath VOC levels. Conclusion: Given our previous findings of a vastly distinct breath metabolome in IBD patients naive to surgery compared to healthy controls, this data shows no difference in VOCs of patients with IPAA, irrespective of occurrence of inflammation or not. This suggests that the IBD specific VOC patterns may evolve from the intramural or intraluminal milieu of the colon.Figure 1

PWE-136 The non-invasive detection of non-alcoholic fatty liver disease using urinary volatile organic compound analysis: a pilot study

Introduction Non Alcoholic Fatty Liver Disease (NAFLD) is the commonest cause of chronic liver disease in the western world. Current diagnostic methods, including that of the Fibroscan, have limitations, thus there is a clinical need for more robust, non-invasive screening tools. The gut microbiome is altered in several gastrointestinal and hepatic disorders resulting in altered, unique gut fermentation patterns. These patterns are detectable by analysis of volatile organic compounds (VOCs) in urine, breath and faeces. We aimed to determine if progressive fatty liver disease produced an altered VOC pattern in urine; specifically NAFLD and Non-Alcoholic Steatohepatitis (NASH). Method 34 patients were recruited; 8 NASH cirrhotics (NASH-C); 7 non-cirrhotic NASH; 4 NAFLD and 15 controls. Urine was collected and stored frozen. For assay, the samples were defrosted and aliquoted into vials, which were heated to 40 ± 0.1°C and the headspace analysed by FAIMS (Field Asymmetric Ion Mobility Spectroscopy). A previously used data processing pipeline employing a ‘Random Forrest’ classification algorithm and a 10 fold cross validation method was applied. Results Urinary VOCs could distinguish liver disease patients from controls with a sensitivity of 0.58 (0.33–0.88) but specificity of 0.93 (0.68 – 1.00); Area Under Curve (AUC) 0.73 (0.55 – 0.90). NASH/NASH-C was similarly separated from the NAFLD/control patients with a sensitivity of 0.73 (0.45–0.92), a specificity of 0.79 (0.54–0.94) and AUC of 0.79 (0.64–0.95), respectively. Conclusion This pilot study offers the potential for early non-invasive tracking of liver disease using urinary VOC bio-odorant fingerprints to distinguish patients with a disease within the spectrum of fatty liver disease from healthy controls, but also to distinguish subsets of the spectrum from each other, such as NASH from NAFLD. This may develop into a viable alternative surveillance or diagnostic tool in patients unable or unwilling to undergo liver biopsy. Disclosure of interest None Declared.

Non-invasive distinction of non-alcoholic fatty liver disease using urinary volatile organic compound analysis: early results.

Non-Alcoholic Fatty Liver Disease (NAFLD) is the commonest cause of chronic liver disease in the western world. Current diagnostic methods including Fibroscan have limitations, thus there is a need for more robust non-invasive screening methods. The gut microbiome is altered in several gastrointestinal and hepatic disorders resulting in altered, unique gut fermentation patterns, detectable by analysis of volatile organic compounds (VOCs) in urine, breath and faeces. We performed a proof of principle pilot study to determine if progressive fatty liver disease produced an altered urinary VOC pattern; specifically NAFLD and Non-Alcoholic Steatohepatitis (NASH). 34 patients were recruited: 8 NASH cirrhotics (NASH-C); 7 non-cirrhotic NASH; 4 NAFLD and 15 controls. Urine was collected and stored frozen. For assay, the samples were defrosted and aliquoted into vials, which were heated to 40±0.1°C and the headspace analyzed by FAIMS (Field Asymmetric Ion Mobility Spectroscopy). A previously used data processing pipeline employing a Random Forrest classification algorithm and using a 10 fold cross validation method was applied. Urinary VOC results demonstrated sensitivity of 0.58 (0.33 - 0.88), but specificity of 0.93 (0.68 - 1.00) and an Area Under Curve (AUC) 0.73 (0.55 - 0.90) to distinguish between liver disease and controls. However, NASH/NASH-C was separated from the NAFLD/controls with a sensitivity of 0.73 (0.45 - 0.92), specificity of 0.79 (0.54 - 0.94) and AUC of 0.79 (0.64 - 0.95), respectively. This pilot study suggests that urinary VOCs detection may offer the potential for early non-invasive characterisation of liver disease using 'smell prints' to distinguish between NASH and NAFLD.

