Tutorial on estimating the limit of detection using LC-MS analysis, part I: Theoretical review.

A large body of literature exists on the limit of detection (LOD), but there is still a lot of confusion about this important validation parameter. This confusion mainly stems from its statistically complex background. The goal of this two-part tutorial is to discuss and clarify the topic of LOD for practitioners. The two main conclusions of this tutorial are: (1) the choice of how to estimate LOD should be based on the purpose of the analytical method that is being validated (e.g. considerable effort should not be made to estimate LOD for a method that is not used for detecting traces in the vicinity of LOD), and (2) LOD estimates are strongly dependent on different assumptions and the approach used, and therefore caution must be exercised when using the estimate or when comparing different estimates. Part I of the tutorial contains a theoretical discussion (without excessively sophisticated statistics) and part II contains examples based on experimental data. In Part I, LOD and other definitions related to it are reviewed, and their estimation and use are discussed. The assumptions and practicality of different approaches to estimate LOD are compared. Different aspects of the analytical method that strongly influence LOD estimates (e.g. linearity, scedasticity and day-to-day variability of LOD) together with experimental design considerations when estimating LOD are discussed. In part II, LOD is estimated for the LC-MS/MS analysis method to detect pesticides on separate days. The performance of different tests on the data necessary for LOD estimation are discussed and the results of different approaches under review in this tutorial are compared. In conclusion, a decision tree is proposed for estimating and monitoring LOD. A detailed working procedure for estimating LOD is presented. Several topics are pointed out in which further research and discussion is needed.