The currently accepted 'gold standard' tuberculosis (TB) detection method for veterinary applications is that of culturing from a tissue sample post mortem. The test is accurate, but growing Mycobacterium bovis is difficult and the process can take up to 12 weeks to return a diagnosis. In this paper we evaluate a much faster screening approach based on serum headspace analysis using selected ion flow tube mass spectrometry (SIFT-MS). SIFT-MS is a rapid, quantitative gas analysis technique, with sample analysis times of as little as a few seconds. Headspace from above serum samples from wild badgers, captured as part of a randomised trial, was analysed. Multivariate classification algorithms were then employed to extract a simple TB diagnosis from the complex multivariate response provided by the SIFT-MS instrument. This is the first time that such multivariate analysis has been applied to SIFT-MS data. An accuracy of TB discrimination of approximately 88% true positive was achieved which shows promise, but the corresponding false positive rate of 38% indicates that there is more work to do before this approach could replace the culture test. Recommendations for future work that could increase the performance are therefore proposed.
a b s t r a c tIn past years, numerous electronic nose (e-nose) developments have been published describing analyses of solid-, liquid-or gaseous media in microbiological-, environmental-, agricultural-or medical applications. However, little has been reported about complex methodological pitfalls that might be associated with commercially available e-nose technology. In this paper, some of these pitfalls such as temperature, the use of filters and mass flow using different sampling methods (static-and dynamic sampling) are described for two generations of conducting polymer e-noses (ST114/214, CPs, both Scensive Tech. Ltd.). A comparison with metal oxide semiconducting field effect transistor/metal oxide semiconductor (MOSFET/MOS) e-noses regarding stability across replicates and over time was made. Changes in temperature were found to give larger sensor responses, whereas the application of filters led to quantitative and qualitative changes in sensor responses due to a change in mass flow which was also affected by the sampling method. Static sampling provided more stable flows across replicates. Variation was investigated for CPs and MOSFET/MOS e-noses that gave different responses over time and across replicates. These methodological factors cause a lack of stability and reproducibility, demonstrating the pitfalls of e-nose technology and therefore limit their utility for discriminating between samples.
Since the idea of electronic noses was published, numerous electronic nose (e-nose) developments and applications have been used in analyzing solid, liquid and gaseous samples in the food and automotive industry or for medical purposes. However, little is known about methodological pitfalls that might be associated with e-nose technology. Some of the methodological variation caused by changes in ambient temperature, using different filters and changes in mass flow rates are described. Reasons for a lack of stability and reproducibility are given, explaining why methodological variation influences sensor responses and why e-nose technology may not always be sufficiently robust for headspace analysis. However, the potential of e-nose technology is also discussed.
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