Physiological variability in volatile organic compounds (VOCs) in exhaled breath and released from faeces due to nutrition and somatic growth in a standardized caprine animal model44Part of the data was presented at the 8th International Conference on Breath Research and Cancer Diagnosis (Breath Analysis 2014), Torun (Poland), 6–9th July 2014. The relating poster was honoured with the 1. Prize (best

Physiological effects may change volatile organic compound (VOC) concentrations and may therefore act as confounding factors in the definition of VOCs as disease biomarkers. To evaluate the extent of physiological background variability, this study assessed the effects of feed composition and somatic growth on VOC patterns in a standardized large animal model. Fifteen clinically healthy goats were followed during their first year of life. VOCs present in the headspace over faeces, exhaled breath and ambient air inside the stable were repeatedly assessed in parallel with the concentrations of glucose, protein, and albumin in venous blood. VOCs were collected and analysed using solid-phase or needle-trap microextraction and gas chromatograpy together with mass spectroscopy. The concentrations of VOCs in exhaled breath and above faeces varied significantly with increasing age of the animals. The largest variations in volatiles detected in the headspace over faeces occurred with the change from milk feeding to plant-based diet. VOCs above faeces and in exhaled breath correlated significantly with blood components. Among VOCs exhaled, the strongest correlations were found between exhaled nonanal concentrations and blood concentrations of glucose and albumin. Results stress the importance of a profound knowledge of the physiological backgrounds of VOC composition before defining reliable and accurate marker sets for diagnostic purposes.

Potential application of PTR-TOFMS for the detection of deoxynivalenol (DON) in durum wheat

Deoxynivalenol (DON) is the most common Fusarium-mycotoxin among the type B group of trichothecenes found at high concentration in cereals mainly produced by Fusarium graminearum and Fusarium culmorum. Analytical methods for the rapid evaluation of mycotoxins contamination of grains are highly needed in order to prevent the food chain contamination. The synthesis of mycotoxins is often associated with the production of volatile organic compounds (VOCs). These, when detectable through the use of suitable methods of analysis, represent a “fingerprint” of the contaminated samples. PTR-TOFMS technology was used for a rapid and efficient detection of DON in naturally contaminated durum wheat grain samples, by evaluating the correlations between selected patterns of volatile organic compounds with DON concentration. A partial least square (PLS) regression was used to efficiently quantify the DON value (r = 0.94 in the test set). A partial least squares discriminant analysis (PLSDA) was used to classify the samples above or below the fixed DON value of 1750 μg/kg (91.9% of correct classification in the test set). Eight VOCs have been proven as good indicators in discriminating wheat samples, with DON concentration values above/below the legal limit 1750 μg/kg, using both class-modeling approaches, partial least square (PLS) analysis and partial least squares discriminant analysis (PLSDA). The obtained results show a promising use of the PTR-TOFMS for the application of rapid, non-destructive, and massive screening of DON contaminated durum wheat samples.

