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Volatile organic compounds (VOCs) emitted from breath, faeces or skin may reflect physiological and pathological processes in vivo. Our setup employs real-time proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS) to explore VOC emissions of dairy cows in stable air under field conditions. Within one herd of 596 cows, seven groups (8-117 cows per group) were assessed. Groups differed in milk yield and health status (two contained cows with paratuberculosis, a chronic intestinal infection). Each group arrived one after another in the area of air measurement in front of the milking parlour. A customised PTR-TOF-MS system with a 6 m long and heated transfer line, was used for measuring VOCs continuously for 7 h, 1.5 m above the cows. Three consecutive time periods were investigated. Twenty-seven VOCs increased while the animals were gathering in the waiting area, and decreased when the animals entered the milking parlour. Linear correlations between the number of animals present and VOC concentrations were found for (C4H6)H+ and (C3H6O)H+. A relatively high concentration of acetone above the cows that had recently given birth to a calf might be related to increased fat turnover due to calving and different nutrition. Changes in VOC emissions were related to the presence of animals with paratuberculosis, to different average milk yields per group and to the time of the day (morning versus noon milking time). We found that VOC monitoring of stable air may provide additional immediate information on an animal's metabolic or health status and foster novel applications in the field of breath research.

Background. The analysis of volatile organic compounds (VOCs) in breath allows non-invasive investigations of diseases. Animal studies are conducted as a model to perform research of VOCs and their relation to diseases. In large animal models ruminants were often used as experimental targets. The effect of their physiological eructation on VOC exhalation has not been examined yet and is the objective of this study. Methods. Continuous breath profiles of two young cattle, four adult goats and four adult sheep were measured through a mask, covering mouth and nose, in real-time (200 ms) by means of proton transfer reaction time of flight mass spectrometry. Each animal was analysed twelve times for 3 consecutive minutes. Results. Real-time monitoring yielded a distinction of different episodes in the breath profiles of ruminants. An algorithm to separate eructation episodes and alveolar breath was established. In the first exhalation after eructation at least 19 VOC concentrations increased (up to 36-fold) and went back to initial levels in subsequent exhalations in all investigated ruminants. Decay of concentrations was substance specific. In goats, less VOCs were affected by the eructation compared to cattle and sheep. Breath profiles without exclusion of eructation episodes showed higher variations and median values than profiles where eructation episodes were excluded. Conclusion. Real-time breath analysis of ruminants enables the discrimination and characterisation of alveolar breath and eructation episodes. This leads to a better understanding of variation in breath data and possible origins of VOCs: breath or digestion related. To avoid impairment of breath gas results and to gain further information on bacterial products from the rumen, eructation and alveolar breath data should be analysed separately.

Background Species of Mycobacteriaceae cause serious zoonotic diseases in mammals, for example tuberculosis in humans, dogs, parrots, and elephants (caused by Mycobacterium tuberculosis) and in ruminants and humans (caused by M. bovis and M. caprae). Pulmonary diseases, lymphadenitis, skin diseases, and disseminated diseases can be caused by non-tuberculous mycobacteria (NTM). Diagnosis and differentiation among Mycobacterium species are currently done by culture isolation. The established diagnostic protocols comprise several steps that allow species identification. Detecting volatile organic compounds (VOCs) above bacterial cultures is a promising approach towards accelerating species identification via culture isolation. The aims of this project were to analyse VOCs in the headspace above 13 different species of mycobacteria, to define VOC profiles that are unique for each species, and to compile a set of substances that indicate the presence of growing mycobacteria in general. Materials & methods VOCs were measured in the headspace above 17 different mycobacterial strains, all cultivated on Herrold’s Egg Yolk Medium and above pure media slants that served as controls. For pre-concentration of VOCs, needle-trap micro-extraction was employed. Samples were subsequently analysed using gas chromatography-mass spectrometry. All volatiles were identified and calibrated by analysing pure reference substances. Results More than 130 VOCs were detected in headspace above mycobacteria-inoculated and control slants. Results confirmed significant VOC emissions above all mycobacterial species that had grown well. Concentration changes were measurable in vials with visually assessed bacterial growth and vials without apparent growth. VOCs above mycobacterial cultures could be grouped into substances that were either higher or equally concentrated, lower or equally concentrated, or both as those above control slants. Hence, we were able to identify 17 substances as potential biomarkers of the presence of growing mycobacteria in general. Conclusions This study revealed species-specific VOC profiles for eleven species of mycobacteria that showed visually apparent bacterial growth at the time point of analysis.

