Correcting measurement errors using reference materials in method validation

Abstract

Quality assurance and method validation are needed to reduce false decisions due to measurement errors. In this context accuracy and standard uncertainty for the analytical method need to be considered to ensure that the performance characteristics of the method are understood. Therefore, analytical methods ought to be validated before implementation and controlled on a regular basis during usage. For this purpose reference materials (RMs) are useful to determine the performance characteristics of methods under development. These performance parameters may be documented in the light of a method evaluation study and the documentation related to international standards and guidelines. In a method evaluation study of Pb in blood using reference samples from the Laboratoire Toxicologie du Quèbec, Canada, a difference between the systematic errors was observed using a Perkin-Elmer Model 5100 atomic absorption spectrometer and a Perkin-Elmer Model 4100 atomic absorption spectrometer, both with Zeeman background correction. For measurement of blood samples, the performance parameters obtained in the method evaluation studies, i.e. slopes and intercepts of the method evaluation function (MEF), were intended to be used for correcting the systematic errors. However, the number of MEF samples was insufficient to produce an acceptable SD for the MEF slopes to be used for correction. In a method evaluation study on valproate in plasma using the SYVA's EMIT assay on COBAS MIRA S a significant systematic error above the concentration 300 mmol dm⁻³ was demonstrated (slope 0.9541) and consequently the slope was used for correction of results. For analytes, where certified RMs (CRMs) exist, a systematic error of measurements can be reduced by correcting errors by assessment of the trueness as recommended in international guidelines issued by ISO or the National Institute of Standards and Technology (NIST). When possible, the analysis of several RMs, covering the concentration range of interest, is the most useful way to investigate measurement bias. Unfortunately, until recently only few RMs existed and only few had been produced and certified by specialized organizations such as NIST or the Standards, Measurements and Testing (SMT, previously BCR) programme. Due to the lack of such RMs, network organizations are nowadays established with the aim of supporting the correct use and production of high-quality CRMs.