Analysis of breath volatile organic compounds in children with chronic liver disease compared to healthy controls

Breath testing is increasingly being used as a non-invasive diagnostic tool for disease states across medicine. The purpose of this study was to compare the levels of volatile organic compounds (VOCs) as measured by mass spectrometry in healthy children and children with chronic liver disease (CLD). Patients between the ages of 6 and 21 were recruited for the study. Control subjects were recruited from a general pediatric population during well-child visits, while patients with CLD were recruited from pediatric gastroenterology clinic visits. The diagnosis of CLD was confirmed by clinical, laboratory, and/or histologic data. A single exhaled breath was collected and analyzed by means of selected-ion flow-tube mass spectrometry per protocol. A total of 104 patients were included in the study (49 with CLD and 55 healthy controls). Of the patients with CLD, 20 had advanced liver fibrosis (F3–F4). In the CLD cohort, levels of exhaled 1-decene, 1-heptene, 1-octene and 3 methylhexane were found to be significantly higher when compared to the control population (p < 0.001, p = 0.035, p < 0.001 and p = 0.004, respectively). Exhaled 1-nonene, (E)-2-nonene, and dimethyl sulfide levels were found to be significantly lower in patients with CLD patients when compared to controls (p < 0.001, p < 0.001 and p = 0.007, respectively). By utilizing a combination of five of the VOCs, the accuracy for predicting the presence of CLD was excellent (AUROC = 0.97). Our study demonstrates that children with CLD have a unique pattern of exhaled VOCs. Utilization of a combination of these VOCs represents a promising non-invasive diagnostic tool and may provide further insight into the pathophysiologic processes and pathways leading to pediatric liver disease. Further analysis of these compounds in external cohorts are needed to validate our findings.

An electronic nose in the discrimination of obese patients with and without obstructive sleep apnoea

Exhaled breath contains thousands of volatile organic compounds (VOCs) in gaseous form, which may be used as markers of airway inflammation and lung disease. Electronic noses enable quick and real-time pattern analysis of VOC spectra. It has been shown that the exhaled breath of patients with obstructive sleep apnoea (OSA) differs from that of non-obese controls. We aimed to assess the influence of obesity in the composition of exhaled VOCs by comparing obese subjects with and without OSA. Moreover, we aimed to identify the discriminant VOCs in the two groups.19 obese patients with established OSA (OO; age 51.2 ± 6.8; body mass index (BMI) 34.3 ± 3.5), 14 obese controls without OSA (ONO; age 46.5 ± 7.6; BMI 33.5 ± 4.1) and 20 non-obese healthy controls (HC; age 41.1 ± 12.6; BMI 24.9 ± 3.8) participated in a cross-sectional study. Exhaled breath was collected by a previously described method and sampled by using an electronic nose (Cyranose 320) and by gas chromatography–mass spectrometry (GC–MS) analysis. Breathprints were analyzed by canonical discriminant analysis on principal component reduction. Cross-validation accuracy (CVA) was calculated.Breathprints from the HC group were separated from those of OO (CVA = 97.4%) and ONO (CVA = 94.1%). Breathprints from OO were moderately separated from those of ONO (CVA = 67.6%).The presence of OSA alters the exhaled VOC pattern in obese subjects. The incomplete separation of breathprints between OO and ONO may be due to the same underlying inflammation caused by obesity.

Current Challenges in Volatile Organic Compounds Analysis as Potential Biomarkers of Cancer.

An early diagnosis and appropriate treatment are crucial in reducing mortality among people suffering from cancer. There is a lack of characteristic early clinical symptoms in most forms of cancer, which highlights the importance of investigating new methods for its early detection. One of the most promising methods is the analysis of volatile organic compounds (VOCs). VOCs are a diverse group of carbon-based chemicals that are present in exhaled breath and biofluids and may be collected from the headspace of these matrices. Different patterns of VOCs have been correlated with various diseases, cancer among them. Studies have also shown that cancer cells in vitro produce or consume specific VOCs that can serve as potential biomarkers that differentiate them from noncancerous cells. This review identifies the current challenges in the investigation of VOCs as potential cancer biomarkers, by the critical evaluation of available matrices for the in vivo and in vitro approaches in this field and by comparison of the main extraction and detection techniques that have been applied to date in this area of study. It also summarises complementary in vivo, ex vivo, and in vitro studies conducted to date in order to try to identify volatile biomarkers of cancer.

Sex and Smoking Status Effects on the Early Detection of Early Lung Cancer in High-Risk Smokers Using an Electronic Nose.