Volatile organic compounds (VOCs) emitted from in vitro cultures may reveal information on species and metabolism. Owing to low nmol L-1 concentration ranges, pre-concentration techniques are required for gas chromatography-mass spectrometry (GC-MS) based analyses. This study was intended to compare the efficiency of established micro-extraction techniques - solid-phase micro-extraction (SPME) and needle-trap micro-extraction (NTME) - for the analysis of complex VOC patterns. For SPME, a 75 μm Carboxen®/polydimethylsiloxane fiber was used. The NTME needle was packed with divinylbenzene, Carbopack X and Carboxen 1000. The headspace was sampled bi-directionally. Seventy-two VOCs were calibrated by reference standard mixtures in the range of 0.041-62.24 nmol L-1 by means of GC-MS. Both pre-concentration methods were applied to profile VOCs from cultures of Mycobacterium avium ssp. paratuberculosis. Limits of detection ranged from 0.004 to 3.93 nmol L-1 (median = 0.030 nmol L-1 ) for NTME and from 0.001 to 5.684 nmol L-1 (median = 0.043 nmol L-1 ) for SPME. NTME showed advantages in assessing polar compounds such as alcohols. SPME showed advantages in reproducibility but disadvantages in sensitivity for N-containing compounds. Micro-extraction techniques such as SPME and NTME are well suited for trace VOC profiling over cultures if the limitations of each technique is taken into account.

Modern statistical methods which were developed for pattern recognition are increasingly being used for data analysis in studies on emissions of volatile organic compounds (VOCs). With the detection of disease-related VOC profiles, novel non-invasive diagnostic tools could be developed for clinical applications. However, it is important to bear in mind that not all statistical methods are equally suitable for the investigation of VOC profiles. In particular, univariate methods are not able to discover VOC patterns as they consider each compound separately. The present study demonstrates this fact in practice. Using VOC samples from a controlled animal study on paratuberculosis, the random forest classification method was applied for pattern recognition and disease prediction. This strategy was compared with a prediction approach based on single compounds. Both methods were framed within a cross-validation procedure. A comparison of both strategies based on these VOC data reveals that random forests achieves higher sensitivities and specificities than predictions based on single compounds. Therefore, it will most likely be more fruitful to further investigate VOC patterns instead of single biomarkers for paratuberculosis. All methods used are thoroughly explained to aid the transfer to other data analyses.

Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic granulomatous enteritis in ruminants. Bacterial growth is still the diagnostic 'gold standard', but is very time consuming. MAP-specific volatile organic compounds (VOCs) above media could accelerate cultural diagnosis. The aim of this project was to assess the kinetics of a VOC profile linked to the growth of MAP in vitro. The following sources of variability were taken into account: five different culture media, three different MAP strains, inoculation with different bacterial counts, and different periods of incubation. Needle-trap microextraction was employed for pre-concentration of VOCs, and gas chromatography-mass spectrometry for subsequent analysis. All volatiles were identified and calibrated by analysing pure references at different concentration levels. More than 100 VOCs were measured in headspaces above MAP-inoculated and control slants. Results confirmed different VOC profiles above different culture media. Emissions could be assigned to either egg-containing media or synthetic ingredients. 43 VOCs were identified as potential biomarkers of MAP growth on Herrold's Egg Yolk Medium without significant differences between the tree MAP strains. Substances belonged to the classes of alcohols, aldehydes, esters, ketones, aliphatic and aromatic hydrocarbons. With increasing bacterial density the VOC concentrations above MAP expressed different patterns: the majority of substances increased (although a few decreased after reaching a peak), but nine VOCs clearly decreased. Data support the hypotheses that (i) bacteria emit different metabolites on different culture media; (ii) different MAP strains show uniform VOC patterns; and (iii) cultural diagnosis can be accelerated by taking specific VOC profiles into account.