Evaluation of a combination of SIFT-MS and multivariate data analysis for the diagnosis of Mycobacterium bovis in wild badgers

Abstract

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.