Volatile organic compounds (VOCs) in exhaled breath as measured by electronic nose (e-nose) have utility as biomarkers to detect subjects at risk of having lung cancer in a screening setting. We hypothesize that breath analysis using an e-nose chemo-resistive sensor array could be used as a screening tool to discriminate patients diagnosed with lung cancer from high-risk smokers. Breath samples from 191 subjects-25 lung cancer patients and 166 high-risk smoker control subjects without cancer-were analyzed. For clinical relevancy, subjects in both groups were matched for age, sex, and smoking histories. Classification and regression trees and discriminant functions classifiers were used to recognize VOC patterns in e-nose data. Cross-validated results were used to assess classification accuracy. Repeatability and reproducibility of e-nose data were assessed by measuring subject-exhaled breath in parallel across two e-nose devices. e-Nose measurements could distinguish lung cancer patients from high-risk control subjects, with a better than 80% classification accuracy. Subject sex and smoking status impacted classification as area under the curve results (ex-smoker males 0.846, ex-smoker female 0.816, current smoker male 0.745, and current smoker female 0.725) demonstrated. Two e-nose systems could be calibrated to give equivalent readings across subject-exhaled breath measured in parallel. e-Nose technology may have significant utility as a noninvasive screening tool for detecting individuals at increased risk for lung cancer. The results presented further the case that VOC patterns could have real clinical utility to screen for lung cancer in the important growing ex-smoker population.

Real time analysis of volatile organic compounds (VOCs) in centenarians

Centenarians are a model to study human longevity and the physiological process of aging. A plethora of studies on this model show the complexity of the system. Laboratory studies fail to find a biomarker of senescence. The real time exhaled breath volatile organic compounds (VOCs) has been suggested as a new biomarker to detect and monitor physiological processes in the respiratory system. VOCs exhaled by centenarians have not been studied in the general population and across-age-groups. In the present study we investigated, in real time, the breath properties and VOC exhaled content in healthy centenarians as compared with non-centenarian seniors and young healthy subjects. We found distinctly different breath pattern and distribution profiles of VOCs in the centenarians. Thus, the VOCs measurement allowed to discriminate the differences between the age-groups. We propose a VOCs fingerprint as a biomarker underlying the physiological mechanisms of aging and longevity. Longevity should be considered physiologically as a new phase of life, characteristic of the well adapted subject.

Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air

BackgroundSeveral classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients.MethodsBreath samples were analyzed for VOC profiles by gas chromatography–mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes.ResultsWe identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics.ConclusionThis study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-014-0136-8) contains supplementary material, which is available to authorized users.

A rapid method for breath analysis in cystic fibrosis patients.

For easy handling and speed of lung diseases diagnostics, approaches based on volatile organic compounds (VOCs), including those emitted by pathogenic microorganisms, are considered but currently require considerable sampling efforts. We tested whether easy-to-handle and fast detection of lung infections is possible using solid-phase microextraction (SPME) of 100ml of exhaled breath. An analytical procedure for the detection of VOCs from the headspace of epithelial lung cells infected with four human pathogens was developed. The feasibility of this method was tested in a cystic fibrosis (CF) outpatient clinic in vivo. Exhaled breath was extracted by SPME and analyzed by gas chromatography-mass spectrometry (GC-MS). The compositions of VOCs released in the infection model were characteristic for all individual pathogens tested. Exhaled breath of CF patients allowed clear distinction of CF patients and controls by their VOC compositions using multivariate analyses. Interestingly, the major specific VOCs detected in the exhaled breath of infected CF patients in vivo differed from those monitored during bacterial in vitro growth. SPME extraction of VOCs from 100ml of human breath allowed the distinction between CF patients and healthy probands. Our results highlight the importance of assessing the entire pattern of VOCs instead of selected biomarkers for diagnostic purposes, as well as the need to use clinical samples to identify reliable biomarkers. This study provides the proof-of-concept for the approach using the composition of exhaled VOCs in human breath for the rapid identification of infectious agents in patients with lower respiratory tract infections.

