Individuals are thought to have their own distinctive scent, analogous to a signature or fingerprint. To test this idea, we collected axillary sweat, urine and saliva from 197 adults from a village in the Austrian Alps, taking five sweat samples per subject over 10 weeks using a novel skin sampling device. We analysed samples using stir bar sorptive extraction in connection with thermal desorption gas chromatograph-mass spectrometry (GC-MS), and then we statistically analysed the chromatographic profiles using pattern recognition techniques. We found more volatile compounds in axillary sweat than in urine or saliva, and among these we found 373 peaks that were consistent over time (detected in four out of five samples per individual). Among these candidate compounds, we found individually distinct and reproducible GC-MS fingerprints, a reproducible difference between the sexes, and we identified the chemical structures of 44 individual and 12 gender-specific volatile compounds. These individual compounds provide candidates for major histocompatibility complex and other genetically determined odours. This is the first study on human axillary odour to sample a large number of subjects, and our findings are relevant to understanding the chemical nature of human odour, and efforts to design electronic sensors (e-nose) for biometric fingerprinting and disease diagnoses.

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Human saliva not only helps control oral health (with anti-microbial proteins), but it may also play a role in chemical communication. As is the case with other mammalian species, human saliva contains peptides, proteins, and numerous volatile organic compounds (VOCs). A high-throughput analytical method is described for profiling a large number of saliva samples to screen the profiles of VOCs. Saliva samples were collected in a non-stimulated fashion. The method utilized static stir bar extraction followed by gas chromatography-mass spectrometry (GC-MS). The method provided excellent reproducibility for a wide range of salivary compounds, including alcohols, aldehydes, ketones, carboxylic acids, esters, amines, amides, lactones, and hydrocarbons. Furthermore, substantial overlap of salivary VOCs and the previously reported skin VOCs in the same subject group was found in this study by using pattern recognition analyses. Sensitivity, precision, and reproducibility of the method suggest that this technique has potential in physiological, metabolomic, pharmacokinetic, forensic, and toxicological studies of small organic compounds where a large number of human saliva samples are involved.

A new approach for peak detection and matching has been developed and applied to two data sets. The first consisted of the Gas Chromatography-Mass Spectrometry (GC-MS) samples of 965 human sweat samples obtained from a population of 197 individuals. The second data set contained 500 synthetic chromatograms, and was generated to validate the peak detection and matching methods. The size of both of the data sets (around 500 000 detectable peaks over all chromatograms in data set 1, and around 100 000 in data set 2) would make it unfeasible to check manually whether peaks are matched. In the method described, the first procedure involves pre-processing the data before carrying out the second procedure of peak detection. The final procedure of peak matching consists of three stages: (a) finding potential target peaks in the full data set over all chromatograms; (b) matching peaks in the chromatograms to these targets to form clusters of spectra associated with each target; (c) merging targets where appropriate. Peak detection and matching were applied to both data sets, and the importance of stage (c) of peak matching described. In addition to the analysis of the synthetic chromatograms, the method was also validated by shuffling the original order of the sweat chromatograms and performing the methods independently on the newly shuffled data.

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