Analytical performance parameters Ruggedness

A number of experimental considerations must be addressed in order to use XRF as a quantitative tool, and these have been discussed at length [75,76]. The effects on the usual analytical performance parameters (accuracy, precision, linearity, limits of detection and quantitation, and ruggedness) associated with instrument are usually minimal. 

The United States Pharmacopoeia (U.S.P.) [5] in a chapter on validation of compendial methods, defines analytical performance parameters (accuracy, precision, specificity, limit of detection, limit of quantitation, linearity and range, ruggedness, and robustness) that are to be used for validating analytical methods. A proposed United States Pharmacopeia (U.S.P.) general chapter on near-infrared spectrophotometry [6] addresses the suitability of instrumentation for use in a particular method through a discussion of operational qualifications and performance verifications. 

Variation in chamber systems and in environmental coruiitions One of the most important aspects of ruggedness testing is to determine the effects of variations in chamber systems and environmental conditions on the analytical performance parameters. The quality of the chromatograms obtained is highly influenced by the running conditions (type, shape, and size of chambers, presaturation conditions, temperature, humidity, etc.). 

For non-compendial procedures, the performance parameters that should be determined in validation studies include specificity/selectivity, linearity, accuracy, precision (repeatability and intermediate precision), detection limit (DL), quantitation limit (QL), range, ruggedness, and robustness [6]. Other method validation information, such as the stability of analytical sample preparations, degradation/ stress studies, legible reproductions of representative instrumental output, identification and characterization of possible impurities, should be included [7], The parameters that are required to be validated depend on the type of analyses, so therefore different test methods require different validation schemes. 

The purpose of an analytical method is the deliverance of a qualitative and/or quantitative result with an acceptable uncertainty level. Therefore, theoretically, validation boils down to measuring uncertainty . In practice, method validation is done by evaluating a series of method performance characteristics, such as precision, trueness, selectivity/specificity, linearity, operating range, recovery, LOD, limit of quantification (LOQ), sensitivity, ruggedness/robustness, and applicability. Calibration and traceability have been mentioned also as performance characteristics of a method [2, 4]. To these performance parameters, MU can be added, although MU is a key indicator for both fitness for purpose of a method and constant reliability of analytical results achieved in a laboratory (IQC). MU is a comprehensive parameter covering all sources of error and thus more than method validation alone. 

Extent of Validation Depends on Type of Method On the one hand, the extent of validation and the choice of performance parameters to be evaluated depend on the status and experience of the analytical method. On the other hand, the validation plan is determined by the analytical requirement(s) as defined on the basis of customer needs or as laid down in regulations. When the method has been fully validated according to an international protocol [63,68] before, the laboratory does not need to conduct extensive in-house validation studies. It must only verify that it can achieve the same performance characteristics as outlined in the collaborative study. As a minimum, precision, bias, linearity, and ruggedness studies should be undertaken. Similar limited vahdation is required in cases where it concerns a fully validated method which is apphed to a new matrix, a well-established but noncol-laboratively studied method, and a scientifically pubhshed method with characteristics given. More profound validation is needed for methods pubhshed as such in the literature, without any characteristic given, and for methods developed in-house [84]. 

Apart from establishing analytical validation parameters, other activities should include experimental optimization of each procedural step or method manipulation to determine the critical control steps that have a substantial impact on method performance. The ruggedness or process variability that may be employed in any particular method step, without reducing method performance, should be determined. It should be identified, for example, whether an analytical method may be stopped without adversely affecting the result. 

In evaluation of the performance characteristics of a candidate method, precision, accuracy (trueness), analytical range, detection limit, and analytical specificity are of prime importance. The sections in this chapter on method evaluation and comparison contain a detailed outline of these concepts and their assessment. The estimated performance parameters for a method can then be related to quality goals that ensure acceptable medical use of the test results (see section on Analytical Goals), From a practical point of view, the ruggedness of the method in routine use is of importance. Reliable performance when used by different operators and with different batches of reagents over longer time periods is essential. 

The first stage in deciding how to treat the results from a ruggedness test is to select a range of parameters to measure which will provide both qualitative and quantitative information on the method s performance. The second stage is to decide how best to evaluate the main effects, standard errors and interaction effects provided by the selected experimental design. For this discussion we will consider only the application of HPLC, normally one of the most complex analytical methods to evaluate. 

The ruggedness of an analytical method is the absence of undue adverse influence on its reliability of performance by minor changes in the laboratory environment [43]. This validation parameter is not recognized by all organizations with testing oversight, as this characteristic is implied by collaborative validation programs (see Section IV.D). 

The ruggedization of the analytical procedure was performed by applying statistical screening techniques to minimize the effort required and, therefore, reduce the time and the cost substantially. The statistical approaches used in this study were those first introduced by Plackett-Burman (3.) and Youden-Steiner (4). Both techniques reduce the required effort since they use balanced incomplete block design experiments which can clearly indicate the non-affecting parameters from those that may have an effect. In this study the important variables of the analytical method were identified by using the Plackett-Burman technique. 

Once the method is vahdated, any modification requires revalidation to demonstrate that it still works as defined. If the new parameter is within the tolerance range of the method as specified during the ruggedness test of method validation, the method does not need to be revalidated. In other cases, it should go through revalidation. With the system suitability software frequently offered by analytical equipment vendors, methods can be automatically revalidated with little operator interaction. The validation can be performed overnight. 

Ruggedness and Its Uncertainly. Ruggedness tests provide a means of simultaneously investigating the effect of several parameters on the performance of the method by the deliberate introduction of reasonable variations and the observation of their consequences. Factors that could potentially influence the results are identifled and are modifled by an amount to match the deviations that could reasonably occur within or between laboratory analyses. Examples include factors such as the analyst, the temperature, the source and age of reagents and analytical standards, the pH value at various steps, and the batch number of solvents or SPE cartridges. 

For obtaining reliable analysis results, the (high-performance) thin-layer chromatographic (TLC) method should he validated before using it as a quality control tool. The validation parameters that should he evaluated are stability of the analyte, specificity/selectivity, linearity, accuracy, precision, range, detection limit, quantification limit, and robustness/ruggedness. 

---