Comorbidity modulates non invasive ventilation-induced changes in breath print of obstructive sleep apnea syndrome patients.

In obstructive sleep apnea syndrome (OSAS), exhaled volatile organic compounds (VOCs) change after long-term continuous positive airway pressure (CPAP). The objective of the study was to verify whether changes in VOCs pattern are detectable after the first night of CPAP and to identify correlates, if any, of these changes. Fifty OSAS patients underwent a multidimensional assessment and breath print (BP) analysis through 28 sensors e-nose at baseline and after the first night of CPAP. Boxplots of individual BP evolution after CPAP and groups were compared by ANOVA and Fisher's exact t. Partial least square discriminant analysis (PLS-DA), with leave-one-out as cross-validation was used to calculate to which extent basal BP could predicts changes in apnea-hypopnea index (AHI). CPAP was effective in all the patients (delta AHI 35.8 events/h; residual AHI 6.0 events/h). BP dramatically changed after a single-night CPAP and changes conformed to two well-distinguished patterns: pattern C (n = 29), characterized by consonant boxplots, and pattern D (n = 21), with variably discordant boxplots. The average number of comorbid diseases (1.55 [standard deviation, SD 1.0] in group C, 3.14 [SD 1.8] in group D, p < 0.001) and the prevalence of selected comorbidity (diabetes mellitus, metabolic syndrome, and chronic heart failure), were the only features distinguishing groups. We found that BP change after a single night of CPAP largely depends upon comorbidity. Comorbidity likely contributes to phenotypic variability in OSAS population. BP might qualify as a surrogate index of the response to and, later, compliance with CPAP.

Differentiating coeliac disease from irritable bowel syndrome by urinary volatile organic compound analysis--a pilot study.

Coeliac disease (CD), a T-cell-mediated gluten sensitive enteropathy, affects ∼1% of the UK population and can present with wide ranging clinical features, often being mistaken for Irritable Bowel Syndrome (IBS). Heightened clinical awareness and serological screening identifies those with potential coeliac disease; the diagnosis is confirmed with duodenal biopsies, and symptom improvement with a gluten-free diet. Limitations to diagnosis are false negative serology and reluctance to undergo biopsy. The gut microbiome is altered in several gastrointestinal disorders, causing altered gut fermentation patterns recognisable by volatile organic compounds (VOC) analysis in urine, breath and faeces. We aimed to determine if CD alters the urinary VOC pattern, distinguishing it from IBS. 47 patients were recruited, 27 with established CD, on gluten free diets, and 20 with diarrhoea-predominant IBS (D-IBS). Collected urine was stored frozen in 10 ml aliquots. For assay, the specimens were heated to 40±0.1°C and the headspace analysed by Field Asymmetric Ion Mobility Spectrometry (FAIMS). Machine learning algorithms were used for statistical evaluation. Samples were also analysed using Gas chromatography and mass spectroscopy (GC-MS). Sparse logistic regression showed that FAIMS distinguishes VOCs in CD vs D-IBS with ROC curve AUC of 0.91 (0.83–0.99), sensitivity and specificity of 85% respectively. GCMS showed a unique peak at 4′67 found only in CD, not D-IBS, which correlated with the compound 1,3,5,7 cyclooctatetraene. This study suggests that FAIMS offers a novel, non-invasive approach to identify those with possible CD, and distinguishes from D-IBS. It offers the potential for monitoring compliance with a gluten-free diet at home. The presence of cyclooctatetraene in CD specimens will need further validation.

Aboveground endophyte affects root volatile emission and host plant selection of a belowground insect.

