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The recently presented in-house validation concept developed on the basis of a variance component model is extended and optimised by basing it on a fractional factorial sampling scheme. The presented approach relies on a comprehensive mathematical model to assess simultaneously the in-house reproducibility and its components in addition to uncertainty-related performance characteristics like power functions and critical concentrations. The influence of individual factors and the corresponding uncertainties can be determined separately in a single experiment with optimum utilization of the generated data, in this way achieving a favourable ratio between costs and result.

A matrix-considering in-house validation concept for analytical methods is presented which takes into account the uncertainty due to matrix- and time-induced deviations. It is based on a variance component model for univariate quantitative measurement data that can be adapted to both screening and confirmation methods and to both zero-tolerance and threshold decisions. The model allows the calculation of critical concentrations for given α-errors and the calculation of the corresponding power function to evaluate the performance of an analytical method. The model is applied to a real-life validation experiment.

The International Harmonized Protocol for the Proficiency Testing of (Chemical) Analytical Laboratories (Thomson, M., and Wood, R., J. AOAC Int., 1993, 76, 926) lays down the minimum requirements for proficiency testing schemes. A prerequisite of statistically-based laboratory-performance characteristics is a suitable statistical model. A detailed investigation of statistical modelling, laboratory performance characteristics and quality limits is presented in this paper.