Currently, a small number of diseases, particularly cardiovascular (CVDs), oncologic (ODs), neurodegenerative (NDDs), chronic respiratory diseases, as well as diabetes, form a severe burden to most of the countries worldwide. Hence, there is an urgent need for development of efficient diagnostic tools, particularly those enabling reliable detection of diseases, at their early stages, preferably using non-invasive approaches. Breath analysis is a non-invasive approach relying only on the characterisation of volatile composition of the exhaled breath (EB) that in turn reflects the volatile composition of the bloodstream and airways and therefore the status and condition of the whole organism metabolism. Advanced sampling procedures (solid-phase and needle traps microextraction) coupled with modern analytical technologies (proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, e-noses, etc.) allow the characterisation of EB composition to an unprecedented level. However, a key challenge in EB analysis is the proper statistical analysis and interpretation of the large and heterogeneous datasets obtained from EB research. There is no standard statistical framework/protocol yet available in literature that can be used for EB data analysis towards discovery of biomarkers for use in a typical clinical setup. Nevertheless, EB analysis has immense potential towards development of biomarkers for the early disease diagnosis of diseases.

Background:Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer.Methods:To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxen-polydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 50°C for 60 min using samples with high ionic strengths (17% sodium chloride, w v−1) and under agitation.Results:A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (P<0.05). A significant increase in the peak area of 2-methyl-3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients.Conclusions:Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.

The influence of the age in the volatile composition of Madeira wines made with Boal, Malvazia, Sercial and Verdelho varieties and aged in oak barrel during 1, 11 and 25 years old was been studied. For this purpose, the evolution of volatile compounds: higher alcohols, ethyl esters, fatty acids, furan compounds, enolic compounds, γ-lactones, dioxanes and dioxolanes, of the four most utilised varieties were determined using liquid-liquid extraction with dichloromeihane. Octan-3-ol was used as internal standard. The wines made with these varieties showed great differences in sugar content and small variations on pH and alcoholic degree.The results show that during ageing, the concentration of fatty acids ethyl esters, acetates and fatty acids decrease significantly contrarily to the great increase of ethyl esters of diprotic acids. There is a strong correlation between sotolon, 2-furfural, 5-methyl-2-furfural, 5-hydroxymethyl-2-furfural and 5-ethoxymethyl-2-furfural with wine ageing. These findings indicate that these compounds can be used as ageing wine markers. Among the molecules studied, sotolon [3-hydroxy-4,5-dimethyl-2(5H)-furanone] was one of the few molecules present in concentrations above the perception threshold in Madeira wines. 5-Eihoxymethyl-2-furfural formed from 5-hydroxymethyl-2-furfural and 2-furfural, derived from sugars, are also involved in the aroma of sweet fortified white wines aged in oxidative conditions. The sensory properties change significantly after long periods of conservation.

a b s t r a c tA sensitive assay to identify volatile organic metabolites (VOMs) as biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. Therefore the aim of this study was to establish the urinary metabolomic profile of breast cancer patients and healthy individuals (control group) and to explore the VOMs as potential biomarkers in breast cancer diagnosis at early stage. Solid-phase microextraction (SPME) using CAR/PDMS sorbent combined with gas chromatography-mass spectrometry was applied to obtain metabolomic information patterns of 26 breast cancer patients and 21 healthy individuals (controls). A total of seventy-nine VOMs, belonging to distinct chemical classes, were detected and identified in control and breast cancer groups. Ketones and sulfur compounds were the chemical classes with highest contribution for both groups. Results showed that excretion values of 6 VOMs among the total of 79 detected were found to be statistically different (p < 0.05). A significant increase in the peak area of (−)-4-carene, 3-heptanone, 1,2,4-trimethylbenzene, 2-methoxythiophene and phenol, in VOMs of cancer patients relatively to controls was observed. Statiscally significant lower abundances of dimethyl disulfide were found in cancer patients. Bioanalytical data were submitted to multivariate statistics [principal component analysis (PCA)], in order to visualize clusters of cases and to detect the VOMs that are able to differentiate cancer patients from healthy individuals. Very good discrimination within breast cancer and control groups was achieved. Nevertheless, a deep study using a larger number of patients must be carried out to confirm the results.

A dynamic headspace solid-phase microextraction methodology was developed for analysis of varietal aroma compounds in must and Madeira wine samples, a spirit wine with an ethanol content of 18% (v/v). The factors with influence in the headspace solid-phase microextraction efficiency such as: fibre coating, extraction time and temperature, pH, ionic strength, ethanol content, desorption time and temperature, were optimised and the method validated. The best results were obtained for a 85 m polyacrylate fibre, with a 60 min headspace for must and 120 min for wine samples, in a 2.4 ml sample at 40 • C with 30% of NaCl. The extract is injected in the splitless mode in a GC-MS Varian system, Saturn III, and separated on a Stabilwax capillary column. The linear dynamic range of the method covers the normal range of occurrence of analytes in wine with typical r 2 between 0.985 (␤-ionone) and 0.998 (linalool) for musts and between 0.980 (␣-terpineol) and 0.999 (linalool) for must and wine samples, respectively. For must samples the reproducibility ranges from 2.5% (citronellol) to 14.4% (nerolidol) (as R.S.D.), and from 4.8% (citronellol) to 14.2% (nerolidol) for wine samples. The analysis of spiked samples has shown that matrix effects do not significantly affect method performance. Limits of detection obtained are in low g l −1 range for all compounds analysed in this study.