Plants emit specific blends of volatile organic compounds (VOCs) that serve as multitrophic, multifunctional signals. Fungi colonizing aboveground (AG) or belowground (BG) plant structures can modify VOC patterns, thereby altering the information content for AG insects. Whether AG microbes affect the emission of root volatiles and thus influence soil insect behaviour is unknown. The endophytic fungus Neotyphodium uncinatum colonizes the aerial parts of the grass hybrid Festuca pratensis × Lolium perenne and is responsible for the presence of insect-toxic loline alkaloids in shoots and roots. We investigated whether endophyte symbiosis had an effect on the volatile emission of grass roots and if the root herbivore Costelytra zealandica was able to recognize endophyte-infected plants by olfaction. In BG olfactometer assays, larvae of C. zealandica were more strongly attracted to roots of uninfected than endophyte-harbouring grasses. Combined gas chromatography-mass spectrometry and proton transfer reaction-mass spectrometry revealed that endophyte-infected roots emitted less VOCs and more CO2. Our results demonstrate that symbiotic fungi in plants may influence soil insect distribution by changing their behaviour towards root volatiles. The well-known defensive mutualism between grasses and Neotyphodium endophytes could thus go beyond bioactive alkaloids and also confer protection by being chemically less apparent for soil herbivores.

Detection of colorectal cancer (CRC) by urinary volatile organic compound analysis.

Colorectal cancer (CRC) is a leading cause of cancer related death in Europe and the USA. There is no universally accepted effective non-invasive screening test for CRC. Guaiac based faecal occult blood (gFOB) testing has largely been superseded by Faecal Immunochemical testing (FIT), but sensitivity still remains poor. The uptake of population based FOBt testing in the UK is also low at around 50%. The detection of volatile organic compounds (VOCs) signature(s) for many cancer subtypes is receiving increasing interest using a variety of gas phase analytical instruments. One such example is FAIMS (Field Asymmetric Ion Mobility Spectrometer). FAIMS is able to identify Inflammatory Bowel disease (IBD) patients by analysing shifts in VOCs patterns in both urine and faeces. This study extends this concept to determine whether CRC patients can be identified through non-invasive analysis of urine, using FAIMS. 133 patients were recruited; 83 CRC patients and 50 healthy controls. Urine was collected at the time of CRC diagnosis and headspace analysis undertaken using a FAIMS instrument (Owlstone, Lonestar, UK). Data was processed using Fisher Discriminant Analysis (FDA) after feature extraction from the raw data. FAIMS analyses demonstrated that the VOC profiles of CRC patients were tightly clustered and could be distinguished from healthy controls. Sensitivity and specificity for CRC detection with FAIMS were 88% and 60% respectively. This study suggests that VOC signatures emanating from urine can be detected in patients with CRC using ion mobility spectroscopy technology (FAIMS) with potential as a novel screening tool.

Fingerprinting oils in water via their dissolved VOC pattern using mid-infrared sensors.

An infrared attenuated total reflection (IR-ATR) method for detecting, differentiating, and quantifying hydrocarbons dissolved in water relevant for oil spills by evaluating the "fingerprint" of the volatile organic compounds (VOCs) associated with individual oil types in the mid-infrared spectral range (i.e., 800-600 cm(-1)) is presented. In this spectral regime, these hydrocarbons provide distinctive absorption features, which may be used to identify specific hydrocarbon patterns that are characteristic for different crude and refined oils. For analyzing the "VOC fingerprint" resulting from various oil samples, aqueous solutions containing the dissolved hydrocarbons from different crude oils (i.e., types "Barrow", "Goodwyn", and "Saladin") and refined oils (i.e., "Petrol" and "Diesel") were analyzed using a ZnSe ATR waveguide as the optical sensing element. To minimize interferences from the surrounding water matrix and for amplifying the VOC signatures by enrichment, a thin layer of poly(ethylene-co-propylene) was coated onto the ATR waveguide surface, thereby enabling the establishment of suitable calibration functions for the quantification of characteristic concentration patterns of the detected VOCs. Multivariate data analysis was then used for a prelininary classification of various oil-types via their VOC patterns.