The assessment of accuracy, which involves the estimation of precision and the determination of trueness, refers to the process of evaluating whether the results provided by analytical methods are close to accepted reference values. The different references available in chromatographic analysis and useful guidelines to perform such a comparison are described.

Screening for important factors during method optimization or in robustness testing involves two-level screening designs, such as fractional factorial and Plackett–Burman designs, as described in Part 1. This second part on screening designs discusses the experimental protocol for executing these designs and the data analysis of their results.

Screening designs are used to screen for important factors during method optimization or in robustness testing. Usually, two-level screening designs, such as fractional factorial and Plackett–Burman designs, are applied. This column discusses the properties of these designs.

In a good laboratory practice (GLP) environment, data-handling software cannot be used until it is validated. This even applies to the most simple program that performs calculations or stores data. A detailed documentation of the set-up and the performance of the software — called software validation — is required. The development and validation requirements are described in this article and illustrated with a software for robustness testing (SRT), which guides the user step-by-step through the experimental set-up and interpretation of robustness tests. This software was developed in an Excel (Windows XP) environment and is used as part of method validation in laboratories that require compliance with GLP and 21 CFR Part 11. The software was subjected to software validation regulations and is compliant with electronic records and signature rules (21 CFR Part 11) as it creates, delivers and stores electronic data. The validation tests are based on the computerized system validation (CSV) —..

It would help to have a restricted set of chromatographic systems (CS) that together serve as potential starting points in method development.

Analytical chemists are concerned with the quality of their methods and results. An important question in this context is whether the precision of a newly developed and validated method is up to standard. In other words: is the precision of the newly developed method comparable to what could be expected? This article looks at how the Horwitz equation can answer this. It also describes the results of an extensive study involving 10000 laboratories which indicates that the relative reproducibility approximately doubles for every 100-fold decrease in concentration and that, surprisingly, it does not depend on the type of material or method.

The sample size is determined by three factors: the size of the difference between the means that should be detected, the precision of the methods being compared and the significance levels at which the test is performed.

The authors explain how to construct box plots and how they can help you to learn more about your data.

The second part in this series focuses on the variables in large data tables, looking at the vocabulary used by PCA experts, and explaining some of the background to the method. It further provides practical examples of how these variables operate in practice.