Quantifying uncertainty in qualitative analysis

The feasibility of adopting a consistent approach to the expression of uncertainties relating to identification is discussed. It is argued that qualitative analysis,can be viewed as a classification problem, that it is at least as important as quantitative analysis and that inferences drawn from qualitative tests should take relevant uncertainties into account. A brief review of systems of reasoning under uncertainty is presented, and it is concluded that Bayes' theorem provides the most suitable framework, providing for combination of separate items of evidence and implicitly allowing for both false positive and false negative probabilities in a single parameter. The chemical significance and practical evaluation of relevant probabilities are considered, and the applications and reporting of 'identification certainty' figures are discussed.