尺度不变特征变换
多元分析
多元统计
金标准(测试)
牛分枝杆菌
结核分枝杆菌
样品(材料)
肺结核
色谱法
计算机科学
化学
人工智能
统计
数学
医学
病理
特征提取
作者
Andrew Spooner,Conrad Bessant,Claire Turner,Henri Knobloch,Mark A. Chambers
出处
期刊:Analyst
[The Royal Society of Chemistry]
日期:2009-01-01
卷期号:134 (9): 1922-1922
被引量:27
摘要
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.
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